Computational and experimental approaches for Investigating NanoparticleBased Drug Delivery Systems M. Ramezanpour, S.S.W. Leung, K.H. Delgado-Magnero, B.Y.M. Bashe, J. Thewalt, D.P. Tieleman PII: DOI: Reference:
S0005-2736(16)30066-9 doi: 10.1016/j.bbamem.2016.02.028 BBAMEM 82153
To appear in:
BBA - Biomembranes
Received date: Revised date: Accepted date:
23 January 2016 20 February 2016 23 February 2016
Please cite this article as: M. Ramezanpour, S.S.W. Leung, K.H. Delgado-Magnero, B.Y.M. Bashe, J. Thewalt, D.P. Tieleman, Computational and experimental approaches for Investigating Nanoparticle-Based Drug Delivery Systems, BBA - Biomembranes (2016), doi: 10.1016/j.bbamem.2016.02.028
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Computational and Experimental Approaches for
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Investigating Nanoparticle-based Drug Delivery Systems
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M. Ramezanpour a, S.S.W. Leung b, K.H. Delgado-Magnero a, B.Y.M. Bashe c, J. Thewalt b,c,*, D.P. Tieleman a,* Centre for Molecular Simulation, Department of Biological Sciences, University of Calgary, Calgary,
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AB, T2N 1N4, Canada b
Department of Physics, Simon Fraser University, Burnaby, BC, V5A 1S6, Canada
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Department of Molecular Biology and Biochemistry, Simon Fraser University, Burnaby, BC, V5A
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Abstract
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1S6, Canada
Most therapeutic agents suffer from poor solubility, rapid clearance from the blood stream, a lack of targeting, and often poor translocation ability across cell membranes. Drug/gene delivery systems (DDSs) are capable of overcoming some of these barriers to enhance delivery of drugs to their right place of action, e.g. inside cancer cells. In this review, we focus on nanoparticles as DDSs. Complementary experimental and computational studies have enhanced our understanding of the mechanism of action of nanocarriers and their underlying interactions with drugs, biomembranes and other biological molecules. We review key biophysical aspects of DDSs and discuss how computer modeling can assist in rational design of DDSs with improved and optimized properties. We summarize commonly used experimental techniques for the study of DDSs. Then we review computational studies for several major categories of nanocarriers, including dendrimers and dendrons, polymer-, peptide-, nucleic acid-, lipid-, and carbon-based DDSs, and gold nanoparticles.
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Keywords Drug delivery system, Gene nanocarrier, Nanoparticle, Biological membrane, Computer modeling,
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Experimental techniques
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Abbreviations
coarse-grained;
CNP,
carbon-based
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5-FU, 5-fluorouracil; AuNPs, gold nanoparticles; BSA, bovine serum albumin; CD, cyclodextrin; CG, nanoparticle;
CNT,
carbon
nanotube;
CPe,
inverse-
phosphatidylcholine; CPNT, cyclic peptide based nanotube; CPP, cell penetrating peptide; DDS, drug
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delivery system; DLS, dynamic light scattering; DOX, doxorubicin; DPD, Dissipative Particle
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Dynamics; DPPC, dipalmitoylphosphatidylcholine; DSC, differential scanning calorimetry; ESR, electron spin resonance spectroscopy; FT-IR, Fourier transform-infrared spectroscopy; gamma-PGA,
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poly(gamma-glutamic acid); HSA, human serum albumin; IDP, intrinsically disordered proteins; IFN, interferon alpha-1b; IR, infrared; ITC, isothermal titration calorimetry; LD, laser diffraction; LNP, lipid nanoparticle; MD, Molecular Dynamics; NMR, nuclear magnetic resonance spectroscopy; NP, nanoparticle; p-THPP, 5,10,15,20-tetrakis(4-hydroxyphenyl)porphyrin; PAMAM, poly(amido amine); PEI, polyethylenimine; PPI, poly(propylene imine); QD, quantum dot; SAXS, small angle X-ray scattering; SCPs, star copolymers; SEM, scanning electron microscopy; SSMs, sterically stabilized micelles; TAT, trans-activator of transcription; TEM, transmission electron microscopy; VIP, vasoactive intestinal peptide; WAXS, wide angle X-ray scattering; XRD, X-ray diffraction.
1. Introduction
In medicine, the delivery of a drug can be as important as the drug itself. Physiology poses key challenges to effective drug delivery; an administered drug must penetrate obstacles such as endo- or 2
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epithelial membranes and also survive the host's defenses in order to be effective. Addressing such
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challenges requires some form of drug encapsulation, and the entity forming the capsule, which has a defined molecular architecture, is known as a "drug delivery system (DDS)." From a functional point
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of view, an ideal DDS is easy to administer, non-toxic, carries the drug to its desired destination, and then releases it (Fig. 1) [1,2]. Practically, an ideal DDS is cheap and straightforward to make, as well as
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stable prior to its administration. Intense research into DDS design is providing increasingly effective
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disease treatment.
A central aim for specialized DDS development is the optimization of tiny drug encapsulation
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vehicles known as nanoparticles (NPs). NPs typically have diameters in the range of 10 to 100 nm [3]. These small DDS can circulate freely even in capillaries [4], and are intrinsically better at traversing biological barriers than larger DDS. It is worth mentioning that nanomedicines, however, have a more
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complicated and time consuming FDA approval process than parent unimolecular therapeutics [5]. In
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addition to efficacy and side effect evaluations common between both small molecules and nanomedicines, there are other concerns about nanocarriers which need further evaluation. Nanocarrier
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aggregation in vivo and their drug release, as well as evaluation of each component of the formulation are some of those concerns. FDA regulations for nanomedicines, the complexity of nanocarriers, and the prospect of only a small increase in performance upon reformulating small drugs in nanocarriers can discourage pharmaceutical companies from investing in these systems. In fact, it seems that investigations into DDS are much more successful in generating papers than in developing new treatment methods [5]. However, considering the number of approved drugs and current ones in clinical trials [6,7], as well as clearer guidelines for nanomedicine approval by the FDA, there is growing momentum in transferring these systems from publication to clinical development [5]. Many different types of NPs exist, including liposomes [7,8], dendrimers [9–11], carbon nanotubes (CNTs) [12–14], inorganic [15–20], and polymer-based [21,22] NPs, with each having its own unique properties (Fig. 2). Physicochemical properties including size, shape, deformability, surface charge and chemical composition affect how well they evade phagocytosis and how they
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interact with vasculature, traverse cell membranes and escape from endosomes prior to drug
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degradation [1]. Thus, physical characterization of NPs is an important step in the DDS design process. Despite the increasing sophistication of experimental efforts to measure, design and optimize
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the structure and dynamics of NPs, such research inevitably faces intrinsic and practical limitations. Resolution of structural details can be difficult or impossible, and systematic variation of properties
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such as composition, size and surface charge can be prohibitively time consuming and expensive. As a result, general biophysical principles connecting the effectiveness of a DDS with its composition are
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difficult to elucidate. Predicting optimum DDS design solely on the basis of experimental research is therefore unlikely. Another challenge in DDS development is that many DDSs show promise in vitro
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but fail in vivo [6]. This is mainly because of the lack of mechanistic insight obtainable by experiments which are based on trial and error. Theoretical methods, both analytical and computational, can allow primary screening of variables in order to predict suitable conditions for further experiments [23].
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Computer modeling techniques provide detailed information about molecular interactions and other
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physicochemical properties of drugs and carriers, and have found broad applications in biology, biochemistry, and biophysics [24]. Computer simulation is capable of complementing experiments and
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assisting in the rational design of new formulations with improved efficacies. In this review, we describe the main experimental approaches to characterize NP-based DDSs and focus in more detail on current applications of computer simulations aimed at understanding molecular aspects of DDSs.
In the next sections, we first give a brief overview of commonly used experimental techniques, followed by a section that describes several common computational approaches. Section 4 highlights recent computational studies of what currently are the major classes of NP-based DDSs. We conclude with a brief outlook on the role of computer simulations in drug delivery technology.
2. Commonly used experimental techniques for nanoparticle characterization
Here we provide a brief summary of techniques regularly used to characterize NP size, zeta potential, surface morphology, and the existence of colloidal structures (Table 1). We first describe the 4
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importance of each of these properties and the experimental techniques used to measure them. Next we
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describe some sophisticated physical chemistry tools that are used in DDS development to provide data that can parameterize and validate computational simulations. Experiments used to visualize cellular
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uptake, NP-cell interactions, as well as those used to determine cell viability will be omitted, even though they are vital to pharmaceutical development and are also commonly performed, since details
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on these can be found elsewhere (e.g. [25]).
Particle size is an important property because the size of the carrier can affect circulation time,
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encapsulation efficiency, and cellular uptake [1,26,27]. For example, in the case of cancer treatment, nano-drug carriers of the appropriate sizes can extravasate from the bloodstream to tumour tissues (380
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– 780 nm) [1,28], but not from the tighter capillaries (50 – 200 nm) in healthy tissues [29]. This contributes to the well-known enhanced permeability and retention effect [30–33]. Light scattering techniques such as laser diffraction (LD) and dynamic light scattering (DLS) can be used to measure
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NP size [34]. In both of these experiments, a laser beam is directed at a dilute sample (e.g. a suspension
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or solution of NPs). In LD, the diffraction angle is used to determine the particle size [35]. In DLS, the time dependence of the scattered light intensity is detected at a known scattering angle. The Brownian
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motion of suspended particles causes the scattered light intensity to fluctuate. These fluctuations are correlated with the particle’s velocity, and can be used to determine particle size via an appropriate theoretical model (e.g. Stokes-Einstein equation). Most models assume the particles are spherical and monodisperse; artifacts may arise if they are not. DLS can be used to measure the size of particles in the 2 nm – 3 m range, and LD can be used in the 20 nm – 2 mm range [34,36]. Resolution of small differences in particle size can be improved by combining light scattering techniques with a separation technique, such as field flow fractionation [37,38]. Microscopy techniques such as transmission electron microscopy (TEM), scanning electron microscopy (SEM), and atomic force microscopy can be used to measure NP size, as well as size distribution (dispersity), shape, and surface morphology. Dispersity – also known as polydispersity in older literature – in a formulation can result in varying body-residence time and immunogenicity [39,40]. Particle shape can affect cellular uptake mechanisms and kinetics [41–45] as well as margination dynamics – the lateral drift of NPs to endothelial walls [1]. The particle surface is the first 5
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point of contact between the NP and its environment, and surface morphology is known to affect drug
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release kinetics [46]. In electron microscopy, an electron beam is directed at the sample, and differences in electron density in the sample gives contrast to structures. Since electron microscopy can
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give high-resolution images approaching molecular resolution, all motions on the supramolecular scale have to be halted. For SEM, the NPs are deposited onto a carbon conductive tape, dried, and covered
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with a thin layer of metal [47]. In cryo-TEM, samples are applied onto an electron microscopy grid, plunge-frozen rapidly in ethane to prevent the formation of damaging cubic ice and then imaged under
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cryogenic conditions to prevent radiation damage to the sample during imaging [48]. Magnifications on the order of 50,000X, with a resolution of 0.3 nm, are possible [49]. Atomic force microscopy probes
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surface terrain using a nanoscale mechanical probe with a vertical resolution as small as 0.01 nm [35]. A NP’s shape can influence the physiological fate of its cargo [1]. Particle shape can be an indication of undesirable aggregate structures. Aggregate structures can also cause inaccurate particle
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size determination by LD or DLS [34]. Light microscopy can be used for determining if microparticles
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are aggregates of smaller particles [36,50], even though it cannot be used for directly observing NP structure due to limitations in optical resolution (~ 200 nm). Isothermal titration calorimetry (ITC),
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which will be described in greater detail below, has been used to study aggregate formation in polycationic nucleic acid carriers [51]. Aggregation of NPs can be prevented via electrostatic repulsion [31,52]. Surface charge measurements can be a predictor of aggregation behaviour, and consequently a predictor of colloidal dispersion stability during storage. Surface charge can affect cellular uptake, cytotoxicity, and circulation times [1,31,53,54]. Zeta potential is used to characterize the surface charge of NPs. When a particle moves in a liquid, a thin layer of liquid moves with it. The zeta potential is defined as the electrical potential at the boundary of this moving liquid, defined as the shear plane. It is a function of surface charge density, shear plane location, and surface structure [55]. Zeta potential is determined by measuring light scattering caused by particle motion in an applied electric field. Charged particles with larger zeta potential (|zeta| > 25 mV) are less likely to form aggregates but neutral particles generally have longer circulation times [56]. Surface charge effects are very complex; positive and negative zeta potentials correlate with higher cellular uptake in non-phagocytic and phagocytic cells, respectively [54]. Future DDS designs may 6
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require NPs to switch zeta potential at the target site to maximize circulation time while targeting a
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particular type of cell [1]. It should be noted that protein adsorption in cellular media can change the surface charge [52]. Conclusions on surface-charge effects, therefore, are only valid when comparing
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functionalized or non-functionalized particles of similar sizes [54].
Encapsulation efficiency – the ratio of encapsulated drug to the amount of drug used during
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formulation preparation – is an important parameter [46]. Low encapsulation efficiency implies that more carrier material is needed, heightening the risk of carrier material toxicity [2,57]. Encapsulation
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efficiency can be determined in a number of ways, depending on the particular type of NP. Drug loading can be determined by weight [16]. Gel electrophoresis has been used to determine
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encapsulation efficiency by analyzing protection from RNase degradation in siRNA lipid nanoparticles (LNPs). Physical chemistry techniques can also be used to identify and quantify the degree to which components have been encapsulated. For example, fluorescence spectroscopy can be used if the drug is
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fluorescent [16]. Ultraviolet-visible(UV-Vis) light spectroscopy can be used to determine the amount
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of cargo (e.g. siRNA, anticancer drug doxorubicin (DOX)) present [58,59]. High performance liquid chromatography can be used to separate, identify, and quantify the components of a mixture [46,60].
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Novel single molecule measurements have been applied to measure encapsulation efficiency in individual lipid vesicles [61].
An array of physical chemistry techniques has been instrumental in evaluating the chemical and physical properties of NP components. Dendrimer structures have been simulated based on experimental data from nuclear magnetic resonance spectroscopy (NMR) and X-ray [62]. LNP matrix state, polymorphism, and phase behaviour have been characterized by differential scanning calorimetry (DSC), X-ray diffraction (XRD), and neutron scattering [63]. Lipid crystallinity of solid LNPs has been studied using X-ray scattering, DSC, NMR, and electron spin resonance spectroscopy (ESR) [64]. CNTs have been characterized using XRD, UV-Vis spectroscopy, Fourier transform-infrared spectroscopy (FT-IR), ESR, SEM, and energy dispersive X-ray diffraction [65]. Iron oxide NPs, which are magnetic, can be studied using magnetometry, NMR relaxation dispersion profiles [55], and magnetic field flow fractionation [66]. Some more common physical chemistry tools will be briefly described here. 7
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In optical spectroscopy, the absorption of electromagnetic radiation by NPs occurs at
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frequencies characteristic of the molecular structures present. UV-Vis light is absorbed if it supplies the right amount of energy to excite valence electrons, while infrared (IR) light is absorbed if it excites
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bond vibrations. FT-IR is a more efficient variant of IR spectroscopy where all frequencies are sampled simultaneously. It has been used to monitor cholesterol-induced changes in liposomal membrane
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structure [67] and to verify conjugation of block copolymers onto iron oxide NPs [68]. UV-Vis spectroscopy has been used extensively in NP characterization. For example it was used to confirm the
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presence of cleavable disulphide bond in CNT-nucleic acid conjugates [69]; monitor the metal to ligand charge transfer band (5d(Pt11) to π*(diimine)) in dendrimer synthesis [70]; measure dye and drug
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encapsulation in poly(lactic-co-glycolic acid) polymeric carriers [71,72] and study drug release kinetics of liposomal formulations [67].
NMR and ESR both rely on spin manipulations in the presence of an applied external magnetic
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field. NMR monitors the nuclear spin of nuclei with non-zero nuclear spins, and ESR monitors the
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electron spin of excited unpaired electrons [73]. NMR can provide a wealth of information on molecular arrangements. NMR is sensitive to small changes in the local environment: it can be used to
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differentiate between various layers up to the fourth generation in dendrimers [74], and between neighbouring carbon atoms in lipids [75]. 2D NMR techniques can be used to ascertain chemical connectivity (e.g. COSY, HMQC, HSQC) and distances (e.g. NOESY and ROESY) between specific atoms [74,76,77]. Dynamic information is also accessible to NMR. Pulsed field-gradient spin echo 1H NMR and diffusion-ordered spectroscopy have been used to determine diffusion coefficients in aliphatic polyester and poly(propylene imine) (PPI) dendrimers [74,78].
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determine the mobility of siRNA encapsulated in LNPs [79]. NMR relaxation was used to monitor molecular tumbling activities [80] and this information, in turn, was used to determine the location of a spin-labeled anticancer drug in micelles formed by PEG modified with long alkyl groups and of paclitaxel in cyclodextrin (CD) vesicles [77,80]. Most DDSs are invisible to ESR, requiring the addition of paramagnetic spin probes [81]. Lipophilic ESR probes have been used as model lipophilic drugs to help determine where these drugs localized in solid LNPs [35]. In gadolinium poly(amido amine) (PAMAM) dendrimer containing spin probes, ESR was used to determine the location and 8
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concentration of the magnetic resonance imaging contrast agent gadolinium [78]. ESR has also been
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used to prove that the terminal interbranch H-bonded groups preclude backfolding in PAMAM dendrimers [78]. Since ESR can probe membrane fluidity, ESR has been used to study interactions
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between polymeric micelles and lipid membranes, which are models for cell membranes [68]. ESR can also give microviscosity and micropolarity information [81]. Viscoelastic properties of dispersions can
which are less deformable than nano-emulsions [64].
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affect DDS interactions with the body. For example, capillary blockage can occur with solid LNPs,
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Scattering techniques can be used to study the structure of colloidal systems. In scattering studies, a monochromatic beam of light, neutrons, or X-ray is focused on the sample, and the intensity
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of the scattered beam is measured. Light scattering was covered earlier in this section. X-ray scattering results from variations in electron density and neutron scattering results from variations in the spatial distribution of atomic nuclei [82]. The interference pattern formed by the scattered beam can be used to
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determine characteristic lengths using Bragg’s Law. Larger angle measurements contain information
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for shorter length scales: small and wide angle X-ray scattering (SAXS and WAXS) are used to look at liquids and solids with structures on the length scale of 1-200 nm and sub-nm, respectively. For
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example, SAXS is used to monitor monolayer and bilayer repeat spacing, and hydrophobic thickness in lipid membranes, while WAXS is used to determine chain packing and extent of lipid motion [83,84]. SAXS has been used to study NPs with cubosome structures [85,86], to provide micelle shape, size and density of nonsteroidal anti-inflammatory drug celecoxib-loaded protein micelles [50], and to observe in situ shell growth of polymeric nanocapsules [87]. WAXS has been used to study lipid polymorphic transformations in solid LNPs and crystallinity of encapsulated drugs [88], and crystallinity of polyelectrolyte complexes made with polysaccharides [89]. SANS, useful for studying structures of 1100 nm [90], has been used to determine molecular weight and end group locations of dendrimers [74]. Neutron scattering can also be used to study drug diffusion and internal molecular motions in LNPs [63]. Fluorescence comprises a family of spectroscopy and microscopy techniques relying on excitation of fluorescent species. An extrinsic fluorescent probe is added when no fluorescent moieties are present in the material of interest. Absorption of a photon excites the fluorophore and fluorescence 9
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occurs when a photon is later emitted. Between excitation and emission, the fluorophore’s motions and local environment (e.g. viscosity, polarity, temperature, pH, ionconcentration) influence the
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fluorophore’s spectrum [91]. Polarity sensitive fluorescent probes are commonly used to determine the
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critical association concentration of polymeric micelles – analogous to the critical micellar concentration of surfactant micelles [92]. Fluorescence quenching assays have been done to evaluate
been used to evaluate drug release characteristics [93].
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nucleic acid-polycation binding in polycationic nucleic acid carriers [51] and dye leakage assays have
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Thermal analysis is a versatile tool for DDS characterization [94,95]. DSC measures enthalpy changes – the heat required for a sample to undergo a physical phase transition (e.g. glass transition,
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melting, decomposition, isomerization or heats of solution, water sorption-desorption) [95,96]. DSC was used to monitor the physical state of the antineoplastic drug paclitaxel inside polymeric microspheres, for example [46]. More specific to DDS design, DSC has been used to measure the gel-
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to-liquid crystalline transition temperature of lipids in liposomal formulations, which can be used to
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predict release rates of encapsulated drugs [95,97]. In a closely related technique, ITC, the evolved heat is measured as concentrated aliquots of one substance are added to a solution of a second substance.
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The released heat is directly proportional to the amount of substance added, and can be used to measure enthalpy changes, stoichiometry, and binding constants. ITC has been used to measure cyclodextranguest molecule interactions, micelle-drug interactions, and polyelectrolyte aggregation [98].
NP DDSs are complex systems that can evolve over time. Dynamic processes cannot be captured in steady-state measurements, and can pose as a challenge to characterization [35]. For example, PEGylated NPs are known to lose their PEG coating before reaching target cells [99–101]. Timeresolved DLS combined with TEM was used to investigate morphology transition kinetics of multiblock copolymer micelles [102]. Time-resolved fluorescence lifetime measurements have been used to follow the release of fluorescent compounds from polymeric carriers [71], and fluorescence lifetime imaging microscopy could extend drug release studies to cellular systems [103,104].
3. Computational approaches to study nanoparticles 10
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Computer simulations in general describe the physics of materials at a suitable level of detail for a particular application. A simulation is described by the level of detail in the physics used to model the
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system of interest, and the algorithms used to generate enough simulation data to draw statistically valid conclusions about the behaviour of the system of interest. NPs can be described at different levels
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of detail (Fig. 3), but for most of the applications in this review the appropriate levels of detail are limited to atomistic and semi-atomistic, slightly coarser, levels.
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At the highest level of detail, a system is described by quantum mechanics in one of its approximations. Quantum mechanical calculations have been widely applied to CNTs and play a role
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in optimizing less detailed simulations, but in practice they are only useful for small systems with limited degrees of freedom and generally not in solution. They are essential to correctly model electronic properties and can be used to calculate electrical, optical, magnetic and mechanical
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properties of CNTs, quantum dots (QDs), magnetic NPs and similar systems. For soft-condensed
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matter systems such as liposomes or dendrimers in solution their usefulness is currently limited. If we average over electronic properties by representing electrons as partial charges on atoms
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rather than taking them into account explicitly, we significantly simplify calculations. At this level of detail, atoms are described as point charges that interact through simplified empirical potential terms including Coulomb interactions, Van der Waals interactions, and simplified terms describing bonds, angles, and dihedrals as harmonic terms and cosine expansions [105]. The resulting technique is called Molecular Dynamics (MD) and is widely used for biological and soft-condensed matter systems. The vast majority of the papers reviewed below use this approach, which accounts for a substantial fraction of super computer time on the worlds’ major academic computer centers. MD is a mature technique that is implemented in a number of widely-used software packages, including GROMACS [106], NAMD [107], LAMPPS [108], CHARMM [109], and AMBER [110]. MD simulations essentially generate a movie of a set of molecules over time, incorporating every degree or nearly every degree of freedom, from which through statistical mechanics thermodynamic properties can be calculated. Its main limitations include the computational cost and the corresponding limited time scale (realistically, microseconds in most cases) and length scale (~10 nm per dimension) and the limitations implicit in 11
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the physics that describes the interactions between atoms: point charges mean electronic effects like
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polarization, pi-electron clouds possibly important for CNTs, and charge transfer are typically ignored. In addition, the simplified interaction function sometimes imposes other limitations.
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For many properties of interest, especially in mesoscopic-scale systems like NPs with a size of 20-200 nm, atomistic detail may not always be necessary. Many papers below use coarse-grained (CG)
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simulations, often based on the MARTINI model [111]. Here CG means that several atoms have been grouped into interaction sites, which are no longer recognizable as atoms but instead are ‘beads’ that
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represent molecular fragments. Depending on the level of coarse-graining, these beads can still maintain a significant amount of chemical specificity, or they can represent very generic properties
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such as those that represent a lipid by one head group bead and two tail beads. The same physics applies as in atomistic simulations, and sufficient conformations of a system of interest have to be generated by a sampling algorithm to accurately calculate thermodynamic and structural properties.
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This can be done by MD, Monte Carlo, or various other algorithms including Langevin dynamics or
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Dissipative Particle Dynamics (DPD) [105].
At the CG level, individual molecules and usually fragments of molecules are still clearly
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recognizable. In material science, there exists a whole hierarchy of additional models. Beyond molecules, DPD solves hydrodynamics equations for materials by representing materials as discrete particles that can be much larger than individual molecules. DPD blurs the line with CG MD a little by its choice of particle volume: if the volume is chosen to coincide with molecules or molecular fragments its resolution is similar to CG MD, although it typically treats problems that are more generic. Beyond DPD there are many other possibilities, including field-based treatments where a system is described by density fields. Simulations coarser than DPD are beyond the scope of this review.
4. Computational studies on drug and gene delivery systems
Computer modeling, as complementary tools to experiments, is excellent in shedding light into the structural and dynamical properties of systems of interest at atomistic or molecular levels of detail 12
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[112,113]. Applied to DDSs, computer simulations have been used to address a broad range of
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questions (Table 2). Computer simulations can be used to study self-assembly [114–116], the structural and dynamical characteristics of the resulting aggregates [117,118], drug loading capacity, mechanism
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and rate [119–121], drug distribution/localization in DDS [121], complex stability [118,122], drug retention, release mechanism and release rate [123–125], dominant drug-DDS interactions
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[119,126,127], and to design or optimize DDSs targeting capabilities [128,129]. Environmental conditions, e.g. pH, temperature, salt type and concentration, counterions [126,130], and external
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stimulus such as external magnetic fields [131–133], as well as interactions with other biomolecules (e.g. serum proteins, miRNA, heparin), all might affect the aforementioned aspects of DDSs [126,134–
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137] and can be studied computationally.
The way DDSs interact with cell membranes is one of the most commonly studied steps in drug delivery by computational studies [138,139]. Computer modeling can investigate the driving forces for
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NP-membrane interactions, as well as how factors such as design parameters and environmental
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conditions affect these. These parameters include size, shape, surface chemistry, and concentration of NPs, as well as mechanical and elastic properties of both NPs and membranes [138–143]. Surface
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chemistry refers to NPs hydrophobicity/hydrophilicity, charge density and distribution, as well as coating ligand’s length, grafting density and distribution, rigidity, and their affinity to receptors [144– 147]. Since, in real systems the membrane often interacts with more than one NP simultaneously, NP aggregation and interaction of multiple NPs with the membrane also is a relevant factor [138,139]. Membrane properties such as lipid phase, membrane composition, surface tension, charge density, receptor types and density also play important roles [148–150]. In addition to all of these, external macromolecules, e.g. proteins in the bloodstream, and different environmental conditions in different cell types [134], as well as differences between external and internal cellular environments, are also of great importance and worth further investigation. Of course, many of these factors are coupled and have to be taken into account together in the design of NPs [138,139]. NPs are usually functionalized by coating with polymers, lipids, or ligands, to increase their stability, targeting capability, cellular uptake efficiency, and also to reduce their toxicity [131]. Both the type of coating molecule and the coating pattern and density strongly affect the physicochemical 13
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properties of these NPs, and as a result their interactions with their cargo and target lipid membranes
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[147,151–155]. Simulations can be used to study these coatings, how they affect NP properties including their interactions with other molecules [152,156], and can be used to optimize these coatings
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towards DDS with improved delivery capabilities [151,154]. Functionalization with PEG polymers, a process called PEGylation, is a good example of functionalization [157,158]. Drugs/proteins/nucleic
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acids and NPs’ surfaces are usually PEGylated to improve their water solubility, stability, and circulation lifetime, and to reduce their aggregation, toxicity, and immune response [157,159].
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Considering the importance of PEG and its wide usage in drug delivery applications, PEG is worth further discussion. PEG is the most commonly used non-ionic polymer with stealth behaviour for
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drug delivery purposes [160]. These biocompatible hydrophilic polymers can solubilize hydrophobic molecules, and reduce renal clearance and toxicity of drugs and nanoparticles. They also inhibit the aggregation of PEGylated agents by steric stabilization, as well as mask therapeutic agents from the
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host immune system and consequently suppress immunogenicity and antigenicity. This protective
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polymer layer, which is known as stealth sheath, can reduce the fast recognition by the immune system and reduce the non-specific interactions with blood components, resulting in longer blood circulation
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times. These properties have made it the gold standard polymer for drug delivery and other biomedical applications and have been the topic of several computational modeling studies reviewed in more detail below. Despite the widespread use of PEG, there are several drawback to PEG including immunogenicity and antigenicity, e.g. hypersensitivity reactions and the development of antibodies against PEGs [160], which adversely affects pharmacokinetics [161], and the search for alternatives, e.g. poly(amino acids), continues, in part aided by computer simulations. In addition to different aspects of DDS mechanism, different types of DDS have been simulated. There are many types of nanoparticulate DDS. DDSs based on polymers, peptides, nucleic acids, lipids, carbon, dendrimers and dendrons, and gold have been investigated via computer modeling and are described in more detail below. Combined, these application show that computer simulation has become a powerful technique for rational design and optimization of DDS, and demonstrates the growing importance of computational pharmaceutics.
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For the rest of this review, we will present examples of computational studies on each aspect of
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interest for each nanoparticulate DDS type in separate sections, and then comment on challenges in this field. We will focus on atomistic, CG MD simulations, and DPD simulations of nanoparticulate DDSs;
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these fall mainly in categories defined as dendrimers/dendrons, polymer-, protein/peptide-, nucleic acid-, lipid-, carbon-based DDS, as well as gold nanoparticles (AuNPs). We emphasize studies
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published since 2010. There are computational studies on other DDSs that are outside the scope of this review, e.g. graphene oxides and reduced graphene oxides[19,162], silicon NPs [19,163], nanodiamond
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[164,165], layered drug delivery carriers [166,167], zeolites [168–170], metal-organic frameworks [171,172], and other porous materials. These are excluded because they are not NPs, there are limited
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computational studies available, or because their focus is different from the studies described below.
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4.1. Dendrimers and dendrons
Dendrimers are a class of branched globular materials with broad application in nanotechnology
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and nanomedicine, including for drug/gene delivery [9–11]. Their monodispersity, modifiable surface, shell and core characteristics, and their high loading capacity make them suitable carriers for therapeutic agents, with promising applications in gene delivery through complex formation with nucleic acids, so-called dendriplexes [173–177]. Drugs can be covalently or noncovalently incorporated in dendrimers to overcome solubility problems and to improve targeting and cellular uptake. Dendrimers have been studied in many computational papers to characterize their structure in solution, their interactions with drugs and biomolecules [62,175], and their response to external stimuli[178]. All parts of dendrimers can be modified chemically; core, shell and surface. The surface can be optimized for different biological activities by functionalizing the terminal groups. Thus dendrimers are a flexible class of materials with the potential for almost infinite variation.
Different dendrimers have been modeled including PPI, triazines and PAMAM. Simulations can be used to investigate their interactions with other molecules, the effect of solvents and counterions, pH, salt concentration, different generation number, the nature of terminal groups and chemical 15
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modifications. Computer simulations have also been used to study deformation and conformational
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changes of dendrimers upon interaction with nanopores [179], lipid membranes [180], and nucleic acids [181] as a function of generation number, pH, functionalization, and ionic strength. For example,
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Nandy et al. [181] simulated a 38 base pair double stranded DNA and three different generations of PAMAM dendrimer (G3, G4, G5). They found that DNA caused deformations in lower-generation
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dendrimers (G3) while higher generation PAMAM (G5) bent DNA (Fig. 4). Generation number, pH, and architecture have been shown to affect dendron’s flexibility and rigidity, and as a result influence
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their binding efficiency to nucleic acids [182,183]. Several studies have investigated the effect of chemical modification on self-assembly, conformation, and drug/gene binding [184–186].
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Self-assembly of Janus dendrimers, comprised of hydrophobic and hydrophilic parts, has been studied both experimentally and computationally. They form onion-like dendrimers and other complex architectures [187,188].
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Computer simulation has been used to study how chemical modification and surface
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functionalization (e.g. PEGylation, acetylation, folic acid groups, peptides, and targeting ligands of the surface) influences the structure [189], and the interactions with cargos [190,191]. Computer modeling
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can also be used to determine the optimal grafting densities and patterns based on structural and drugloading properties, and interactions with target membranes [154,192–195]. Effect of PEGylation size and grafting density on the structure of dendrimers, and also the structure of the PEG layer itself have been widely studied [154,196–198]. PEGylation expands the core of dendrimers, effectively reduces their surface charge density and increases the overall size of dendrimers [196]. The effect of PEGylation on structure and drug loading capacity of PAMAM-G4 dendrimers was investigated by an all-atom MD simulation [154]. Different PEG densities (25%, 50%, 75% and 100%), were tested for complexation and loading capacity for 5-fluorouracil (5-FU), as a model anticancer drug. The simulations suggested that 25% PEG is the most suitable PEGylation density for drug delivery purposes. This grafting density can retain the internal drug complexation capability, and also assist in retaining drugs. For higher PEGylation degrees than 25%, 5-FU molecules to a high extent interact with external PEG chains than dendrimer branches. Pearson et al.[193] used MD simulation to understand why PEGylated dendron-based copolymers functionalized with different terminal groups, 16
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NH2, -COOH, and –Ac, do not exhibit a charge-dependent cellular interaction. Simulations suggest
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that interactions between positively charged terminal groups with PEG molecules sequester the charges, and cause this low level of cellular interaction for NH2 terminated micelles.
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Interactions between dendrimers with lipid membranes, permeation of dendrimers as a function of generation number (size), protonation state, functionalization, and dendrimer concentration, as well
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as the effect of bilayer asymmetry, bilayer tension, lipid tail length, and lipid phase (temperature) all have been studied by simulations [124,180,195,199–204]. Tian and Ma [124] studied interactions of
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G4-PAMAM dendrimers with both symmetric and asymmetric negatively charged bilayers, with and without tension, at both neutral and low pH. It was shown that both membrane tension and electrostatic
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interactions between charged dendrimers and tensed membrane play important role in dendrimer penetration through membrane. Based on their simulation results authors proposed a mechanism of endosomal escape for pH-responsive gene delivery vectors (Fig. 5). Xie et al. [204] found that low
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generations leave gel-phase membranes intact while higher generations fluidize the membrane and
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cause a gel-fluid phase transition around the dendrimer binding site. Higher generation cationic dendrimers are capable of membrane disruption in both low and high temperatures [204].
dendrimers [195].
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Functionalization can influence these interactions since it affects the physicochemical properties of
Dendrimers can condense DNA and siRNA, and stabilize small molecules in their core, shell and interface. Several studies have considered the loading, release, interactions and distribution of small molecules in dendrimers [174,205–213]. For example, Jain et al. [206] used all atom MD simulations and molecular mechanics Poisson-Boltzmann surface area analysis to study the solubility and drug release profile of the hydrophobic drugs famotidine and indomethacin in G5 ethylenediamine cored PPI dendrimers under different pH conditions. They found fast drug release in low pH, intermediate values in neutral pH and a slow release in high pH. These results and further simulations on the effect of dendrimer chemistry and topology on drug loading and release suggest that both the pKa of the drug and pH-dependent changes in the dendrimer influence these dendrimer-drug interactions and complex stability. Loading and release of the antibiotic rifampicin, from G4-PAMAM dendrimer has been studied by Bellini et al. [205] in a combined experimental and simulation approach. 17
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Experimentally, approximately 20 drug molecules were found as the maximum potential loading
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capacity of this dendrimer at neutral pH. In simulation, the model of the dendrimer with 20 drug molecules in neutral pH was more stable than in low pH, where drug molecules got expelled rapidly
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and simultaneously to the solvent. This suggests a pH dependent drug release desirable for drug delivery applications. Simulations can also be used to study the effect of PEG lipids on loading
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capacity of dendrimers, and on surface ligand’s targeting capability [154,211,214]. Based on a comparison of several generations of PAMAM dendrimers, G4 PAMAM might be
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optimal as vector for gene delivery, based on PAMAM interactions with bilayers and siRNA complexation [203,215]. For gene delivery applications, generation number (size), flexibility, hydrogen
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bonding capability, pH, ion concentration and salt type have been considered in simulations [183,216– 219]. Electrostatic interactions and the entropy gain from counterion release upon binding have been proposed as important driving forces determining the interactions between dendrimers and nucleic
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acids [181,212,213].
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As one final example, we would like to design drug specific nanocarriers by including optimal drug-binding molecules in the structure. Shi et al. [220] used de novo design of dendrimers via
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optimizing building blocks and including optimal drug-binding molecules with a final goal the design of a drug-specific nanocarrier. They combined virtual screening with molecular docking and MD simulations on DOX-dendrimer complexes. Using Rhein, a molecule with strong DOX binding, they achieved Rhein-containing nanocarriers with a suitable DOX profile.
4.2. Polymer-based delivery systems Polymeric NPs are of interest in drug delivery applications and more generally because of their stability and easily modifiable surface [21,31]. Block-copolymers [92] composed of hydrophobic and hydrophilic parts can form a variety of self-assembled structures [221] of interest in drug delivery applications. These structures, e.g. polymeric micelles [222], are capable of accumulating drug molecules in their core, interface and shell [121]. There are several reviews of computational studies on different aspects or specific type of polymers [23,221,223–225]. Here, we will discuss some examples. 18
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Amphiphilic polymers form a variety of structures in solvents, e.g. core-shell, Janus particles, micelles, and rod-like micelles [102,226,227]. Several factors affecting aggregate structures, stability
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and phase transitions between different structures have been studied: for example, solvent polarity [130], polymer architecture, concentration and physicochemical properties [226,228,229], mixture
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components and mixing ratios of comprising polymers [230,231], pH, temperature [226,232], and the addition of components, e.g. lipids, cholesterol and ligands [102,233,234]. Huynh et al.[122] performed
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all atom MD simulations on star copolymers (SCPs) to understand why SCP micelles are unstable and form multimolecular micelles in solution, and to assist in the rational design of stable SCP
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unimolecular micelles in solution. Each SCP has a central connecting part and six attached arms, with each arm comprised of a hydrophilic part (PEG) and a hydrophobic one (PCL). These SCPs were different in their PCL and PEG length and the resulting molecular weight. They form core-shell
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structures, in which PCL blocks form a dense hydrophobic core while PEG blocks form a shell around
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the PCL core. The simulations suggested that partial water exposure of the PCL core drives aggregation into multimolecular micelles. Increasing PEG length protects the PCL core from exposure to the water
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but it also increases the size of micelles. Therefore, since smaller micelles are preferred in DDS applications, predicting the minimum number of PEG units for a specific number of PCL blocks required to fully protect the PCL core is important. The authors found a quantitative relation between hydration of PCL core and both number and molecular weight of PEG and PCL blocks [122]. The internal structure of the thermoresponsive polymer blend NPs, and the phase transition between core-shell and Janus structures were investigated by DPD simulations by Guo et al. [226]. Chen and Ruckenstein [230] looked at the structure and mechanism of formation and the degradation process of multicomponent multicore micelles via DPD simulations. Taresco et al.[231] used a combined computational/experimental approach to study the self-assembly and structure of copolymer micelles formed by two different types of monomers. Wang et al.[233] studied the structural characterization of cholesterol-functionalized CD micelles by all-atom MD simulation. Tan et al.[102] by both experiment and DPD simulations showed that introducing a second hydrophilic
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phosphatidylcholine group into the polymer chains causes a phase transition from sphere to rod-like in
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multiblock polyurethane micelles. Hybrid systems composed of polymers and other NPs, e.g. AuNPs and QDs, also show interesting
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structural and phase transition behaviour [227,235]. Using both experiments and DPD simulations, Cai et al.[227] studied the effect of adding AuNPs on block copolymer self-assembly. Adding AuNPs
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caused a transformation from long cylindrical micelles to short cylindrical micelles and finally to spherical micelles.
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Interactions with and translocation through cell membranes for polymer-based DDSs have been studied both by experiment and simulations [236–238]. Li et al. [236] studied the interactions between
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a novel amphiphilic polymer, PMAL, and siRNAs, combining experiment and CG MD simulations. Potential of mean force calculations showed that siRNA by itself experiences a large free energy barrier for translocation while the siRNA-PMAL complex entered the membrane spontaneously,
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consistent with experiment. The PMAL polymers induced pore formation to facilitate siRNA
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translocation. Srinivas et al.[237] used CG MD simulation and showed that these patchy polymeric micelles are capable of accommodating and transporting hydrophilic contents across a lipid membrane
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into a lipid vesicle.
Ding and Ma [238], using DPD simulations, investigated receptor-mediated endocytosis for a new type of pH-responsive DDS composed of NP (radius of 4 nm) and some pH-sensitive polymers (of twelve beads length). Beads on the NP surface were treated as ligands and assigned +e charges. The polymer had N randomly distributed negatively charged (-e) beads, where N depends on the pKa of the polymer and the system’s pH. Half of the model lipids in the membrane were treated as receptors by replacing charged beads in the lipid’s head group with neutral beads. They introduced a modified Lennard-Jones potential to account for receptor-ligand interactions. Eighteen-microsecond simulations for three different pH conditions combined with potential of mean force calculations showed a triplepH-response. The NP can only be engulfed by cell membrane when the pH is higher or lower than the polymer’s pKa, whereas endocytosis is blocked when the pH equals the polymer’s pKa. It was also found that the properties of NPs, pH-sensitive polymer and cell membranes, and the external environment, all affect NP engulfment by the membrane, at least in this simplified DPD model (Fig. 6). 20
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The effect of functionalization on interactions with the membrane, uptake mechanism, as well as
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effect on the structure and conformation of drugs/polymer complexes have been studied by simulation [118,239–243]. Liao et al. [240] used both experiment and all atom MD simulations to study the uptake
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of chitosan/DNA complexes coated with anionic poly(gamma-glutamic acid) (gamma-PGA). This coating enhanced the cellular uptake of chitosan/DNA complexes via a specific protein-mediated
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endocytosis. Both the structure of this ternary complex (CS/DNA/PGA), as well as the interaction between gamma-PGA and glutamyl transpeptidase proteins were studied by MD simulations. Sun et al.
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[239,244] studied the effect of polyethylenimine polymer (PEI) modification by lipids on nucleic acid compaction and aggregation. They found that lipid association as an additional mechanism of
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aggregation resulted in more compact and stable structures.
Several studies have focused on interactions between polymeric DDS and small molecule cargos, investigating driving forces for partitioning [60,245–249], complex stability [250], drug loading
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capacity and rate, and drug distribution and release [121,247,251–255] as a function of temperature
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[256], pH [228,232,257,258], concentration of drugs [232], physicochemical and structural properties of polymers and cargos [259,260]. CD is a well-known family of cyclic oligosaccharides with great
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promise as solubilizing agent for poorly soluble drugs [246,261]. He et al. [246] performed MD simulations to investigate the binding mode and affinities of the antifungal drug amphotericin B drug with γ- and β-CD. Consistent with experiments, simulations showed a significantly higher binding affinity for γ-CD than β-CD. The binding mechanism of the drugs bexarotene and human vasoactive intestinal peptide (VIP) to highly PEGylated sterically stabilized micelles (SSMs) has been investigated by Vukovic et al. [121] both experimentally and computationally. Using free energy profiles, they predicted the drug distribution in SSMs. Single bexarotene, as a poorly water-soluble drug, resides in the ionic interface with its polar end exposed to solvent. With multiple bexarotene molecules, the preferred distribution is as clusters in the alkane core of SSM (Fig. 7). Dominated by electrostatic interactions, VIP molecules, with two regions with positively charged residues, interact with negatively charged -PO4 groups in the ionic interface. These simulations suggest the importance of the balance between hydrophobic and Coulomb interactions between drugs and phospholipid polymers in stabilization of drugs in SSMs. 21
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The interaction and complexation of nucleic acids, including DNA and siRNA with cationic
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polymers has been studied extensively [223]. PEI, as an example of cationic polymers, is one of the most promising polymers which has been widely utilized in studies for gene delivery applications. Both
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experimental and computational studies have provided a better understanding of PEIs’ binding modes with DNA/siRNA [223]. Molecular modeling has had a significant impact on our understanding of
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critical steps in gene delivery, including complexation of carriers with nucleic acids, as well as nucleic acid condensation and aggregation by gene carriers [177]. Simulations can be used to investigate the
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factors determining interactions between cationic polymers and nucleic acids, including complexation and decomplexation. Such factors, similar to those in other DDSs, include chemical surface
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composition as amine-to-phosphate ratio [51,262], charge density, protonation state, polymer molecular weight, polymer chemical structure and charge distribution [262–267], the effect of endogenous molecules [136], and multivalent ions, e.g. Fe(III) [137,268]. miRNA as endogenous molecule might
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cause decomplexation. To test this hypothesis, Meneksedag-Erol et al. [136] used both experiment and
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all atom MD to study the interactions between miRNA and pre-formed PEI-siRNA complexes. PEI was found to bind more strongly to miRNA than siRNA. However, miRNA could not disrupt the
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integrity of PEI-siRNA but formed a layer on the complex, leading to a siRNA-PEI-miRNA complex. Interactions between polymeric DDS and nucleic acids for gene delivery purposes can be studied indirectly using polymers. Polycations and polyanions can be considered as models for DDS and nucleic acids, respectively [269–271]. Zhao et al. [270] utilized polycations and polyanions and studied the effect of charge distribution along the chain on complexation behavior and the structure of the resulting complexes. They found that charge distribution has a significant influence on aggregation and the complex structure.
4.3. Peptide- and nucleic acid-based delivery systems There are several peptide-based DDSs, e.g. protein- [272] and cyclic peptide-based [273,274], and cell penetrating peptides (CPPs) [275]. There are also DDSs based on nucleic acids. RNA and DNA have been used as either building blocks or surface ligands to design a variety of three-dimensional 22
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nanostructures, including rings, tubes, cubes, cages, and spheres [276–279]. As with other DDSs, the
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structural properties of these systems, and their interactions with endogeneous biological molecules, as a function of environmental conditions are of interest [280–282]. Badu et al. [281] performed atomistic
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MD simulations on RNA nanotubes and investigated their structural properties. Juul et al. [282] studied DNA cages loaded with active enzymes both experimentally and by all-atom MD simulation, and
enabling controllable encapsulation and release (Fig. 8).
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found a temperature-dependent conformational change from an ‘open’ to a ‘closed’ state, in principle
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Protein-based NPs are promising because of their unique characteristics, including biocompatibility, biodegradability, low cytotoxicity, and non-antigenicity [283,284]. Some proteins, e.g. albumin, can be
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used for targeted delivery to tumor cells and inflamed tissues since they are preferentially taken up by these types of cells [284]. Luo et al. [272] using both theory and experimentations investigated the binding mode, affinity and interactions between bovine serum albumin (BSA) and interferon alpha-1b
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(IFN) as delivery system and protein drug, respectively. Simulations predicted domain III of BSA as
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the most probable binding sites, as well as hydrogen bonds and salt bridges as the main contributors in binding between BSA and IFN. Cyclic peptide based nanotubes (CPNTs) can be used as a transporter
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for ions and small molecules [273,274]. Vijayaraj et al. [273] investigated the transport mechanism of 5-FU through a variety of CPNTs and determined the driving forces and free energy barriers affecting this transport. They found that transport was driven by direct or water-mediated hydrogen bonding and hydrophobic interactions between CPNT and 5-FU. CPPs are of interest because of their intrinsic ability to enter cells, their low cytotoxicity, and their versatility. They can facilitate the transport of small molecules (e.g. drugs, imaging agents), macromolecules (e.g. DNA, siRNA, proteins, peptides) and NPs/DDS. Therefore, CPPs can be used as drug/gene delivery systems, as well as carriers for imaging agents, proteins and peptides [275]. Recently, it has been of great interest to combine the benefits of nanomaterials and CPPs. CPPs can be functionalized with other delivery systems, like QDs, liposomes, and dendrimers, potentially improving CPPs intracellular drug release ability and decreasing their toxicity. DDSs such as polymeric micelles, dendrimers, liposomes, QDs, and inorganic nanocarriers (e.g. gold-, silver-, and iron-based NPs) can be functionalized with CPPs to enable their cellular uptake [275]. CPPs' unique physicochemical 23
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properties, their uptake mechanisms, classifications, and applications in medicine and biotechnology
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have recently been reviewed by Durzynska et al. [275]. Key elements involved in CPPs' roles in drug delivery have been studied computationally [285–
interaction
between
two
CPPs,
penetratin
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288]. Atomistic MD simulations revealed the importance of arginine, lysine and tryptophan in the [285]
and
transportan
[286],
and
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dipalmitoylphosphatidylcholine (DPPC) membranes, as well as their translocation mechanism. CPP conjugation can help translocate hydrophobic and hydrophilic drugs across lipid membranes [289,290].
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Teixeira et al. [290], via both MD simulations and experiments, found that lysine-based surfactants enhanced the transdermal permeation of two topically administered hydrophilic drugs, tetracaine and
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ropivacaine hydrochloride. As mentioned earlier, NPs can be functionalized with CPPs. The concentration and distribution of CPPs on the NP's surface influence the conformation of the peptide layer, which in turn affects the CPP-functionalized NPs’ activity and the efficiency of cellular
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internalization. So, designing a DDS of desired activity requires knowledge of the structure and
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dynamics of CPPs on NPs in solution prior to interacting with membranes. Todorova et al. [291] using both experimental and atomistic MD simulations investigated the effect of the grafting density and
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surface distribution of HIV-derived trans-activator of transcription (TAT) peptide on NPs cell internalization. They found a correlation between the surface properties, e.g. positive charge distribution, of these TAT conjugated NPs in solution, and their membrane permeation.
New DDS design strategies combine the benefits of several systems such as block copolymers and CPPs [292], or mimic natural systems such as intrinsically disordered proteins (IDPs) [293]. Sanchez-Sanchez et al. [293], using both experiments and MD simulations, designed and characterized single chain NPs called artificial IDP mimetics. These IDP mimetics are capable of simultaneous release of two dermal bioactive molecules, folic acid and hinokitiol, into aqeous solution in a pHdependent manner. Computer simulations can provide insight into drug loading, distribution/complex structure and release from peptide self-assemblies [294,295], as well as the influence of pH and temperature [282,294,296] on these processes. Guo et al., using both experiments and DPD simulations investigated 24
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the effect of pH on microstructure of peptidic micelles [296]. Micelles were self-assemblies of HR-20
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(Histidine10-Arginine10) peptides conjugated with cholesterol, either loaded or not with DOX. Micelles had more compact structure at pH > 6 (Fig. 9). For pH < 6, channels in swollen micelles
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might cause drugs to diffuse out of micelles. This was verified experimentally as the DOX release rate was influenced by pH values, consistent with simulation results.
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Finally, physicochemical properties of peptides and solvent conditions can govern selfassembly and structural properties of peptide-based carriers. Computer simulation can be used to
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design peptides which can self-assemble, and to study the mechanism of self-assembly into nanostructures. Computational approaches can also be used to study how factors such as solvent and
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4.4. Carbon-based delivery systems
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temperature influence both self-assembly and transitions between different morphologies [297–301].
Several different types of carbon-based nanoparticles (CNPs) have the potential to act as DDS
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[12,302]. Here we focus on DDS based on CNTs and fullerenes but DDS using graphene, graphene oxide, and nanodiamond [19,162,165,168] are also of interest. Computational research has played a major role in exploring drug loading of [164,303] and release [132,133,304,305] from CNPs, as well as interactions of cargos with carbon nanocarriers [306] as a function of internal and external stimulus, e.g. pH, temperature, torsion, and external magnetic field. CNTs are capable of loading therapeutic agents on their surface or inside their cylindrical hollow [19,307]. Saikia et al.[305] performed MD simulation to study the C60 fullerene-mediated release of multiple pyrazinamide molecules from the interior of a single walled CNT as a function of temperature. Chaban et al. [304] investigated the effect of temperature and concentration on drug release from CNTs using MD simulations. The effect of external magnetic fields has been studied on hybrid systems composed of CNTs with magnetic NP caps [132]. Understanding the interactions of CNTs with biological macromolecules is also of great interest [308–313]. Wu et al. [313] studied DNA ejection from CNTs as a function of temperature, torsion loading and CNT size by MD simulation. Santosh et al. [309] studied the binding of siRNA and DNA 25
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to the surface of single walled CNTs via MD simulations. Chen et al. [311] studied the dynamic
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mechanism of encapsulation of HIV replication inhibitor peptide into CNTs using MD simulation. It is also important to understand the interactions of carbon-based DDS with human serum albumin (HSA),
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the most abundant protein in blood. Interaction of DDS with HSA is crucial in DDS absorption, distribution and metabolism [314].
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The membrane associations of carbon-based DDS have been studied by computer simulation [315–317]. Several factors affect these associations: DDS structural properties [318–321]; clustering
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[320–322]; functionalization [319,323,324]; concentration [319] and lipid membrane properties [321,325]. The effects of size and clustering of fullerene NPs, and fullerene concentration on a DPPC
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monolayer as a model for pulmonary surfactant was investigated by CG MD simulation by Chiu et al.[321]. Free energy calculations suggest that all fullerene systems in this study (C60, C180, C540, and cluster of five C60 fullerenes) spontaneously diffuse into the hydrophobic region of both
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monolayer and bilayer membranes. Large fullerene molecules were found to prefer partitioning into
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bilayers rather than monolayers, however, which can influence the monolayer-to-bilayer transition in
inhalation.
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the respiratory cycle. This may suggest a possible mechanism of internalization of CNPs through lung
The effects of using polymers, peptides, and lipids as surface functionalization agents in carbon-based DDS were studied by computational methods. Chehel Amirani and Tang [308] comprehensively reviewed studies, mainly at the quantum mechanical level, of graphene and CNT functionalization, and their binding energies with nucleobases. Lai and Barnard [131] have reviewed recent studies on functionalized nanodiamonds and their importance for biomedical applications. Functionalization can prevent NPs from aggregating [323,326], increase their stability [327], and influence their interactions with lipid membranes [323,326,328]. Surface functionalization can also promote drug delivery through improving translocation across lipid bilayers [328]. Sridhar et al.[328] used CG MD simulations to look at effects on fullerene (C60) translocation through the membrane due to polar and nonpolar functionalization, temperature, and fullerene concentration. Although none of their models showed complete translocation, they found that Janus particles having half the surface
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modified by polar groups were the most promising form of functionalized fullerenes in terms of bilayer
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translocation. A NP's behavior depends on the conformation of the functionalization layer on its surface. The
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coating mechanism used to functionalize the NP, e.g. covalent vs. non-covalent attachment, as well as the grafting density are two factors which can affect this conformation [329,330]. For example, Lee
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[330] found, using CG MD simulation, that the PEGylation method can influence PEG distribution on CNTs. They also found that PEG size and grafting density affected the PEG layer's conformation (Fig.
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10). Designing functionalized NPs with desired properties will require a detailed understanding of these
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4.5. Lipid-based delivery system
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factors.
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Recently, lipid-based DDS such as liposomes, micelles and LNPs have attracted much attention [7,8,331,332] due to their ability to encapsulate and transport drugs as well as biomolecules, the
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versatility of their structures and compositions, and their inherent selectivity to tumor cells and inflammation sites. There are currently more than ten approved lipid-based drug formulations and many more in clinical trials [7,8]. Specifically, sterically stabilized liposomes, i.e. PEGylated liposomes, are widely used as DDS. PEGylation achieves long circulation times for liposomes in vivo due to the formation of a protective PEG layer over the liposomal surface. This layer sterically prevents the coating of liposomes by opsonins, thus reducing drug uptake by cells of the immune system [333]. However, the mechanism through which PEGylated lipids interact with different components in the liposomes, its function in drug distribution, localization and retention as well as the influence of external environmental factors such as salt concentration are not completely understood. Since atomistic simulations of full liposomes are computationally expensive, a common approach is to infer results on liposomes from lipid bilayer simulations. Dzieciuch et al., [334] for instance, performed atomistic MD simulation to study the effect of liposome PEGylation on their drugloading efficiency. They compared the effect of zwitterionic and PEGylated membranes on the location and orientation of a model hydrophobic compound, 5,10,15,20-tetrakis(4-hydroxyphenyl)porphyrin (p27
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THPP). p-THPP enters both types of lipid bilayers, which agreed with experimental results, but in
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PEGylated liposomes p-THPP also localizes to the outer PEG corona where porphyrins are wrapped by PEG chains. Thus, in PEGylated liposomes p-THPP showed a greater exposure to the water than in
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zwitterionic liposomes. These results highlight that PEGylation enhances the drug-loading efficiency of membranes and support fluorescence experiments carried out in this study [334]. Simulations also
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predicted strong interaction between PEG lipids and porphyrin [334]. This interaction is particularly important in drug delivery studies as it may be an extra barrier to drug release. This finding supports
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previous computational studies that showed that PEG interacts with hydrophobic molecules [127,335,336]. Similarly, MD simulations combined with free energy calculations were used to
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elucidate binding mechanisms and divulge the exact location and organization of two small drugs, bexarotene and human VIP, within PEGylated micellar nanocarriers [121]. PEGs strongly interact with salts. This property has been utilized in lithium ion batteries with
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PEGs as polymer electrolytes [337,338]. PEGs are soluble in a wide variety of both polar and nonpolar
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solvents [339]. Although both of these general properties have been shown previously by computational studies [337,338,340] and experiments [341,342], for DDS applications it is important to
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understand how the PEG layer on DDS, e.g. PEGylated liposomes, interacts with salts in physiological conditions and how these interactions affect the structure of the PEG layer [118,343,344]. Stepniewski et al. [344] modeled the effect of NaCl on the surface structure of PEGylated liposomes. They varied the salt concentration in Langmuir monolayer film experiments and added ions at the physiological level in atomistic MD simulation studies. The results showed that the PEG surface layer should not be treated as generic hydrophilic molecules completely outside the bilayer, nor should it be considered as totally neutral as was previously accepted. PEG molecules are able to penetrate into the liquidcrystalline lipid bilayer, which may affect the permeability and structure of the membrane. It was also noted that Na+ ions bind to PEG chains, changing the surface charge of the liposome and enhance opsonization, whereas Ca2+ did not interact with PEG [343]. These results are not surprising, however. In fact, when PEGs are attached on DDS surface, e.g. liposomes, they seem to show similar properties as previously reported for other applications [337,338,340–342]. As another example, Pannuzo et al. [345] performed CG simulations to study the effect of Ca2+ and PEG molecules on membrane fusion. 28
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They showed that for a rapid fusion between negatively charged apposed membranes both Ca2+ and
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PEG were required.
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An important property governing drug release from liposomal DDS is bilayer permeability [346,347]. Magarkar et al. [346] performed atomistic simulations using the OPLS-AA force field to
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investigate the high permeability observed for "inverse-phosphatidylcholine" (CPe) liposomes in experimental studies [348]. CPe is a synthetic analog of phosphatidylcholine (PC) with a reversed
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zwitterionic headgroup and has been proposed for use in liposomal DDS formulations. The larger surface area observed for the CPe bilayer compared with the PC bilayer may cause CPe's higher
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permeability. Adding cholesterol to liposomal formulations reduces leakage from liposomes due to its well-known role in lipid packing and stability, but the mechanism for this is not understood. Magarkar et al.[336], by performing MD simulation using OPLS force field, proposed a model to explain the
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interaction between PEG molecules and cholesterol. PEG molecules tend to interact with the ß side of
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cholesterol, disrupting the membrane structure. Contrary to the expectation that cholesterol should make the membranes more stable and compact, adding cholesterol in the presence of PEG caused
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membrane destabilization. This result may explain possible effects of cholesterol on the permeability and compressibility of PEGylated liposomes. Self-assembly is another feature of special interest in nanotechnology and nanomedicine as its understanding provides a framework for developing new nanoscale materials with desirable properties [116]. CG and DPD simulations have been performed for the investigation of lipid-based aggregates relevant for drug delivery [115,349–352]. For example, Lee and Pastor [349] used the Martini CG force field to study mixtures of lipids and PEGylated lipids in water at different sizes and concentrations of PEGylated lipids. They found that the mixtures self-assembled to liposomes, bicelles and micelles. The analyses and simulations indicated that the average aggregate sizes decreased when the concentration of PEGylated lipid increased, in agreement with experimental results. Janke et al.[352] used CG MD simulations to study the phase behavior of oleic acid aggregation at various concentration and protonation states. They observed a range of structures including micelles, vesicles and oil phases depending on the protonation state of the oleic acid head group. 29
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Simulations can be used to calculate drug release rates from lipid-based DDS [353,354]. For instance, the release of encapsulated materials from systems including emulsions (100% liquid lipid
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phase) and nanostructured lipid carriers, which have a combination of solid and liquid lipid domains, was investigated by Dan using Monte Carlo simulations [353]. The results obtained suggest that the
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size of lipid domains does not significantly affect the rate of release, but the location and distribution of the solid domains have a notable impact. When solid domains are concentrated near the solution
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interface, transport is inhibited due to a reduction in the accessible surface area. However, when the solid domain is located in the center of the LNP the release rate is increased and may even be higher
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than in the corresponding emulsion particle. In another study by Dan [354], Monte Carlo simulations were performed to study drug release in response to electric field-induced liposome pores. There are also other interesting studies, three of which are mentioned briefly here. Computer
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simulations were combined with experiments to investigate the effect of penetration and membrane
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behaviour of three terpenes, which are effective chemical permeation enhancers in nanostructured lipid carriers [355]. A detailed model of the distribution of drug molecules inside a liposome was obtained
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using the Martini force field [356]. Jämbeck et al. [356] performed large scale simulations of hypericin, a photosensitizing drug, in a DPPC liposome (Fig. 11) and calculated the distribution and orientation of the drugs in the lipid bilayer. Hoof et al.[357] studied the encapsulation of proteins in spontaneously forming vesicles using MD simulation with a CG force field. They showed that the interactions of proteins with membranes govern the encapsulation efficiency, but the size of the encapsulated proteins and the speed of the vesicle formation did not seem to have a significant effect. A promising gene therapy approach is based on siRNA. Sophisticated delivery systems are required to protect and deliver siRNA effectively to the target tissues [358,359]. CG molecular simulations together with a variety of experimental techniques were able to give a more detailed view of the structure of a LNP containing siRNA (Fig. 12) [79,360]. Simulations of small systems containing 8 duplex 12 base pair DNA complexes, DLinKC2-DMA (an ionizable cationic lipid), DSPC, cholesterol, a PEG-lipid, water and ion molecules were performed. These molecules self-assembled and organized in an ordered structure where, for instance, the PEG-lipids were distributed in the outer 30
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layer of the LNP. This was the first study to show that LNP siRNA systems likely have a nanostructured core, with the encapsulated siRNA located in internalized inverted micelles complexed
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to cationic lipids. The in silico finding agreed with 31P NMR, FRET, cryo-TEM, and RNase digestion
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data and it was possible to understand the mechanism whereby LNP siRNA systems are formed. This insight is being applied to the design and construction of new LNP systems. In addition, both CG and
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atomistic computer simulation have been used to study the DNA adsorption onto anionic lipid
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membranes induced by multivalent cations [361].
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4.6. Gold-nanoparticles
AuNPs are of particular importance in drug delivery research due to their unique properties
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including their stability, ease of synthesis, and the ability to manufacture a range of sizes [15,362].
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They can be loaded with various therapeutics including small molecules, peptides, proteins, and nucleic acids [19], either conjugated to AuNPs covalently or noncovalently. There are several computational
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studies on the properties of AuNPs and their interactions with other molecules [135,227,363–370]. The effect of AuNP conjugation on peptide conformational flexibility and structure was studied by Lee and Ytreberg [366]. They examined the structure and dynamics of six peptides that were either free or conjugated to AuNPs in water. Conjugation affected both structure and dynamics in an amino acid sequence dependent way. Peptides with little or no secondary structure in solution were adsorbed on the AuNP surface. This causes peptides to lose their specific interactions with cell components. For drug delivery purposes, this suggests that peptides with significant secondary structures in solution are suitable candidates for peptide-NP conjugation. Interactions between ubiquitin and AuNPs have also been studied by Brancolini et al. [135] using computer simulation. Van Lehn and co-workers performed a series of computational studies on AuNPs coated by lipids. Among other properties, they investigated the structure of the monolayer coating the AuNP under different conditions [369], lipid composition and AuNP size [368], the interactions of a
31
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monolayer-coated AuNP with model membranes relevant for uptake [367], suggesting similarities in
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uptake process with bilayer-bilayer fusion [371], and interactions between AuNPs [370].
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5. Conclusions and future perspectives
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We have reviewed common biophysical and computational approaches to studying DDSs, focusing in particular on computational studies of several major classes of NPs used in drug delivery research. The
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strength of computer simulations in general is their ability to give very detailed insight into the structure of NPs and their interactions with drugs and model membranes under a range of conditions.
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Clearly, the efficiency of DDSs depends on a large number of other parameters, including stability in the bloodstream, targeting to the desired tissues, uptake by endocytosis or other mechanisms, and final release of the drugs. Although experimental biophysical and computational studies can characterize
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important aspects of DDSs, a major challenge for the near future is to go beyond individual drug
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DDSs.
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carriers and to investigate at a mechanistic level the larger-scale processes that determine the success of
At the computational level, currently we are limited by some of the standard limitations of biomolecular simulation. In general, these involve limited sampling time, limited length scale, and expensive or difficult access to important thermodynamic variables. Simulations can access time scales of the order of microseconds or for coarser models tens or hundreds of microseconds, at length scales of ca. 10 x 10 nm for atomistic and up to 150 x 150 nm for CG simulations. Thus it is now possible to simulate entire nanocarriers, which will open up new areas of study for simulations. However, the process of NP formation by e.g. microfluidics remains outside reach of direct detailed simulations. Questions regarding drug loading, the stability of complexes, and the interactions of NPs with membranes essentially involve free energies of binding, partitioning, and membrane pore formation. These can all be studied by free energy methods, but the current state of the literature remains somewhat qualitative. This is an obvious area for future work, which is already feasible with current methods and computational resources. Some of the most interesting simulations currently available use 32
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CG MD or DPD. This is an exciting development, but also comes with its own limitations; in particular,
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CG models need to be carefully tested for their ability to reproduce the effect of important factors in DDSs such as salt concentration, the distribution of chemical functional groups over the surface of a
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DDS, and the effect of pH.
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DDSs are an interesting example of multi-scale computational problems, with a growing amount of data from biophysical characterization. They have an obvious biomedical and biotechnological
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importance, and bridge chemistry, nanoscience, materials, basic biology and medical applications in a unique way. Although there already is a large body of literature as reviewed in this paper, in our
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assessment the impact of computational work in this important area is likely to grow significantly in the near future as model systems become more realistic and more tightly coupled to experiment.
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Conflict of interest
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The authors declare no conflicts of interest.
Acknowledgements
This work was supported by a Natural Sciences and Engineering Council (Canada) Strategic Project Grant (DPT, JT). DPT is an Alberta Innovates Health Solutions Scientist and Alberta Innovates Technology Futures Strategic Chair in (Bio)Molecular Simulation.
Tables and Figures (tables/figures in separate file) Table 1. Questions relevant to drug delivery system design that can be answered by computational simulations.
Table 2. Experimental methods used to study drug delivery systems.
Figure 1. Drug delivery pathway for a drug-nanocarrier system. After the DDSs enter the bloodstream (1), drug (green circle) leakage might occur if the drug-DDS complex is unstable (2). 33
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Depending on the surface chemistry, DDSs might interact with biomolecules (3) and ions (4) in
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circulation. Eventually, they will extravasate to the extracellular space (5). After reaching the target cell, they interact with the cell membrane (6) and can be internalized either via direct penetration (7) or
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endocytosis (8). In the latter case, the nanocarrier will be entrapped in endosomes and must be released (9) before endosomal maturation. Drug-DDS dissociation (10) is necessary for drugs to be effective in
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the cytoplasm or nucleus (11). This dissociation can be mediated by interactions with endogeneous
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biomolecules (12). Not drawn to scale.
Figure 2. Examples of nanoparticulate DDS. From top-left to bottom-right, monolayer-protected
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gold nanoparticle, polymeric micelle, DNA cage, carbon nanotube, solid lipid nanoparticle, polymer, dendrimer and dendron, liposome, and protein-based DDS. For dendrimer: orange, green, red, and blue represents G0, G1, G2, and G3, respectively. Dendron has also been shown as black branch. Not drawn
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to scale.
Figure 3. Categories of simulation methods and their respective spatio-temporal domains of
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applicability. The question of interest dictates the level of resolution required. Briefly, if electronic motion play an important role in the property we are studying, quantum mechanics (QM) level is appropriate. However, if there is no bond formation and cleavage, all atom (AA) level works well; we can safely ignore electronic motion by assigning point charges to each atom. If electrostatic and hydrophobic interactions are the dominant contributors, coarse grain models (CG) can be used: individual atoms can be ignored and a group of atoms (e.g. 4 heavy atoms in MARTINI) can be treated as one interaction point/bead. CG models allow the exploration of a larger area in phase space, at the expense of losing atomistic details. Coarser levels of simulations (e.g. DPD and continuum models) are appropriate for systems and processes which require longer times and lengths.
Figure 4. PAMAM Dendrimer-DNA interaction and deformation as a function of generation number. (a) For the G3 dendrimer, very little deformation in DNA was observed, while the dendrimer was bent considerably by the DNA. (b) For G4, both DNA and dendrimer were deformed. (c) For G5, 34
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DNA was deformed, but the dendrimer behaved almost as a rigid body. Adapted with permission from
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B. Nandy, P.K. Maiti, A. Bunker, Force Biased Molecular Dynamics Simulation Study of Effect of
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Dendrimer Generation on Interaction with DNA, (2013). Copyright 2013 American Chemical Society.
Figure 5. Proposed mechanism of endosomal escape for pH-responsive dendrimers. In endosomes
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(pH ~ 5), dendrimers are protonated, leading to increasing osmotic pressure and dendrimer swelling, putting the membrane under tension. Meanwhile, electrostatic interactions between charged dendrimers
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and asymmetric negatively charged endosomal membrane cause a drop in the critical membrane tension required for membrane disruption, allowing NP escape from endosomes. Bottom row:
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snapshots of a MARTINI simulation of interactions between a G4 dendrimer (orange beads) and a membrane (with lipid tails represented with cyan beads, and lipid headgroups represented with green and blue beads) at different pH levels. Reproduced from [124] with permission from The Royal Society
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of Chemistry.
Figure 6. Time evolution of nanoparticle-polymer complex endocytosis as a function of pH. The
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endocytosis process of a nanoparticle-polymer complex (nanoparticle with pH-sensitive polymers absorbed on its surface) depends on the pH. For pH values lower (a) and higher (c) than the polymer’s pKa, the nanoparticle can be fully engulfed by the membrane. However, for pH = pKa (b), endocytosis is blocked. Membrane lipid head groups are shown in green, purple, and blue correspond to +e, -e, and neutral beads, respectively. Lipid tails are in orange, receptor heads are in red, and nanoparticle beads are in yellow. Polymer beads are in cyan (-e) and pink. Reprinted by permission from Macmillan Publishers Ltd: Scientific reports H. Ding, Y. Ma, Controlling Cellular Uptake of Nanoparticles with pH-Sensitive Polymers., Sci. Rep. 3 (2013) 2804., Copyright 2013.
Figure 7. Solubilization of bexarotene in sterically stabilized micelles (SSM). (Left) Free energy profiles for systems composed of 1, 3, and 5 bexarotenes in SSMs. SSMs are either composed of 10 (small) or 90 (large) monomers, in water and 0.16 M NaCl, respectively. Single drugs prefer the ionic interface, represented with vertical arrows, in both SSMs. For multiple drugs accumulated in the SSM 35
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core (shown as a gold surface), a deeper minimum develops in the core. This is because several
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bexarotene (e.g. 5) cluster together (Right) via a hydrogen bond network between their –COOH groups. Reprinted (adapted) with permission from L. Vukovic, A. Madriaga, A. Kuzmis, A. Banerjee, A. Tang,
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K. Tao, et al., Solubilization of Therapeutic Agents in Micellar Nanomedicines, (2013). Copyright
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2013 American Chemical Society.
Figure 8. DNA nanocage with temperature-controlled conformational transition capability. The
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cage is closed at 4C (a), and in a more open conformation at 37C (b). Heating the nanocage to 37C in presence of horseradish peroxidase (HRP), followed by cooling to 4C, allows HRP enzymes to be
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encapsulated in nanocages. Reheating to 37C causes enzyme (orange) release (not shown). Reprinted with permission from S. Juul, F. Iacovelli, M. Falconi, S.L. Kragh, B. Christensen, R. Frøhlich, et al.,
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Encapsulation and Release of an Active Enzyme in the Cavity of a Self-Assembled DNA Nanocage,
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(2013) 9724–9734. Copyright 2013 American Chemical Society.
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Figure 9. Effect of pH on microstructure of self-assembled micelles. Starting from a homogeneous state (a), cholesterol conjugated peptides form compact core/shell micelles at pH > 6.0 (b). Decreasing the pH loosens these micelles (c) and the swelling facilitates DOX release. Arginine, histidine, and cholesterol, are shown in green, brawn, and black, respectively. Water has not been shown for clarity. Reprinted with permission from X.D. Guo, L.J. Zhang, Z.M. Wu, Y. Qian, Dissipative Particle Dynamics Studies on Microstructure of pH-Sensitive Micelles for Sustained Drug Delivery, Macromolecules. 43 (2010) 7839–7844. Copyright 2010 American Chemical Society.
Figure 10. Effect of PEGylation method, and PEG chain size and grafting density on the conformation of PEG layer on carbon nanotube. Non-covalently modified single walled CNT (SWNT) (Left) is more exposed to water than covalently modified SWNT (Right). PEG (red) size and grafting density also influence the conformation of PEG layer on SWNT (grey cylinder). RF is the mushroom radius and L is the thickness of brush state. SWNT is shown as grey cylinders. Reprinted with permission from H. Lee, Molecular Dynamics Studies of PEGylated Single-Walled Carbon 36
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Nanotubes: The Effect of PEG Size and Grafting Density, (2013). Copyright 2013 American Chemical
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Society.
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Figure 11. Distribution and orientation of hypericin within liposome. a) Snapshots from MD simulations taken after 10 μs. Phosphorus groups (orange), choline groups (blue) and hydrophobic tails
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(green) of the lipids are represented, with different numbers of drug molecules (yellow). b) Liposomes with some lipids removed to show the binding of hypericin to DPPC; the hydrophilic parts of hypericin
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are shown in red and the hydrophobic parts in yellow. Reprinted with permission from J.P.M. Jämbeck, E.S.E. Eriksson, A. Laaksonen, A.P. Lyubartsev, L.A. Eriksson, Molecular Dynamics Studies of
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Liposomes as Carriers for Photosensitizing Drugs: Development, Validation and Simulations with a Coarse-Grained Model, (2013). Copyright 2013 American Chemical Society.
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Figure 12. Lipid nanoparticle. External and a cross-sectional view of the LNP (top). Components are
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represented by their molecular densities: protective PEG-lipid (magenta), therapeutic siRNA (red), cholesterol (green), DSPC (yellow), ionizable cationic lipid (DLin-KC2-DMA) (blue) and water (cyan).
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Adapted from D. Rozmanov, S. Baoukina, D.P. Tieleman, Density Based Visualization for Molecular Simulation., Faraday Discuss. 169 (2014) 225–43. doi:10.1039/c3fd00124e. Under a Creative Commons Attribution 3.0 Unported License.
References: [1]
E. Blanco, H. Shen, M. Ferrari, Principles of nanoparticle design for overcoming biological barriers to drug delivery, Nat. Biotechnol. 33 (2015) 941–951.
[2]
T.M. Allen, P.R. Cullis, Drug delivery systems: Entering the mainstream, Science. 303 (2004) 1818–1822.
[3]
S. Parveen, R. Misra, S.K. Sahoo, Nanoparticles: A boon to drug delivery, therapeutics, diagnostics and imaging, Nanomed. Nanotechn. Biol. Med. 8 (2012) 147–166. 37
ACCEPTED MANUSCRIPT
[4]
D.S. Kohane, Microparticles and nanoparticles for drug delivery, Biotechnol. Bioeng. 96 (2007)
[5]
PT
203–209. V.J. Venditto, F.C. Szoka Jr., Cancer nanomedicines: So many papers and so few drugs!, Adv.
[6]
RI
Drug Deliv. Rev. 65 (2013) 80–88.
Y. Zhang, H.F. Chan, K.W. Leong, Advanced materials and processing for drug delivery: The
B.S. Pattni, V.V. Chupin, V.P. Torchilin, New developments in liposomal drug delivery, Chem. Rev. 115 (2015) 10938–10966.
[8]
T.M. Allen, P.R. Cullis, Liposomal drug delivery systems: From concept to clinical applications,
MA
Adv. Drug Deliv. Rev. 65 (2013) 36–48. [9]
E.R. Gillies, J.M.J. Frechet, Dendrimers and dendritic polymers in drug delivery, Drug Discov. Today. 10 (2005) 35–43.
C.C. Lee, J.A. MacKay, J.M.J. Fréchet, F.C. Szoka, Designing dendrimers for biological
D
[10]
NU
[7]
SC
past and the future, Adv. Drug Deliv. Rev. 65 (2013) 104–120.
[11]
TE
applications, Nat. Biotechnol. 23 (2005) 1517–1526. S. Svenson, D.A. Tomalia, Dendrimers in biomedical applications—reflections on the field,
[12]
AC CE P
Adv. Drug Deliv. Rev. 57 (2005) 2106–2129. A. Bianco, K. Kostarelos, M. Prato, Applications of carbon nanotubes in drug delivery, Curr. Opin. Chem. Biol. 9 (2005) 674–679. [13]
L. Lacerda, A. Bianco, M. Prato, K. Kostarelos, Carbon nanotubes as nanomedicines: From toxicology to pharmacology, Adv. Drug Deliv. Rev. 58 (2006) 1460–1470.
[14]
A. Bianco, K. Kostarelos, C.D. Partidos, M. Prato, Biomedical applications of functionalised carbon nanotubes, Chem. Commun. (2005) 571–577.
[15]
P. Ghosh, G. Han, M. De, C.K. Kim, V.M. Rotello, Gold nanoparticles in delivery applications, Adv. Drug Deliv. Rev. 60 (2008) 1307–1315.
[16]
T.K. Jain, M. A. Morales, S.K. Sahoo, D.L. Leslie-Pelecky, V. Labhasetwar, Iron oxide nanoparticles for sustained delivery of anticancer agents, Mol. Pharm. 2 (2005) 194–205.
[17]
G. Bao, S. Mitragotri, S. Tong, Multifunctional nanoparticles for drug delivery and molecular imaging, Annu. Rev. Biomed. Eng. 15 (2013) 253–282. 38
ACCEPTED MANUSCRIPT
[18]
C. Sun, J.S.H. Lee, M. Zhang, Magnetic nanoparticles in MR imaging and drug delivery, Adv.
[19]
PT
Drug Deliv. Rev. 60 (2008) 1252–1265. X. Sun, Z. Feng, T. Hou, Y. Li, Computational simulation of inorganic nanoparticle drug
RI
delivery systems at the molecular level, in: Computational Pharmaceutics: Application of Molecular Modelling in Drug Delivery, John Wiley & Sons, 2015: pp. 149-168.
SC
[20] C.S. Kim, G.Y. Tonga, D. Solfiell, V.M. Rotello, Inorganic nanosystems for therapeutic delivery: Status and prospects, Adv. Drug Deliv. Rev. 65 (2013) 93–99. R. Duncan, The dawning era of polymer therapeutics, Nat. Rev. Drug Discov. 2 (2003) 347–360.
[22]
K.S. Soppimath, T.M. Aminabhavi, A.R. Kulkarni, W.E. Rudzinski, Biodegradable polymeric
NU
[21]
[23]
MA
nanoparticles as drug delivery devices, J. Control. Release. 70 (2001) 1–20. L. Huynh, C. Neale, R. Pomès, C. Allen, Computational approaches to the rational design of
Biol. Med. 8 (2012) 20–36.
W.F. van Gunsteren, D. Bakowies, R. Baron, I. Chandrasekhar, M. Christen, X. Daura, et al.,
TE
[24]
D
nanoemulsions, polymeric micelles, and dendrimers for drug delivery, Nanomed. Nanotechn.
Biomolecular modeling: Goals, problems, perspectives, Angew. Chemie Int. Ed. 45 (2006)
[25]
AC CE P
4064–4092.
B. Kann, H.L. Offerhaus, M. Windbergs, C. Otto, Raman microscopy for cellular investigations — From single cell imaging to drug carrier uptake visualization, Adv. Drug Deliv. Rev. 89 (2015) 71–90.
[26]
A. Sharma, U.S. Sharma, Liposomes in drug delivery: Progress and limitations, Int. J. Pharm. 154 (1997) 123–140.
[27]
R.W. Niven, M. Speer, H. Schreier, Nebulization of liposomes. II. The effects of size and modeling of solute release profiles, Pharm. Res. 8 (1991) 217–221.
[28]
S.K. Hobbs, W.L. Monsky, F. Yuan, W.G. Roberts, L. Griffith, V.P. Torchilin, et al., Regulation of transport pathways in tumor vessels: Role of tumor type and microenvironment, Proc. Natl. Acad. Sci. U. S. A. 95 (1998) 4607–4612.
[29]
C.R. Dass, Drug delivery in cancer using liposomes, in: Drug Delivery Systems, Humana Press, 2008: pp. 177–182. 39
ACCEPTED MANUSCRIPT
[30]
P.Y. Lu, M.C. Woodle, Delivering small interfering RNA for novel therapeutics, in: Drug
[31]
PT
Delivery Systems, Humana Press, 2008: pp. 93–107. R. Singh, J.W. Lillard, Nanoparticle-based targeted drug delivery, Exp. Mol. Pathol. 86 (2009)
[32]
RI
215–223.
S.H. Jang, M.G. Wientjes, D. Lu, J.L.S. Au, Drug delivery and transport to solid tumors, Pharm.
[33]
SC
Res. 20 (2003) 1337–1350.
A.R. Kirtane, R. A. Siegel, J. Panyam, A pharmacokinetic model for quantifying the effect of
NU
vascular permeability on the choice of drug carrier: A framework for personalized nanomedicine, J. Pharm. Sci. 104 (2015) 1174–1186.
S.K. Brar, M. Verma, Measurement of nanoparticles by light-scattering techniques, Trends Anal.
MA
[34]
Chem. 30 (2011) 4–17. [35]
W. Mehnert, K. Mäder, Solid lipid nanoparticles: Production, characterization and applications,
S.A. Kübart, C.M. Keck, Laser diffractometry of nanoparticles: Frequent pitfalls & overlooked
TE
[36]
D
Adv. Drug Deliv. Rev. 64 (2012) 83–101.
opportunities, J. Pharm. Technol. Drug Res. 2 (2013) 17. A. Moquin, F.M. Winnik, The use of field-flow fractionation for the analysis of drug and gene
AC CE P
[37]
delivery systems, in: Field-Flow Fractionation in Biopolymer Analysis, Springer Vienna, 2012: pp. 187-206 [38]
A. Zattoni, B. Roda, F. Borghi, V. Marassi, P. Reschiglian, Flow field-flow fractionation for the analysis of nanoparticles used in drug delivery, J. Pharm. Biomed. Anal. 87 (2014) 53–61.
[39]
F.M. Veronese, G. Pasut, PEGylation, successful approach to drug delivery, Drug Discov. Today. 10 (2005) 1451–1458.
[40]
N.S. Berchane, K.H. Carson, A.C. Rice-Ficht, M.J. Andrews, Effect of mean diameter and polydispersity of PLG microspheres on drug release: Experiment and theory, Int. J. Pharm. 337 (2007) 118–126.
[41]
C.M. Beddoes, C.P. Case, W.H. Briscoe, Understanding nanoparticle cellular entry: A physicochemical perspective, Adv. Colloid Interface Sci. 218C (2015) 48–68.
40
ACCEPTED MANUSCRIPT
[42]
M. Mu llner, S.J. Dodds, T.-H. Nguyen, D. Senyschyn, C.J.H. Porter, B.J. Boyd, et al., Size
PT
and rigidity of cylindrical polymer brushes dictate long circulating properties in vivo, ACS Nano. 9 (2015) 1294–1304.
S.E.A. Gratton, P. A. Ropp, P.D. Pohlhaus, J.C. Luft, V.J. Madden, M.E. Napier, et al., The
RI
[43]
effect of particle design on cellular internalization pathways, Proc. Natl. Acad. Sci. U. S. A. 105
[44]
SC
(2008) 11613–11618.
J. A. Champion, S. Mitragotri, Role of target geometry in phagocytosis, Proc. Natl. Acad. Sci.
[45]
NU
U. S. A. 103 (2006) 4930–4934.
S. Fusco, H.W. Huang, K.E. Peyer, C. Peters, M. Häberli, A. Ulbers, et al., Shape-switching
MA
microrobots for medical applications: The influence of shape in drug delivery and locomotion, ACS Appl. Mater. & Interfaces. 7 (2015) 6803–6811. [46]
G. Ruan, S.S. Feng, Preparation and characterization of poly(lactic acid)–poly(ethylene glycol)–
D
poly(lactic acid) (PLA–PEG–PLA) microspheres for controlled release of paclitaxel,
[47]
TE
Biomaterials. 24 (2003) 5037–5044.
E. Fattal, H. Hillaireau, S. Mura, J. Nicolas, N. Tsapis, Targeted delivery using biodegradable
AC CE P
polymeric nanoparticles, in: Fundamentals and Applications of Controlled Release Drug Delivery, Springer US, 2012: pp. 255–288. [48]
J.L.S. Milne, M.J. Borgnia, A. Bartesaghi, E.E.H. Tran, L.A. Earl, D.M. Schauder, et al., Cryoelectron microscopy - A primer for the non-microscopist, FEBS J. 280 (2013) 28–45.
[49]
A. Bartesaghi, A. Merk, S. Banerjee, D. Matthies, X. Wu, J.L.S. Milne, et al., 2.2 Å resolution cryo-EM structure of β-galactosidase in complex with a cell-permeant inhibitor, Science. 348 (2015) 1147–1151.
[50]
T. Turovsky, R. Khalfin, S. Kababya, A. Schmidt, Y. Barenholz, D. Danino, Celecoxib encapsulation in β-Casein Micelles: Structure, interactions, and conformation, Langmuir. 31 (2015) 7183–7192.
[51]
M. Zheng, G.M. Pavan, M. Neeb, A.K. Schaper, A. Danani, G. Klebe, et al., Targeting the blind spot of polycationic nanocarrier-based siRNA delivery, ACS Nano. 6 (2012) 9447–9454.
[52]
A.M. Alkilany, C.J. Murphy, Toxicity and cellular uptake of gold nanoparticles: What we have 41
ACCEPTED MANUSCRIPT
learned so far?, J. Nanoparticle Res. 12 (2010) 2313–2333. K. Murugan, Y.E. Choonara, P. Kumar, D. Bijukumar, L.C. du Toit, V. Pillay, Parameters and
PT
[53]
characteristics governing cellular internalization and trans-barrier trafficking of nanostructures,
[54]
E. Fröhlich, The role of surface charge in cellular uptake and cytotoxicity of medical
SC
nanoparticles, Int. J. Nanomed. 7 (2012) 5577–5591. [55]
RI
Int. J. Nanomed. 10 (2015) 2191–2206.
M. Di Marco, C. Sadun, M. Port, I. Guilbert, P. Couvreur, C. Dubernet, Physicochemical
NU
characterization of ultrasmall superparamagnetic iron oxide particles (USPIO) for biomedical application as MRI contrast agents, Int. J. Nanomed. 2 (2007) 609–622. K. Xiao, Y. Li, J. Luo, J.S. Lee, W. Xiao, A.M. Gonik, et al., The effect of surface charge on in
MA
[56]
vivo biodistribution of PEG-oligocholic acid based micellar nanoparticles, Biomaterials 32 (2011) 3435–3446.
L.P. Mendes, J.M.F. Delgado, A.D.A. Costa, M.S. Vieira, P.L. Benfica, E.M. Lima, et al.,
D
[57]
TE
Biodegradable nanoparticles designed for drug delivery: The number of nanoparticles impacts on cytotoxicity, Toxicol. Vitr. 29 (2015) 1268–1274. M. Law, M. Jafari, P. Chen, Physicochemical characterization of SiRNA-peptide complexes,
AC CE P
[58]
Biotechnol. Prog. 24 (2008) 957–963. [59]
A.M. Chen, M. Zhang, D. Wei, D. Stueber, O. Taratula, T. Minko, et al., Co-delivery of Doxorubicin and Bcl-2 siRNA by mesoporous silica nanoparticles enhances the efficacy of chemotherapy in multidrug-resistant cancer cells, Small. 5 (2009) 2673–2677.
[60]
X.Y. Wang, L. Zhang, X.H. Wei, Q. Wang, Molecular dynamics of paclitaxel encapsulated by salicylic acid-grafted chitosan oligosaccharide aggregates, Biomaterials. 34 (2013) 1843–1851.
[61]
B. Sun, D.T. Chiu, Determination of the encapsulation efficiency of individual vesicles using single-vesicle photolysis and confocal single-molecule detection, Anal. Chem. 77 (2005) 2770– 2776.
[62]
N. Martinho, H. Florindo, L. Silva, S. Brocchini, M. Zloh, T. Barata, Molecular modeling to study dendrimers for biomedical applications, Molecules. 19 (2014) 20424–20467.
[63]
H. Bunjes, T. Unruh, Characterization of lipid nanoparticles by differential scanning 42
ACCEPTED MANUSCRIPT
calorimetry, X-ray and neutron scattering, Adv. Drug Deliv. Rev. 59 (2007) 379–402. W. Mehnert, Solid lipid nanoparticles: Production, characterization and applications, Adv. Drug
PT
[64]
Deliv. Rev. 47 (2001) 165–196.
M. Deborah, A. Jawahar, T. Mathavan, M.K. Dhas, A. M.F. Benial, Spectroscopic studies on
RI
[65]
Spectrochim. Acta Part A. 139 (2015) 138–144. [66]
SC
sidewall carboxylic acid functionalization of multi-walled carbon nanotubes with valine,
P.S. Williams, F. Carpino, M. Zborowski, Magnetic nanoparticle drug carriers and their study
[67]
NU
by quadrupole magnetic field-flow fractionation, Molecules. 6 (2009) 1290–1306. M.L. Briuglia, C. Rotella, A. McFarlane, D.A. Lamprou, Influence of cholesterol on liposome
[68]
MA
stability and on in vitro drug release, Drug Deliv. Transl. Res. 5 (2015) 231–242. J. Wang, Y. Wang, W. Liang, Delivery of drugs to cell membranes by encapsulation in PEG– PE micelles, J. Control. Release. 160 (2012) 637–651. N.W.S. Kam, Z. Liu, H. Dai, Functionalization of carbon nanotubes via cleavable disulfide
D
[69]
127 (2005) 12492–12493.
S. Achar, R.J. Puddephatt, Organoplatinum dendrimers formed by oxidative addition, Angew.
AC CE P
[70]
TE
bonds for efficient intracellular delivery of siRNA and potent gene silencing, J. Am. Chem. Soc.
Chemie Int. Ed. 33 (1994) 847–849. [71]
M.L. Viger, W. Sheng, C.L. McFearin, M.Y. Berezin, A. Almutairi, Application of timeresolved fluorescence for direct and continuous probing of release from polymeric delivery vehicles, J. Control. Release. 171 (2013) 308–314.
[72]
A. A. Metwally, R.M. Hathout, Computer-assisted drug formulation design: Novel approach in drug delivery, Mol. Pharm. 12 (2015) 2800–2810.
[73]
M.B. Sankaram, T.E. Thompson, Interaction of cholesterol with various glycerophospholipids and sphingomyelin, Biochemistry. 29 (1990) 10670–10675.
[74]
A. Caminade, R. Laurent, J. Majoral, Characterization of dendrimers, Adv. Drug Deliv. Rev. 57 (2005) 2130–2146.
[75]
Y.W. Hsueh, M. Zuckermann, J. Thewalt, Phase diagram determination for phospholipid/sterol membranes using deuterium NMR, Concepts Magn. Reson. Part A. 26A (2005) 35–46. 43
ACCEPTED MANUSCRIPT
[76]
T. Io, T. Fukami, K. Yamamoto, T. Suzuki, J. Xu, K. Tomono, et al., Homogeneous
PT
nanoparticles to enhance the efficiency of a hydrophobic drug, antihyperlipidemic probucol, characterized by solid-state NMR, Mol. Pharm. 7 (2009) 299–305.
T. Sun, Q. Guo, C. Zhang, J. Hao, P. Xing, J. Su, et al., Self-assembled vesicles prepared from
RI
[77]
amphiphilic cyclodextrins as drug carriers, Langmuir. 28 (2012) 8625–8636. D. Astruc, E. Boisselier, C. Ornelas, Dendrimers designed for functions: From physical,
SC
[78]
photophysical, and supramolecular properties to applications in sensing, catalysis, molecular
[79]
NU
electronics, photonics, and nanomedicine, Chem. Rev. 110 (2010) 1857–1959. A.K.K. Leung, I.M. Hafez, S. Baoukina, N.M. Belliveau, I. V Zhigaltsev, E. Afshinmanesh, et
MA
al., Lipid nanoparticles containing siRNA synthesized by microfluidic mixing exhibit an electron-dense nanostructured core, J. Phys. Chem. C. 116 (2012) 18440–18450. [80]
E. V Mathias, X. Liu, O. Franco, I. Khan, Y. Ba, J. A. Kornfield, Model of drug-loaded
D
fluorocarbon-based micelles studied by electron-spin induced 19F relaxation NMR and
[81]
G. Martini, L. Ciani, Electron spin resonance spectroscopy in drug delivery, Phys. Chem. Chem.
AC CE P
Phys. 11 (2009) 211–254. [82]
TE
molecular dynamics simulation, Langmuir. 24 (2008) 692–700.
H. Wennerström, D.F. Evans, The colloidal domain: Where physics, chemistry, biology, and technology meet, Wiley-VCH: New York, 1999.
[83]
C. Nicolini, J. Kraineva, M. Khurana, N. Periasamy, S.S. Funari, R. Winter, Temperature and pressure effects on structural and conformational properties of POPC/SM/cholesterol model raft mixtures–an FT-IR, SAXS, DSC, PPC and Laurdan fluorescence spectroscopy study, Biochim. Biophys. Acta. 1758 (2006) 248–258.
[84]
T.T. Mills, S. Tristram-Nagle, F. a Heberle, N.F. Morales, J. Zhao, J. Wu, et al., Liquid-liquid domains in bilayers detected by wide angle X-ray scattering, Biophys. J. 95 (2008) 682–690.
[85]
V. Meli, C. Caltagirone, A.M. Falchi, S.T. Hyde, V. Lippolis, M. Monduzzi, et al., Docetaxelloaded fluorescent liquid-crystalline nanoparticles for cancer theranostics, Langmuir. 31 (2015) 9566–9575.
[86]
A. Angelova, B. Angelov, R. Mutafchieva, S. Lesieur, P. Couvreur, Self-assembled 44
ACCEPTED MANUSCRIPT
multicompartment liquid crystalline lipid carriers for protein, peptide and nucleic acid drug
[87]
PT
delivery, Acc. Chem. Res. 44 (2010) 147–156. R.H. Utama, M. Dulle, S. Förster, M.H. Stenzel, P.B. Zetterlund, SAXS analysis of shell
RI
formation during nanocapsule synthesis via inverse miniemulsion periphery RAFT polymerization, Macromol. Rapid Commun. (2015) 1267-1271.
R.H. Müller, S.A. Runge, V. Ravelli, A.F. Thünemann, W. Mehnert, E.B. Souto, Cyclosporine-
SC
[88]
loaded solid lipid nanoparticles (SLN®): drug–lipid physicochemical interactions and
[89]
NU
characterization of drug incorporation, Eur. J. Pharm. Biopharm. 68 (2008) 535–544. A.F. Martins, A.G.B. Pereira, A.R. Fajardo, A.F. Rubira, E.C. Muniz, Characterization of
MA
polyelectrolytes complexes based on N,N,N-trimethyl chitosan/heparin prepared at different pH conditions, Carbohydr. Polym. 86 (2011) 1266–1272. [90]
J. Pan, F.A. Heberle, R.S. Petruzielo, J. Katsaras, Using small-angle neutron scattering to detect
J.R. Lakowicz, Principles of fluorescence spectroscopy, Springer Sceince & Business Media, 2006.
K. Kataoka, A. Harada, Y. Nagasaki, Block copolymer micelles for drug delivery: design,
AC CE P
[92]
TE
[91]
D
nanoscopic lipid domains, Chem. Phys. Lipids. 170-171 (2013) 19–32.
characterization and biological significance, Adv. Drug Deliv. Rev. 64 (2012) 37-48. [93]
S.R. Pygall, J. Whetstone, P. Timmins, C.D. Melia, Pharmaceutical applications of confocal laser scanning microscopy: The physical characterisation of pharmaceutical systems, Adv. Drug Deliv. Rev. 59 (2007) 1434–1452.
[94]
C. Dubernet, Thermoanalysis of microspheres, Thermochim. Acta. 248 (1995) 259–269.
[95]
D. Giron, Applications of thermal analysis and coupled techniques in pharmaceutical industry, J. Therm. Anal. Calorim. 68 (2002) 335–357.
[96]
H. Reinl, T. Brumm, T.M. Bayerl, The physical properties of the liquid-ordered phase with temperature in binary mixtures of DPPC with cholesterol: A 2H-NMR, FT-IR, DSC, and neutron scattering study, Biophys. J. 61 (1992) 1025–1035.
[97]
N.R. Labiris, M.B. Dolovich, Pulmonary drug delivery. Part I: Physiological factors affecting therapeutic effectiveness of aerosolized medications, Br. J. Clin. Pharmacol. 56 (2003) 588–599. 45
ACCEPTED MANUSCRIPT
[98]
K. Bouchemal, New challenges for pharmaceutical formulations and drug delivery systems
[99]
PT
characterization using isothermal titration calorimetry, Drug Discov. Today. 13 (2008) 960–972. R. Gref, A. Domb, P. Quellec, T. Blunk, R.H. Müller, J.M. Verbavatz, et al., The controlled
RI
intravenous delivery of drugs using PEG-coated sterically stabilized nanospheres, Adv. Drug Deliv. Rev. 64 (2012) 316–326.
SC
[100] K. Avgoustakis, A. Beletsi, Z. Panagi, P. Klepetsanis, A. G. Karydas, D.S. Ithakissios, PLGAmPEG nanoparticles of cisplatin: In vitro nanoparticle degradation, in vitro drug release and in
NU
vivo drug residence in blood properties, J. Control. Release. 79 (2002) 123–135. [101] B.L. Mui, Y.K. Tam, M. Jayaraman, S.M. Ansell, X. Du, Y.Y.C. Tam, et al., Influence of
MA
Polyethylene Glycol Lipid Desorption Rates on Pharmacokinetics and Pharmacodynamics of siRNA Lipid Nanoparticles, Mol. Ther. Acids. 2 (2013) e139. [102] H. Tan, Z. Wang, J. Li, Z. Pan, M. Ding, Q. Fu, An approach for the sphere-to-rod transition of
D
multiblock copolymer micelles, ACS Macro Lett. 2 (2013) 146–151.
TE
[103] A. Almutairi, W.J. Akers, M.Y. Berezin, S. Achilefu, J.M.J. Frechet, Monitoring the biodegradation of dendritic near-infrared nanoprobes by in vivo fluorescence imaging, Mol.
AC CE P
Pharm. 5 (2008) 1103–1110.
[104] X. Dai, Z. Yue, M.E. Eccleston, J. Swartling, N.K.H. Slater, C.F. Kaminski, Fluorescence intensity and lifetime imaging of free and micellar-encapsulated doxorubicin in living cells, Nanomed. Nanotechn. Biol. Med. 4 (2008) 49–56. [105] D. Frenkel, B. Smit, Understanding molecular simulation: From algorithms to applications, Academic Press, 2001.
[106] B. Hess, C. Kutzner, D. Van Der Spoel, E. Lindahl, GROMACS 4: Algorithms for highly efficient, load-balanced and scalable molecular simulation, J. Chem. Theory Comput. 4 (2008) 435–447. [107] J.C. Phillips, R. Braun, W. Wang, J. Gumbart, E. Tajkhorshid, E. Villa, et al., Scalable molecular dynamics with NAMD, J. Comput. Chem. 26 (2005) 1781–1802. [108] S. Plimpton, Fast parallel algorithms for short-range molecular dynamics, J. Comput. Phys. 117 (1995) 1–19. 46
ACCEPTED MANUSCRIPT
[109] B.R. Brooks, C.L. Brooks, A.D. MacKerell, L. Nilsson, R.J. Petrella, B. Roux, et al.,
PT
CHARMM: The biomolecular simulation program, J. Comput. Chem. 30 (2009) 1545–1614. [110] D.A. Case, T.E. Cheatham, T. Darden, H. Gohlke, R. Luo, K.M. Merz, et al., The Amber
RI
biomolecular simulation programs, J. Comput. Chem. 26 (2005) 1668–1688. [111] S.J. Marrink, D.P. Tieleman, Perspective on the Martini model, Chem. Soc. Rev. 42 (2013)
SC
6801–6822.
[112] M. Karplus, J.A. McCammon, Molecular dynamics simulations of biomolecules, Nat. Struct. &
NU
Mol. Biol. 9 (2002) 646–652.
[113] W.F. van Gunsteren, J. Dolenc, A.E. Mark, Molecular simulation as an aid to experimentalists,
MA
Curr. Opin. Struct. Biol. 18 (2008) 149–153.
[114] M. Velinova, D. Sengupta, A. V Tadjer, S.J. Marrink, Sphere-to-rod transitions of nonionic surfactant micelles in aqueous solution modeled by molecular dynamics simulations, Langmuir.
D
27 (2011) 14071–14077.
TE
[115] W. Shinoda, R. DeVane, M.L. Klein, Computer simulation studies of self-assembling macromolecules, Curr. Opin. Struct. Biol. 22 (2012) 175–186.
AC CE P
[116] P. V Messina, J. Miguel Besada-Porto, J. M Ruso, Self-assembly drugs: From micelles to nanomedicine, Curr. Top. Med. Chem. 14 (2014) 555–571. [117] B. Aoun, V.K. Sharma, E. Pellegrini, S. Mitra, M. Johnson, R. Mukhopadhyay, Structure and dynamics of ionic micelles: MD simulation and neutron scattering study, J. Phys. Chem. B. 119 (2015) 5079–5086.
[118] L. Vukovic, F.A. Khatib, S.P. Drake, A. Madriaga, K.S. Brandenburg, P. Král, et al., Structure and dynamics of highly PEG-ylated sterically stabilized micelles in aqueous media, J. Am. Chem. Soc. 133 (2011) 13481–13488. [119] J. Gupta, C. Nunes, S. Vyas, S. Jonnalagadda, Prediction of solubility parameters and miscibility of pharmaceutical compounds by molecular dynamics simulations, J. Phys. Chem. B. 115 (2011) 2014–2023. [120] S.M. Loverde, M.L. Klein, D.E. Discher, Nanoparticle shape improves delivery: Rational coarse grain molecular dynamics (rCG-MD) of taxol in worm-like PEG-PCL micelles, Adv. Mater. 24 47
ACCEPTED MANUSCRIPT
(2012) 3823–3830. ukovi , A. Madriaga, A. Kuzmis, A. Banerjee, A. Tang, K. Tao, et al., Solubilization of
PT
[121] L.
therapeutic agents in micellar nanomedicines, Langmuir. 29 (2013) 15747–15754.
RI
[122] L. Huynh, C. Neale, R. Pomès, C. Allen, Systematic design of unimolecular star copolymer micelles using molecular dynamics simulations, Soft Matter. 6 (2010) 5491–5501. .
cha, F.J. Martinez-Veracoechea, D. Frenkel, Intracellular release of endocytosed
SC
[123]
nanoparticles upon a change of ligand–receptor interaction, ACS Nano. 6 (2012) 10598–10605.
NU
[124] W. Tian, Y.Q. Ma, Insights into the endosomal escape mechanism via investigation of dendrimer–membrane interactions, Soft Matter. 8 (2012) 6378–6384.
MA
[125] N. Arai, K. Yasuoka, X.C. Zeng, A vesicle cell under collision with a Janus or homogeneous nanoparticle: translocation dynamics and late-stage morphology, Nanoscale. 5 (2013) 9089– 9100.
A.J. Makarucha, N. Todorova, I. Yarovsky, Nanomaterials in biological environment: A review
D
[126]
TE
of computer modelling studies, Eur. Biophys. J. 40 (2011) 103–115. [127] Y.C. Li, S. Rissanen, M. Stepniewski, O. Cramariuc, T. Róg, S. Mirza, et al., Study of
AC CE P
interaction between PEG carrier and three relevant drug molecules: piroxicam, paclitaxel, and hematoporphyrin, J. Phys. Chem. B. 116 (2012) 7334–7341. [128] F.J. Martinez-Veracoechea, D. Frenkel, Designing super selectivity in multivalent nano-particle binding, Proc. Natl. Acad. Sci. U. S. A. 108 (2011) 10963–10968. [129] S. Wang, E.E. Dormidontova, Nanoparticle design optimization for enhanced targeting: Monte Carlo simulations, Biomacromolecules. 11 (2010) 1785–1795. [130] L. Liu, S. Parameswaran, A. Sharma, S.M. Grayson, H.S. Ashbaugh, S.W. Rick, Molecular dynamics simulations of linear and cyclic amphiphilic polymers in aqueous and organic environments, J. Phys. Chem. B. 118 (2014) 6491–6497. [131] L. Lai, A.S. Barnard, Functionalized nanodiamonds for biological and medical applications, J. Nanosci. Nanotechnol. 15 (2015) 989–999. [132] T. Panczyk, T.P. Warzocha, P.J. Camp, A magnetically controlled molecular nanocontainer as a drug delivery system: The effects of carbon nanotube and magnetic nanoparticle parameters 48
ACCEPTED MANUSCRIPT
from monte carlo simulations, J. Phys. Chem. C. 114 (2010) 21299–21308.
PT
[133] T. Panczyk, A. Jagusiak, G. Pastorin, W.H. Ang, J. Narkiewicz-Michalek, Molecular dynamics study of cisplatin release from carbon nanotubes capped by magnetic nanoparticles, J. Phys.
RI
Chem. C. 117 (2013) 17327–17336.
[134] H.M. Ding, Y.Q. Ma, Computer simulation of the role of protein corona in cellular delivery of
SC
nanoparticles, Biomaterials. 35 (2014) 8703–8710.
[135] G. Brancolini, D.B. Kokh, L. Calzolai, R.C. Wade, S. Corni, Docking of ubiquitin to gold
NU
nanoparticles, ACS Nano. 6 (2012) 9863–9878.
[136] D. Meneksedag-Erol, T. Tang, H. Uludağ, Probing the effect of mi NA on si NA–PEI
MA
polyplexes, J. Phys. Chem. B. 119 (2015) 5475–5486. [137] A.F. Jorge, R.S. Dias, A.A. Pais, Enhanced condensation and facilitated release of DNA using mixed cationic agents: a combined experimental and Monte Carlo study, Biomacromolecules.
D
13 (2012) 3151–3161.
TE
[138] F. Tian, T. Yue, Y. Li, X. Zhang, Computer simulation studies on the interactions between nanoparticles and cell membrane, Sci. China Chem. 57 (2014) 1662–1671.
AC CE P
[139] H.M. Ding, Y.Q. Ma, Theoretical and computational investigations of nanoparticlebiomembrane interactions in cellular delivery, Small. 11 (2015) 1055–1071. [140] Y. Li, X. Zhang, D. Cao, Nanoparticle hardness controls the internalization pathway for drug delivery, Nanoscale. 7 (2015) 2758–2769. [141] Y. Li, T. Yue, K. Yang, X. Zhang, Molecular modeling of the relationship between nanoparticle shape anisotropy and endocytosis kinetics, Biomaterials. 33 (2012) 4965–4973. [142] R. Vácha, F.J. Martinez-Veracoechea, D. Frenkel, Receptor-mediated endocytosis of nanoparticles of various shapes, Nano Lett. 11 (2011) 5391–5395. [143] X. Lin, Y. Li, N. Gu, Nanoparticle’s size effect on its translocation across a lipid bilayer: A molecular dynamics simulation, J. Comput. Theor. Nanosci. 7 (2010) 269–276. [144] E.L. da Rocha, G.F. Caramori, C.R. Rambo, Nanoparticle translocation through a lipid bilayer tuned by surface chemistry, Phys. Chem. Chem. Phys. 15 (2013) 2282–2290. [145] R.C. Van Lehn, A. Alexander-Katz, Penetration of lipid bilayers by nanoparticles with 49
ACCEPTED MANUSCRIPT
environmentally-responsive surfaces: Simulations and theory, Soft Matter. 7 (2011) 11392-
PT
11404. [146] J. Lin, H. Zhang, Z. Chen, Y. Zheng, Penetration of lipid membranes by gold nanoparticles:
RI
Insights into cellular uptake, cytotoxicity, and their relationship, ACS Nano. 4 (2010) 5421– 5429.
SC
[147] H.M. Ding, Y.Q. Ma, Role of physicochemical properties of coating ligands in receptormediated endocytosis of nanoparticles, Biomaterials. 33 (2012) 5798–5802.
NU
[148] Y. Li, Computer investigations of influences of molar fraction and acyl chain length of lipids on the nanoparticle–biomembrane interactions, RSC Adv. 5 (2015) 11049–11057.
MA
[149] H.M. Ding, W. Tian, Y.Q. Ma, Designing nanoparticle translocation through membranes by computer simulations, ACS Nano. 6 (2012) 1230–1238. [150] S. Wang, E.E. Dormidontova, Selectivity of ligand-receptor interactions between nanoparticle
D
and cell surfaces, Phys. Rev. Lett. 109 (2012) 238102.
TE
[151] L. Zhang, M. Becton, X. Wang, Designing nanoparticle translocation through cell membranes by varying amphiphilic polymer coatings, J. Phys. Chem. B. 119 (2015) 3786–3794.
AC CE P
[152] Y. Li, X. Zhang, D. Cao, A spontaneous penetration mechanism of patterned nanoparticles across a biomembrane, Soft Matter. 10 (2014) 6844–6856. [153] T. Yue, X. Zhang, Molecular understanding of receptor-mediated membrane responses to ligand-coated nanoparticles, Soft Matter. 7 (2011) 9104-9112. [154] L.F. Barraza, V. A. Jiménez, J.B. Alderete, Effect of PEGylation on the structure and drug loading capacity of PAMAM-G4 dendrimers: A molecular modeling approach on the complexation of 5-Fluorouracil with native and PEGylated PAMAM-G4, Macromol. Chem. Phys. 216 (2015) 1689–1701. [155] O.S. Lee, G.C. Schatz, Computational simulations of the interaction of lipid membranes with DNA-functionalized gold nanoparticles, in: Biomedical Nanotechnolgy, Springer, 2011: pp. 283–296. [156] Q. Mu, T. Hu, J. Yu, Molecular insight into the steric shielding effect of PEG on the conjugated staphylokinase: Biochemical characterization and molecular dynamics simulation, PLoS One. 8 50
ACCEPTED MANUSCRIPT
(2013) e68559.
PT
[157] H. Lee, Molecular modeling of PEGylated peptides, dendrimers, and single-walled carbon nanotubes for biomedical applications, Polymers. 6 (2014) 776–798.
RI
[158] A. Bunker, Molecular modeling as a tool to understand the role of poly (ethylene) glycol in drug delivery, in: Computational Pharmaceutics: Application of Molecular Modelling in Drug
SC
Delivery, John Wiley & Sons, 2015: pp. 217-233.
[159] Y. Li, M. Kröger, W.K. Liu, Endocytosis of PEGylated nanoparticles accompanied by structural
NU
and free energy changes of the grafted polyethylene glycol, Biomaterials. 35 (2014) 8467–8478. [160] K. Knop, R. Hoogenboom, D. Fischer, U.S. Schubert, Poly(ethylene glycol) in drug delivery:
MA
Pros and cons as well as potential alternatives, Angew. Chemie Int. Ed. 49 (2010) 6288–6308. [161] J.K. Armstrong, G. Hempel, S. Koling, L.S. Chan, T. Fisher, H.J. Meiselman, et al., Antibody against poly(ethylene glycol) adversely affects PEG-asparaginase therapy in acute
D
lymphoblastic leukemia patients, Cancer. 110 (2007) 103–111.
TE
[162] L. Zhang, Z. Wang, Z. Lu, H. Shen, J. Huang, Q. Zhao, et al., PEGylated reduced graphene oxide as a superior ssRNA delivery system, J. Mater. Chem. B. 1 (2013) 749–755.
AC CE P
[163] X. Sun, Z. Feng, L. Zhang, T. Hou, Y. Li, The selective interaction between silica nanoparticles and enzymes from molecular dynamics simulations, PLoS One. 9 (2014) e107696. [164] A. Adnan, R. Lam, H. Chen, J. Lee, D.J. Schaffer, A.S. Barnard, et al., Atomistic simulation and measurement of pH dependent cancer therapeutic interactions with nanodiamond carrier, Mol. Pharm. 8 (2011) 368–374. [165] L. Lai, A.S. Barnard, Molecular and analytical modeling of nanodiamond for drug delivery applications, in: Computational Pharmaceutics: Application of Molecular Modelling in Drug Delivery, John Wiley & Sons, 2015: pp. 169-196. [166] M.L. Bello, A.M. Junior, B.A. Vieira, L.R.S. Dias, V.P. de Sousa, H.C. Castro, et al., Sodium montmorillonite/amine-containing drugs complexes: New insights on intercalated drugs arrangement into layered carrier material, PLoS One. 10 (2015) e0121110. [167] V. Murthy, Z.P. Xu, S.C. Smith, Molecular modeling of layered double hydroxide nanoparticles for drug delivery, in: Computational Pharmaceutics: Application of Molecular Modelling in 51
ACCEPTED MANUSCRIPT
Drug Delivery, John Wiley & Sons, 2015: pp. 197-216.
PT
[168] P. Cox, Atomistic modelling of drug delivery systems, in: Drug Design Strategies: Computational Techniques and Applications, Royal Society of Chemistry, 2012: pp. 210–232.
RI
[169] D.G. Fatouros, D. Douroumis, V. Nikolakis, S. Ntais, A.M. Moschovi, V. Trivedi, et al., In vitro and in silico investigations of drug delivery via zeolite BEA, J. Mater. Chem. 21 (2011)
SC
7789-7794.
[170] M. Spanakis, N. Bouropoulos, D. Theodoropoulos, L. Sygellou, S. Ewart, A.M. Moschovi, et al.,
NU
Controlled release of 5-fluorouracil from microporous zeolites, Nanomed. Nanotech. Biol. Med. 10 (2014) 197–205.
MA
[171] M.C. Bernini, D. Fairen-Jimenez, M. Pasinetti, A.J. Ramirez-Pastor, R.Q. Snurr, Screening of bio-compatible metal–organic frameworks as potential drug carriers using Monte Carlo simulations, J. Mater. Chem. B. 2 (2014) 766-774.
D
[172] S.T. Meek, J.A. Greathouse, M.D. Allendorf, Metal - organic frameworks: A rapidly growing
TE
class of versatile nanoporous materials, Adv. Mater. 23 (2011) 249–267. [173] G.M. Pavan, Modeling the interaction between dendrimers and nucleic acids: A molecular
AC CE P
perspective through hierarchical scales, ChemMedChem. 9 (2014) 2623–2631. [174] V. Jain, P. V Bharatam, Pharmacoinformatic approaches to understand complexation of dendrimeric nanoparticles with drugs, Nanoscale. 6 (2014) 2476–2501. [175] D. Shcharbin, A. Shakhbazau, M. Bryszewska, Poly(amidoamine) dendrimer complexes as a platform for gene delivery, Expert Opin. Drug Deliv. 10 (2013) 1687–1698. [176] P. Posocco, E. Laurini, V. Dal Col, D. Marson, K. Karatasos, M. Fermeglia, et al., Tell me something I do not know. Multiscale molecular modeling of dendrimer/dendron organization and self-assembly in gene therapy, Curr. Med. Chem. 19 (2012) 5062–5087. [177] D. Meneksedag-Erol, T. Tang, H. Uludağ, Molecular modeling of polynucleotide complexes, Biomaterials. 35 (2014) 7068–7076. [178] M.R. Molla, P. Rangadurai, G.M. Pavan, S. Thayumanavan, Experimental and theoretical investigations in stimuli responsive dendrimer-based assemblies, Nanoscale. 7 (2015) 3817– 3837. 52
ACCEPTED MANUSCRIPT
[179] E. Ficici, I. Andricioaei, S. Howorka, Dendrimers in nanoscale confinement: The interplay
PT
between conformational change and nanopore entrance, Nano Lett. 15 (2015) 4822–4828. [180] Y.L. Wang, Z.Y. Lu, A. Laaksonen, Specific binding structures of dendrimers on lipid bilayer
RI
membranes, Phys. Chem. Chem. Phys. 14 (2012) 8348–8359.
[181] B. Nandy, P.K. Maiti, A. Bunker, Force biased molecular dynamics simulation study of effect
SC
of dendrimer generation on interaction with DNA, J. Chem. Theory Comput. 9 (2013) 722–729. [182] G.M. Pavan, A. Danani, The influence of dendrons architecture on the “rigid” and “flexible”
NU
behaviour in binding DNA—A modelling study, Phys. Chem. Chem. Phys. 12 (2010) 13914– 13917.
MA
[183] G.M. Pavan, L. Albertazzi, A. Danani, Ability to adapt: Different generations of PAMAM dendrimers show different behaviors in binding siRNA, J. Phys. Chem. B. 114 (2010) 2667– 2675.
D
[184] P. Posocco, S. Pricl, S. Jones, A. Barnard, D.K. Smith, Less is more – multiscale modelling of
(2010) 393–404.
TE
self-assembling multivalency and its impact on DNA binding and gene delivery, Chem. Sci. 1
AC CE P
[185] S.P. Jones, N.P. Gabrielson, C.H. Wong, H.F. Chow, D.W. Pack, P. Posocco, et al., Hydrophobically modified dendrons: Developing structure - Activity relationships for DNA binding and gene transfection, Mol. Pharm. 8 (2011) 416–429. [186] S.P. Jones, G.M. Pavan, A. Danani, S. Pricl, D.K. Smith, Quantifying the effect of surface ligands on dendron – DNA interactions : Insights into multivalency through a combined experimental and theoretical approach, Chem. Eur. J. 16 (2010) 4519–4532. [187] V. Percec, D.A. Wilson, P. Leowanawat, C.J. Wilson, A.D. Hughes, M.S. Kaucher, et al., Selfassembly of Janus dendrimers into uniform dendrimersomes and other complex architectures, Science. 328 (2010) 1009–1014. [188] S. Zhang, H.J. Sun, A.D. Hughes, R.O. Moussodia, A. Bertin, Y. Chen, et al., Self-assembly of amphiphilic Janus dendrimers into uniform onion-like dendrimersomes with predictable size and number of bilayers, Proc. Natl. Acad. Sci. U. S. A. 111 (2014) 9058-9063. [189] S. Kavyani, S. Amjad-Iranagh, H. Modarress, Aqueous poly(amidoamine) dendrimer G3 and 53
ACCEPTED MANUSCRIPT
G4 generations with several interior cores at pHs 5 and 7: A molecular dynamics simulation
PT
study, J. Phys. Chem. B. 118 (2014) 3257–3266. [190] A. Barnard, P. Posocco, S. Pricl, M. Calderon, R. Haag, M.E. Hwang, et al., Degradable self-
RI
assembling dendrons for gene delivery: Experimental and theoretical insights into the barriers to cellular uptake, J. Am. Chem. Soc. 133 (2011) 20288–20300.
SC
[191] V. Carrasco-Sánchez, A. Vergara-Jaque, M. Zuñiga, J. Comer, A. John, F.M. Nachtigall, et al., In situ and in silico evaluation of amine- and folate-terminated dendrimers as nanocarriers of
NU
anesthetics, Eur. J. Med. Chem. 73 (2014) 250–257.
[192] C. Shi, D. Yuan, S. Nangia, G. Xu, K.S. Lam, J. Luo, A structure-property relationship study of
MA
the well-defined telodendrimers to improve hemocompatibility of nanocarriers for anticancer drug delivery, Langmuir. 30 (2014) 6878–6888. [193] R.M. Pearson, N. Patra, H. . Hsu,
. Uddin, P. Kr l, S. Hong, Positively charged dendron
D
micelles display negligible cellular interactions, ACS Macro Lett. 2 (2012) 77–81.
TE
[194] H. Lee, J.S. Choi, R.G. Larson, Molecular dynamics studies of the size and internal structure of
8686.
AC CE P
the PAMAM dendrimer grafted with arginine and histidine, Macromolecules. 44 (2011) 8681–
[195] H. Lee, R.G. Larson, Membrane pore formation induced by acetylated and polyethylene glycolconjugated polyamidoamine dendrimers, J. Phys. Chem. C. 115 (2011) 5316–5322. [196] L. Yang, S.R.P. Rocha, PEGylated, NH2‑terminated PAMAM dendrimers: A microscopic view from atomistic computer simulations, Mol. Pharm. 11 (2014) 1459–1470. [197] L. Albertazzi, F.M. Mickler, G.M. Pavan, F. Salomone, G. Bardi, M. Panniello, et al., Enhanced bioactivity of internally functionalized cationic dendrimers with PEG cores, Biomacromolecules. 13 (2012) 4089–4097. [198] H. Lee, R.G. Larson, Effects of PEGylation on the size and internal structure of dendrimers: Self-penetration of long PEG chains into the dendrimer core, Macromolecules. 44 (2011) 2291– 2298. [199] R. Bhattacharya,S. Kanchi, C. Roobala, A. Lakshminarayanan, O.H. Seeck, P.K. Maiti, K.G. Ayappa, N. Jayaraman, J.K. Basu, A new microscopic insight into membrane penetration and 54
ACCEPTED MANUSCRIPT
reorganization by PETIM dendrimers, Soft Matter. 10 (2014) 7577–7587.
PT
[200] R. Guo, J. Mao, L.T. Yan, Unique dynamical approach of fully wrapping dendrimer-like soft nanoparticles by lipid bilayer membrane, ACS Nano. 7 (2013) 10646–10653.
RI
[201] X. He, Z. Qu, F. Xu, M. Lin, J. Wang, X. Shi, et al., Molecular analysis of interactions between dendrimers and asymmetric membranes at different transport stages, Soft Matter. 10 (2014)
SC
139–148.
[202] C.K. Tu, K. Chen, W.D. Tian, Y.Q. Ma, Computational investigations of a peptide-modified
NU
dendrimer interacting with lipid membranes, Macromol. Rapid Commun. 34 (2013) 1237–1242. [203] Y.Q. Ma, pH-responsive dendrimers interacting with lipid membranes, Soft Matter. 8 (2012)
MA
2627–2632.
[204] L.Q. Xie, J.W. Feng, W.D. Tian, Y.Q. Ma, Generation-dependent gel-fluid phase transition of membrane caused by a PAMAM dendrimer, Macromol. Theory Simul. 23 (2014) 531–536.
D
[205] R.G. Bellini, A.P. Guimarães, M. A. Pacheco, D.M. Dias, V.R. Furtado, R.B. De Alencastro, et
TE
al., Association of the anti-tuberculosis drug rifampicin with a PAMAM dendrimer, J. Mol. Graph. Model. 60 (2015) 34–42.
AC CE P
[206] V. Jain, V. Maingi, P.K. Maiti, P. V Bharatam, Molecular dynamics simulations of PPI dendrimer–drug complexes, Soft Matter. 9 (2013) 6482-6496. [207] Y. Jiang, D. Zhang, Y. Zhang, Z. Deng, L. Zhang, The adsorption-desorption transition of double-stranded DNA interacting with an oppositely charged dendrimer induced by multivalent anions, J. Chem. Phys. 140 (2014) 204912. [208] X.F. Wen, J.L. Lan, Z.Q. Cai, P.H. Pi, S.P. Xu, L.J. Zhang, et al., Dissipative particle dynamics simulation on drug loading/release in polyester-PEG dendrimer, J. Nanoparticle Res. 16 (2014) 2403. [209] V. Maingi, M.V.S. Kumar, P.K. Maiti, PAMAM dendrimer-drug interactions: Effect of pH on the binding and release pattern, J. Phys. Chem. B. 116 (2012) 4370–4376. [210] F. Avila- alas, . andoval, . aballero, . Gui ez-Molinos, L.S. Santos, R.E. Cachau, et al., Study of interaction energies between the PAMAM dendrimer and nonsteroidal antiinflammatory drug using a distributed computational strategy and experimental analysis by ESI55
ACCEPTED MANUSCRIPT
MS/MS, J. Phys. Chem. B. 116 (2012) 2031–2039.
PT
[211] J. Lim, G.M. Pavan, O. Annunziata, E.E. Simanek, Experimental and computational evidence for an inversion in guest capacity in high-heneration triazine dendrimer hosts, J. Am. Chem. Soc.
RI
134 (2012) 1942–1945.
[212] D. Ouyang, H. Zhang, H.S. Parekh, S.C. Smith, The effect of pH on PAMAM dendrimer–
SC
siRNA complexation—Endosomal considerations as determined by molecular dynamics simulation, Biophys. Chem. 158 (2011) 126–133.
NU
[213] B. Nandy, P.K. Maiti, DNA compaction by a dendrimer, J. Phys. Chem. B. 115 (2010) 217–230. [214] D. Amado Torres, M. Garzoni, A. V Subrahmanyam, G.M. Pavan, S. Thayumanavan, Protein-
MA
triggered supramolecular disassembly: Insights based on variations in ligand location in amphiphilic dendrons, J. Am. Chem. Soc. 136 (2014) 5385–5399. [215] L.B. Jensen, G.M. Pavan, M.R. Kasimova, S. Rutherford, A. Danani, H.M. Nielsen, et al.,
D
Elucidating the molecular mechanism of PAMAM – siRNA dendriplex self-assembly: Effect of
TE
dendrimer charge density, Int. J. Pharm. 416 (2011) 410–418. [216] K. Karatasos, P. Posocco, E. Laurini, S. Pricl, Poly (amidoamine)-based dendrimer/siRNA
AC CE P
complexation studied by computer simulations: Effects of pH and generation on dendrimer structure and siRNA binding, Macromol. Biochem. 12 (2012) 225–240. [217] G. M. Pavan, S. Monteagudo, J. Guerra, B. Carrion, V. Ocana, J. Rodriguez-Lopez, et al., Role of generation, architecture, pH and ionic strength on successful siRNA delivery and transfection by hybrid PPV-PAMAM dendrimers, Curr. Med. Chem. 19 (2012) 4929–4941. [218] G.M. Pavan, M.A. Mintzer, E.E. Simanek, O.M. Merkel, T. Kissel, A. Danani, Computational insights into the interactions between DNA and si NA with “ rigid ” and “ flexible ” triazine dendrimers, Biomacromolecules. 11 (2010) 721–730. [219] G.M. Pavan, P. Posocco, A. Tagliabue, M. Maly, A. Malek, A. Danani, et al., PAMAM dendrimers for siRNA delivery: Computational and experimental insights, Chem. Eur. J. 16 (2010) 7781–7795. [220] C. Shi, D. Guo, K. Xiao, X. Wang, L. Wang, J. Luo, A drug-specific nanocarrier design for efficient anticancer therapy, Nat. Commun. 6 (2015) 7449. 56
ACCEPTED MANUSCRIPT
[221] M. Kang, D. Lam, D.E. Discher, S.M. Loverde, Molecular modeling of block copolymer self-
PT
assembly and micellar drug delivery, in: Computational Pharmaceutics: Application of Molecular Modelling in Drug Delivery, John Wiley & Sons, 2015: pp. 53-80.
RI
[222] A. Rösler, G.W.M. Vandermeulen, H.A. Klok, Advanced drug delivery devices via selfassembly of amphiphilic block copolymers, Adv. Drug Deliv. Rev. 64 (2012) 270–279.
SC
[223] C. Sun, T. Tang, Study on the role of polyethylenimine as gene delivery carrier using molecular dynamics simulations, J. Adhes. Sci. Technol. 28 (2014) 399–416.
NU
[224] S.M. Loverde, Computer simulation of polymer and biopolymer self-assembly for drug delivery, Mol. Simul. 40 (2014) 794–801.
MA
[225] D. Meneksedag-Erol, C. Sun, T. Tang, H. Uludag, Molecular dynamics simulations of polyplexes and lipoplexes employed in gene delivery, in: Intracellular Delivery II , Springer, 2014: pp. 277–311.
D
[226] H. Guo, X. Qiu, J. Zhou, Self-assembled core-shell and Janus microphase separated structures
TE
of polymer blends in aqueous solution, J. Chem. Phys. 139 (2013) 84907. [227] C. Cai, L. Wang, J. Lin, X. Zhang, Morphology transformation of hybrid micelles self-
AC CE P
assembled from rod–coil block copolymer and nanoparticles, Langmuir. 28 (2012) 4515–4524. [228] H.Y. Chang, Y.L. Lin, Y.J. Sheng, H.K. Tsao, Structural characteristics and fusion pathways of onion-like multilayered polymersome formed by amphiphilic comb-like graft copolymers, Macromolecules. 46 (2013) 5644–5656. [229] Y. Sheng, J. An, Y. Zhu, Self-assembly of ABA triblock copolymers under soft confinement, Chem. Phys. 452 (2015) 46–52. [230] H. Chen, E. Ruckenstein, Formation and degradation of multicomponent multicore micelles: insights from dissipative particle dynamics simulations, Langmuir. 29 (2013) 5428–5434. [231]
. Taresco, L. Gontrani, F.
risante, I. Francolini, A. Martinelli, L. D’Ilario, et al.,
elf-
assembly of catecholic moiety-containing cationic random acrylic copolymers, J. Phys. Chem. B. 119 (2015) 8369–8379. [232] S.Y. Nie, Y. Sun, W.J. Lin, W.S. Wu, X.D. Guo, Y. Qian, et al., Dissipative particle dynamics studies of doxorubicin-loaded micelles assembled from four-arm star triblock polymers 4AS57
ACCEPTED MANUSCRIPT
PCL-b-PDEAEMA-b-PPEGMA and their pH-release mechanism, J. Phys. Chem. B. 117 (2013)
PT
13688–13697. [233] T. Wang, C. Chipot, X. Shao, W. Cai, Structural characterization of micelles formed of
RI
cholesteryl-functionalized cyclodextrins, Langmuir. 27 (2010) 91–97.
[234] L.X. Peng, A. Ivetac, A.S. Chaudhari, S. Van, G. Zhao, L. Yu, et al., Characterization of a
SC
clinical polymer-drug conjugate using multiscale modeling, Biopolymers. 93 (2010) 936–951. [235] P. Pi, D. Qin, J. Lan, Z. Cai, X. Yuan, S. Xu, et al., Dissipative particle dynamics simulation on
NU
the nanocomposites delivery system of quantum dots and poly (styrene-b-ethylene oxide) copolymer, Ind. & Eng. Chem. Res. 54 (2015) 6123–6134.
MA
[236] J. Li, Y. Ouyang, X. Kong, J. Zhu, D. Lu, Z. Liu, A multi-scale molecular dynamics simulation of PMAL facilitated delivery of siRNA, RSC Adv. 5 (2015) 68227–68233. [237] G. Srinivas, R. V Mohan, A.D. Kelkar, Polymer micelle assisted transport and delivery of
D
model hydrophilic components inside a biological lipid vesicle: a coarse-grain simulation study,
TE
J. Phys. Chem. B. 117 (2013) 12095–12104. [238] H.M. Ding, Y.Q. Ma, Controlling cellular uptake of nanoparticles with pH-sensitive polymers,
AC CE P
Sci. Rep. 3 (2013) 2804.
[239] C. Sun, T. Tang, H. Uludag, A molecular dynamics simulation study on the effect of lipid substitution on polyethylenimine mediated siRNA complexation, Biomaterials. 34 (2013) 2822– 2833.
[240] Z.X. Liao, S.F. Peng, Y.C. Ho, F.L. Mi, B. Maiti, H.W. Sung, Mechanistic study of transfection of chitosan/DNA complexes coated by anionic poly(γ-glutamic acid), Biomaterials. 33 (2012) 3306–3315. [241] S.F. Peng, M.T. Tseng, Y.C. Ho, M.C. Wei, Z.X. Liao, H.W. Sung, Mechanisms of cellular uptake and intracellular trafficking with chitosan/DNA/poly(γ-glutamic acid) complexes as a gene delivery vector, Biomaterials. 32 (2011) 239–248. [242] M. Kepczynski, D. Jamróz, M. Wytrwal, J. Bednar, E. Rzad, M. Nowakowska, Interactions of a hydrophobically modified polycation with zwitterionic lipid membranes, Langmuir. 28 (2011) 676–688. 58
ACCEPTED MANUSCRIPT
[243] Y.L. Chiu, Y.C. Ho, Y.M. Chen, S.F. Peng, C.J. Ke, K.J. Chen, et al., The characteristics,
PT
cellular uptake and intracellular trafficking of nanoparticles made of hydrophobically-modified chitosan, J. Control. Release. 146 (2010) 152–159.
. un, T. Tang, H. Uludag , Probing the effects of lipid substitution on polycation mediated
RI
[244]
DNA aggregation: A molecular dynamics simulations study, Biomacromolecules. 13 (2012)
SC
2982–2988.
[245] S. Samanta, D. Roccatano, Interaction of curcumin with PEO-PPO-PEO block copolymers: A
NU
molecular dynamics study, J. Phys. Chem. B. 117 (2013) 3250–3257. [246] J. He, C. Chipot, X. Shao, W. Cai, Cyclodextrin-mediated recruitment and delivery of
MA
Amphotericin B, J. Phys. Chem. C. 117 (2013) 11750–11756. [247] P. Shan, J.W. Shen, D.H. Xu, L.Y. Shi, J. Gao, Y.W. Lan, et al., Molecular dynamics study on the interaction between doxorubicin and hydrophobically modified chitosan oligosaccharide,
D
RSC Adv. 4 (2014) 23730-23739.
TE
[248] M. Subashini, P.V. Devarajan, G.S. Sonavane, M. Doble, Molecular dynamics simulation of drug uptake by polymer, J. Mol. Model. 17 (2011) 1141–1147.
AC CE P
[249] R. Anand, S. Ottani, F. Manoli, I. Manet, S. Monti, A close-up on doxorubicin binding to γcyclodextrin: An elucidating spectroscopic, photophysical and conformational study, RSC Adv. 2 (2012) 2346-2357.
[250] R. Namgung, Y.M. Lee, J. Kim, Y. Jang, B.H. Lee, I.S. Kim, et al., Poly-cyclodextrin and polypaclitaxel nano-assembly for anticancer therapy, Nat. Commun. 5 (2014) 3702. [251] Z. Wang, J. Jiang, Dissipative particle dynamics simulation on paclitaxel loaded PEO–PPO– PEO block copolymer micelles, J. Nanosci. Nanotechnol. 14 (2014) 2644–2647. [252] Z. Wang, F. Wang, C. Su, Y. Zhang, Computer simulation of polymer delivery system by dissipative particle dynamics, J. Comput. Theor. Nanosci. 10 (2013) 2323–2327. [253] F. Razmimanesh, S. Amjad-Iranagh, H. Modarress, Molecular dynamics simulation study of chitosan and gemcitabine as a drug delivery system, J. Mol. Model. 21 (2015) 165. [254] S.Y. Nie, W.J. Lin, N. Yao, X.D. Guo, L.J. Zhang, Drug release from pH-Sensitive polymeric micelles with different drug distributions: Insight from coarse-grained simulations, ACS Appl. 59
ACCEPTED MANUSCRIPT
Mater. & Interfaces. 6 (2014) 17668–17678.
PT
[255] Z. Luo, J. Jiang, pH-sensitive drug loading/releasing in amphiphilic copolymer PAE-PEG: Integrating molecular dynamics and dissipative particle dynamics simulations, J. Control.
RI
Release. 162 (2012) 185–193.
[256] J. Hao, Y. Cheng, R.J.K.U. Ranatunga, S. Senevirathne, M.C. Biewer, S.O. Nielsen, et al., A
SC
combined experimental and computational study of the substituent effect on micellar behavior of γ-substituted thermoresponsive amphiphilic poly (ε-caprolactone), Macromolecules. 46
NU
(2013) 4829–4838.
[257] L.S. Zheng, Y.Q. Yang, X.D. Guo, Y. Sun, Y. Qian, L.J. Zhang, Mesoscopic simulations on the
Interface Sci. 363 (2011) 114–121.
MA
aggregation behavior of pH-responsive polymeric micelles for drug delivery, J. Colloid
[258] M.R. Rodríguez-Hidalgo, C. Soto-Figueroa, L. Vicente, Mesoscopic simulation of the drug
TE
(2011) 8224-8230.
D
release mechanism on the polymeric vehicle P(ST-DVB) in an acid environment, Soft Matter. 7
[259] R. Malik, J. Genzer, C.K. Hall, Proteinlike copolymers as encapsulating agents for small-
AC CE P
molecule solutes, Langmuir. 31 (2015) 3518–3526. [260] S.K. Patel, A. Lavasanifar, P. Choi, Molecular dynamics study of the encapsulation capability of a PCL-PEO based block copolymer for hydrophobic drugs with different spatial distributions of hydrogen bond donors and acceptors, Biomaterials. 31 (2010) 1780–1786. [261] S.S. Thakur, H.S. Parekh, C.H. Schwable, Y. Gan, D. Ouyang, Solubilization of poorly soluble drugs, in: Computational Pharmaceutics: Application of Molecular Modelling in Drug Delivery, John Wiley & Sons, 2015: pp. 31-51. [262] S. Bagai, C. Sun, T. Tang, Potential of mean force of polyethylenimine-mediated DNA attraction, J. Phys. Chem. B. 117 (2012) 49–56. [263] H.S. Antila, M. Härkönen, M. Sammalkorpi, Chemistry specificity of DNA–polycation complex salt response: A simulation study of DNA, polylysine and polyethyleneimine, Phys. Chem. Chem. Phys. 17 (2015) 5279–5289. [264] P. Pereira, A.F. Jorge, R. Martins, A.A. Pais, F. Sousa, A. Figueiras, Characterization of 60
ACCEPTED MANUSCRIPT
polyplexes involving small RNA, J. Colloid Interface Sci. 387 (2012) 84–94.
PT
[265] C. Sun, T. Tang, H. Uludag, Molecular dynamics simulations for complexation of dna with 2 kDa PEI reveal profound effect of PEI architecture on complexation, J. Phys. Chem. B. 116
RI
(2012) 2405–2413.
[266] D. Ouyang, H. Zhang, D. Herten, H.S. Parekh, S.C. Smith, Structure, dynamics and energetics
SC
of siRNA - Cationic vector complexation: A molecular dynamics study, J. Phys. Chem. B. 114 (2010) 9220–9230.
NU
[267] D. Ouyang, H. Zhang, H.S. Parekh, S.C. Smith, Structure and dynamics of multiple cationic vectors - siRNA complexation by all-atomic molecular dynamics simulations, J. Phys. Chem. B.
MA
114 (2010) 9231–9237.
[268] A.F. Jorge, R.F.P. Pereira, S.C.C. Nunes, A.J.M. Valente, R.S. Dias, A.A.C.C. Pais, Interpreting the rich behavior of ternary DNA-PEI-Fe(III) complexes, Biomacromolecules. 15 (2014) 478–
D
491.
TE
[269] B. Zhan, K. Shi, Z. Dong, W. Lv, S. Zhao, X. Han, et al., Coarse-grained simulation of polycation/DNA-like complexes: Role of neutral block, Mol. Pharm. 12 (2015) 2834–2844.
AC CE P
[270] M. Zhao, J. Zhou, C. Su, L. Niu, D. Liang, B. Li, Complexation behavior of oppositely charged polyelectrolytes: Effect of charge distribution, J. Chem. Phys. 142 (2015) 204902. [271] J. Ziebarth, Y. Wang, Coarse-grained molecular dynamics simulations of DNA condensation by block copolymer and formation of core-corona structures, J. Phys. Chem. B. 114 (2010) 6225– 6232.
[272] Q. Luo, Y. Wang, H. Yang, C. Liu, Y. Ding, H. Xu, et al., Modeling the interaction of interferon α-1b to bovine serum albumin as a drug delivery system, J. Phys. Chem. B. 118 (2014) 8566–8574. [273] R. Vijayaraj, S. Van Damme, P. Bultinck, V. Subramanian, Theoretical studies on the transport mechanism of 5-fluorouracil through cyclic peptide based nanotubes, Phys. Chem. Chem. Phys. 15 (2013) 1260–1270. [274] H. Liu, J. Chen, Q. Shen, W. Fu, W. Wu, Molecular insights on the cyclic peptide nanotubemediated transportation of antitumor drug 5-fluorouracil, Mol. Pharm. 7 (2010) 1985–1994. 61
ACCEPTED MANUSCRIPT
[275]
. Ł. Durzyńska, Ł. Przysiecka, . Nawrot, . Barylski, G. Nowicki, A. Warowicka, et al.,
iral
PT
and other cell-penetrating peptides as vectors of therapeutic agents in medicine, J. Pharmacol. Exp. Ther. 354 (2015) 32-42.
RI
[276] Y. Krishnan, F.C. Simmel, Nucleic acid based molecular devices, Angew. Chemie Int. Ed. 50 (2011) 3124–3156.
SC
[277] C.K. McLaughlin, G.D. Hamblin, H.F. Sleiman, Supramolecular DNA assembly, Chem. Soc. Rev. 40 (2011) 5647–5656.
NU
[278] J.I. Cutler, E. Auyeung, C.A. Mirkin, Spherical nucleic acids, J. Am. Chem. Soc. 134 (2012) 1376–1391.
MA
[279] S.N. Barnaby, T.L. Sita, S.H. Petrosko, A.H. Stegh, C.A. Mirkin, Therapeutic applications of spherical nucleic acids, in: Nanotechnology-Based Precision Tools for the Detection and Treatment of Cancer, Springer, 2015: pp. 23–50.
D
[280] K.A. Afonin, W.K. Kasprzak, E. Bindewald, M. Kireeva, M. Viard, M. Kashlev, B.A. Shapiro,
(2014) 1731–1741.
TE
In silico design and enzymatic synthesis of functional RNA nanoparticles, Acc. Chem. Res. 47
AC CE P
[281] S.R. Badu, R. Melnik, M. Paliy, S. Prabhakar, A. Sebetci, B.A. Shapiro, Modeling of RNA nanotubes using molecular dynamics simulation, Eur. Biophys. J. 43 (2014) 555–564. [282] S. Juul, F. Iacovelli, M. Falconi, S.L. Kragh, B. Christensen, R. Frøhlich, et al., Temperaturecontrolled encapsulation and release of an active enzyme in the cavity of a self-assembled DNA nanocage, ACS Nano. 7 (2013) 9724–9734. [283] A.O. Elzoghby, W.M. Samy, N.A. Elgindy, Protein-based nanocarriers as promising drug and gene delivery systems, J. Control. Release. 161 (2012) 38–49. [284] A.O. Elzoghby, W.M. Samy, N.A. Elgindy, Albumin-based nanoparticles as potential controlled release drug delivery systems, J. Control. Release. 157 (2012) 168–182. [285] M. Pourmousa, M. Karttunen, Early stages of interactions of cell-penetrating peptide penetratin with a DPPC bilayer, Chem. Phys. Lipids. 169 (2013) 85–94. [286] M. Pourmousa, J. Wong-Ekkabut, M. Patra, M. Karttunen, Molecular dynamic studies of transportan interacting with a DPPC lipid bilayer, J. Phys. Chem. B. 117 (2012) 230–241. 62
ACCEPTED MANUSCRIPT
[287] X. He, M. Lin, B. Sha, S. Feng, X. Shi, Z. Qu, et al., Coarse-grained molecular dynamics
PT
studies of the translocation mechanism of polyarginines across asymmetric membrane under tension, Sci. Rep. 5 (2015) 12808.
RI
[288] M. Islami, F. Mehrnejad, F. Doustdar, M. Alimohammadi, M. Khadem-Maaref, M. MirDerikvand, et al., Study of orientation and penetration of LAH4 into lipid bilayer membranes:
SC
pH and composition dependence, Chem. Biol. & Drug Des. 84 (2014) 242–252. [289] Z.L. Li, H.M. Ding, Y.Q. Ma, Translocation of polyarginines and conjugated nanoparticles
NU
across asymmetric membranes, Soft Matter. 9 (2012) 1281–1286. [290] R.S. Teixeira, T.F. Cova, S.M. Silva, R. Oliveira, M.J. Araújo, E.F. Marques, et al., Lysine-
MA
based surfactants as chemical permeation enhancers for dermal delivery of local anesthetics, Int. J. Pharm. 474 (2014) 212–222.
[291] N. Todorova, C. Chiappini, M. Mager, B. Simona, I.I. Patel, M.M. Stevens, I. Yarovsky,
D
Surface presentation of functional peptides in solution determines cell internalization efficiency
TE
of TAT conjugated nanoparticles, Nano Lett. 14 (2014) 5229–5237. [292] Y. Li, D. Feng, X. Zhang, D. Cao, Design strategy of cell-penetrating copolymers for high
AC CE P
efficient drug delivery, Biomaterials. 52 (2015) 171–179. [293] A. Sanchez-Sanchez, S. Akbari, A.J. Moreno, F. L. Verso, A. Arbe, J. Colmenero, J.A. Pomposo, Design and preparation of single-chain nanocarriers mimicking disordered proteins for combined delivery of dermal bioactive cargos, Macromol. Rapid Commun. 34 (2013) 1681– 1686.
[294] L. Chen, T. Jiang, C. Cai, L. Wang, J. Lin, X. Cao, Polypeptide-based “smart” micelles for dual-drug delivery: A combination study of experiments and simulations, Adv. Healthcare Mater. 3 (2014) 1508–1517. [295] E. Jabbari, X. Yang, S. Moeinzadeh, X. He, Drug release kinetics, cell uptake, and tumor toxicity of hybrid VVVVVVKK peptide-assembled polylactide nanoparticles, Eur. J. Pharm. Biopharm. 84 (2013) 49–62. [296] X.D. Guo, L.J. Zhang, Z.M. Wu, Y. Qian, Dissipative particle dynamics studies on microstructure of pH-sensitive micelles for sustained drug delivery, Macromolecules. 43 (2010) 63
ACCEPTED MANUSCRIPT
7839–7844.
PT
[297] X. Tian, F. Sun, X.R. Zhou, S.Z. Luo, L. Chen, Role of peptide self-assembly in antimicrobial peptides, J. Pept. Sci. 21 (2015) 530–539.
RI
[298] I.W. Fu, C.B. Markegard, H.D. Nguyen, Solvent effects on kinetic mechanisms of selfassembly by peptide amphiphiles via molecular dynamics simulations, Langmuir. 31 (2014)
SC
315–324.
[299] I.W. Fu, C.B. Markegard, B.K. Chu, H.D. Nguyen, The role of electrostatics and temperature on
NU
morphological transitions of hydrogel nanostructures self-assembled by peptide amphiphiles via molecular dynamics simulations, Adv. Healthcare Mater. 2 (2013) 1388–1400.
MA
[300] N. Thota, Z. Luo, Z. Hu, J. Jiang, Self-assembly of amphiphilic peptide (AF) 6H5K15: Coarsegrained molecular dynamics simulation, J. Phys. Chem. B. 117 (2013) 9690–9698. [301]
. Garc a-Fandi o, J.R. Granja, Effect of organochloride guest molecules on the stability of
D
homo/hetero self-assembled α, γ-cyclic peptide structures: A computational study toward the
TE
control of nanotube length, J. Phys. Chem. C. 117 (2013) 10143–10162. [302] R. Bakry, R.M. Vallant, M. Najam-ul-Haq, M. Rainer, Z. Szabo, C.W. Huck, G.K. Bonn,
AC CE P
Medicinal applications of fullerenes, Int. J. Nanomed. 2 (2007) 639-649. [303] M. Martincic, G. Tobias, Filled carbon nanotubes in biomedical imaging and drug delivery, Expert Opin. Drug Deliv. 12 (2015) 563–581. [304] V.V. Chaban, T.I. Savchenko, S.M. Kovalenko, O.V. Prezhdo, Heat-driven release of a drug molecule from carbon nanotubes: A molecular dynamics study, J. Phys. Chem. B. 114 (2010) 13481–13486.
[305] N. Saikia, A.N. Jha, R.C. Deka, Dynamics of fullerene-mediated heat-driven release of drug molecules from carbon nanotubes, J. Phys. Chem. Lett. 4 (2013) 4126–4132. [306] U. Arsawang, O. Saengsawang, T. Rungrotmongkol, P. Sornmee, K. Wittayanarakul, T. Remsungnen, et al., How do carbon nanotubes serve as carriers for gemcitabine transport in a drug delivery system?, J. Mol. Graph. & Model. 29 (2011) 591–596. [307] Y. Li, T. Hou, Computational simulation of drug delivery at molecular level, Curr. Med. Chem. 17 (2010) 4482–4491. 64
ACCEPTED MANUSCRIPT
[308] M. Chehel Amirani, T. Tang, Binding of nucleobases with graphene and carbon nanotube: A
PT
review of computational studies, J. Biomol. Struct. Dyn. 33 (2015) 1567–1597. [309] M. Santosh, S. Panigrahi, D. Bhattacharyya, A.K. Sood, P.K. Maiti, Unzipping and binding of
RNA delivery, J. Chem. Phys. 136 (2012) 065106.
RI
small interfering RNA with single walled carbon nanotube: A platform for small interfering
SC
[310] B. Nandy, M. Santosh, P.K. Maiti, Interaction of nucleic acids with carbon nanotubes and dendrimers, J. Biosci. 37 (2012) 457–474.
NU
[311] B.D. Chen, C.L. Yang, J.S. Yang, M.S. Wang, X.G. Ma, Dynamic mechanism of HIV replication inhibitor peptide encapsulated into carbon nanotubes, Curr. Appl. Phys. 13 (2013)
MA
1001–1007.
[312] J.W. Shen, T. Tang, X.H. Wei, W. Zheng, T.Y. Sun, Z. Zhang, L. Liang, Q. Wang, On the loading mechanism of ssDNA into carbon nanotubes, RSC Adv. 5 (2015) 56896–56903.
TE
(2012) 4945–4952.
D
[313] N. Wu, Q. Wang, B. Arash, Ejection of DNA molecules from carbon nanotubes, Carbon. 50
[314] J. Li, L. Jiang, X. Zhu, Computational studies of the binding mechanisms of fullerenes to
AC CE P
human serum albumin, J. Mol. Model. 21 (2015) 177. [315] J. Wong-Ekkabut, S. Baoukina, W. Triampo, I.M. Tang, D.P. Tieleman, L. Monticelli, Computer simulation study of fullerene translocation through lipid membranes, Nat. Nanotechnol. 3 (2008) 363–368. [316] J. Barnoud, L. Urbini, L. Monticelli, C60 fullerene promotes lung monolayer collapse, J. R. Soc. Interface. 12 (2015) 20140931. [317] N. Nisoh, M. Karttunen, L. Monticelli, J. Wong-Ekkabut, Lipid monolayer disruption caused by aggregated carbon nanoparticles, RSC Adv. 5 (2015) 11676–11685. [318] A.A. Skandani, R. Zeineldin, M. Al-Haik, Effect of chirality and length on the penetrability of single-walled carbon nanotubes into lipid bilayer cell membranes, Langmuir. 28 (2012) 7872– 7879. [319] H. Lee, Membrane penetration and curvature induced by single-walled carbon nanotubes: The effect of diameter, length, and concentration, Phys. Chem. Chem. Phys. 15 (2013) 16334–16340. 65
ACCEPTED MANUSCRIPT
[320] S. Baoukina, L. Monticelli, D.P. Tieleman, Interaction of pristine and functionalized carbon
PT
nanotubes with lipid membranes, J. Phys. Chem. B. 117 (2013) 12113–12123. [321] C.C. Chiu, W. Shinoda, R.H. DeVane, S.O. Nielsen, Effects of spherical fullerene nanoparticles
RI
on a dipalmitoyl phosphatidylcholine lipid monolayer: A coarse grain molecular dynamics approach, Soft Matter. 8 (2012) 9610-9616.
SC
[322] A. Jusufi, R.H. DeVane, W. Shinoda, M.L. Klein, Nanoscale carbon particles and the stability of lipid bilayers, Soft Matter. 7 (2011) 1139–1146.
NU
[323] H. Lee, Interparticle dispersion, membrane curvature, and penetration induced by single-walled carbon nanotubes wrapped with lipids and PEGylated lipids, J. Phys. Chem. B. 117 (2013)
MA
1337–1344.
[324] S. Kraszewski, A. Bianco, M. Tarek, C. Ramseyer, Insertion of short amino-functionalized single-walled carbon nanotubes into phospholipid bilayer occurs by passive diffusion, PLoS
D
One. 7 (2012) e40703.
TE
[325] S. Kraszewski, F. Picaud, I. Elhechmi, T. Gharbi, C. Ramseyer, How long a functionalized carbon nanotube can passively penetrate a lipid membrane, Carbon. 50 (2012) 5301–5308.
AC CE P
[326] H. Lee, Dispersion and bilayer interaction of single-walled carbon nanotubes modulated by covalent and noncovalent PEGylation, Mol. Simul. 41 (2014) 1254–1263. [327] A. Di Crescenzo, M. Aschi, A. Fontana, Toward a better understanding of steric stabilization when using block copolymers as stabilizers of single-walled carbon nanotubes (SWCNTs) aqueous dispersions, Macromolecules. 45 (2012) 8043–8050. [328] A. Sridhar, B. Srikanth, A. Kumar, A.K. Dasmahapatra, Coarse-grain molecular dynamics study of fullerene transport across a cell membrane, J. Chem. Phys. 143 (2015) 24907. [329] E. Sarukhanyan, G. Milano, D. Roccatano, Coating mechanisms of single-walled carbon nanotube by linear polyether surfactants: Insights from computer simulations, J. Phys. Chem. C. 118 (2014) 18069–18078. [330] H. Lee, Molecular dynamics studies of PEGylated single-walled carbon nanotubes: The effect of PEG size and grafting density, J. Phys. Chem. C. 117 (2013) 26334–26341. [331] H. Shrestha, R. Bala, S. Arora, Lipid-based drug delivery systems, J. Pharm. 2014 (2014) 66
ACCEPTED MANUSCRIPT
801820.
PT
[332] S. Mashaghi, T. Jadidi, G. Koenderink, A. Mashaghi, Lipid nanotechnology, Int. J. Mol. Sci. 14 (2013) 4242–4282.
RI
[333] A.L. Klibanov, K. Maruyama, V.P. Torchilin, L. Huang, Amphipathic polyethyleneglycols effectively prolong the circulation time of liposomes, FEBS Lett. 268 (1990) 235–237. .
issanen, N.
zydłowska, A. Bunker, M. Kumorek, D. amróz, et al.,
SC
[334] M. Dzieciuch,
PEGylated liposomes as carriers of hydrophobic porphyrins, J. Phys. Chem. B. 119 (2015)
NU
6646–6657.
[335] J. Lehtinen, A. Magarkar, M. Stepniewski, S. Hakola, M. Bergman, T. Róg, et al., Analysis of
MA
cause of failure of new targeting peptide in PEGylated liposome: Molecular modeling as rational design tool for nanomedicine, Eur. J. Pharm. Sci. 46 (2012) 121–130. [336] A. Magarkar, T. Róg, A. Bunker, Molecular dynamics simulation of PEGylated membranes
TE
15541–15549.
D
with cholesterol: Building toward the DOXIL formulation, J. Phys. Chem. C. 118 (2014)
[337] K. Laasonen, M.L. Klein, Molecular dynamics simulations of the structure and ion diffusion in
AC CE P
poly(ethylene oxide), J. Chem. Soc., Faraday Trans. 91 (1995) 2633–2638. [338] F. Müller-Plathe, W.F. van Gunsteren, Computer simulation of a polymer electrolyte: Lithium iodide in amorphous poly(ethylene oxide), J. Chem. Phys. 103 (1995) 4745-4756. [339] C. Özdemir Dinç, G. Kibarer, A. Güner, Solubility profiles of poly(ethylene glycol)/solvent systems. II. comparison of thermodynamic parameters from viscosity measurements, J. Appl. Polym. Sci. 117 (2010) 1100–1119. [340] O. Borodin, G.D. Smith, R.L. Jaffe, Ab initio quantum chemistry and molecular dynamics simulations studies of LiPF6/poly(ethylene oxide) interactions, J. Comput. Chem. 22 (2001) 641–654. [341] M. Nowakowska, K. Nawalany, M. Kepczynski, Z. Krawczyk, Novel nanostructural hybride materials for photodynamic theraphy, Macromol. Symp. 279 (2009) 132–137. [342] I.F. Hakem, J. Lal, M.R. Bockstaller, Binding of monovalent ions to PEO in Solution: Relevant parameters and structural transitions, Macromolecules. 37 (2004) 8431–8440. 67
ACCEPTED MANUSCRIPT
[343] A. Magarkar, E. Karakas, M. Stepniewski, T. Róg, A. Bunker, Molecular dynamics simulation
PT
of PEGylated bilayer interacting with salt ions: A model of the liposome surface in the bloodstream, J. Phys. Chem. B. 116 (2012) 4212–4219.
RI
[344] M. Stepniewski, M. Pasenkiewicz-Gierula, T. Róg, R. Danne, A. Orlowski, M. Karttunen, et al., Study of PEGylated lipid layers as a model for PEGylated liposome surfaces: molecular
SC
dynamics simulation and Langmuir monolayer studies, Langmuir. 27 (2011) 7788–7798. [345] M. Pannuzzo, D.H. De Jong, A. Raudino, S.J. Marrink, Simulation of polyethylene glycol and
NU
calcium-mediated membrane fusion, J. Chem. Phys. 140 (2014) 124905. [346] A. Magarkar, T. Róg, A. Bunker, Molecular dynamics simulation of inverse-phosphocholine [347] N.D. Winter,
.K. . Murphy, T.
MA
lipids, J. Phys. Chem. C. 118 (2014) 19444–19449.
O’Halloran, G. . chatz, Development and modeling of
arsenic-trioxide-loaded thermosensitive liposomes for anticancer drug delivery, J. Liposome
D
Res. 21 (2011) 106–115.
TE
[348] E.K. Perttu, A.G. Kohli, F.C. Szoka, Inverse-phosphocholine lipids: A remix of a common phospholipid, J. Am. Chem. Soc. 134 (2012) 4485–4488.
AC CE P
[349] H. Lee, R.W. Pastor, Coarse-grained model for PEGylated lipids: Effect of PEGylation on the size and shape of self-assembled structures, J. Phys. Chem. B. 115 (2011) 7830–7837. [350] J.C. Shillcock, Spontaneous vesicle self-assembly: A mesoscopic view of membrane dynamics, Langmuir. 28 (2011) 541–547. [351] W. Shinoda, D.E. Discher, M.L. Klein, S.M. Loverde, Probing the structure of PEGylated-lipid assemblies by coarse-grained molecular dynamics, Soft Matter. 9 (2013) 11549-11556. [352] J.J. Janke, W.F.D. Bennett, D.P. Tieleman, Oleic acid phase behavior from molecular dynamics simulations, Langmuir. 30 (2014) 10661–10667. [353] N. Dan, Nanostructured lipid carriers: Effect of solid phase fraction and distribution on the release of encapsulated materials, Langmuir. 30 (2014) 13809–13814. [354] N. Dan, Drug release through liposome pores, Colloids Surf. B 126 (2015) 80–86. [355] C. Vitorino, J. Almeida, L.M. Gonçalves, A J. Almeida, J.J. Sousa,
A.A.C.C. Pais, Co-
encapsulating nanostructured lipid carriers for transdermal application: From experimental 68
ACCEPTED MANUSCRIPT
design to the molecular detail, J. Control. Release. 167 (2013) 301–314.
PT
[356] J.P.M. Jämbeck, E.S.E. Eriksson, A. Laaksonen, A.P. Lyubartsev, L. A Eriksson, Molecular dynamics studies of liposomes as carriers for photosensitizing drugs: Development, validation,
RI
and simulations with a coarse-grained model, J. Chem. Theory Comput. 10 (2014) 5–13. [357] B. van Hoof, A.J. Markvoort, R.A. van Santen, P.A.J. Hilbers, Molecular simulation of protein
SC
encapsulation in vesicle formation, J. Phys. Chem. B. 118 (2014) 3346–3354. [358] C.D. Novina, P.A. Sharp, The RNAi revolution, Nature. 430 (2004) 161–164. Wang,
Optimizing
drug
delivery :
haracterization
NU
[359] S.L.
of
DLin-KC2-DMA
/
distearoylphosphatidylserine by 31P and 2H NMR spectroscopy, Diss. Sci. Dep. Mol. Biol.
MA
Biochem, (2013).
[360] D. Rozmanov, S. Baoukina, D.P. Tieleman, Density based visualization for molecular simulation, Faraday Discuss. 169 (2014) 225–243.
D
[361] A. Martín-Molina, G. Luque-Caballero, J. Faraudo, M. Quesada-Pérez, J. Maldonado-
(2014) 172–185.
TE
Valderrama, Adsorption of DNA onto anionic lipid surfaces, Adv. Colloid Interface Sci. 206
AC CE P
[362] G. Ajnai, A. Chiu, T. Kan, C.C. Cheng, T.H. Tsai, J. Chang, Trends of gold nanoparticle-based drug delivery system in cancer therapy, J. Exp. & Clin. Med. 6 (2014) 172–178. [363] A.C. Yang, C.I. Weng, Structural and dynamic properties of water near monolayer-protected gold clusters with various alkanethiol tail groups, J. Phys. Chem. C. 114 (2010) 8697–8709. [364] G. Milano, G. Santangelo, F. Ragone, L. Cavallo, A. Di Matteo, Gold nanoparticle/polymer interfaces: All atom structures from molecular dynamics simulations, J. Phys. Chem. C. 115 (2011) 15154–15163. [365] J. Yu, M.L. Becker, G.A. Carri, The influence of amino acid sequence and functionality on the binding process of peptides onto gold surfaces, Langmuir. 28 (2011) 1408–1417. [366] K.H. Lee, F.M. Ytreberg, Effect of gold nanoparticle conjugation on peptide dynamics and structure, Entropy. 14 (2012) 630–641. [367] R.C. Van Lehn, M. Ricci, P.H.J. Silva, P. Andreozzi, J. Reguera, K. Voïtchovsky, et al., Lipid tail protrusions mediate the insertion of nanoparticles into model cell membranes, Nat. Commun. 69
ACCEPTED MANUSCRIPT
5 (2014) 4482.
PT
[368] R.C. Van Lehn, A. Alexander-Katz, Free energy change for insertion of charged, monolayerprotected nanoparticles into lipid bilayers, Soft Matter. 10 (2014) 648–658.
RI
[369] R.C. Van Lehn, A. Alexander-Katz, Structure of mixed-monolayer-protected nanoparticles in aqueous salt solution from atomistic molecular dynamics simulations, J. Phys. Chem. C. 117
SC
(2013) 20104–20115.
[370] R.C. Van Lehn, A. Alexander-Katz, Ligand-mediated short-range attraction drives aggregation
NU
of charged monolayer-protected gold nanoparticles, Langmuir. 29 (2013) 8788–8798. [371] S.J. Marrink, A.H. de Vries, D.P. Tieleman, Lipids on the move: Simulations of membrane
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pores, domains, stalks and curves, Biochim. Biophys. Acta. 1788 (2009) 149–168.
Physicochemical characterization
Properties of Interest
Self-assembly of DDS components Structure and dynamics of drug-DDS complex Drug loading Drug distribution Drug-DDS interactions Functionalization Drug leakage Aggregation
Circulation
Interaction with biomolecules Stability 70
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Interaction with ions Cell-membrane
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Interactions with membrane
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Cellular internalization
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Interaction with membrane proteins Intracellular
Drug-DDS dissociation
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Drug release from endosome
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Interaction with endogenous molecules Nucleus translocation
Abbreviation
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Experimental Technique
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Table 1: Questions relevant to drug delivery system design that can be answered by computational simulations. Properties
Dynamic light scattering
DLS
Particle size, aggregation
Laser diffraction
LD
Particle size, aggregation
Transmission electron microscopy
TEM
Particle size, dispersity, shape, surface morphology
Scanning electron microscopy
SEM
Particle size, dispersity, shape, surface morphology
Atomic force microscopy
AFM
Fluorescence
FLIM
Particle size, dispersity, shape, surface morphology Drug release
Zeta potential
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Surface charge
Gel electrophoresis
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Encapsulation efficiency of nucleic acids Encapsulation efficiency
Ultraviolet-visible light spectroscopy
UV-vis
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Bond vibrations
X-ray scattering
Repeat spacing, chemical connectivity
SAXS and WAXS -
NMR
Electron spin resonance spectroscopy
ESR
DSC ITC
Carrier fluidity, gadolinium concentration, microviscosity, micropolarity Viscosity, polarity, concentration, drug release Phase transitions Phase transitions
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Isothermal calorimetry
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Crystallinity, molecular mobility, chemical connectivity
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Nuclear magnetic resonance spectroscopy
Separate, identify and quantify components in mixture
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HPLC
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High performance liquid chromatography
Crystallinity
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Neutron scattering
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Fourier transform-infrared FT-IR light spectroscopy
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Table 2: Experimental methods used to study drug delivery systems.
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Highlights Nanoparticles have the potential to improve drug delivery and enable precision medicine.
Nanoparticles for drug delivery can be based on many different chemistries.
We review experimental and computational approaches to study the structure and properties of
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nanoparticles and review computational studies of the major classes of nanoparticles for drug
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S0005-2736(16)30066-9 doi: 10.1016/j.bbamem.2016.02.028 BBAMEM 82153
To appear in:
BBA - Biomembranes
Received date: Revised date: Accepted date:
23 January 2016 20 February 2016 23 February 2016
Please cite this article as: M. Ramezanpour, S.S.W. Leung, K.H. Delgado-Magnero, B.Y.M. Bashe, J. Thewalt, D.P. Tieleman, Computational and experimental approaches for Investigating Nanoparticle-Based Drug Delivery Systems, BBA - Biomembranes (2016), doi: 10.1016/j.bbamem.2016.02.028
This is a PDF file of an unedited manuscript that has been accepted for publication. As a service to our customers we are providing this early version of the manuscript. The manuscript will undergo copyediting, typesetting, and review of the resulting proof before it is published in its final form. Please note that during the production process errors may be discovered which could affect the content, and all legal disclaimers that apply to the journal pertain.
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Computational and Experimental Approaches for
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Investigating Nanoparticle-based Drug Delivery Systems
a
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M. Ramezanpour a, S.S.W. Leung b, K.H. Delgado-Magnero a, B.Y.M. Bashe c, J. Thewalt b,c,*, D.P. Tieleman a,* Centre for Molecular Simulation, Department of Biological Sciences, University of Calgary, Calgary,
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AB, T2N 1N4, Canada b
Department of Physics, Simon Fraser University, Burnaby, BC, V5A 1S6, Canada
c
Department of Molecular Biology and Biochemistry, Simon Fraser University, Burnaby, BC, V5A
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Abstract
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1S6, Canada
Most therapeutic agents suffer from poor solubility, rapid clearance from the blood stream, a lack of targeting, and often poor translocation ability across cell membranes. Drug/gene delivery systems (DDSs) are capable of overcoming some of these barriers to enhance delivery of drugs to their right place of action, e.g. inside cancer cells. In this review, we focus on nanoparticles as DDSs. Complementary experimental and computational studies have enhanced our understanding of the mechanism of action of nanocarriers and their underlying interactions with drugs, biomembranes and other biological molecules. We review key biophysical aspects of DDSs and discuss how computer modeling can assist in rational design of DDSs with improved and optimized properties. We summarize commonly used experimental techniques for the study of DDSs. Then we review computational studies for several major categories of nanocarriers, including dendrimers and dendrons, polymer-, peptide-, nucleic acid-, lipid-, and carbon-based DDSs, and gold nanoparticles.
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Keywords Drug delivery system, Gene nanocarrier, Nanoparticle, Biological membrane, Computer modeling,
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Experimental techniques
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Abbreviations
coarse-grained;
CNP,
carbon-based
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5-FU, 5-fluorouracil; AuNPs, gold nanoparticles; BSA, bovine serum albumin; CD, cyclodextrin; CG, nanoparticle;
CNT,
carbon
nanotube;
CPe,
inverse-
phosphatidylcholine; CPNT, cyclic peptide based nanotube; CPP, cell penetrating peptide; DDS, drug
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delivery system; DLS, dynamic light scattering; DOX, doxorubicin; DPD, Dissipative Particle
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Dynamics; DPPC, dipalmitoylphosphatidylcholine; DSC, differential scanning calorimetry; ESR, electron spin resonance spectroscopy; FT-IR, Fourier transform-infrared spectroscopy; gamma-PGA,
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poly(gamma-glutamic acid); HSA, human serum albumin; IDP, intrinsically disordered proteins; IFN, interferon alpha-1b; IR, infrared; ITC, isothermal titration calorimetry; LD, laser diffraction; LNP, lipid nanoparticle; MD, Molecular Dynamics; NMR, nuclear magnetic resonance spectroscopy; NP, nanoparticle; p-THPP, 5,10,15,20-tetrakis(4-hydroxyphenyl)porphyrin; PAMAM, poly(amido amine); PEI, polyethylenimine; PPI, poly(propylene imine); QD, quantum dot; SAXS, small angle X-ray scattering; SCPs, star copolymers; SEM, scanning electron microscopy; SSMs, sterically stabilized micelles; TAT, trans-activator of transcription; TEM, transmission electron microscopy; VIP, vasoactive intestinal peptide; WAXS, wide angle X-ray scattering; XRD, X-ray diffraction.
1. Introduction
In medicine, the delivery of a drug can be as important as the drug itself. Physiology poses key challenges to effective drug delivery; an administered drug must penetrate obstacles such as endo- or 2
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epithelial membranes and also survive the host's defenses in order to be effective. Addressing such
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challenges requires some form of drug encapsulation, and the entity forming the capsule, which has a defined molecular architecture, is known as a "drug delivery system (DDS)." From a functional point
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of view, an ideal DDS is easy to administer, non-toxic, carries the drug to its desired destination, and then releases it (Fig. 1) [1,2]. Practically, an ideal DDS is cheap and straightforward to make, as well as
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stable prior to its administration. Intense research into DDS design is providing increasingly effective
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disease treatment.
A central aim for specialized DDS development is the optimization of tiny drug encapsulation
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vehicles known as nanoparticles (NPs). NPs typically have diameters in the range of 10 to 100 nm [3]. These small DDS can circulate freely even in capillaries [4], and are intrinsically better at traversing biological barriers than larger DDS. It is worth mentioning that nanomedicines, however, have a more
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complicated and time consuming FDA approval process than parent unimolecular therapeutics [5]. In
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addition to efficacy and side effect evaluations common between both small molecules and nanomedicines, there are other concerns about nanocarriers which need further evaluation. Nanocarrier
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aggregation in vivo and their drug release, as well as evaluation of each component of the formulation are some of those concerns. FDA regulations for nanomedicines, the complexity of nanocarriers, and the prospect of only a small increase in performance upon reformulating small drugs in nanocarriers can discourage pharmaceutical companies from investing in these systems. In fact, it seems that investigations into DDS are much more successful in generating papers than in developing new treatment methods [5]. However, considering the number of approved drugs and current ones in clinical trials [6,7], as well as clearer guidelines for nanomedicine approval by the FDA, there is growing momentum in transferring these systems from publication to clinical development [5]. Many different types of NPs exist, including liposomes [7,8], dendrimers [9–11], carbon nanotubes (CNTs) [12–14], inorganic [15–20], and polymer-based [21,22] NPs, with each having its own unique properties (Fig. 2). Physicochemical properties including size, shape, deformability, surface charge and chemical composition affect how well they evade phagocytosis and how they
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interact with vasculature, traverse cell membranes and escape from endosomes prior to drug
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degradation [1]. Thus, physical characterization of NPs is an important step in the DDS design process. Despite the increasing sophistication of experimental efforts to measure, design and optimize
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the structure and dynamics of NPs, such research inevitably faces intrinsic and practical limitations. Resolution of structural details can be difficult or impossible, and systematic variation of properties
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such as composition, size and surface charge can be prohibitively time consuming and expensive. As a result, general biophysical principles connecting the effectiveness of a DDS with its composition are
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difficult to elucidate. Predicting optimum DDS design solely on the basis of experimental research is therefore unlikely. Another challenge in DDS development is that many DDSs show promise in vitro
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but fail in vivo [6]. This is mainly because of the lack of mechanistic insight obtainable by experiments which are based on trial and error. Theoretical methods, both analytical and computational, can allow primary screening of variables in order to predict suitable conditions for further experiments [23].
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Computer modeling techniques provide detailed information about molecular interactions and other
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physicochemical properties of drugs and carriers, and have found broad applications in biology, biochemistry, and biophysics [24]. Computer simulation is capable of complementing experiments and
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assisting in the rational design of new formulations with improved efficacies. In this review, we describe the main experimental approaches to characterize NP-based DDSs and focus in more detail on current applications of computer simulations aimed at understanding molecular aspects of DDSs.
In the next sections, we first give a brief overview of commonly used experimental techniques, followed by a section that describes several common computational approaches. Section 4 highlights recent computational studies of what currently are the major classes of NP-based DDSs. We conclude with a brief outlook on the role of computer simulations in drug delivery technology.
2. Commonly used experimental techniques for nanoparticle characterization
Here we provide a brief summary of techniques regularly used to characterize NP size, zeta potential, surface morphology, and the existence of colloidal structures (Table 1). We first describe the 4
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importance of each of these properties and the experimental techniques used to measure them. Next we
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describe some sophisticated physical chemistry tools that are used in DDS development to provide data that can parameterize and validate computational simulations. Experiments used to visualize cellular
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uptake, NP-cell interactions, as well as those used to determine cell viability will be omitted, even though they are vital to pharmaceutical development and are also commonly performed, since details
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on these can be found elsewhere (e.g. [25]).
Particle size is an important property because the size of the carrier can affect circulation time,
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encapsulation efficiency, and cellular uptake [1,26,27]. For example, in the case of cancer treatment, nano-drug carriers of the appropriate sizes can extravasate from the bloodstream to tumour tissues (380
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– 780 nm) [1,28], but not from the tighter capillaries (50 – 200 nm) in healthy tissues [29]. This contributes to the well-known enhanced permeability and retention effect [30–33]. Light scattering techniques such as laser diffraction (LD) and dynamic light scattering (DLS) can be used to measure
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NP size [34]. In both of these experiments, a laser beam is directed at a dilute sample (e.g. a suspension
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or solution of NPs). In LD, the diffraction angle is used to determine the particle size [35]. In DLS, the time dependence of the scattered light intensity is detected at a known scattering angle. The Brownian
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motion of suspended particles causes the scattered light intensity to fluctuate. These fluctuations are correlated with the particle’s velocity, and can be used to determine particle size via an appropriate theoretical model (e.g. Stokes-Einstein equation). Most models assume the particles are spherical and monodisperse; artifacts may arise if they are not. DLS can be used to measure the size of particles in the 2 nm – 3 m range, and LD can be used in the 20 nm – 2 mm range [34,36]. Resolution of small differences in particle size can be improved by combining light scattering techniques with a separation technique, such as field flow fractionation [37,38]. Microscopy techniques such as transmission electron microscopy (TEM), scanning electron microscopy (SEM), and atomic force microscopy can be used to measure NP size, as well as size distribution (dispersity), shape, and surface morphology. Dispersity – also known as polydispersity in older literature – in a formulation can result in varying body-residence time and immunogenicity [39,40]. Particle shape can affect cellular uptake mechanisms and kinetics [41–45] as well as margination dynamics – the lateral drift of NPs to endothelial walls [1]. The particle surface is the first 5
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point of contact between the NP and its environment, and surface morphology is known to affect drug
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release kinetics [46]. In electron microscopy, an electron beam is directed at the sample, and differences in electron density in the sample gives contrast to structures. Since electron microscopy can
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give high-resolution images approaching molecular resolution, all motions on the supramolecular scale have to be halted. For SEM, the NPs are deposited onto a carbon conductive tape, dried, and covered
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with a thin layer of metal [47]. In cryo-TEM, samples are applied onto an electron microscopy grid, plunge-frozen rapidly in ethane to prevent the formation of damaging cubic ice and then imaged under
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cryogenic conditions to prevent radiation damage to the sample during imaging [48]. Magnifications on the order of 50,000X, with a resolution of 0.3 nm, are possible [49]. Atomic force microscopy probes
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surface terrain using a nanoscale mechanical probe with a vertical resolution as small as 0.01 nm [35]. A NP’s shape can influence the physiological fate of its cargo [1]. Particle shape can be an indication of undesirable aggregate structures. Aggregate structures can also cause inaccurate particle
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size determination by LD or DLS [34]. Light microscopy can be used for determining if microparticles
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are aggregates of smaller particles [36,50], even though it cannot be used for directly observing NP structure due to limitations in optical resolution (~ 200 nm). Isothermal titration calorimetry (ITC),
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which will be described in greater detail below, has been used to study aggregate formation in polycationic nucleic acid carriers [51]. Aggregation of NPs can be prevented via electrostatic repulsion [31,52]. Surface charge measurements can be a predictor of aggregation behaviour, and consequently a predictor of colloidal dispersion stability during storage. Surface charge can affect cellular uptake, cytotoxicity, and circulation times [1,31,53,54]. Zeta potential is used to characterize the surface charge of NPs. When a particle moves in a liquid, a thin layer of liquid moves with it. The zeta potential is defined as the electrical potential at the boundary of this moving liquid, defined as the shear plane. It is a function of surface charge density, shear plane location, and surface structure [55]. Zeta potential is determined by measuring light scattering caused by particle motion in an applied electric field. Charged particles with larger zeta potential (|zeta| > 25 mV) are less likely to form aggregates but neutral particles generally have longer circulation times [56]. Surface charge effects are very complex; positive and negative zeta potentials correlate with higher cellular uptake in non-phagocytic and phagocytic cells, respectively [54]. Future DDS designs may 6
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require NPs to switch zeta potential at the target site to maximize circulation time while targeting a
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particular type of cell [1]. It should be noted that protein adsorption in cellular media can change the surface charge [52]. Conclusions on surface-charge effects, therefore, are only valid when comparing
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functionalized or non-functionalized particles of similar sizes [54].
Encapsulation efficiency – the ratio of encapsulated drug to the amount of drug used during
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formulation preparation – is an important parameter [46]. Low encapsulation efficiency implies that more carrier material is needed, heightening the risk of carrier material toxicity [2,57]. Encapsulation
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efficiency can be determined in a number of ways, depending on the particular type of NP. Drug loading can be determined by weight [16]. Gel electrophoresis has been used to determine
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encapsulation efficiency by analyzing protection from RNase degradation in siRNA lipid nanoparticles (LNPs). Physical chemistry techniques can also be used to identify and quantify the degree to which components have been encapsulated. For example, fluorescence spectroscopy can be used if the drug is
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fluorescent [16]. Ultraviolet-visible(UV-Vis) light spectroscopy can be used to determine the amount
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of cargo (e.g. siRNA, anticancer drug doxorubicin (DOX)) present [58,59]. High performance liquid chromatography can be used to separate, identify, and quantify the components of a mixture [46,60].
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Novel single molecule measurements have been applied to measure encapsulation efficiency in individual lipid vesicles [61].
An array of physical chemistry techniques has been instrumental in evaluating the chemical and physical properties of NP components. Dendrimer structures have been simulated based on experimental data from nuclear magnetic resonance spectroscopy (NMR) and X-ray [62]. LNP matrix state, polymorphism, and phase behaviour have been characterized by differential scanning calorimetry (DSC), X-ray diffraction (XRD), and neutron scattering [63]. Lipid crystallinity of solid LNPs has been studied using X-ray scattering, DSC, NMR, and electron spin resonance spectroscopy (ESR) [64]. CNTs have been characterized using XRD, UV-Vis spectroscopy, Fourier transform-infrared spectroscopy (FT-IR), ESR, SEM, and energy dispersive X-ray diffraction [65]. Iron oxide NPs, which are magnetic, can be studied using magnetometry, NMR relaxation dispersion profiles [55], and magnetic field flow fractionation [66]. Some more common physical chemistry tools will be briefly described here. 7
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In optical spectroscopy, the absorption of electromagnetic radiation by NPs occurs at
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frequencies characteristic of the molecular structures present. UV-Vis light is absorbed if it supplies the right amount of energy to excite valence electrons, while infrared (IR) light is absorbed if it excites
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bond vibrations. FT-IR is a more efficient variant of IR spectroscopy where all frequencies are sampled simultaneously. It has been used to monitor cholesterol-induced changes in liposomal membrane
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structure [67] and to verify conjugation of block copolymers onto iron oxide NPs [68]. UV-Vis spectroscopy has been used extensively in NP characterization. For example it was used to confirm the
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presence of cleavable disulphide bond in CNT-nucleic acid conjugates [69]; monitor the metal to ligand charge transfer band (5d(Pt11) to π*(diimine)) in dendrimer synthesis [70]; measure dye and drug
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encapsulation in poly(lactic-co-glycolic acid) polymeric carriers [71,72] and study drug release kinetics of liposomal formulations [67].
NMR and ESR both rely on spin manipulations in the presence of an applied external magnetic
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field. NMR monitors the nuclear spin of nuclei with non-zero nuclear spins, and ESR monitors the
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electron spin of excited unpaired electrons [73]. NMR can provide a wealth of information on molecular arrangements. NMR is sensitive to small changes in the local environment: it can be used to
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differentiate between various layers up to the fourth generation in dendrimers [74], and between neighbouring carbon atoms in lipids [75]. 2D NMR techniques can be used to ascertain chemical connectivity (e.g. COSY, HMQC, HSQC) and distances (e.g. NOESY and ROESY) between specific atoms [74,76,77]. Dynamic information is also accessible to NMR. Pulsed field-gradient spin echo 1H NMR and diffusion-ordered spectroscopy have been used to determine diffusion coefficients in aliphatic polyester and poly(propylene imine) (PPI) dendrimers [74,78].
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determine the mobility of siRNA encapsulated in LNPs [79]. NMR relaxation was used to monitor molecular tumbling activities [80] and this information, in turn, was used to determine the location of a spin-labeled anticancer drug in micelles formed by PEG modified with long alkyl groups and of paclitaxel in cyclodextrin (CD) vesicles [77,80]. Most DDSs are invisible to ESR, requiring the addition of paramagnetic spin probes [81]. Lipophilic ESR probes have been used as model lipophilic drugs to help determine where these drugs localized in solid LNPs [35]. In gadolinium poly(amido amine) (PAMAM) dendrimer containing spin probes, ESR was used to determine the location and 8
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concentration of the magnetic resonance imaging contrast agent gadolinium [78]. ESR has also been
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used to prove that the terminal interbranch H-bonded groups preclude backfolding in PAMAM dendrimers [78]. Since ESR can probe membrane fluidity, ESR has been used to study interactions
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between polymeric micelles and lipid membranes, which are models for cell membranes [68]. ESR can also give microviscosity and micropolarity information [81]. Viscoelastic properties of dispersions can
which are less deformable than nano-emulsions [64].
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affect DDS interactions with the body. For example, capillary blockage can occur with solid LNPs,
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Scattering techniques can be used to study the structure of colloidal systems. In scattering studies, a monochromatic beam of light, neutrons, or X-ray is focused on the sample, and the intensity
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of the scattered beam is measured. Light scattering was covered earlier in this section. X-ray scattering results from variations in electron density and neutron scattering results from variations in the spatial distribution of atomic nuclei [82]. The interference pattern formed by the scattered beam can be used to
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determine characteristic lengths using Bragg’s Law. Larger angle measurements contain information
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for shorter length scales: small and wide angle X-ray scattering (SAXS and WAXS) are used to look at liquids and solids with structures on the length scale of 1-200 nm and sub-nm, respectively. For
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example, SAXS is used to monitor monolayer and bilayer repeat spacing, and hydrophobic thickness in lipid membranes, while WAXS is used to determine chain packing and extent of lipid motion [83,84]. SAXS has been used to study NPs with cubosome structures [85,86], to provide micelle shape, size and density of nonsteroidal anti-inflammatory drug celecoxib-loaded protein micelles [50], and to observe in situ shell growth of polymeric nanocapsules [87]. WAXS has been used to study lipid polymorphic transformations in solid LNPs and crystallinity of encapsulated drugs [88], and crystallinity of polyelectrolyte complexes made with polysaccharides [89]. SANS, useful for studying structures of 1100 nm [90], has been used to determine molecular weight and end group locations of dendrimers [74]. Neutron scattering can also be used to study drug diffusion and internal molecular motions in LNPs [63]. Fluorescence comprises a family of spectroscopy and microscopy techniques relying on excitation of fluorescent species. An extrinsic fluorescent probe is added when no fluorescent moieties are present in the material of interest. Absorption of a photon excites the fluorophore and fluorescence 9
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occurs when a photon is later emitted. Between excitation and emission, the fluorophore’s motions and local environment (e.g. viscosity, polarity, temperature, pH, ionconcentration) influence the
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fluorophore’s spectrum [91]. Polarity sensitive fluorescent probes are commonly used to determine the
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critical association concentration of polymeric micelles – analogous to the critical micellar concentration of surfactant micelles [92]. Fluorescence quenching assays have been done to evaluate
been used to evaluate drug release characteristics [93].
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nucleic acid-polycation binding in polycationic nucleic acid carriers [51] and dye leakage assays have
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Thermal analysis is a versatile tool for DDS characterization [94,95]. DSC measures enthalpy changes – the heat required for a sample to undergo a physical phase transition (e.g. glass transition,
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melting, decomposition, isomerization or heats of solution, water sorption-desorption) [95,96]. DSC was used to monitor the physical state of the antineoplastic drug paclitaxel inside polymeric microspheres, for example [46]. More specific to DDS design, DSC has been used to measure the gel-
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to-liquid crystalline transition temperature of lipids in liposomal formulations, which can be used to
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predict release rates of encapsulated drugs [95,97]. In a closely related technique, ITC, the evolved heat is measured as concentrated aliquots of one substance are added to a solution of a second substance.
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The released heat is directly proportional to the amount of substance added, and can be used to measure enthalpy changes, stoichiometry, and binding constants. ITC has been used to measure cyclodextranguest molecule interactions, micelle-drug interactions, and polyelectrolyte aggregation [98].
NP DDSs are complex systems that can evolve over time. Dynamic processes cannot be captured in steady-state measurements, and can pose as a challenge to characterization [35]. For example, PEGylated NPs are known to lose their PEG coating before reaching target cells [99–101]. Timeresolved DLS combined with TEM was used to investigate morphology transition kinetics of multiblock copolymer micelles [102]. Time-resolved fluorescence lifetime measurements have been used to follow the release of fluorescent compounds from polymeric carriers [71], and fluorescence lifetime imaging microscopy could extend drug release studies to cellular systems [103,104].
3. Computational approaches to study nanoparticles 10
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Computer simulations in general describe the physics of materials at a suitable level of detail for a particular application. A simulation is described by the level of detail in the physics used to model the
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system of interest, and the algorithms used to generate enough simulation data to draw statistically valid conclusions about the behaviour of the system of interest. NPs can be described at different levels
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of detail (Fig. 3), but for most of the applications in this review the appropriate levels of detail are limited to atomistic and semi-atomistic, slightly coarser, levels.
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At the highest level of detail, a system is described by quantum mechanics in one of its approximations. Quantum mechanical calculations have been widely applied to CNTs and play a role
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in optimizing less detailed simulations, but in practice they are only useful for small systems with limited degrees of freedom and generally not in solution. They are essential to correctly model electronic properties and can be used to calculate electrical, optical, magnetic and mechanical
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properties of CNTs, quantum dots (QDs), magnetic NPs and similar systems. For soft-condensed
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matter systems such as liposomes or dendrimers in solution their usefulness is currently limited. If we average over electronic properties by representing electrons as partial charges on atoms
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rather than taking them into account explicitly, we significantly simplify calculations. At this level of detail, atoms are described as point charges that interact through simplified empirical potential terms including Coulomb interactions, Van der Waals interactions, and simplified terms describing bonds, angles, and dihedrals as harmonic terms and cosine expansions [105]. The resulting technique is called Molecular Dynamics (MD) and is widely used for biological and soft-condensed matter systems. The vast majority of the papers reviewed below use this approach, which accounts for a substantial fraction of super computer time on the worlds’ major academic computer centers. MD is a mature technique that is implemented in a number of widely-used software packages, including GROMACS [106], NAMD [107], LAMPPS [108], CHARMM [109], and AMBER [110]. MD simulations essentially generate a movie of a set of molecules over time, incorporating every degree or nearly every degree of freedom, from which through statistical mechanics thermodynamic properties can be calculated. Its main limitations include the computational cost and the corresponding limited time scale (realistically, microseconds in most cases) and length scale (~10 nm per dimension) and the limitations implicit in 11
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the physics that describes the interactions between atoms: point charges mean electronic effects like
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polarization, pi-electron clouds possibly important for CNTs, and charge transfer are typically ignored. In addition, the simplified interaction function sometimes imposes other limitations.
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For many properties of interest, especially in mesoscopic-scale systems like NPs with a size of 20-200 nm, atomistic detail may not always be necessary. Many papers below use coarse-grained (CG)
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simulations, often based on the MARTINI model [111]. Here CG means that several atoms have been grouped into interaction sites, which are no longer recognizable as atoms but instead are ‘beads’ that
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represent molecular fragments. Depending on the level of coarse-graining, these beads can still maintain a significant amount of chemical specificity, or they can represent very generic properties
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such as those that represent a lipid by one head group bead and two tail beads. The same physics applies as in atomistic simulations, and sufficient conformations of a system of interest have to be generated by a sampling algorithm to accurately calculate thermodynamic and structural properties.
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This can be done by MD, Monte Carlo, or various other algorithms including Langevin dynamics or
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Dissipative Particle Dynamics (DPD) [105].
At the CG level, individual molecules and usually fragments of molecules are still clearly
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recognizable. In material science, there exists a whole hierarchy of additional models. Beyond molecules, DPD solves hydrodynamics equations for materials by representing materials as discrete particles that can be much larger than individual molecules. DPD blurs the line with CG MD a little by its choice of particle volume: if the volume is chosen to coincide with molecules or molecular fragments its resolution is similar to CG MD, although it typically treats problems that are more generic. Beyond DPD there are many other possibilities, including field-based treatments where a system is described by density fields. Simulations coarser than DPD are beyond the scope of this review.
4. Computational studies on drug and gene delivery systems
Computer modeling, as complementary tools to experiments, is excellent in shedding light into the structural and dynamical properties of systems of interest at atomistic or molecular levels of detail 12
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[112,113]. Applied to DDSs, computer simulations have been used to address a broad range of
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questions (Table 2). Computer simulations can be used to study self-assembly [114–116], the structural and dynamical characteristics of the resulting aggregates [117,118], drug loading capacity, mechanism
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and rate [119–121], drug distribution/localization in DDS [121], complex stability [118,122], drug retention, release mechanism and release rate [123–125], dominant drug-DDS interactions
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[119,126,127], and to design or optimize DDSs targeting capabilities [128,129]. Environmental conditions, e.g. pH, temperature, salt type and concentration, counterions [126,130], and external
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stimulus such as external magnetic fields [131–133], as well as interactions with other biomolecules (e.g. serum proteins, miRNA, heparin), all might affect the aforementioned aspects of DDSs [126,134–
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137] and can be studied computationally.
The way DDSs interact with cell membranes is one of the most commonly studied steps in drug delivery by computational studies [138,139]. Computer modeling can investigate the driving forces for
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NP-membrane interactions, as well as how factors such as design parameters and environmental
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conditions affect these. These parameters include size, shape, surface chemistry, and concentration of NPs, as well as mechanical and elastic properties of both NPs and membranes [138–143]. Surface
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chemistry refers to NPs hydrophobicity/hydrophilicity, charge density and distribution, as well as coating ligand’s length, grafting density and distribution, rigidity, and their affinity to receptors [144– 147]. Since, in real systems the membrane often interacts with more than one NP simultaneously, NP aggregation and interaction of multiple NPs with the membrane also is a relevant factor [138,139]. Membrane properties such as lipid phase, membrane composition, surface tension, charge density, receptor types and density also play important roles [148–150]. In addition to all of these, external macromolecules, e.g. proteins in the bloodstream, and different environmental conditions in different cell types [134], as well as differences between external and internal cellular environments, are also of great importance and worth further investigation. Of course, many of these factors are coupled and have to be taken into account together in the design of NPs [138,139]. NPs are usually functionalized by coating with polymers, lipids, or ligands, to increase their stability, targeting capability, cellular uptake efficiency, and also to reduce their toxicity [131]. Both the type of coating molecule and the coating pattern and density strongly affect the physicochemical 13
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properties of these NPs, and as a result their interactions with their cargo and target lipid membranes
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[147,151–155]. Simulations can be used to study these coatings, how they affect NP properties including their interactions with other molecules [152,156], and can be used to optimize these coatings
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towards DDS with improved delivery capabilities [151,154]. Functionalization with PEG polymers, a process called PEGylation, is a good example of functionalization [157,158]. Drugs/proteins/nucleic
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acids and NPs’ surfaces are usually PEGylated to improve their water solubility, stability, and circulation lifetime, and to reduce their aggregation, toxicity, and immune response [157,159].
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Considering the importance of PEG and its wide usage in drug delivery applications, PEG is worth further discussion. PEG is the most commonly used non-ionic polymer with stealth behaviour for
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drug delivery purposes [160]. These biocompatible hydrophilic polymers can solubilize hydrophobic molecules, and reduce renal clearance and toxicity of drugs and nanoparticles. They also inhibit the aggregation of PEGylated agents by steric stabilization, as well as mask therapeutic agents from the
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host immune system and consequently suppress immunogenicity and antigenicity. This protective
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polymer layer, which is known as stealth sheath, can reduce the fast recognition by the immune system and reduce the non-specific interactions with blood components, resulting in longer blood circulation
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times. These properties have made it the gold standard polymer for drug delivery and other biomedical applications and have been the topic of several computational modeling studies reviewed in more detail below. Despite the widespread use of PEG, there are several drawback to PEG including immunogenicity and antigenicity, e.g. hypersensitivity reactions and the development of antibodies against PEGs [160], which adversely affects pharmacokinetics [161], and the search for alternatives, e.g. poly(amino acids), continues, in part aided by computer simulations. In addition to different aspects of DDS mechanism, different types of DDS have been simulated. There are many types of nanoparticulate DDS. DDSs based on polymers, peptides, nucleic acids, lipids, carbon, dendrimers and dendrons, and gold have been investigated via computer modeling and are described in more detail below. Combined, these application show that computer simulation has become a powerful technique for rational design and optimization of DDS, and demonstrates the growing importance of computational pharmaceutics.
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For the rest of this review, we will present examples of computational studies on each aspect of
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interest for each nanoparticulate DDS type in separate sections, and then comment on challenges in this field. We will focus on atomistic, CG MD simulations, and DPD simulations of nanoparticulate DDSs;
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these fall mainly in categories defined as dendrimers/dendrons, polymer-, protein/peptide-, nucleic acid-, lipid-, carbon-based DDS, as well as gold nanoparticles (AuNPs). We emphasize studies
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published since 2010. There are computational studies on other DDSs that are outside the scope of this review, e.g. graphene oxides and reduced graphene oxides[19,162], silicon NPs [19,163], nanodiamond
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[164,165], layered drug delivery carriers [166,167], zeolites [168–170], metal-organic frameworks [171,172], and other porous materials. These are excluded because they are not NPs, there are limited
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computational studies available, or because their focus is different from the studies described below.
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4.1. Dendrimers and dendrons
Dendrimers are a class of branched globular materials with broad application in nanotechnology
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and nanomedicine, including for drug/gene delivery [9–11]. Their monodispersity, modifiable surface, shell and core characteristics, and their high loading capacity make them suitable carriers for therapeutic agents, with promising applications in gene delivery through complex formation with nucleic acids, so-called dendriplexes [173–177]. Drugs can be covalently or noncovalently incorporated in dendrimers to overcome solubility problems and to improve targeting and cellular uptake. Dendrimers have been studied in many computational papers to characterize their structure in solution, their interactions with drugs and biomolecules [62,175], and their response to external stimuli[178]. All parts of dendrimers can be modified chemically; core, shell and surface. The surface can be optimized for different biological activities by functionalizing the terminal groups. Thus dendrimers are a flexible class of materials with the potential for almost infinite variation.
Different dendrimers have been modeled including PPI, triazines and PAMAM. Simulations can be used to investigate their interactions with other molecules, the effect of solvents and counterions, pH, salt concentration, different generation number, the nature of terminal groups and chemical 15
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modifications. Computer simulations have also been used to study deformation and conformational
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changes of dendrimers upon interaction with nanopores [179], lipid membranes [180], and nucleic acids [181] as a function of generation number, pH, functionalization, and ionic strength. For example,
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Nandy et al. [181] simulated a 38 base pair double stranded DNA and three different generations of PAMAM dendrimer (G3, G4, G5). They found that DNA caused deformations in lower-generation
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dendrimers (G3) while higher generation PAMAM (G5) bent DNA (Fig. 4). Generation number, pH, and architecture have been shown to affect dendron’s flexibility and rigidity, and as a result influence
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their binding efficiency to nucleic acids [182,183]. Several studies have investigated the effect of chemical modification on self-assembly, conformation, and drug/gene binding [184–186].
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Self-assembly of Janus dendrimers, comprised of hydrophobic and hydrophilic parts, has been studied both experimentally and computationally. They form onion-like dendrimers and other complex architectures [187,188].
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Computer simulation has been used to study how chemical modification and surface
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functionalization (e.g. PEGylation, acetylation, folic acid groups, peptides, and targeting ligands of the surface) influences the structure [189], and the interactions with cargos [190,191]. Computer modeling
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can also be used to determine the optimal grafting densities and patterns based on structural and drugloading properties, and interactions with target membranes [154,192–195]. Effect of PEGylation size and grafting density on the structure of dendrimers, and also the structure of the PEG layer itself have been widely studied [154,196–198]. PEGylation expands the core of dendrimers, effectively reduces their surface charge density and increases the overall size of dendrimers [196]. The effect of PEGylation on structure and drug loading capacity of PAMAM-G4 dendrimers was investigated by an all-atom MD simulation [154]. Different PEG densities (25%, 50%, 75% and 100%), were tested for complexation and loading capacity for 5-fluorouracil (5-FU), as a model anticancer drug. The simulations suggested that 25% PEG is the most suitable PEGylation density for drug delivery purposes. This grafting density can retain the internal drug complexation capability, and also assist in retaining drugs. For higher PEGylation degrees than 25%, 5-FU molecules to a high extent interact with external PEG chains than dendrimer branches. Pearson et al.[193] used MD simulation to understand why PEGylated dendron-based copolymers functionalized with different terminal groups, 16
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NH2, -COOH, and –Ac, do not exhibit a charge-dependent cellular interaction. Simulations suggest
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that interactions between positively charged terminal groups with PEG molecules sequester the charges, and cause this low level of cellular interaction for NH2 terminated micelles.
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Interactions between dendrimers with lipid membranes, permeation of dendrimers as a function of generation number (size), protonation state, functionalization, and dendrimer concentration, as well
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as the effect of bilayer asymmetry, bilayer tension, lipid tail length, and lipid phase (temperature) all have been studied by simulations [124,180,195,199–204]. Tian and Ma [124] studied interactions of
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G4-PAMAM dendrimers with both symmetric and asymmetric negatively charged bilayers, with and without tension, at both neutral and low pH. It was shown that both membrane tension and electrostatic
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interactions between charged dendrimers and tensed membrane play important role in dendrimer penetration through membrane. Based on their simulation results authors proposed a mechanism of endosomal escape for pH-responsive gene delivery vectors (Fig. 5). Xie et al. [204] found that low
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generations leave gel-phase membranes intact while higher generations fluidize the membrane and
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cause a gel-fluid phase transition around the dendrimer binding site. Higher generation cationic dendrimers are capable of membrane disruption in both low and high temperatures [204].
dendrimers [195].
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Functionalization can influence these interactions since it affects the physicochemical properties of
Dendrimers can condense DNA and siRNA, and stabilize small molecules in their core, shell and interface. Several studies have considered the loading, release, interactions and distribution of small molecules in dendrimers [174,205–213]. For example, Jain et al. [206] used all atom MD simulations and molecular mechanics Poisson-Boltzmann surface area analysis to study the solubility and drug release profile of the hydrophobic drugs famotidine and indomethacin in G5 ethylenediamine cored PPI dendrimers under different pH conditions. They found fast drug release in low pH, intermediate values in neutral pH and a slow release in high pH. These results and further simulations on the effect of dendrimer chemistry and topology on drug loading and release suggest that both the pKa of the drug and pH-dependent changes in the dendrimer influence these dendrimer-drug interactions and complex stability. Loading and release of the antibiotic rifampicin, from G4-PAMAM dendrimer has been studied by Bellini et al. [205] in a combined experimental and simulation approach. 17
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Experimentally, approximately 20 drug molecules were found as the maximum potential loading
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capacity of this dendrimer at neutral pH. In simulation, the model of the dendrimer with 20 drug molecules in neutral pH was more stable than in low pH, where drug molecules got expelled rapidly
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and simultaneously to the solvent. This suggests a pH dependent drug release desirable for drug delivery applications. Simulations can also be used to study the effect of PEG lipids on loading
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capacity of dendrimers, and on surface ligand’s targeting capability [154,211,214]. Based on a comparison of several generations of PAMAM dendrimers, G4 PAMAM might be
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optimal as vector for gene delivery, based on PAMAM interactions with bilayers and siRNA complexation [203,215]. For gene delivery applications, generation number (size), flexibility, hydrogen
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bonding capability, pH, ion concentration and salt type have been considered in simulations [183,216– 219]. Electrostatic interactions and the entropy gain from counterion release upon binding have been proposed as important driving forces determining the interactions between dendrimers and nucleic
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acids [181,212,213].
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As one final example, we would like to design drug specific nanocarriers by including optimal drug-binding molecules in the structure. Shi et al. [220] used de novo design of dendrimers via
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optimizing building blocks and including optimal drug-binding molecules with a final goal the design of a drug-specific nanocarrier. They combined virtual screening with molecular docking and MD simulations on DOX-dendrimer complexes. Using Rhein, a molecule with strong DOX binding, they achieved Rhein-containing nanocarriers with a suitable DOX profile.
4.2. Polymer-based delivery systems Polymeric NPs are of interest in drug delivery applications and more generally because of their stability and easily modifiable surface [21,31]. Block-copolymers [92] composed of hydrophobic and hydrophilic parts can form a variety of self-assembled structures [221] of interest in drug delivery applications. These structures, e.g. polymeric micelles [222], are capable of accumulating drug molecules in their core, interface and shell [121]. There are several reviews of computational studies on different aspects or specific type of polymers [23,221,223–225]. Here, we will discuss some examples. 18
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Amphiphilic polymers form a variety of structures in solvents, e.g. core-shell, Janus particles, micelles, and rod-like micelles [102,226,227]. Several factors affecting aggregate structures, stability
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and phase transitions between different structures have been studied: for example, solvent polarity [130], polymer architecture, concentration and physicochemical properties [226,228,229], mixture
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components and mixing ratios of comprising polymers [230,231], pH, temperature [226,232], and the addition of components, e.g. lipids, cholesterol and ligands [102,233,234]. Huynh et al.[122] performed
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all atom MD simulations on star copolymers (SCPs) to understand why SCP micelles are unstable and form multimolecular micelles in solution, and to assist in the rational design of stable SCP
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unimolecular micelles in solution. Each SCP has a central connecting part and six attached arms, with each arm comprised of a hydrophilic part (PEG) and a hydrophobic one (PCL). These SCPs were different in their PCL and PEG length and the resulting molecular weight. They form core-shell
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structures, in which PCL blocks form a dense hydrophobic core while PEG blocks form a shell around
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the PCL core. The simulations suggested that partial water exposure of the PCL core drives aggregation into multimolecular micelles. Increasing PEG length protects the PCL core from exposure to the water
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but it also increases the size of micelles. Therefore, since smaller micelles are preferred in DDS applications, predicting the minimum number of PEG units for a specific number of PCL blocks required to fully protect the PCL core is important. The authors found a quantitative relation between hydration of PCL core and both number and molecular weight of PEG and PCL blocks [122]. The internal structure of the thermoresponsive polymer blend NPs, and the phase transition between core-shell and Janus structures were investigated by DPD simulations by Guo et al. [226]. Chen and Ruckenstein [230] looked at the structure and mechanism of formation and the degradation process of multicomponent multicore micelles via DPD simulations. Taresco et al.[231] used a combined computational/experimental approach to study the self-assembly and structure of copolymer micelles formed by two different types of monomers. Wang et al.[233] studied the structural characterization of cholesterol-functionalized CD micelles by all-atom MD simulation. Tan et al.[102] by both experiment and DPD simulations showed that introducing a second hydrophilic
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phosphatidylcholine group into the polymer chains causes a phase transition from sphere to rod-like in
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multiblock polyurethane micelles. Hybrid systems composed of polymers and other NPs, e.g. AuNPs and QDs, also show interesting
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structural and phase transition behaviour [227,235]. Using both experiments and DPD simulations, Cai et al.[227] studied the effect of adding AuNPs on block copolymer self-assembly. Adding AuNPs
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caused a transformation from long cylindrical micelles to short cylindrical micelles and finally to spherical micelles.
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Interactions with and translocation through cell membranes for polymer-based DDSs have been studied both by experiment and simulations [236–238]. Li et al. [236] studied the interactions between
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a novel amphiphilic polymer, PMAL, and siRNAs, combining experiment and CG MD simulations. Potential of mean force calculations showed that siRNA by itself experiences a large free energy barrier for translocation while the siRNA-PMAL complex entered the membrane spontaneously,
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consistent with experiment. The PMAL polymers induced pore formation to facilitate siRNA
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translocation. Srinivas et al.[237] used CG MD simulation and showed that these patchy polymeric micelles are capable of accommodating and transporting hydrophilic contents across a lipid membrane
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into a lipid vesicle.
Ding and Ma [238], using DPD simulations, investigated receptor-mediated endocytosis for a new type of pH-responsive DDS composed of NP (radius of 4 nm) and some pH-sensitive polymers (of twelve beads length). Beads on the NP surface were treated as ligands and assigned +e charges. The polymer had N randomly distributed negatively charged (-e) beads, where N depends on the pKa of the polymer and the system’s pH. Half of the model lipids in the membrane were treated as receptors by replacing charged beads in the lipid’s head group with neutral beads. They introduced a modified Lennard-Jones potential to account for receptor-ligand interactions. Eighteen-microsecond simulations for three different pH conditions combined with potential of mean force calculations showed a triplepH-response. The NP can only be engulfed by cell membrane when the pH is higher or lower than the polymer’s pKa, whereas endocytosis is blocked when the pH equals the polymer’s pKa. It was also found that the properties of NPs, pH-sensitive polymer and cell membranes, and the external environment, all affect NP engulfment by the membrane, at least in this simplified DPD model (Fig. 6). 20
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The effect of functionalization on interactions with the membrane, uptake mechanism, as well as
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effect on the structure and conformation of drugs/polymer complexes have been studied by simulation [118,239–243]. Liao et al. [240] used both experiment and all atom MD simulations to study the uptake
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of chitosan/DNA complexes coated with anionic poly(gamma-glutamic acid) (gamma-PGA). This coating enhanced the cellular uptake of chitosan/DNA complexes via a specific protein-mediated
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endocytosis. Both the structure of this ternary complex (CS/DNA/PGA), as well as the interaction between gamma-PGA and glutamyl transpeptidase proteins were studied by MD simulations. Sun et al.
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[239,244] studied the effect of polyethylenimine polymer (PEI) modification by lipids on nucleic acid compaction and aggregation. They found that lipid association as an additional mechanism of
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aggregation resulted in more compact and stable structures.
Several studies have focused on interactions between polymeric DDS and small molecule cargos, investigating driving forces for partitioning [60,245–249], complex stability [250], drug loading
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capacity and rate, and drug distribution and release [121,247,251–255] as a function of temperature
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[256], pH [228,232,257,258], concentration of drugs [232], physicochemical and structural properties of polymers and cargos [259,260]. CD is a well-known family of cyclic oligosaccharides with great
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promise as solubilizing agent for poorly soluble drugs [246,261]. He et al. [246] performed MD simulations to investigate the binding mode and affinities of the antifungal drug amphotericin B drug with γ- and β-CD. Consistent with experiments, simulations showed a significantly higher binding affinity for γ-CD than β-CD. The binding mechanism of the drugs bexarotene and human vasoactive intestinal peptide (VIP) to highly PEGylated sterically stabilized micelles (SSMs) has been investigated by Vukovic et al. [121] both experimentally and computationally. Using free energy profiles, they predicted the drug distribution in SSMs. Single bexarotene, as a poorly water-soluble drug, resides in the ionic interface with its polar end exposed to solvent. With multiple bexarotene molecules, the preferred distribution is as clusters in the alkane core of SSM (Fig. 7). Dominated by electrostatic interactions, VIP molecules, with two regions with positively charged residues, interact with negatively charged -PO4 groups in the ionic interface. These simulations suggest the importance of the balance between hydrophobic and Coulomb interactions between drugs and phospholipid polymers in stabilization of drugs in SSMs. 21
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The interaction and complexation of nucleic acids, including DNA and siRNA with cationic
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polymers has been studied extensively [223]. PEI, as an example of cationic polymers, is one of the most promising polymers which has been widely utilized in studies for gene delivery applications. Both
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experimental and computational studies have provided a better understanding of PEIs’ binding modes with DNA/siRNA [223]. Molecular modeling has had a significant impact on our understanding of
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critical steps in gene delivery, including complexation of carriers with nucleic acids, as well as nucleic acid condensation and aggregation by gene carriers [177]. Simulations can be used to investigate the
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factors determining interactions between cationic polymers and nucleic acids, including complexation and decomplexation. Such factors, similar to those in other DDSs, include chemical surface
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composition as amine-to-phosphate ratio [51,262], charge density, protonation state, polymer molecular weight, polymer chemical structure and charge distribution [262–267], the effect of endogenous molecules [136], and multivalent ions, e.g. Fe(III) [137,268]. miRNA as endogenous molecule might
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cause decomplexation. To test this hypothesis, Meneksedag-Erol et al. [136] used both experiment and
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all atom MD to study the interactions between miRNA and pre-formed PEI-siRNA complexes. PEI was found to bind more strongly to miRNA than siRNA. However, miRNA could not disrupt the
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integrity of PEI-siRNA but formed a layer on the complex, leading to a siRNA-PEI-miRNA complex. Interactions between polymeric DDS and nucleic acids for gene delivery purposes can be studied indirectly using polymers. Polycations and polyanions can be considered as models for DDS and nucleic acids, respectively [269–271]. Zhao et al. [270] utilized polycations and polyanions and studied the effect of charge distribution along the chain on complexation behavior and the structure of the resulting complexes. They found that charge distribution has a significant influence on aggregation and the complex structure.
4.3. Peptide- and nucleic acid-based delivery systems There are several peptide-based DDSs, e.g. protein- [272] and cyclic peptide-based [273,274], and cell penetrating peptides (CPPs) [275]. There are also DDSs based on nucleic acids. RNA and DNA have been used as either building blocks or surface ligands to design a variety of three-dimensional 22
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nanostructures, including rings, tubes, cubes, cages, and spheres [276–279]. As with other DDSs, the
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structural properties of these systems, and their interactions with endogeneous biological molecules, as a function of environmental conditions are of interest [280–282]. Badu et al. [281] performed atomistic
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MD simulations on RNA nanotubes and investigated their structural properties. Juul et al. [282] studied DNA cages loaded with active enzymes both experimentally and by all-atom MD simulation, and
enabling controllable encapsulation and release (Fig. 8).
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found a temperature-dependent conformational change from an ‘open’ to a ‘closed’ state, in principle
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Protein-based NPs are promising because of their unique characteristics, including biocompatibility, biodegradability, low cytotoxicity, and non-antigenicity [283,284]. Some proteins, e.g. albumin, can be
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used for targeted delivery to tumor cells and inflamed tissues since they are preferentially taken up by these types of cells [284]. Luo et al. [272] using both theory and experimentations investigated the binding mode, affinity and interactions between bovine serum albumin (BSA) and interferon alpha-1b
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(IFN) as delivery system and protein drug, respectively. Simulations predicted domain III of BSA as
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the most probable binding sites, as well as hydrogen bonds and salt bridges as the main contributors in binding between BSA and IFN. Cyclic peptide based nanotubes (CPNTs) can be used as a transporter
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for ions and small molecules [273,274]. Vijayaraj et al. [273] investigated the transport mechanism of 5-FU through a variety of CPNTs and determined the driving forces and free energy barriers affecting this transport. They found that transport was driven by direct or water-mediated hydrogen bonding and hydrophobic interactions between CPNT and 5-FU. CPPs are of interest because of their intrinsic ability to enter cells, their low cytotoxicity, and their versatility. They can facilitate the transport of small molecules (e.g. drugs, imaging agents), macromolecules (e.g. DNA, siRNA, proteins, peptides) and NPs/DDS. Therefore, CPPs can be used as drug/gene delivery systems, as well as carriers for imaging agents, proteins and peptides [275]. Recently, it has been of great interest to combine the benefits of nanomaterials and CPPs. CPPs can be functionalized with other delivery systems, like QDs, liposomes, and dendrimers, potentially improving CPPs intracellular drug release ability and decreasing their toxicity. DDSs such as polymeric micelles, dendrimers, liposomes, QDs, and inorganic nanocarriers (e.g. gold-, silver-, and iron-based NPs) can be functionalized with CPPs to enable their cellular uptake [275]. CPPs' unique physicochemical 23
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properties, their uptake mechanisms, classifications, and applications in medicine and biotechnology
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have recently been reviewed by Durzynska et al. [275]. Key elements involved in CPPs' roles in drug delivery have been studied computationally [285–
interaction
between
two
CPPs,
penetratin
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288]. Atomistic MD simulations revealed the importance of arginine, lysine and tryptophan in the [285]
and
transportan
[286],
and
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dipalmitoylphosphatidylcholine (DPPC) membranes, as well as their translocation mechanism. CPP conjugation can help translocate hydrophobic and hydrophilic drugs across lipid membranes [289,290].
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Teixeira et al. [290], via both MD simulations and experiments, found that lysine-based surfactants enhanced the transdermal permeation of two topically administered hydrophilic drugs, tetracaine and
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ropivacaine hydrochloride. As mentioned earlier, NPs can be functionalized with CPPs. The concentration and distribution of CPPs on the NP's surface influence the conformation of the peptide layer, which in turn affects the CPP-functionalized NPs’ activity and the efficiency of cellular
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internalization. So, designing a DDS of desired activity requires knowledge of the structure and
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dynamics of CPPs on NPs in solution prior to interacting with membranes. Todorova et al. [291] using both experimental and atomistic MD simulations investigated the effect of the grafting density and
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surface distribution of HIV-derived trans-activator of transcription (TAT) peptide on NPs cell internalization. They found a correlation between the surface properties, e.g. positive charge distribution, of these TAT conjugated NPs in solution, and their membrane permeation.
New DDS design strategies combine the benefits of several systems such as block copolymers and CPPs [292], or mimic natural systems such as intrinsically disordered proteins (IDPs) [293]. Sanchez-Sanchez et al. [293], using both experiments and MD simulations, designed and characterized single chain NPs called artificial IDP mimetics. These IDP mimetics are capable of simultaneous release of two dermal bioactive molecules, folic acid and hinokitiol, into aqeous solution in a pHdependent manner. Computer simulations can provide insight into drug loading, distribution/complex structure and release from peptide self-assemblies [294,295], as well as the influence of pH and temperature [282,294,296] on these processes. Guo et al., using both experiments and DPD simulations investigated 24
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the effect of pH on microstructure of peptidic micelles [296]. Micelles were self-assemblies of HR-20
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(Histidine10-Arginine10) peptides conjugated with cholesterol, either loaded or not with DOX. Micelles had more compact structure at pH > 6 (Fig. 9). For pH < 6, channels in swollen micelles
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might cause drugs to diffuse out of micelles. This was verified experimentally as the DOX release rate was influenced by pH values, consistent with simulation results.
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Finally, physicochemical properties of peptides and solvent conditions can govern selfassembly and structural properties of peptide-based carriers. Computer simulation can be used to
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design peptides which can self-assemble, and to study the mechanism of self-assembly into nanostructures. Computational approaches can also be used to study how factors such as solvent and
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4.4. Carbon-based delivery systems
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temperature influence both self-assembly and transitions between different morphologies [297–301].
Several different types of carbon-based nanoparticles (CNPs) have the potential to act as DDS
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[12,302]. Here we focus on DDS based on CNTs and fullerenes but DDS using graphene, graphene oxide, and nanodiamond [19,162,165,168] are also of interest. Computational research has played a major role in exploring drug loading of [164,303] and release [132,133,304,305] from CNPs, as well as interactions of cargos with carbon nanocarriers [306] as a function of internal and external stimulus, e.g. pH, temperature, torsion, and external magnetic field. CNTs are capable of loading therapeutic agents on their surface or inside their cylindrical hollow [19,307]. Saikia et al.[305] performed MD simulation to study the C60 fullerene-mediated release of multiple pyrazinamide molecules from the interior of a single walled CNT as a function of temperature. Chaban et al. [304] investigated the effect of temperature and concentration on drug release from CNTs using MD simulations. The effect of external magnetic fields has been studied on hybrid systems composed of CNTs with magnetic NP caps [132]. Understanding the interactions of CNTs with biological macromolecules is also of great interest [308–313]. Wu et al. [313] studied DNA ejection from CNTs as a function of temperature, torsion loading and CNT size by MD simulation. Santosh et al. [309] studied the binding of siRNA and DNA 25
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to the surface of single walled CNTs via MD simulations. Chen et al. [311] studied the dynamic
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mechanism of encapsulation of HIV replication inhibitor peptide into CNTs using MD simulation. It is also important to understand the interactions of carbon-based DDS with human serum albumin (HSA),
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the most abundant protein in blood. Interaction of DDS with HSA is crucial in DDS absorption, distribution and metabolism [314].
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The membrane associations of carbon-based DDS have been studied by computer simulation [315–317]. Several factors affect these associations: DDS structural properties [318–321]; clustering
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[320–322]; functionalization [319,323,324]; concentration [319] and lipid membrane properties [321,325]. The effects of size and clustering of fullerene NPs, and fullerene concentration on a DPPC
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monolayer as a model for pulmonary surfactant was investigated by CG MD simulation by Chiu et al.[321]. Free energy calculations suggest that all fullerene systems in this study (C60, C180, C540, and cluster of five C60 fullerenes) spontaneously diffuse into the hydrophobic region of both
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monolayer and bilayer membranes. Large fullerene molecules were found to prefer partitioning into
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bilayers rather than monolayers, however, which can influence the monolayer-to-bilayer transition in
inhalation.
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the respiratory cycle. This may suggest a possible mechanism of internalization of CNPs through lung
The effects of using polymers, peptides, and lipids as surface functionalization agents in carbon-based DDS were studied by computational methods. Chehel Amirani and Tang [308] comprehensively reviewed studies, mainly at the quantum mechanical level, of graphene and CNT functionalization, and their binding energies with nucleobases. Lai and Barnard [131] have reviewed recent studies on functionalized nanodiamonds and their importance for biomedical applications. Functionalization can prevent NPs from aggregating [323,326], increase their stability [327], and influence their interactions with lipid membranes [323,326,328]. Surface functionalization can also promote drug delivery through improving translocation across lipid bilayers [328]. Sridhar et al.[328] used CG MD simulations to look at effects on fullerene (C60) translocation through the membrane due to polar and nonpolar functionalization, temperature, and fullerene concentration. Although none of their models showed complete translocation, they found that Janus particles having half the surface
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modified by polar groups were the most promising form of functionalized fullerenes in terms of bilayer
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translocation. A NP's behavior depends on the conformation of the functionalization layer on its surface. The
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coating mechanism used to functionalize the NP, e.g. covalent vs. non-covalent attachment, as well as the grafting density are two factors which can affect this conformation [329,330]. For example, Lee
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[330] found, using CG MD simulation, that the PEGylation method can influence PEG distribution on CNTs. They also found that PEG size and grafting density affected the PEG layer's conformation (Fig.
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10). Designing functionalized NPs with desired properties will require a detailed understanding of these
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4.5. Lipid-based delivery system
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factors.
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Recently, lipid-based DDS such as liposomes, micelles and LNPs have attracted much attention [7,8,331,332] due to their ability to encapsulate and transport drugs as well as biomolecules, the
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versatility of their structures and compositions, and their inherent selectivity to tumor cells and inflammation sites. There are currently more than ten approved lipid-based drug formulations and many more in clinical trials [7,8]. Specifically, sterically stabilized liposomes, i.e. PEGylated liposomes, are widely used as DDS. PEGylation achieves long circulation times for liposomes in vivo due to the formation of a protective PEG layer over the liposomal surface. This layer sterically prevents the coating of liposomes by opsonins, thus reducing drug uptake by cells of the immune system [333]. However, the mechanism through which PEGylated lipids interact with different components in the liposomes, its function in drug distribution, localization and retention as well as the influence of external environmental factors such as salt concentration are not completely understood. Since atomistic simulations of full liposomes are computationally expensive, a common approach is to infer results on liposomes from lipid bilayer simulations. Dzieciuch et al., [334] for instance, performed atomistic MD simulation to study the effect of liposome PEGylation on their drugloading efficiency. They compared the effect of zwitterionic and PEGylated membranes on the location and orientation of a model hydrophobic compound, 5,10,15,20-tetrakis(4-hydroxyphenyl)porphyrin (p27
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THPP). p-THPP enters both types of lipid bilayers, which agreed with experimental results, but in
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PEGylated liposomes p-THPP also localizes to the outer PEG corona where porphyrins are wrapped by PEG chains. Thus, in PEGylated liposomes p-THPP showed a greater exposure to the water than in
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zwitterionic liposomes. These results highlight that PEGylation enhances the drug-loading efficiency of membranes and support fluorescence experiments carried out in this study [334]. Simulations also
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predicted strong interaction between PEG lipids and porphyrin [334]. This interaction is particularly important in drug delivery studies as it may be an extra barrier to drug release. This finding supports
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previous computational studies that showed that PEG interacts with hydrophobic molecules [127,335,336]. Similarly, MD simulations combined with free energy calculations were used to
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elucidate binding mechanisms and divulge the exact location and organization of two small drugs, bexarotene and human VIP, within PEGylated micellar nanocarriers [121]. PEGs strongly interact with salts. This property has been utilized in lithium ion batteries with
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PEGs as polymer electrolytes [337,338]. PEGs are soluble in a wide variety of both polar and nonpolar
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solvents [339]. Although both of these general properties have been shown previously by computational studies [337,338,340] and experiments [341,342], for DDS applications it is important to
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understand how the PEG layer on DDS, e.g. PEGylated liposomes, interacts with salts in physiological conditions and how these interactions affect the structure of the PEG layer [118,343,344]. Stepniewski et al. [344] modeled the effect of NaCl on the surface structure of PEGylated liposomes. They varied the salt concentration in Langmuir monolayer film experiments and added ions at the physiological level in atomistic MD simulation studies. The results showed that the PEG surface layer should not be treated as generic hydrophilic molecules completely outside the bilayer, nor should it be considered as totally neutral as was previously accepted. PEG molecules are able to penetrate into the liquidcrystalline lipid bilayer, which may affect the permeability and structure of the membrane. It was also noted that Na+ ions bind to PEG chains, changing the surface charge of the liposome and enhance opsonization, whereas Ca2+ did not interact with PEG [343]. These results are not surprising, however. In fact, when PEGs are attached on DDS surface, e.g. liposomes, they seem to show similar properties as previously reported for other applications [337,338,340–342]. As another example, Pannuzo et al. [345] performed CG simulations to study the effect of Ca2+ and PEG molecules on membrane fusion. 28
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They showed that for a rapid fusion between negatively charged apposed membranes both Ca2+ and
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PEG were required.
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An important property governing drug release from liposomal DDS is bilayer permeability [346,347]. Magarkar et al. [346] performed atomistic simulations using the OPLS-AA force field to
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investigate the high permeability observed for "inverse-phosphatidylcholine" (CPe) liposomes in experimental studies [348]. CPe is a synthetic analog of phosphatidylcholine (PC) with a reversed
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zwitterionic headgroup and has been proposed for use in liposomal DDS formulations. The larger surface area observed for the CPe bilayer compared with the PC bilayer may cause CPe's higher
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permeability. Adding cholesterol to liposomal formulations reduces leakage from liposomes due to its well-known role in lipid packing and stability, but the mechanism for this is not understood. Magarkar et al.[336], by performing MD simulation using OPLS force field, proposed a model to explain the
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interaction between PEG molecules and cholesterol. PEG molecules tend to interact with the ß side of
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cholesterol, disrupting the membrane structure. Contrary to the expectation that cholesterol should make the membranes more stable and compact, adding cholesterol in the presence of PEG caused
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membrane destabilization. This result may explain possible effects of cholesterol on the permeability and compressibility of PEGylated liposomes. Self-assembly is another feature of special interest in nanotechnology and nanomedicine as its understanding provides a framework for developing new nanoscale materials with desirable properties [116]. CG and DPD simulations have been performed for the investigation of lipid-based aggregates relevant for drug delivery [115,349–352]. For example, Lee and Pastor [349] used the Martini CG force field to study mixtures of lipids and PEGylated lipids in water at different sizes and concentrations of PEGylated lipids. They found that the mixtures self-assembled to liposomes, bicelles and micelles. The analyses and simulations indicated that the average aggregate sizes decreased when the concentration of PEGylated lipid increased, in agreement with experimental results. Janke et al.[352] used CG MD simulations to study the phase behavior of oleic acid aggregation at various concentration and protonation states. They observed a range of structures including micelles, vesicles and oil phases depending on the protonation state of the oleic acid head group. 29
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Simulations can be used to calculate drug release rates from lipid-based DDS [353,354]. For instance, the release of encapsulated materials from systems including emulsions (100% liquid lipid
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phase) and nanostructured lipid carriers, which have a combination of solid and liquid lipid domains, was investigated by Dan using Monte Carlo simulations [353]. The results obtained suggest that the
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size of lipid domains does not significantly affect the rate of release, but the location and distribution of the solid domains have a notable impact. When solid domains are concentrated near the solution
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interface, transport is inhibited due to a reduction in the accessible surface area. However, when the solid domain is located in the center of the LNP the release rate is increased and may even be higher
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than in the corresponding emulsion particle. In another study by Dan [354], Monte Carlo simulations were performed to study drug release in response to electric field-induced liposome pores. There are also other interesting studies, three of which are mentioned briefly here. Computer
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simulations were combined with experiments to investigate the effect of penetration and membrane
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behaviour of three terpenes, which are effective chemical permeation enhancers in nanostructured lipid carriers [355]. A detailed model of the distribution of drug molecules inside a liposome was obtained
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using the Martini force field [356]. Jämbeck et al. [356] performed large scale simulations of hypericin, a photosensitizing drug, in a DPPC liposome (Fig. 11) and calculated the distribution and orientation of the drugs in the lipid bilayer. Hoof et al.[357] studied the encapsulation of proteins in spontaneously forming vesicles using MD simulation with a CG force field. They showed that the interactions of proteins with membranes govern the encapsulation efficiency, but the size of the encapsulated proteins and the speed of the vesicle formation did not seem to have a significant effect. A promising gene therapy approach is based on siRNA. Sophisticated delivery systems are required to protect and deliver siRNA effectively to the target tissues [358,359]. CG molecular simulations together with a variety of experimental techniques were able to give a more detailed view of the structure of a LNP containing siRNA (Fig. 12) [79,360]. Simulations of small systems containing 8 duplex 12 base pair DNA complexes, DLinKC2-DMA (an ionizable cationic lipid), DSPC, cholesterol, a PEG-lipid, water and ion molecules were performed. These molecules self-assembled and organized in an ordered structure where, for instance, the PEG-lipids were distributed in the outer 30
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layer of the LNP. This was the first study to show that LNP siRNA systems likely have a nanostructured core, with the encapsulated siRNA located in internalized inverted micelles complexed
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to cationic lipids. The in silico finding agreed with 31P NMR, FRET, cryo-TEM, and RNase digestion
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data and it was possible to understand the mechanism whereby LNP siRNA systems are formed. This insight is being applied to the design and construction of new LNP systems. In addition, both CG and
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atomistic computer simulation have been used to study the DNA adsorption onto anionic lipid
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membranes induced by multivalent cations [361].
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4.6. Gold-nanoparticles
AuNPs are of particular importance in drug delivery research due to their unique properties
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including their stability, ease of synthesis, and the ability to manufacture a range of sizes [15,362].
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They can be loaded with various therapeutics including small molecules, peptides, proteins, and nucleic acids [19], either conjugated to AuNPs covalently or noncovalently. There are several computational
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studies on the properties of AuNPs and their interactions with other molecules [135,227,363–370]. The effect of AuNP conjugation on peptide conformational flexibility and structure was studied by Lee and Ytreberg [366]. They examined the structure and dynamics of six peptides that were either free or conjugated to AuNPs in water. Conjugation affected both structure and dynamics in an amino acid sequence dependent way. Peptides with little or no secondary structure in solution were adsorbed on the AuNP surface. This causes peptides to lose their specific interactions with cell components. For drug delivery purposes, this suggests that peptides with significant secondary structures in solution are suitable candidates for peptide-NP conjugation. Interactions between ubiquitin and AuNPs have also been studied by Brancolini et al. [135] using computer simulation. Van Lehn and co-workers performed a series of computational studies on AuNPs coated by lipids. Among other properties, they investigated the structure of the monolayer coating the AuNP under different conditions [369], lipid composition and AuNP size [368], the interactions of a
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monolayer-coated AuNP with model membranes relevant for uptake [367], suggesting similarities in
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uptake process with bilayer-bilayer fusion [371], and interactions between AuNPs [370].
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5. Conclusions and future perspectives
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We have reviewed common biophysical and computational approaches to studying DDSs, focusing in particular on computational studies of several major classes of NPs used in drug delivery research. The
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strength of computer simulations in general is their ability to give very detailed insight into the structure of NPs and their interactions with drugs and model membranes under a range of conditions.
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Clearly, the efficiency of DDSs depends on a large number of other parameters, including stability in the bloodstream, targeting to the desired tissues, uptake by endocytosis or other mechanisms, and final release of the drugs. Although experimental biophysical and computational studies can characterize
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important aspects of DDSs, a major challenge for the near future is to go beyond individual drug
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DDSs.
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carriers and to investigate at a mechanistic level the larger-scale processes that determine the success of
At the computational level, currently we are limited by some of the standard limitations of biomolecular simulation. In general, these involve limited sampling time, limited length scale, and expensive or difficult access to important thermodynamic variables. Simulations can access time scales of the order of microseconds or for coarser models tens or hundreds of microseconds, at length scales of ca. 10 x 10 nm for atomistic and up to 150 x 150 nm for CG simulations. Thus it is now possible to simulate entire nanocarriers, which will open up new areas of study for simulations. However, the process of NP formation by e.g. microfluidics remains outside reach of direct detailed simulations. Questions regarding drug loading, the stability of complexes, and the interactions of NPs with membranes essentially involve free energies of binding, partitioning, and membrane pore formation. These can all be studied by free energy methods, but the current state of the literature remains somewhat qualitative. This is an obvious area for future work, which is already feasible with current methods and computational resources. Some of the most interesting simulations currently available use 32
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CG MD or DPD. This is an exciting development, but also comes with its own limitations; in particular,
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CG models need to be carefully tested for their ability to reproduce the effect of important factors in DDSs such as salt concentration, the distribution of chemical functional groups over the surface of a
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DDS, and the effect of pH.
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DDSs are an interesting example of multi-scale computational problems, with a growing amount of data from biophysical characterization. They have an obvious biomedical and biotechnological
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importance, and bridge chemistry, nanoscience, materials, basic biology and medical applications in a unique way. Although there already is a large body of literature as reviewed in this paper, in our
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assessment the impact of computational work in this important area is likely to grow significantly in the near future as model systems become more realistic and more tightly coupled to experiment.
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Conflict of interest
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The authors declare no conflicts of interest.
Acknowledgements
This work was supported by a Natural Sciences and Engineering Council (Canada) Strategic Project Grant (DPT, JT). DPT is an Alberta Innovates Health Solutions Scientist and Alberta Innovates Technology Futures Strategic Chair in (Bio)Molecular Simulation.
Tables and Figures (tables/figures in separate file) Table 1. Questions relevant to drug delivery system design that can be answered by computational simulations.
Table 2. Experimental methods used to study drug delivery systems.
Figure 1. Drug delivery pathway for a drug-nanocarrier system. After the DDSs enter the bloodstream (1), drug (green circle) leakage might occur if the drug-DDS complex is unstable (2). 33
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Depending on the surface chemistry, DDSs might interact with biomolecules (3) and ions (4) in
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circulation. Eventually, they will extravasate to the extracellular space (5). After reaching the target cell, they interact with the cell membrane (6) and can be internalized either via direct penetration (7) or
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endocytosis (8). In the latter case, the nanocarrier will be entrapped in endosomes and must be released (9) before endosomal maturation. Drug-DDS dissociation (10) is necessary for drugs to be effective in
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the cytoplasm or nucleus (11). This dissociation can be mediated by interactions with endogeneous
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biomolecules (12). Not drawn to scale.
Figure 2. Examples of nanoparticulate DDS. From top-left to bottom-right, monolayer-protected
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gold nanoparticle, polymeric micelle, DNA cage, carbon nanotube, solid lipid nanoparticle, polymer, dendrimer and dendron, liposome, and protein-based DDS. For dendrimer: orange, green, red, and blue represents G0, G1, G2, and G3, respectively. Dendron has also been shown as black branch. Not drawn
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to scale.
Figure 3. Categories of simulation methods and their respective spatio-temporal domains of
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applicability. The question of interest dictates the level of resolution required. Briefly, if electronic motion play an important role in the property we are studying, quantum mechanics (QM) level is appropriate. However, if there is no bond formation and cleavage, all atom (AA) level works well; we can safely ignore electronic motion by assigning point charges to each atom. If electrostatic and hydrophobic interactions are the dominant contributors, coarse grain models (CG) can be used: individual atoms can be ignored and a group of atoms (e.g. 4 heavy atoms in MARTINI) can be treated as one interaction point/bead. CG models allow the exploration of a larger area in phase space, at the expense of losing atomistic details. Coarser levels of simulations (e.g. DPD and continuum models) are appropriate for systems and processes which require longer times and lengths.
Figure 4. PAMAM Dendrimer-DNA interaction and deformation as a function of generation number. (a) For the G3 dendrimer, very little deformation in DNA was observed, while the dendrimer was bent considerably by the DNA. (b) For G4, both DNA and dendrimer were deformed. (c) For G5, 34
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DNA was deformed, but the dendrimer behaved almost as a rigid body. Adapted with permission from
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B. Nandy, P.K. Maiti, A. Bunker, Force Biased Molecular Dynamics Simulation Study of Effect of
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Dendrimer Generation on Interaction with DNA, (2013). Copyright 2013 American Chemical Society.
Figure 5. Proposed mechanism of endosomal escape for pH-responsive dendrimers. In endosomes
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(pH ~ 5), dendrimers are protonated, leading to increasing osmotic pressure and dendrimer swelling, putting the membrane under tension. Meanwhile, electrostatic interactions between charged dendrimers
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and asymmetric negatively charged endosomal membrane cause a drop in the critical membrane tension required for membrane disruption, allowing NP escape from endosomes. Bottom row:
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snapshots of a MARTINI simulation of interactions between a G4 dendrimer (orange beads) and a membrane (with lipid tails represented with cyan beads, and lipid headgroups represented with green and blue beads) at different pH levels. Reproduced from [124] with permission from The Royal Society
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of Chemistry.
Figure 6. Time evolution of nanoparticle-polymer complex endocytosis as a function of pH. The
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endocytosis process of a nanoparticle-polymer complex (nanoparticle with pH-sensitive polymers absorbed on its surface) depends on the pH. For pH values lower (a) and higher (c) than the polymer’s pKa, the nanoparticle can be fully engulfed by the membrane. However, for pH = pKa (b), endocytosis is blocked. Membrane lipid head groups are shown in green, purple, and blue correspond to +e, -e, and neutral beads, respectively. Lipid tails are in orange, receptor heads are in red, and nanoparticle beads are in yellow. Polymer beads are in cyan (-e) and pink. Reprinted by permission from Macmillan Publishers Ltd: Scientific reports H. Ding, Y. Ma, Controlling Cellular Uptake of Nanoparticles with pH-Sensitive Polymers., Sci. Rep. 3 (2013) 2804., Copyright 2013.
Figure 7. Solubilization of bexarotene in sterically stabilized micelles (SSM). (Left) Free energy profiles for systems composed of 1, 3, and 5 bexarotenes in SSMs. SSMs are either composed of 10 (small) or 90 (large) monomers, in water and 0.16 M NaCl, respectively. Single drugs prefer the ionic interface, represented with vertical arrows, in both SSMs. For multiple drugs accumulated in the SSM 35
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core (shown as a gold surface), a deeper minimum develops in the core. This is because several
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bexarotene (e.g. 5) cluster together (Right) via a hydrogen bond network between their –COOH groups. Reprinted (adapted) with permission from L. Vukovic, A. Madriaga, A. Kuzmis, A. Banerjee, A. Tang,
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K. Tao, et al., Solubilization of Therapeutic Agents in Micellar Nanomedicines, (2013). Copyright
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2013 American Chemical Society.
Figure 8. DNA nanocage with temperature-controlled conformational transition capability. The
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cage is closed at 4C (a), and in a more open conformation at 37C (b). Heating the nanocage to 37C in presence of horseradish peroxidase (HRP), followed by cooling to 4C, allows HRP enzymes to be
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encapsulated in nanocages. Reheating to 37C causes enzyme (orange) release (not shown). Reprinted with permission from S. Juul, F. Iacovelli, M. Falconi, S.L. Kragh, B. Christensen, R. Frøhlich, et al.,
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Encapsulation and Release of an Active Enzyme in the Cavity of a Self-Assembled DNA Nanocage,
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(2013) 9724–9734. Copyright 2013 American Chemical Society.
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Figure 9. Effect of pH on microstructure of self-assembled micelles. Starting from a homogeneous state (a), cholesterol conjugated peptides form compact core/shell micelles at pH > 6.0 (b). Decreasing the pH loosens these micelles (c) and the swelling facilitates DOX release. Arginine, histidine, and cholesterol, are shown in green, brawn, and black, respectively. Water has not been shown for clarity. Reprinted with permission from X.D. Guo, L.J. Zhang, Z.M. Wu, Y. Qian, Dissipative Particle Dynamics Studies on Microstructure of pH-Sensitive Micelles for Sustained Drug Delivery, Macromolecules. 43 (2010) 7839–7844. Copyright 2010 American Chemical Society.
Figure 10. Effect of PEGylation method, and PEG chain size and grafting density on the conformation of PEG layer on carbon nanotube. Non-covalently modified single walled CNT (SWNT) (Left) is more exposed to water than covalently modified SWNT (Right). PEG (red) size and grafting density also influence the conformation of PEG layer on SWNT (grey cylinder). RF is the mushroom radius and L is the thickness of brush state. SWNT is shown as grey cylinders. Reprinted with permission from H. Lee, Molecular Dynamics Studies of PEGylated Single-Walled Carbon 36
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Nanotubes: The Effect of PEG Size and Grafting Density, (2013). Copyright 2013 American Chemical
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Society.
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Figure 11. Distribution and orientation of hypericin within liposome. a) Snapshots from MD simulations taken after 10 μs. Phosphorus groups (orange), choline groups (blue) and hydrophobic tails
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(green) of the lipids are represented, with different numbers of drug molecules (yellow). b) Liposomes with some lipids removed to show the binding of hypericin to DPPC; the hydrophilic parts of hypericin
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are shown in red and the hydrophobic parts in yellow. Reprinted with permission from J.P.M. Jämbeck, E.S.E. Eriksson, A. Laaksonen, A.P. Lyubartsev, L.A. Eriksson, Molecular Dynamics Studies of
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Liposomes as Carriers for Photosensitizing Drugs: Development, Validation and Simulations with a Coarse-Grained Model, (2013). Copyright 2013 American Chemical Society.
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Figure 12. Lipid nanoparticle. External and a cross-sectional view of the LNP (top). Components are
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represented by their molecular densities: protective PEG-lipid (magenta), therapeutic siRNA (red), cholesterol (green), DSPC (yellow), ionizable cationic lipid (DLin-KC2-DMA) (blue) and water (cyan).
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Adapted from D. Rozmanov, S. Baoukina, D.P. Tieleman, Density Based Visualization for Molecular Simulation., Faraday Discuss. 169 (2014) 225–43. doi:10.1039/c3fd00124e. Under a Creative Commons Attribution 3.0 Unported License.
References: [1]
E. Blanco, H. Shen, M. Ferrari, Principles of nanoparticle design for overcoming biological barriers to drug delivery, Nat. Biotechnol. 33 (2015) 941–951.
[2]
T.M. Allen, P.R. Cullis, Drug delivery systems: Entering the mainstream, Science. 303 (2004) 1818–1822.
[3]
S. Parveen, R. Misra, S.K. Sahoo, Nanoparticles: A boon to drug delivery, therapeutics, diagnostics and imaging, Nanomed. Nanotechn. Biol. Med. 8 (2012) 147–166. 37
ACCEPTED MANUSCRIPT
[4]
D.S. Kohane, Microparticles and nanoparticles for drug delivery, Biotechnol. Bioeng. 96 (2007)
[5]
PT
203–209. V.J. Venditto, F.C. Szoka Jr., Cancer nanomedicines: So many papers and so few drugs!, Adv.
[6]
RI
Drug Deliv. Rev. 65 (2013) 80–88.
Y. Zhang, H.F. Chan, K.W. Leong, Advanced materials and processing for drug delivery: The
B.S. Pattni, V.V. Chupin, V.P. Torchilin, New developments in liposomal drug delivery, Chem. Rev. 115 (2015) 10938–10966.
[8]
T.M. Allen, P.R. Cullis, Liposomal drug delivery systems: From concept to clinical applications,
MA
Adv. Drug Deliv. Rev. 65 (2013) 36–48. [9]
E.R. Gillies, J.M.J. Frechet, Dendrimers and dendritic polymers in drug delivery, Drug Discov. Today. 10 (2005) 35–43.
C.C. Lee, J.A. MacKay, J.M.J. Fréchet, F.C. Szoka, Designing dendrimers for biological
D
[10]
NU
[7]
SC
past and the future, Adv. Drug Deliv. Rev. 65 (2013) 104–120.
[11]
TE
applications, Nat. Biotechnol. 23 (2005) 1517–1526. S. Svenson, D.A. Tomalia, Dendrimers in biomedical applications—reflections on the field,
[12]
AC CE P
Adv. Drug Deliv. Rev. 57 (2005) 2106–2129. A. Bianco, K. Kostarelos, M. Prato, Applications of carbon nanotubes in drug delivery, Curr. Opin. Chem. Biol. 9 (2005) 674–679. [13]
L. Lacerda, A. Bianco, M. Prato, K. Kostarelos, Carbon nanotubes as nanomedicines: From toxicology to pharmacology, Adv. Drug Deliv. Rev. 58 (2006) 1460–1470.
[14]
A. Bianco, K. Kostarelos, C.D. Partidos, M. Prato, Biomedical applications of functionalised carbon nanotubes, Chem. Commun. (2005) 571–577.
[15]
P. Ghosh, G. Han, M. De, C.K. Kim, V.M. Rotello, Gold nanoparticles in delivery applications, Adv. Drug Deliv. Rev. 60 (2008) 1307–1315.
[16]
T.K. Jain, M. A. Morales, S.K. Sahoo, D.L. Leslie-Pelecky, V. Labhasetwar, Iron oxide nanoparticles for sustained delivery of anticancer agents, Mol. Pharm. 2 (2005) 194–205.
[17]
G. Bao, S. Mitragotri, S. Tong, Multifunctional nanoparticles for drug delivery and molecular imaging, Annu. Rev. Biomed. Eng. 15 (2013) 253–282. 38
ACCEPTED MANUSCRIPT
[18]
C. Sun, J.S.H. Lee, M. Zhang, Magnetic nanoparticles in MR imaging and drug delivery, Adv.
[19]
PT
Drug Deliv. Rev. 60 (2008) 1252–1265. X. Sun, Z. Feng, T. Hou, Y. Li, Computational simulation of inorganic nanoparticle drug
RI
delivery systems at the molecular level, in: Computational Pharmaceutics: Application of Molecular Modelling in Drug Delivery, John Wiley & Sons, 2015: pp. 149-168.
SC
[20] C.S. Kim, G.Y. Tonga, D. Solfiell, V.M. Rotello, Inorganic nanosystems for therapeutic delivery: Status and prospects, Adv. Drug Deliv. Rev. 65 (2013) 93–99. R. Duncan, The dawning era of polymer therapeutics, Nat. Rev. Drug Discov. 2 (2003) 347–360.
[22]
K.S. Soppimath, T.M. Aminabhavi, A.R. Kulkarni, W.E. Rudzinski, Biodegradable polymeric
NU
[21]
[23]
MA
nanoparticles as drug delivery devices, J. Control. Release. 70 (2001) 1–20. L. Huynh, C. Neale, R. Pomès, C. Allen, Computational approaches to the rational design of
Biol. Med. 8 (2012) 20–36.
W.F. van Gunsteren, D. Bakowies, R. Baron, I. Chandrasekhar, M. Christen, X. Daura, et al.,
TE
[24]
D
nanoemulsions, polymeric micelles, and dendrimers for drug delivery, Nanomed. Nanotechn.
Biomolecular modeling: Goals, problems, perspectives, Angew. Chemie Int. Ed. 45 (2006)
[25]
AC CE P
4064–4092.
B. Kann, H.L. Offerhaus, M. Windbergs, C. Otto, Raman microscopy for cellular investigations — From single cell imaging to drug carrier uptake visualization, Adv. Drug Deliv. Rev. 89 (2015) 71–90.
[26]
A. Sharma, U.S. Sharma, Liposomes in drug delivery: Progress and limitations, Int. J. Pharm. 154 (1997) 123–140.
[27]
R.W. Niven, M. Speer, H. Schreier, Nebulization of liposomes. II. The effects of size and modeling of solute release profiles, Pharm. Res. 8 (1991) 217–221.
[28]
S.K. Hobbs, W.L. Monsky, F. Yuan, W.G. Roberts, L. Griffith, V.P. Torchilin, et al., Regulation of transport pathways in tumor vessels: Role of tumor type and microenvironment, Proc. Natl. Acad. Sci. U. S. A. 95 (1998) 4607–4612.
[29]
C.R. Dass, Drug delivery in cancer using liposomes, in: Drug Delivery Systems, Humana Press, 2008: pp. 177–182. 39
ACCEPTED MANUSCRIPT
[30]
P.Y. Lu, M.C. Woodle, Delivering small interfering RNA for novel therapeutics, in: Drug
[31]
PT
Delivery Systems, Humana Press, 2008: pp. 93–107. R. Singh, J.W. Lillard, Nanoparticle-based targeted drug delivery, Exp. Mol. Pathol. 86 (2009)
[32]
RI
215–223.
S.H. Jang, M.G. Wientjes, D. Lu, J.L.S. Au, Drug delivery and transport to solid tumors, Pharm.
[33]
SC
Res. 20 (2003) 1337–1350.
A.R. Kirtane, R. A. Siegel, J. Panyam, A pharmacokinetic model for quantifying the effect of
NU
vascular permeability on the choice of drug carrier: A framework for personalized nanomedicine, J. Pharm. Sci. 104 (2015) 1174–1186.
S.K. Brar, M. Verma, Measurement of nanoparticles by light-scattering techniques, Trends Anal.
MA
[34]
Chem. 30 (2011) 4–17. [35]
W. Mehnert, K. Mäder, Solid lipid nanoparticles: Production, characterization and applications,
S.A. Kübart, C.M. Keck, Laser diffractometry of nanoparticles: Frequent pitfalls & overlooked
TE
[36]
D
Adv. Drug Deliv. Rev. 64 (2012) 83–101.
opportunities, J. Pharm. Technol. Drug Res. 2 (2013) 17. A. Moquin, F.M. Winnik, The use of field-flow fractionation for the analysis of drug and gene
AC CE P
[37]
delivery systems, in: Field-Flow Fractionation in Biopolymer Analysis, Springer Vienna, 2012: pp. 187-206 [38]
A. Zattoni, B. Roda, F. Borghi, V. Marassi, P. Reschiglian, Flow field-flow fractionation for the analysis of nanoparticles used in drug delivery, J. Pharm. Biomed. Anal. 87 (2014) 53–61.
[39]
F.M. Veronese, G. Pasut, PEGylation, successful approach to drug delivery, Drug Discov. Today. 10 (2005) 1451–1458.
[40]
N.S. Berchane, K.H. Carson, A.C. Rice-Ficht, M.J. Andrews, Effect of mean diameter and polydispersity of PLG microspheres on drug release: Experiment and theory, Int. J. Pharm. 337 (2007) 118–126.
[41]
C.M. Beddoes, C.P. Case, W.H. Briscoe, Understanding nanoparticle cellular entry: A physicochemical perspective, Adv. Colloid Interface Sci. 218C (2015) 48–68.
40
ACCEPTED MANUSCRIPT
[42]
M. Mu llner, S.J. Dodds, T.-H. Nguyen, D. Senyschyn, C.J.H. Porter, B.J. Boyd, et al., Size
PT
and rigidity of cylindrical polymer brushes dictate long circulating properties in vivo, ACS Nano. 9 (2015) 1294–1304.
S.E.A. Gratton, P. A. Ropp, P.D. Pohlhaus, J.C. Luft, V.J. Madden, M.E. Napier, et al., The
RI
[43]
effect of particle design on cellular internalization pathways, Proc. Natl. Acad. Sci. U. S. A. 105
[44]
SC
(2008) 11613–11618.
J. A. Champion, S. Mitragotri, Role of target geometry in phagocytosis, Proc. Natl. Acad. Sci.
[45]
NU
U. S. A. 103 (2006) 4930–4934.
S. Fusco, H.W. Huang, K.E. Peyer, C. Peters, M. Häberli, A. Ulbers, et al., Shape-switching
MA
microrobots for medical applications: The influence of shape in drug delivery and locomotion, ACS Appl. Mater. & Interfaces. 7 (2015) 6803–6811. [46]
G. Ruan, S.S. Feng, Preparation and characterization of poly(lactic acid)–poly(ethylene glycol)–
D
poly(lactic acid) (PLA–PEG–PLA) microspheres for controlled release of paclitaxel,
[47]
TE
Biomaterials. 24 (2003) 5037–5044.
E. Fattal, H. Hillaireau, S. Mura, J. Nicolas, N. Tsapis, Targeted delivery using biodegradable
AC CE P
polymeric nanoparticles, in: Fundamentals and Applications of Controlled Release Drug Delivery, Springer US, 2012: pp. 255–288. [48]
J.L.S. Milne, M.J. Borgnia, A. Bartesaghi, E.E.H. Tran, L.A. Earl, D.M. Schauder, et al., Cryoelectron microscopy - A primer for the non-microscopist, FEBS J. 280 (2013) 28–45.
[49]
A. Bartesaghi, A. Merk, S. Banerjee, D. Matthies, X. Wu, J.L.S. Milne, et al., 2.2 Å resolution cryo-EM structure of β-galactosidase in complex with a cell-permeant inhibitor, Science. 348 (2015) 1147–1151.
[50]
T. Turovsky, R. Khalfin, S. Kababya, A. Schmidt, Y. Barenholz, D. Danino, Celecoxib encapsulation in β-Casein Micelles: Structure, interactions, and conformation, Langmuir. 31 (2015) 7183–7192.
[51]
M. Zheng, G.M. Pavan, M. Neeb, A.K. Schaper, A. Danani, G. Klebe, et al., Targeting the blind spot of polycationic nanocarrier-based siRNA delivery, ACS Nano. 6 (2012) 9447–9454.
[52]
A.M. Alkilany, C.J. Murphy, Toxicity and cellular uptake of gold nanoparticles: What we have 41
ACCEPTED MANUSCRIPT
learned so far?, J. Nanoparticle Res. 12 (2010) 2313–2333. K. Murugan, Y.E. Choonara, P. Kumar, D. Bijukumar, L.C. du Toit, V. Pillay, Parameters and
PT
[53]
characteristics governing cellular internalization and trans-barrier trafficking of nanostructures,
[54]
E. Fröhlich, The role of surface charge in cellular uptake and cytotoxicity of medical
SC
nanoparticles, Int. J. Nanomed. 7 (2012) 5577–5591. [55]
RI
Int. J. Nanomed. 10 (2015) 2191–2206.
M. Di Marco, C. Sadun, M. Port, I. Guilbert, P. Couvreur, C. Dubernet, Physicochemical
NU
characterization of ultrasmall superparamagnetic iron oxide particles (USPIO) for biomedical application as MRI contrast agents, Int. J. Nanomed. 2 (2007) 609–622. K. Xiao, Y. Li, J. Luo, J.S. Lee, W. Xiao, A.M. Gonik, et al., The effect of surface charge on in
MA
[56]
vivo biodistribution of PEG-oligocholic acid based micellar nanoparticles, Biomaterials 32 (2011) 3435–3446.
L.P. Mendes, J.M.F. Delgado, A.D.A. Costa, M.S. Vieira, P.L. Benfica, E.M. Lima, et al.,
D
[57]
TE
Biodegradable nanoparticles designed for drug delivery: The number of nanoparticles impacts on cytotoxicity, Toxicol. Vitr. 29 (2015) 1268–1274. M. Law, M. Jafari, P. Chen, Physicochemical characterization of SiRNA-peptide complexes,
AC CE P
[58]
Biotechnol. Prog. 24 (2008) 957–963. [59]
A.M. Chen, M. Zhang, D. Wei, D. Stueber, O. Taratula, T. Minko, et al., Co-delivery of Doxorubicin and Bcl-2 siRNA by mesoporous silica nanoparticles enhances the efficacy of chemotherapy in multidrug-resistant cancer cells, Small. 5 (2009) 2673–2677.
[60]
X.Y. Wang, L. Zhang, X.H. Wei, Q. Wang, Molecular dynamics of paclitaxel encapsulated by salicylic acid-grafted chitosan oligosaccharide aggregates, Biomaterials. 34 (2013) 1843–1851.
[61]
B. Sun, D.T. Chiu, Determination of the encapsulation efficiency of individual vesicles using single-vesicle photolysis and confocal single-molecule detection, Anal. Chem. 77 (2005) 2770– 2776.
[62]
N. Martinho, H. Florindo, L. Silva, S. Brocchini, M. Zloh, T. Barata, Molecular modeling to study dendrimers for biomedical applications, Molecules. 19 (2014) 20424–20467.
[63]
H. Bunjes, T. Unruh, Characterization of lipid nanoparticles by differential scanning 42
ACCEPTED MANUSCRIPT
calorimetry, X-ray and neutron scattering, Adv. Drug Deliv. Rev. 59 (2007) 379–402. W. Mehnert, Solid lipid nanoparticles: Production, characterization and applications, Adv. Drug
PT
[64]
Deliv. Rev. 47 (2001) 165–196.
M. Deborah, A. Jawahar, T. Mathavan, M.K. Dhas, A. M.F. Benial, Spectroscopic studies on
RI
[65]
Spectrochim. Acta Part A. 139 (2015) 138–144. [66]
SC
sidewall carboxylic acid functionalization of multi-walled carbon nanotubes with valine,
P.S. Williams, F. Carpino, M. Zborowski, Magnetic nanoparticle drug carriers and their study
[67]
NU
by quadrupole magnetic field-flow fractionation, Molecules. 6 (2009) 1290–1306. M.L. Briuglia, C. Rotella, A. McFarlane, D.A. Lamprou, Influence of cholesterol on liposome
[68]
MA
stability and on in vitro drug release, Drug Deliv. Transl. Res. 5 (2015) 231–242. J. Wang, Y. Wang, W. Liang, Delivery of drugs to cell membranes by encapsulation in PEG– PE micelles, J. Control. Release. 160 (2012) 637–651. N.W.S. Kam, Z. Liu, H. Dai, Functionalization of carbon nanotubes via cleavable disulfide
D
[69]
127 (2005) 12492–12493.
S. Achar, R.J. Puddephatt, Organoplatinum dendrimers formed by oxidative addition, Angew.
AC CE P
[70]
TE
bonds for efficient intracellular delivery of siRNA and potent gene silencing, J. Am. Chem. Soc.
Chemie Int. Ed. 33 (1994) 847–849. [71]
M.L. Viger, W. Sheng, C.L. McFearin, M.Y. Berezin, A. Almutairi, Application of timeresolved fluorescence for direct and continuous probing of release from polymeric delivery vehicles, J. Control. Release. 171 (2013) 308–314.
[72]
A. A. Metwally, R.M. Hathout, Computer-assisted drug formulation design: Novel approach in drug delivery, Mol. Pharm. 12 (2015) 2800–2810.
[73]
M.B. Sankaram, T.E. Thompson, Interaction of cholesterol with various glycerophospholipids and sphingomyelin, Biochemistry. 29 (1990) 10670–10675.
[74]
A. Caminade, R. Laurent, J. Majoral, Characterization of dendrimers, Adv. Drug Deliv. Rev. 57 (2005) 2130–2146.
[75]
Y.W. Hsueh, M. Zuckermann, J. Thewalt, Phase diagram determination for phospholipid/sterol membranes using deuterium NMR, Concepts Magn. Reson. Part A. 26A (2005) 35–46. 43
ACCEPTED MANUSCRIPT
[76]
T. Io, T. Fukami, K. Yamamoto, T. Suzuki, J. Xu, K. Tomono, et al., Homogeneous
PT
nanoparticles to enhance the efficiency of a hydrophobic drug, antihyperlipidemic probucol, characterized by solid-state NMR, Mol. Pharm. 7 (2009) 299–305.
T. Sun, Q. Guo, C. Zhang, J. Hao, P. Xing, J. Su, et al., Self-assembled vesicles prepared from
RI
[77]
amphiphilic cyclodextrins as drug carriers, Langmuir. 28 (2012) 8625–8636. D. Astruc, E. Boisselier, C. Ornelas, Dendrimers designed for functions: From physical,
SC
[78]
photophysical, and supramolecular properties to applications in sensing, catalysis, molecular
[79]
NU
electronics, photonics, and nanomedicine, Chem. Rev. 110 (2010) 1857–1959. A.K.K. Leung, I.M. Hafez, S. Baoukina, N.M. Belliveau, I. V Zhigaltsev, E. Afshinmanesh, et
MA
al., Lipid nanoparticles containing siRNA synthesized by microfluidic mixing exhibit an electron-dense nanostructured core, J. Phys. Chem. C. 116 (2012) 18440–18450. [80]
E. V Mathias, X. Liu, O. Franco, I. Khan, Y. Ba, J. A. Kornfield, Model of drug-loaded
D
fluorocarbon-based micelles studied by electron-spin induced 19F relaxation NMR and
[81]
G. Martini, L. Ciani, Electron spin resonance spectroscopy in drug delivery, Phys. Chem. Chem.
AC CE P
Phys. 11 (2009) 211–254. [82]
TE
molecular dynamics simulation, Langmuir. 24 (2008) 692–700.
H. Wennerström, D.F. Evans, The colloidal domain: Where physics, chemistry, biology, and technology meet, Wiley-VCH: New York, 1999.
[83]
C. Nicolini, J. Kraineva, M. Khurana, N. Periasamy, S.S. Funari, R. Winter, Temperature and pressure effects on structural and conformational properties of POPC/SM/cholesterol model raft mixtures–an FT-IR, SAXS, DSC, PPC and Laurdan fluorescence spectroscopy study, Biochim. Biophys. Acta. 1758 (2006) 248–258.
[84]
T.T. Mills, S. Tristram-Nagle, F. a Heberle, N.F. Morales, J. Zhao, J. Wu, et al., Liquid-liquid domains in bilayers detected by wide angle X-ray scattering, Biophys. J. 95 (2008) 682–690.
[85]
V. Meli, C. Caltagirone, A.M. Falchi, S.T. Hyde, V. Lippolis, M. Monduzzi, et al., Docetaxelloaded fluorescent liquid-crystalline nanoparticles for cancer theranostics, Langmuir. 31 (2015) 9566–9575.
[86]
A. Angelova, B. Angelov, R. Mutafchieva, S. Lesieur, P. Couvreur, Self-assembled 44
ACCEPTED MANUSCRIPT
multicompartment liquid crystalline lipid carriers for protein, peptide and nucleic acid drug
[87]
PT
delivery, Acc. Chem. Res. 44 (2010) 147–156. R.H. Utama, M. Dulle, S. Förster, M.H. Stenzel, P.B. Zetterlund, SAXS analysis of shell
RI
formation during nanocapsule synthesis via inverse miniemulsion periphery RAFT polymerization, Macromol. Rapid Commun. (2015) 1267-1271.
R.H. Müller, S.A. Runge, V. Ravelli, A.F. Thünemann, W. Mehnert, E.B. Souto, Cyclosporine-
SC
[88]
loaded solid lipid nanoparticles (SLN®): drug–lipid physicochemical interactions and
[89]
NU
characterization of drug incorporation, Eur. J. Pharm. Biopharm. 68 (2008) 535–544. A.F. Martins, A.G.B. Pereira, A.R. Fajardo, A.F. Rubira, E.C. Muniz, Characterization of
MA
polyelectrolytes complexes based on N,N,N-trimethyl chitosan/heparin prepared at different pH conditions, Carbohydr. Polym. 86 (2011) 1266–1272. [90]
J. Pan, F.A. Heberle, R.S. Petruzielo, J. Katsaras, Using small-angle neutron scattering to detect
J.R. Lakowicz, Principles of fluorescence spectroscopy, Springer Sceince & Business Media, 2006.
K. Kataoka, A. Harada, Y. Nagasaki, Block copolymer micelles for drug delivery: design,
AC CE P
[92]
TE
[91]
D
nanoscopic lipid domains, Chem. Phys. Lipids. 170-171 (2013) 19–32.
characterization and biological significance, Adv. Drug Deliv. Rev. 64 (2012) 37-48. [93]
S.R. Pygall, J. Whetstone, P. Timmins, C.D. Melia, Pharmaceutical applications of confocal laser scanning microscopy: The physical characterisation of pharmaceutical systems, Adv. Drug Deliv. Rev. 59 (2007) 1434–1452.
[94]
C. Dubernet, Thermoanalysis of microspheres, Thermochim. Acta. 248 (1995) 259–269.
[95]
D. Giron, Applications of thermal analysis and coupled techniques in pharmaceutical industry, J. Therm. Anal. Calorim. 68 (2002) 335–357.
[96]
H. Reinl, T. Brumm, T.M. Bayerl, The physical properties of the liquid-ordered phase with temperature in binary mixtures of DPPC with cholesterol: A 2H-NMR, FT-IR, DSC, and neutron scattering study, Biophys. J. 61 (1992) 1025–1035.
[97]
N.R. Labiris, M.B. Dolovich, Pulmonary drug delivery. Part I: Physiological factors affecting therapeutic effectiveness of aerosolized medications, Br. J. Clin. Pharmacol. 56 (2003) 588–599. 45
ACCEPTED MANUSCRIPT
[98]
K. Bouchemal, New challenges for pharmaceutical formulations and drug delivery systems
[99]
PT
characterization using isothermal titration calorimetry, Drug Discov. Today. 13 (2008) 960–972. R. Gref, A. Domb, P. Quellec, T. Blunk, R.H. Müller, J.M. Verbavatz, et al., The controlled
RI
intravenous delivery of drugs using PEG-coated sterically stabilized nanospheres, Adv. Drug Deliv. Rev. 64 (2012) 316–326.
SC
[100] K. Avgoustakis, A. Beletsi, Z. Panagi, P. Klepetsanis, A. G. Karydas, D.S. Ithakissios, PLGAmPEG nanoparticles of cisplatin: In vitro nanoparticle degradation, in vitro drug release and in
NU
vivo drug residence in blood properties, J. Control. Release. 79 (2002) 123–135. [101] B.L. Mui, Y.K. Tam, M. Jayaraman, S.M. Ansell, X. Du, Y.Y.C. Tam, et al., Influence of
MA
Polyethylene Glycol Lipid Desorption Rates on Pharmacokinetics and Pharmacodynamics of siRNA Lipid Nanoparticles, Mol. Ther. Acids. 2 (2013) e139. [102] H. Tan, Z. Wang, J. Li, Z. Pan, M. Ding, Q. Fu, An approach for the sphere-to-rod transition of
D
multiblock copolymer micelles, ACS Macro Lett. 2 (2013) 146–151.
TE
[103] A. Almutairi, W.J. Akers, M.Y. Berezin, S. Achilefu, J.M.J. Frechet, Monitoring the biodegradation of dendritic near-infrared nanoprobes by in vivo fluorescence imaging, Mol.
AC CE P
Pharm. 5 (2008) 1103–1110.
[104] X. Dai, Z. Yue, M.E. Eccleston, J. Swartling, N.K.H. Slater, C.F. Kaminski, Fluorescence intensity and lifetime imaging of free and micellar-encapsulated doxorubicin in living cells, Nanomed. Nanotechn. Biol. Med. 4 (2008) 49–56. [105] D. Frenkel, B. Smit, Understanding molecular simulation: From algorithms to applications, Academic Press, 2001.
[106] B. Hess, C. Kutzner, D. Van Der Spoel, E. Lindahl, GROMACS 4: Algorithms for highly efficient, load-balanced and scalable molecular simulation, J. Chem. Theory Comput. 4 (2008) 435–447. [107] J.C. Phillips, R. Braun, W. Wang, J. Gumbart, E. Tajkhorshid, E. Villa, et al., Scalable molecular dynamics with NAMD, J. Comput. Chem. 26 (2005) 1781–1802. [108] S. Plimpton, Fast parallel algorithms for short-range molecular dynamics, J. Comput. Phys. 117 (1995) 1–19. 46
ACCEPTED MANUSCRIPT
[109] B.R. Brooks, C.L. Brooks, A.D. MacKerell, L. Nilsson, R.J. Petrella, B. Roux, et al.,
PT
CHARMM: The biomolecular simulation program, J. Comput. Chem. 30 (2009) 1545–1614. [110] D.A. Case, T.E. Cheatham, T. Darden, H. Gohlke, R. Luo, K.M. Merz, et al., The Amber
RI
biomolecular simulation programs, J. Comput. Chem. 26 (2005) 1668–1688. [111] S.J. Marrink, D.P. Tieleman, Perspective on the Martini model, Chem. Soc. Rev. 42 (2013)
SC
6801–6822.
[112] M. Karplus, J.A. McCammon, Molecular dynamics simulations of biomolecules, Nat. Struct. &
NU
Mol. Biol. 9 (2002) 646–652.
[113] W.F. van Gunsteren, J. Dolenc, A.E. Mark, Molecular simulation as an aid to experimentalists,
MA
Curr. Opin. Struct. Biol. 18 (2008) 149–153.
[114] M. Velinova, D. Sengupta, A. V Tadjer, S.J. Marrink, Sphere-to-rod transitions of nonionic surfactant micelles in aqueous solution modeled by molecular dynamics simulations, Langmuir.
D
27 (2011) 14071–14077.
TE
[115] W. Shinoda, R. DeVane, M.L. Klein, Computer simulation studies of self-assembling macromolecules, Curr. Opin. Struct. Biol. 22 (2012) 175–186.
AC CE P
[116] P. V Messina, J. Miguel Besada-Porto, J. M Ruso, Self-assembly drugs: From micelles to nanomedicine, Curr. Top. Med. Chem. 14 (2014) 555–571. [117] B. Aoun, V.K. Sharma, E. Pellegrini, S. Mitra, M. Johnson, R. Mukhopadhyay, Structure and dynamics of ionic micelles: MD simulation and neutron scattering study, J. Phys. Chem. B. 119 (2015) 5079–5086.
[118] L. Vukovic, F.A. Khatib, S.P. Drake, A. Madriaga, K.S. Brandenburg, P. Král, et al., Structure and dynamics of highly PEG-ylated sterically stabilized micelles in aqueous media, J. Am. Chem. Soc. 133 (2011) 13481–13488. [119] J. Gupta, C. Nunes, S. Vyas, S. Jonnalagadda, Prediction of solubility parameters and miscibility of pharmaceutical compounds by molecular dynamics simulations, J. Phys. Chem. B. 115 (2011) 2014–2023. [120] S.M. Loverde, M.L. Klein, D.E. Discher, Nanoparticle shape improves delivery: Rational coarse grain molecular dynamics (rCG-MD) of taxol in worm-like PEG-PCL micelles, Adv. Mater. 24 47
ACCEPTED MANUSCRIPT
(2012) 3823–3830. ukovi , A. Madriaga, A. Kuzmis, A. Banerjee, A. Tang, K. Tao, et al., Solubilization of
PT
[121] L.
therapeutic agents in micellar nanomedicines, Langmuir. 29 (2013) 15747–15754.
RI
[122] L. Huynh, C. Neale, R. Pomès, C. Allen, Systematic design of unimolecular star copolymer micelles using molecular dynamics simulations, Soft Matter. 6 (2010) 5491–5501. .
cha, F.J. Martinez-Veracoechea, D. Frenkel, Intracellular release of endocytosed
SC
[123]
nanoparticles upon a change of ligand–receptor interaction, ACS Nano. 6 (2012) 10598–10605.
NU
[124] W. Tian, Y.Q. Ma, Insights into the endosomal escape mechanism via investigation of dendrimer–membrane interactions, Soft Matter. 8 (2012) 6378–6384.
MA
[125] N. Arai, K. Yasuoka, X.C. Zeng, A vesicle cell under collision with a Janus or homogeneous nanoparticle: translocation dynamics and late-stage morphology, Nanoscale. 5 (2013) 9089– 9100.
A.J. Makarucha, N. Todorova, I. Yarovsky, Nanomaterials in biological environment: A review
D
[126]
TE
of computer modelling studies, Eur. Biophys. J. 40 (2011) 103–115. [127] Y.C. Li, S. Rissanen, M. Stepniewski, O. Cramariuc, T. Róg, S. Mirza, et al., Study of
AC CE P
interaction between PEG carrier and three relevant drug molecules: piroxicam, paclitaxel, and hematoporphyrin, J. Phys. Chem. B. 116 (2012) 7334–7341. [128] F.J. Martinez-Veracoechea, D. Frenkel, Designing super selectivity in multivalent nano-particle binding, Proc. Natl. Acad. Sci. U. S. A. 108 (2011) 10963–10968. [129] S. Wang, E.E. Dormidontova, Nanoparticle design optimization for enhanced targeting: Monte Carlo simulations, Biomacromolecules. 11 (2010) 1785–1795. [130] L. Liu, S. Parameswaran, A. Sharma, S.M. Grayson, H.S. Ashbaugh, S.W. Rick, Molecular dynamics simulations of linear and cyclic amphiphilic polymers in aqueous and organic environments, J. Phys. Chem. B. 118 (2014) 6491–6497. [131] L. Lai, A.S. Barnard, Functionalized nanodiamonds for biological and medical applications, J. Nanosci. Nanotechnol. 15 (2015) 989–999. [132] T. Panczyk, T.P. Warzocha, P.J. Camp, A magnetically controlled molecular nanocontainer as a drug delivery system: The effects of carbon nanotube and magnetic nanoparticle parameters 48
ACCEPTED MANUSCRIPT
from monte carlo simulations, J. Phys. Chem. C. 114 (2010) 21299–21308.
PT
[133] T. Panczyk, A. Jagusiak, G. Pastorin, W.H. Ang, J. Narkiewicz-Michalek, Molecular dynamics study of cisplatin release from carbon nanotubes capped by magnetic nanoparticles, J. Phys.
RI
Chem. C. 117 (2013) 17327–17336.
[134] H.M. Ding, Y.Q. Ma, Computer simulation of the role of protein corona in cellular delivery of
SC
nanoparticles, Biomaterials. 35 (2014) 8703–8710.
[135] G. Brancolini, D.B. Kokh, L. Calzolai, R.C. Wade, S. Corni, Docking of ubiquitin to gold
NU
nanoparticles, ACS Nano. 6 (2012) 9863–9878.
[136] D. Meneksedag-Erol, T. Tang, H. Uludağ, Probing the effect of mi NA on si NA–PEI
MA
polyplexes, J. Phys. Chem. B. 119 (2015) 5475–5486. [137] A.F. Jorge, R.S. Dias, A.A. Pais, Enhanced condensation and facilitated release of DNA using mixed cationic agents: a combined experimental and Monte Carlo study, Biomacromolecules.
D
13 (2012) 3151–3161.
TE
[138] F. Tian, T. Yue, Y. Li, X. Zhang, Computer simulation studies on the interactions between nanoparticles and cell membrane, Sci. China Chem. 57 (2014) 1662–1671.
AC CE P
[139] H.M. Ding, Y.Q. Ma, Theoretical and computational investigations of nanoparticlebiomembrane interactions in cellular delivery, Small. 11 (2015) 1055–1071. [140] Y. Li, X. Zhang, D. Cao, Nanoparticle hardness controls the internalization pathway for drug delivery, Nanoscale. 7 (2015) 2758–2769. [141] Y. Li, T. Yue, K. Yang, X. Zhang, Molecular modeling of the relationship between nanoparticle shape anisotropy and endocytosis kinetics, Biomaterials. 33 (2012) 4965–4973. [142] R. Vácha, F.J. Martinez-Veracoechea, D. Frenkel, Receptor-mediated endocytosis of nanoparticles of various shapes, Nano Lett. 11 (2011) 5391–5395. [143] X. Lin, Y. Li, N. Gu, Nanoparticle’s size effect on its translocation across a lipid bilayer: A molecular dynamics simulation, J. Comput. Theor. Nanosci. 7 (2010) 269–276. [144] E.L. da Rocha, G.F. Caramori, C.R. Rambo, Nanoparticle translocation through a lipid bilayer tuned by surface chemistry, Phys. Chem. Chem. Phys. 15 (2013) 2282–2290. [145] R.C. Van Lehn, A. Alexander-Katz, Penetration of lipid bilayers by nanoparticles with 49
ACCEPTED MANUSCRIPT
environmentally-responsive surfaces: Simulations and theory, Soft Matter. 7 (2011) 11392-
PT
11404. [146] J. Lin, H. Zhang, Z. Chen, Y. Zheng, Penetration of lipid membranes by gold nanoparticles:
RI
Insights into cellular uptake, cytotoxicity, and their relationship, ACS Nano. 4 (2010) 5421– 5429.
SC
[147] H.M. Ding, Y.Q. Ma, Role of physicochemical properties of coating ligands in receptormediated endocytosis of nanoparticles, Biomaterials. 33 (2012) 5798–5802.
NU
[148] Y. Li, Computer investigations of influences of molar fraction and acyl chain length of lipids on the nanoparticle–biomembrane interactions, RSC Adv. 5 (2015) 11049–11057.
MA
[149] H.M. Ding, W. Tian, Y.Q. Ma, Designing nanoparticle translocation through membranes by computer simulations, ACS Nano. 6 (2012) 1230–1238. [150] S. Wang, E.E. Dormidontova, Selectivity of ligand-receptor interactions between nanoparticle
D
and cell surfaces, Phys. Rev. Lett. 109 (2012) 238102.
TE
[151] L. Zhang, M. Becton, X. Wang, Designing nanoparticle translocation through cell membranes by varying amphiphilic polymer coatings, J. Phys. Chem. B. 119 (2015) 3786–3794.
AC CE P
[152] Y. Li, X. Zhang, D. Cao, A spontaneous penetration mechanism of patterned nanoparticles across a biomembrane, Soft Matter. 10 (2014) 6844–6856. [153] T. Yue, X. Zhang, Molecular understanding of receptor-mediated membrane responses to ligand-coated nanoparticles, Soft Matter. 7 (2011) 9104-9112. [154] L.F. Barraza, V. A. Jiménez, J.B. Alderete, Effect of PEGylation on the structure and drug loading capacity of PAMAM-G4 dendrimers: A molecular modeling approach on the complexation of 5-Fluorouracil with native and PEGylated PAMAM-G4, Macromol. Chem. Phys. 216 (2015) 1689–1701. [155] O.S. Lee, G.C. Schatz, Computational simulations of the interaction of lipid membranes with DNA-functionalized gold nanoparticles, in: Biomedical Nanotechnolgy, Springer, 2011: pp. 283–296. [156] Q. Mu, T. Hu, J. Yu, Molecular insight into the steric shielding effect of PEG on the conjugated staphylokinase: Biochemical characterization and molecular dynamics simulation, PLoS One. 8 50
ACCEPTED MANUSCRIPT
(2013) e68559.
PT
[157] H. Lee, Molecular modeling of PEGylated peptides, dendrimers, and single-walled carbon nanotubes for biomedical applications, Polymers. 6 (2014) 776–798.
RI
[158] A. Bunker, Molecular modeling as a tool to understand the role of poly (ethylene) glycol in drug delivery, in: Computational Pharmaceutics: Application of Molecular Modelling in Drug
SC
Delivery, John Wiley & Sons, 2015: pp. 217-233.
[159] Y. Li, M. Kröger, W.K. Liu, Endocytosis of PEGylated nanoparticles accompanied by structural
NU
and free energy changes of the grafted polyethylene glycol, Biomaterials. 35 (2014) 8467–8478. [160] K. Knop, R. Hoogenboom, D. Fischer, U.S. Schubert, Poly(ethylene glycol) in drug delivery:
MA
Pros and cons as well as potential alternatives, Angew. Chemie Int. Ed. 49 (2010) 6288–6308. [161] J.K. Armstrong, G. Hempel, S. Koling, L.S. Chan, T. Fisher, H.J. Meiselman, et al., Antibody against poly(ethylene glycol) adversely affects PEG-asparaginase therapy in acute
D
lymphoblastic leukemia patients, Cancer. 110 (2007) 103–111.
TE
[162] L. Zhang, Z. Wang, Z. Lu, H. Shen, J. Huang, Q. Zhao, et al., PEGylated reduced graphene oxide as a superior ssRNA delivery system, J. Mater. Chem. B. 1 (2013) 749–755.
AC CE P
[163] X. Sun, Z. Feng, L. Zhang, T. Hou, Y. Li, The selective interaction between silica nanoparticles and enzymes from molecular dynamics simulations, PLoS One. 9 (2014) e107696. [164] A. Adnan, R. Lam, H. Chen, J. Lee, D.J. Schaffer, A.S. Barnard, et al., Atomistic simulation and measurement of pH dependent cancer therapeutic interactions with nanodiamond carrier, Mol. Pharm. 8 (2011) 368–374. [165] L. Lai, A.S. Barnard, Molecular and analytical modeling of nanodiamond for drug delivery applications, in: Computational Pharmaceutics: Application of Molecular Modelling in Drug Delivery, John Wiley & Sons, 2015: pp. 169-196. [166] M.L. Bello, A.M. Junior, B.A. Vieira, L.R.S. Dias, V.P. de Sousa, H.C. Castro, et al., Sodium montmorillonite/amine-containing drugs complexes: New insights on intercalated drugs arrangement into layered carrier material, PLoS One. 10 (2015) e0121110. [167] V. Murthy, Z.P. Xu, S.C. Smith, Molecular modeling of layered double hydroxide nanoparticles for drug delivery, in: Computational Pharmaceutics: Application of Molecular Modelling in 51
ACCEPTED MANUSCRIPT
Drug Delivery, John Wiley & Sons, 2015: pp. 197-216.
PT
[168] P. Cox, Atomistic modelling of drug delivery systems, in: Drug Design Strategies: Computational Techniques and Applications, Royal Society of Chemistry, 2012: pp. 210–232.
RI
[169] D.G. Fatouros, D. Douroumis, V. Nikolakis, S. Ntais, A.M. Moschovi, V. Trivedi, et al., In vitro and in silico investigations of drug delivery via zeolite BEA, J. Mater. Chem. 21 (2011)
SC
7789-7794.
[170] M. Spanakis, N. Bouropoulos, D. Theodoropoulos, L. Sygellou, S. Ewart, A.M. Moschovi, et al.,
NU
Controlled release of 5-fluorouracil from microporous zeolites, Nanomed. Nanotech. Biol. Med. 10 (2014) 197–205.
MA
[171] M.C. Bernini, D. Fairen-Jimenez, M. Pasinetti, A.J. Ramirez-Pastor, R.Q. Snurr, Screening of bio-compatible metal–organic frameworks as potential drug carriers using Monte Carlo simulations, J. Mater. Chem. B. 2 (2014) 766-774.
D
[172] S.T. Meek, J.A. Greathouse, M.D. Allendorf, Metal - organic frameworks: A rapidly growing
TE
class of versatile nanoporous materials, Adv. Mater. 23 (2011) 249–267. [173] G.M. Pavan, Modeling the interaction between dendrimers and nucleic acids: A molecular
AC CE P
perspective through hierarchical scales, ChemMedChem. 9 (2014) 2623–2631. [174] V. Jain, P. V Bharatam, Pharmacoinformatic approaches to understand complexation of dendrimeric nanoparticles with drugs, Nanoscale. 6 (2014) 2476–2501. [175] D. Shcharbin, A. Shakhbazau, M. Bryszewska, Poly(amidoamine) dendrimer complexes as a platform for gene delivery, Expert Opin. Drug Deliv. 10 (2013) 1687–1698. [176] P. Posocco, E. Laurini, V. Dal Col, D. Marson, K. Karatasos, M. Fermeglia, et al., Tell me something I do not know. Multiscale molecular modeling of dendrimer/dendron organization and self-assembly in gene therapy, Curr. Med. Chem. 19 (2012) 5062–5087. [177] D. Meneksedag-Erol, T. Tang, H. Uludağ, Molecular modeling of polynucleotide complexes, Biomaterials. 35 (2014) 7068–7076. [178] M.R. Molla, P. Rangadurai, G.M. Pavan, S. Thayumanavan, Experimental and theoretical investigations in stimuli responsive dendrimer-based assemblies, Nanoscale. 7 (2015) 3817– 3837. 52
ACCEPTED MANUSCRIPT
[179] E. Ficici, I. Andricioaei, S. Howorka, Dendrimers in nanoscale confinement: The interplay
PT
between conformational change and nanopore entrance, Nano Lett. 15 (2015) 4822–4828. [180] Y.L. Wang, Z.Y. Lu, A. Laaksonen, Specific binding structures of dendrimers on lipid bilayer
RI
membranes, Phys. Chem. Chem. Phys. 14 (2012) 8348–8359.
[181] B. Nandy, P.K. Maiti, A. Bunker, Force biased molecular dynamics simulation study of effect
SC
of dendrimer generation on interaction with DNA, J. Chem. Theory Comput. 9 (2013) 722–729. [182] G.M. Pavan, A. Danani, The influence of dendrons architecture on the “rigid” and “flexible”
NU
behaviour in binding DNA—A modelling study, Phys. Chem. Chem. Phys. 12 (2010) 13914– 13917.
MA
[183] G.M. Pavan, L. Albertazzi, A. Danani, Ability to adapt: Different generations of PAMAM dendrimers show different behaviors in binding siRNA, J. Phys. Chem. B. 114 (2010) 2667– 2675.
D
[184] P. Posocco, S. Pricl, S. Jones, A. Barnard, D.K. Smith, Less is more – multiscale modelling of
(2010) 393–404.
TE
self-assembling multivalency and its impact on DNA binding and gene delivery, Chem. Sci. 1
AC CE P
[185] S.P. Jones, N.P. Gabrielson, C.H. Wong, H.F. Chow, D.W. Pack, P. Posocco, et al., Hydrophobically modified dendrons: Developing structure - Activity relationships for DNA binding and gene transfection, Mol. Pharm. 8 (2011) 416–429. [186] S.P. Jones, G.M. Pavan, A. Danani, S. Pricl, D.K. Smith, Quantifying the effect of surface ligands on dendron – DNA interactions : Insights into multivalency through a combined experimental and theoretical approach, Chem. Eur. J. 16 (2010) 4519–4532. [187] V. Percec, D.A. Wilson, P. Leowanawat, C.J. Wilson, A.D. Hughes, M.S. Kaucher, et al., Selfassembly of Janus dendrimers into uniform dendrimersomes and other complex architectures, Science. 328 (2010) 1009–1014. [188] S. Zhang, H.J. Sun, A.D. Hughes, R.O. Moussodia, A. Bertin, Y. Chen, et al., Self-assembly of amphiphilic Janus dendrimers into uniform onion-like dendrimersomes with predictable size and number of bilayers, Proc. Natl. Acad. Sci. U. S. A. 111 (2014) 9058-9063. [189] S. Kavyani, S. Amjad-Iranagh, H. Modarress, Aqueous poly(amidoamine) dendrimer G3 and 53
ACCEPTED MANUSCRIPT
G4 generations with several interior cores at pHs 5 and 7: A molecular dynamics simulation
PT
study, J. Phys. Chem. B. 118 (2014) 3257–3266. [190] A. Barnard, P. Posocco, S. Pricl, M. Calderon, R. Haag, M.E. Hwang, et al., Degradable self-
RI
assembling dendrons for gene delivery: Experimental and theoretical insights into the barriers to cellular uptake, J. Am. Chem. Soc. 133 (2011) 20288–20300.
SC
[191] V. Carrasco-Sánchez, A. Vergara-Jaque, M. Zuñiga, J. Comer, A. John, F.M. Nachtigall, et al., In situ and in silico evaluation of amine- and folate-terminated dendrimers as nanocarriers of
NU
anesthetics, Eur. J. Med. Chem. 73 (2014) 250–257.
[192] C. Shi, D. Yuan, S. Nangia, G. Xu, K.S. Lam, J. Luo, A structure-property relationship study of
MA
the well-defined telodendrimers to improve hemocompatibility of nanocarriers for anticancer drug delivery, Langmuir. 30 (2014) 6878–6888. [193] R.M. Pearson, N. Patra, H. . Hsu,
. Uddin, P. Kr l, S. Hong, Positively charged dendron
D
micelles display negligible cellular interactions, ACS Macro Lett. 2 (2012) 77–81.
TE
[194] H. Lee, J.S. Choi, R.G. Larson, Molecular dynamics studies of the size and internal structure of
8686.
AC CE P
the PAMAM dendrimer grafted with arginine and histidine, Macromolecules. 44 (2011) 8681–
[195] H. Lee, R.G. Larson, Membrane pore formation induced by acetylated and polyethylene glycolconjugated polyamidoamine dendrimers, J. Phys. Chem. C. 115 (2011) 5316–5322. [196] L. Yang, S.R.P. Rocha, PEGylated, NH2‑terminated PAMAM dendrimers: A microscopic view from atomistic computer simulations, Mol. Pharm. 11 (2014) 1459–1470. [197] L. Albertazzi, F.M. Mickler, G.M. Pavan, F. Salomone, G. Bardi, M. Panniello, et al., Enhanced bioactivity of internally functionalized cationic dendrimers with PEG cores, Biomacromolecules. 13 (2012) 4089–4097. [198] H. Lee, R.G. Larson, Effects of PEGylation on the size and internal structure of dendrimers: Self-penetration of long PEG chains into the dendrimer core, Macromolecules. 44 (2011) 2291– 2298. [199] R. Bhattacharya,S. Kanchi, C. Roobala, A. Lakshminarayanan, O.H. Seeck, P.K. Maiti, K.G. Ayappa, N. Jayaraman, J.K. Basu, A new microscopic insight into membrane penetration and 54
ACCEPTED MANUSCRIPT
reorganization by PETIM dendrimers, Soft Matter. 10 (2014) 7577–7587.
PT
[200] R. Guo, J. Mao, L.T. Yan, Unique dynamical approach of fully wrapping dendrimer-like soft nanoparticles by lipid bilayer membrane, ACS Nano. 7 (2013) 10646–10653.
RI
[201] X. He, Z. Qu, F. Xu, M. Lin, J. Wang, X. Shi, et al., Molecular analysis of interactions between dendrimers and asymmetric membranes at different transport stages, Soft Matter. 10 (2014)
SC
139–148.
[202] C.K. Tu, K. Chen, W.D. Tian, Y.Q. Ma, Computational investigations of a peptide-modified
NU
dendrimer interacting with lipid membranes, Macromol. Rapid Commun. 34 (2013) 1237–1242. [203] Y.Q. Ma, pH-responsive dendrimers interacting with lipid membranes, Soft Matter. 8 (2012)
MA
2627–2632.
[204] L.Q. Xie, J.W. Feng, W.D. Tian, Y.Q. Ma, Generation-dependent gel-fluid phase transition of membrane caused by a PAMAM dendrimer, Macromol. Theory Simul. 23 (2014) 531–536.
D
[205] R.G. Bellini, A.P. Guimarães, M. A. Pacheco, D.M. Dias, V.R. Furtado, R.B. De Alencastro, et
TE
al., Association of the anti-tuberculosis drug rifampicin with a PAMAM dendrimer, J. Mol. Graph. Model. 60 (2015) 34–42.
AC CE P
[206] V. Jain, V. Maingi, P.K. Maiti, P. V Bharatam, Molecular dynamics simulations of PPI dendrimer–drug complexes, Soft Matter. 9 (2013) 6482-6496. [207] Y. Jiang, D. Zhang, Y. Zhang, Z. Deng, L. Zhang, The adsorption-desorption transition of double-stranded DNA interacting with an oppositely charged dendrimer induced by multivalent anions, J. Chem. Phys. 140 (2014) 204912. [208] X.F. Wen, J.L. Lan, Z.Q. Cai, P.H. Pi, S.P. Xu, L.J. Zhang, et al., Dissipative particle dynamics simulation on drug loading/release in polyester-PEG dendrimer, J. Nanoparticle Res. 16 (2014) 2403. [209] V. Maingi, M.V.S. Kumar, P.K. Maiti, PAMAM dendrimer-drug interactions: Effect of pH on the binding and release pattern, J. Phys. Chem. B. 116 (2012) 4370–4376. [210] F. Avila- alas, . andoval, . aballero, . Gui ez-Molinos, L.S. Santos, R.E. Cachau, et al., Study of interaction energies between the PAMAM dendrimer and nonsteroidal antiinflammatory drug using a distributed computational strategy and experimental analysis by ESI55
ACCEPTED MANUSCRIPT
MS/MS, J. Phys. Chem. B. 116 (2012) 2031–2039.
PT
[211] J. Lim, G.M. Pavan, O. Annunziata, E.E. Simanek, Experimental and computational evidence for an inversion in guest capacity in high-heneration triazine dendrimer hosts, J. Am. Chem. Soc.
RI
134 (2012) 1942–1945.
[212] D. Ouyang, H. Zhang, H.S. Parekh, S.C. Smith, The effect of pH on PAMAM dendrimer–
SC
siRNA complexation—Endosomal considerations as determined by molecular dynamics simulation, Biophys. Chem. 158 (2011) 126–133.
NU
[213] B. Nandy, P.K. Maiti, DNA compaction by a dendrimer, J. Phys. Chem. B. 115 (2010) 217–230. [214] D. Amado Torres, M. Garzoni, A. V Subrahmanyam, G.M. Pavan, S. Thayumanavan, Protein-
MA
triggered supramolecular disassembly: Insights based on variations in ligand location in amphiphilic dendrons, J. Am. Chem. Soc. 136 (2014) 5385–5399. [215] L.B. Jensen, G.M. Pavan, M.R. Kasimova, S. Rutherford, A. Danani, H.M. Nielsen, et al.,
D
Elucidating the molecular mechanism of PAMAM – siRNA dendriplex self-assembly: Effect of
TE
dendrimer charge density, Int. J. Pharm. 416 (2011) 410–418. [216] K. Karatasos, P. Posocco, E. Laurini, S. Pricl, Poly (amidoamine)-based dendrimer/siRNA
AC CE P
complexation studied by computer simulations: Effects of pH and generation on dendrimer structure and siRNA binding, Macromol. Biochem. 12 (2012) 225–240. [217] G. M. Pavan, S. Monteagudo, J. Guerra, B. Carrion, V. Ocana, J. Rodriguez-Lopez, et al., Role of generation, architecture, pH and ionic strength on successful siRNA delivery and transfection by hybrid PPV-PAMAM dendrimers, Curr. Med. Chem. 19 (2012) 4929–4941. [218] G.M. Pavan, M.A. Mintzer, E.E. Simanek, O.M. Merkel, T. Kissel, A. Danani, Computational insights into the interactions between DNA and si NA with “ rigid ” and “ flexible ” triazine dendrimers, Biomacromolecules. 11 (2010) 721–730. [219] G.M. Pavan, P. Posocco, A. Tagliabue, M. Maly, A. Malek, A. Danani, et al., PAMAM dendrimers for siRNA delivery: Computational and experimental insights, Chem. Eur. J. 16 (2010) 7781–7795. [220] C. Shi, D. Guo, K. Xiao, X. Wang, L. Wang, J. Luo, A drug-specific nanocarrier design for efficient anticancer therapy, Nat. Commun. 6 (2015) 7449. 56
ACCEPTED MANUSCRIPT
[221] M. Kang, D. Lam, D.E. Discher, S.M. Loverde, Molecular modeling of block copolymer self-
PT
assembly and micellar drug delivery, in: Computational Pharmaceutics: Application of Molecular Modelling in Drug Delivery, John Wiley & Sons, 2015: pp. 53-80.
RI
[222] A. Rösler, G.W.M. Vandermeulen, H.A. Klok, Advanced drug delivery devices via selfassembly of amphiphilic block copolymers, Adv. Drug Deliv. Rev. 64 (2012) 270–279.
SC
[223] C. Sun, T. Tang, Study on the role of polyethylenimine as gene delivery carrier using molecular dynamics simulations, J. Adhes. Sci. Technol. 28 (2014) 399–416.
NU
[224] S.M. Loverde, Computer simulation of polymer and biopolymer self-assembly for drug delivery, Mol. Simul. 40 (2014) 794–801.
MA
[225] D. Meneksedag-Erol, C. Sun, T. Tang, H. Uludag, Molecular dynamics simulations of polyplexes and lipoplexes employed in gene delivery, in: Intracellular Delivery II , Springer, 2014: pp. 277–311.
D
[226] H. Guo, X. Qiu, J. Zhou, Self-assembled core-shell and Janus microphase separated structures
TE
of polymer blends in aqueous solution, J. Chem. Phys. 139 (2013) 84907. [227] C. Cai, L. Wang, J. Lin, X. Zhang, Morphology transformation of hybrid micelles self-
AC CE P
assembled from rod–coil block copolymer and nanoparticles, Langmuir. 28 (2012) 4515–4524. [228] H.Y. Chang, Y.L. Lin, Y.J. Sheng, H.K. Tsao, Structural characteristics and fusion pathways of onion-like multilayered polymersome formed by amphiphilic comb-like graft copolymers, Macromolecules. 46 (2013) 5644–5656. [229] Y. Sheng, J. An, Y. Zhu, Self-assembly of ABA triblock copolymers under soft confinement, Chem. Phys. 452 (2015) 46–52. [230] H. Chen, E. Ruckenstein, Formation and degradation of multicomponent multicore micelles: insights from dissipative particle dynamics simulations, Langmuir. 29 (2013) 5428–5434. [231]
. Taresco, L. Gontrani, F.
risante, I. Francolini, A. Martinelli, L. D’Ilario, et al.,
elf-
assembly of catecholic moiety-containing cationic random acrylic copolymers, J. Phys. Chem. B. 119 (2015) 8369–8379. [232] S.Y. Nie, Y. Sun, W.J. Lin, W.S. Wu, X.D. Guo, Y. Qian, et al., Dissipative particle dynamics studies of doxorubicin-loaded micelles assembled from four-arm star triblock polymers 4AS57
ACCEPTED MANUSCRIPT
PCL-b-PDEAEMA-b-PPEGMA and their pH-release mechanism, J. Phys. Chem. B. 117 (2013)
PT
13688–13697. [233] T. Wang, C. Chipot, X. Shao, W. Cai, Structural characterization of micelles formed of
RI
cholesteryl-functionalized cyclodextrins, Langmuir. 27 (2010) 91–97.
[234] L.X. Peng, A. Ivetac, A.S. Chaudhari, S. Van, G. Zhao, L. Yu, et al., Characterization of a
SC
clinical polymer-drug conjugate using multiscale modeling, Biopolymers. 93 (2010) 936–951. [235] P. Pi, D. Qin, J. Lan, Z. Cai, X. Yuan, S. Xu, et al., Dissipative particle dynamics simulation on
NU
the nanocomposites delivery system of quantum dots and poly (styrene-b-ethylene oxide) copolymer, Ind. & Eng. Chem. Res. 54 (2015) 6123–6134.
MA
[236] J. Li, Y. Ouyang, X. Kong, J. Zhu, D. Lu, Z. Liu, A multi-scale molecular dynamics simulation of PMAL facilitated delivery of siRNA, RSC Adv. 5 (2015) 68227–68233. [237] G. Srinivas, R. V Mohan, A.D. Kelkar, Polymer micelle assisted transport and delivery of
D
model hydrophilic components inside a biological lipid vesicle: a coarse-grain simulation study,
TE
J. Phys. Chem. B. 117 (2013) 12095–12104. [238] H.M. Ding, Y.Q. Ma, Controlling cellular uptake of nanoparticles with pH-sensitive polymers,
AC CE P
Sci. Rep. 3 (2013) 2804.
[239] C. Sun, T. Tang, H. Uludag, A molecular dynamics simulation study on the effect of lipid substitution on polyethylenimine mediated siRNA complexation, Biomaterials. 34 (2013) 2822– 2833.
[240] Z.X. Liao, S.F. Peng, Y.C. Ho, F.L. Mi, B. Maiti, H.W. Sung, Mechanistic study of transfection of chitosan/DNA complexes coated by anionic poly(γ-glutamic acid), Biomaterials. 33 (2012) 3306–3315. [241] S.F. Peng, M.T. Tseng, Y.C. Ho, M.C. Wei, Z.X. Liao, H.W. Sung, Mechanisms of cellular uptake and intracellular trafficking with chitosan/DNA/poly(γ-glutamic acid) complexes as a gene delivery vector, Biomaterials. 32 (2011) 239–248. [242] M. Kepczynski, D. Jamróz, M. Wytrwal, J. Bednar, E. Rzad, M. Nowakowska, Interactions of a hydrophobically modified polycation with zwitterionic lipid membranes, Langmuir. 28 (2011) 676–688. 58
ACCEPTED MANUSCRIPT
[243] Y.L. Chiu, Y.C. Ho, Y.M. Chen, S.F. Peng, C.J. Ke, K.J. Chen, et al., The characteristics,
PT
cellular uptake and intracellular trafficking of nanoparticles made of hydrophobically-modified chitosan, J. Control. Release. 146 (2010) 152–159.
. un, T. Tang, H. Uludag , Probing the effects of lipid substitution on polycation mediated
RI
[244]
DNA aggregation: A molecular dynamics simulations study, Biomacromolecules. 13 (2012)
SC
2982–2988.
[245] S. Samanta, D. Roccatano, Interaction of curcumin with PEO-PPO-PEO block copolymers: A
NU
molecular dynamics study, J. Phys. Chem. B. 117 (2013) 3250–3257. [246] J. He, C. Chipot, X. Shao, W. Cai, Cyclodextrin-mediated recruitment and delivery of
MA
Amphotericin B, J. Phys. Chem. C. 117 (2013) 11750–11756. [247] P. Shan, J.W. Shen, D.H. Xu, L.Y. Shi, J. Gao, Y.W. Lan, et al., Molecular dynamics study on the interaction between doxorubicin and hydrophobically modified chitosan oligosaccharide,
D
RSC Adv. 4 (2014) 23730-23739.
TE
[248] M. Subashini, P.V. Devarajan, G.S. Sonavane, M. Doble, Molecular dynamics simulation of drug uptake by polymer, J. Mol. Model. 17 (2011) 1141–1147.
AC CE P
[249] R. Anand, S. Ottani, F. Manoli, I. Manet, S. Monti, A close-up on doxorubicin binding to γcyclodextrin: An elucidating spectroscopic, photophysical and conformational study, RSC Adv. 2 (2012) 2346-2357.
[250] R. Namgung, Y.M. Lee, J. Kim, Y. Jang, B.H. Lee, I.S. Kim, et al., Poly-cyclodextrin and polypaclitaxel nano-assembly for anticancer therapy, Nat. Commun. 5 (2014) 3702. [251] Z. Wang, J. Jiang, Dissipative particle dynamics simulation on paclitaxel loaded PEO–PPO– PEO block copolymer micelles, J. Nanosci. Nanotechnol. 14 (2014) 2644–2647. [252] Z. Wang, F. Wang, C. Su, Y. Zhang, Computer simulation of polymer delivery system by dissipative particle dynamics, J. Comput. Theor. Nanosci. 10 (2013) 2323–2327. [253] F. Razmimanesh, S. Amjad-Iranagh, H. Modarress, Molecular dynamics simulation study of chitosan and gemcitabine as a drug delivery system, J. Mol. Model. 21 (2015) 165. [254] S.Y. Nie, W.J. Lin, N. Yao, X.D. Guo, L.J. Zhang, Drug release from pH-Sensitive polymeric micelles with different drug distributions: Insight from coarse-grained simulations, ACS Appl. 59
ACCEPTED MANUSCRIPT
Mater. & Interfaces. 6 (2014) 17668–17678.
PT
[255] Z. Luo, J. Jiang, pH-sensitive drug loading/releasing in amphiphilic copolymer PAE-PEG: Integrating molecular dynamics and dissipative particle dynamics simulations, J. Control.
RI
Release. 162 (2012) 185–193.
[256] J. Hao, Y. Cheng, R.J.K.U. Ranatunga, S. Senevirathne, M.C. Biewer, S.O. Nielsen, et al., A
SC
combined experimental and computational study of the substituent effect on micellar behavior of γ-substituted thermoresponsive amphiphilic poly (ε-caprolactone), Macromolecules. 46
NU
(2013) 4829–4838.
[257] L.S. Zheng, Y.Q. Yang, X.D. Guo, Y. Sun, Y. Qian, L.J. Zhang, Mesoscopic simulations on the
Interface Sci. 363 (2011) 114–121.
MA
aggregation behavior of pH-responsive polymeric micelles for drug delivery, J. Colloid
[258] M.R. Rodríguez-Hidalgo, C. Soto-Figueroa, L. Vicente, Mesoscopic simulation of the drug
TE
(2011) 8224-8230.
D
release mechanism on the polymeric vehicle P(ST-DVB) in an acid environment, Soft Matter. 7
[259] R. Malik, J. Genzer, C.K. Hall, Proteinlike copolymers as encapsulating agents for small-
AC CE P
molecule solutes, Langmuir. 31 (2015) 3518–3526. [260] S.K. Patel, A. Lavasanifar, P. Choi, Molecular dynamics study of the encapsulation capability of a PCL-PEO based block copolymer for hydrophobic drugs with different spatial distributions of hydrogen bond donors and acceptors, Biomaterials. 31 (2010) 1780–1786. [261] S.S. Thakur, H.S. Parekh, C.H. Schwable, Y. Gan, D. Ouyang, Solubilization of poorly soluble drugs, in: Computational Pharmaceutics: Application of Molecular Modelling in Drug Delivery, John Wiley & Sons, 2015: pp. 31-51. [262] S. Bagai, C. Sun, T. Tang, Potential of mean force of polyethylenimine-mediated DNA attraction, J. Phys. Chem. B. 117 (2012) 49–56. [263] H.S. Antila, M. Härkönen, M. Sammalkorpi, Chemistry specificity of DNA–polycation complex salt response: A simulation study of DNA, polylysine and polyethyleneimine, Phys. Chem. Chem. Phys. 17 (2015) 5279–5289. [264] P. Pereira, A.F. Jorge, R. Martins, A.A. Pais, F. Sousa, A. Figueiras, Characterization of 60
ACCEPTED MANUSCRIPT
polyplexes involving small RNA, J. Colloid Interface Sci. 387 (2012) 84–94.
PT
[265] C. Sun, T. Tang, H. Uludag, Molecular dynamics simulations for complexation of dna with 2 kDa PEI reveal profound effect of PEI architecture on complexation, J. Phys. Chem. B. 116
RI
(2012) 2405–2413.
[266] D. Ouyang, H. Zhang, D. Herten, H.S. Parekh, S.C. Smith, Structure, dynamics and energetics
SC
of siRNA - Cationic vector complexation: A molecular dynamics study, J. Phys. Chem. B. 114 (2010) 9220–9230.
NU
[267] D. Ouyang, H. Zhang, H.S. Parekh, S.C. Smith, Structure and dynamics of multiple cationic vectors - siRNA complexation by all-atomic molecular dynamics simulations, J. Phys. Chem. B.
MA
114 (2010) 9231–9237.
[268] A.F. Jorge, R.F.P. Pereira, S.C.C. Nunes, A.J.M. Valente, R.S. Dias, A.A.C.C. Pais, Interpreting the rich behavior of ternary DNA-PEI-Fe(III) complexes, Biomacromolecules. 15 (2014) 478–
D
491.
TE
[269] B. Zhan, K. Shi, Z. Dong, W. Lv, S. Zhao, X. Han, et al., Coarse-grained simulation of polycation/DNA-like complexes: Role of neutral block, Mol. Pharm. 12 (2015) 2834–2844.
AC CE P
[270] M. Zhao, J. Zhou, C. Su, L. Niu, D. Liang, B. Li, Complexation behavior of oppositely charged polyelectrolytes: Effect of charge distribution, J. Chem. Phys. 142 (2015) 204902. [271] J. Ziebarth, Y. Wang, Coarse-grained molecular dynamics simulations of DNA condensation by block copolymer and formation of core-corona structures, J. Phys. Chem. B. 114 (2010) 6225– 6232.
[272] Q. Luo, Y. Wang, H. Yang, C. Liu, Y. Ding, H. Xu, et al., Modeling the interaction of interferon α-1b to bovine serum albumin as a drug delivery system, J. Phys. Chem. B. 118 (2014) 8566–8574. [273] R. Vijayaraj, S. Van Damme, P. Bultinck, V. Subramanian, Theoretical studies on the transport mechanism of 5-fluorouracil through cyclic peptide based nanotubes, Phys. Chem. Chem. Phys. 15 (2013) 1260–1270. [274] H. Liu, J. Chen, Q. Shen, W. Fu, W. Wu, Molecular insights on the cyclic peptide nanotubemediated transportation of antitumor drug 5-fluorouracil, Mol. Pharm. 7 (2010) 1985–1994. 61
ACCEPTED MANUSCRIPT
[275]
. Ł. Durzyńska, Ł. Przysiecka, . Nawrot, . Barylski, G. Nowicki, A. Warowicka, et al.,
iral
PT
and other cell-penetrating peptides as vectors of therapeutic agents in medicine, J. Pharmacol. Exp. Ther. 354 (2015) 32-42.
RI
[276] Y. Krishnan, F.C. Simmel, Nucleic acid based molecular devices, Angew. Chemie Int. Ed. 50 (2011) 3124–3156.
SC
[277] C.K. McLaughlin, G.D. Hamblin, H.F. Sleiman, Supramolecular DNA assembly, Chem. Soc. Rev. 40 (2011) 5647–5656.
NU
[278] J.I. Cutler, E. Auyeung, C.A. Mirkin, Spherical nucleic acids, J. Am. Chem. Soc. 134 (2012) 1376–1391.
MA
[279] S.N. Barnaby, T.L. Sita, S.H. Petrosko, A.H. Stegh, C.A. Mirkin, Therapeutic applications of spherical nucleic acids, in: Nanotechnology-Based Precision Tools for the Detection and Treatment of Cancer, Springer, 2015: pp. 23–50.
D
[280] K.A. Afonin, W.K. Kasprzak, E. Bindewald, M. Kireeva, M. Viard, M. Kashlev, B.A. Shapiro,
(2014) 1731–1741.
TE
In silico design and enzymatic synthesis of functional RNA nanoparticles, Acc. Chem. Res. 47
AC CE P
[281] S.R. Badu, R. Melnik, M. Paliy, S. Prabhakar, A. Sebetci, B.A. Shapiro, Modeling of RNA nanotubes using molecular dynamics simulation, Eur. Biophys. J. 43 (2014) 555–564. [282] S. Juul, F. Iacovelli, M. Falconi, S.L. Kragh, B. Christensen, R. Frøhlich, et al., Temperaturecontrolled encapsulation and release of an active enzyme in the cavity of a self-assembled DNA nanocage, ACS Nano. 7 (2013) 9724–9734. [283] A.O. Elzoghby, W.M. Samy, N.A. Elgindy, Protein-based nanocarriers as promising drug and gene delivery systems, J. Control. Release. 161 (2012) 38–49. [284] A.O. Elzoghby, W.M. Samy, N.A. Elgindy, Albumin-based nanoparticles as potential controlled release drug delivery systems, J. Control. Release. 157 (2012) 168–182. [285] M. Pourmousa, M. Karttunen, Early stages of interactions of cell-penetrating peptide penetratin with a DPPC bilayer, Chem. Phys. Lipids. 169 (2013) 85–94. [286] M. Pourmousa, J. Wong-Ekkabut, M. Patra, M. Karttunen, Molecular dynamic studies of transportan interacting with a DPPC lipid bilayer, J. Phys. Chem. B. 117 (2012) 230–241. 62
ACCEPTED MANUSCRIPT
[287] X. He, M. Lin, B. Sha, S. Feng, X. Shi, Z. Qu, et al., Coarse-grained molecular dynamics
PT
studies of the translocation mechanism of polyarginines across asymmetric membrane under tension, Sci. Rep. 5 (2015) 12808.
RI
[288] M. Islami, F. Mehrnejad, F. Doustdar, M. Alimohammadi, M. Khadem-Maaref, M. MirDerikvand, et al., Study of orientation and penetration of LAH4 into lipid bilayer membranes:
SC
pH and composition dependence, Chem. Biol. & Drug Des. 84 (2014) 242–252. [289] Z.L. Li, H.M. Ding, Y.Q. Ma, Translocation of polyarginines and conjugated nanoparticles
NU
across asymmetric membranes, Soft Matter. 9 (2012) 1281–1286. [290] R.S. Teixeira, T.F. Cova, S.M. Silva, R. Oliveira, M.J. Araújo, E.F. Marques, et al., Lysine-
MA
based surfactants as chemical permeation enhancers for dermal delivery of local anesthetics, Int. J. Pharm. 474 (2014) 212–222.
[291] N. Todorova, C. Chiappini, M. Mager, B. Simona, I.I. Patel, M.M. Stevens, I. Yarovsky,
D
Surface presentation of functional peptides in solution determines cell internalization efficiency
TE
of TAT conjugated nanoparticles, Nano Lett. 14 (2014) 5229–5237. [292] Y. Li, D. Feng, X. Zhang, D. Cao, Design strategy of cell-penetrating copolymers for high
AC CE P
efficient drug delivery, Biomaterials. 52 (2015) 171–179. [293] A. Sanchez-Sanchez, S. Akbari, A.J. Moreno, F. L. Verso, A. Arbe, J. Colmenero, J.A. Pomposo, Design and preparation of single-chain nanocarriers mimicking disordered proteins for combined delivery of dermal bioactive cargos, Macromol. Rapid Commun. 34 (2013) 1681– 1686.
[294] L. Chen, T. Jiang, C. Cai, L. Wang, J. Lin, X. Cao, Polypeptide-based “smart” micelles for dual-drug delivery: A combination study of experiments and simulations, Adv. Healthcare Mater. 3 (2014) 1508–1517. [295] E. Jabbari, X. Yang, S. Moeinzadeh, X. He, Drug release kinetics, cell uptake, and tumor toxicity of hybrid VVVVVVKK peptide-assembled polylactide nanoparticles, Eur. J. Pharm. Biopharm. 84 (2013) 49–62. [296] X.D. Guo, L.J. Zhang, Z.M. Wu, Y. Qian, Dissipative particle dynamics studies on microstructure of pH-sensitive micelles for sustained drug delivery, Macromolecules. 43 (2010) 63
ACCEPTED MANUSCRIPT
7839–7844.
PT
[297] X. Tian, F. Sun, X.R. Zhou, S.Z. Luo, L. Chen, Role of peptide self-assembly in antimicrobial peptides, J. Pept. Sci. 21 (2015) 530–539.
RI
[298] I.W. Fu, C.B. Markegard, H.D. Nguyen, Solvent effects on kinetic mechanisms of selfassembly by peptide amphiphiles via molecular dynamics simulations, Langmuir. 31 (2014)
SC
315–324.
[299] I.W. Fu, C.B. Markegard, B.K. Chu, H.D. Nguyen, The role of electrostatics and temperature on
NU
morphological transitions of hydrogel nanostructures self-assembled by peptide amphiphiles via molecular dynamics simulations, Adv. Healthcare Mater. 2 (2013) 1388–1400.
MA
[300] N. Thota, Z. Luo, Z. Hu, J. Jiang, Self-assembly of amphiphilic peptide (AF) 6H5K15: Coarsegrained molecular dynamics simulation, J. Phys. Chem. B. 117 (2013) 9690–9698. [301]
. Garc a-Fandi o, J.R. Granja, Effect of organochloride guest molecules on the stability of
D
homo/hetero self-assembled α, γ-cyclic peptide structures: A computational study toward the
TE
control of nanotube length, J. Phys. Chem. C. 117 (2013) 10143–10162. [302] R. Bakry, R.M. Vallant, M. Najam-ul-Haq, M. Rainer, Z. Szabo, C.W. Huck, G.K. Bonn,
AC CE P
Medicinal applications of fullerenes, Int. J. Nanomed. 2 (2007) 639-649. [303] M. Martincic, G. Tobias, Filled carbon nanotubes in biomedical imaging and drug delivery, Expert Opin. Drug Deliv. 12 (2015) 563–581. [304] V.V. Chaban, T.I. Savchenko, S.M. Kovalenko, O.V. Prezhdo, Heat-driven release of a drug molecule from carbon nanotubes: A molecular dynamics study, J. Phys. Chem. B. 114 (2010) 13481–13486.
[305] N. Saikia, A.N. Jha, R.C. Deka, Dynamics of fullerene-mediated heat-driven release of drug molecules from carbon nanotubes, J. Phys. Chem. Lett. 4 (2013) 4126–4132. [306] U. Arsawang, O. Saengsawang, T. Rungrotmongkol, P. Sornmee, K. Wittayanarakul, T. Remsungnen, et al., How do carbon nanotubes serve as carriers for gemcitabine transport in a drug delivery system?, J. Mol. Graph. & Model. 29 (2011) 591–596. [307] Y. Li, T. Hou, Computational simulation of drug delivery at molecular level, Curr. Med. Chem. 17 (2010) 4482–4491. 64
ACCEPTED MANUSCRIPT
[308] M. Chehel Amirani, T. Tang, Binding of nucleobases with graphene and carbon nanotube: A
PT
review of computational studies, J. Biomol. Struct. Dyn. 33 (2015) 1567–1597. [309] M. Santosh, S. Panigrahi, D. Bhattacharyya, A.K. Sood, P.K. Maiti, Unzipping and binding of
RNA delivery, J. Chem. Phys. 136 (2012) 065106.
RI
small interfering RNA with single walled carbon nanotube: A platform for small interfering
SC
[310] B. Nandy, M. Santosh, P.K. Maiti, Interaction of nucleic acids with carbon nanotubes and dendrimers, J. Biosci. 37 (2012) 457–474.
NU
[311] B.D. Chen, C.L. Yang, J.S. Yang, M.S. Wang, X.G. Ma, Dynamic mechanism of HIV replication inhibitor peptide encapsulated into carbon nanotubes, Curr. Appl. Phys. 13 (2013)
MA
1001–1007.
[312] J.W. Shen, T. Tang, X.H. Wei, W. Zheng, T.Y. Sun, Z. Zhang, L. Liang, Q. Wang, On the loading mechanism of ssDNA into carbon nanotubes, RSC Adv. 5 (2015) 56896–56903.
TE
(2012) 4945–4952.
D
[313] N. Wu, Q. Wang, B. Arash, Ejection of DNA molecules from carbon nanotubes, Carbon. 50
[314] J. Li, L. Jiang, X. Zhu, Computational studies of the binding mechanisms of fullerenes to
AC CE P
human serum albumin, J. Mol. Model. 21 (2015) 177. [315] J. Wong-Ekkabut, S. Baoukina, W. Triampo, I.M. Tang, D.P. Tieleman, L. Monticelli, Computer simulation study of fullerene translocation through lipid membranes, Nat. Nanotechnol. 3 (2008) 363–368. [316] J. Barnoud, L. Urbini, L. Monticelli, C60 fullerene promotes lung monolayer collapse, J. R. Soc. Interface. 12 (2015) 20140931. [317] N. Nisoh, M. Karttunen, L. Monticelli, J. Wong-Ekkabut, Lipid monolayer disruption caused by aggregated carbon nanoparticles, RSC Adv. 5 (2015) 11676–11685. [318] A.A. Skandani, R. Zeineldin, M. Al-Haik, Effect of chirality and length on the penetrability of single-walled carbon nanotubes into lipid bilayer cell membranes, Langmuir. 28 (2012) 7872– 7879. [319] H. Lee, Membrane penetration and curvature induced by single-walled carbon nanotubes: The effect of diameter, length, and concentration, Phys. Chem. Chem. Phys. 15 (2013) 16334–16340. 65
ACCEPTED MANUSCRIPT
[320] S. Baoukina, L. Monticelli, D.P. Tieleman, Interaction of pristine and functionalized carbon
PT
nanotubes with lipid membranes, J. Phys. Chem. B. 117 (2013) 12113–12123. [321] C.C. Chiu, W. Shinoda, R.H. DeVane, S.O. Nielsen, Effects of spherical fullerene nanoparticles
RI
on a dipalmitoyl phosphatidylcholine lipid monolayer: A coarse grain molecular dynamics approach, Soft Matter. 8 (2012) 9610-9616.
SC
[322] A. Jusufi, R.H. DeVane, W. Shinoda, M.L. Klein, Nanoscale carbon particles and the stability of lipid bilayers, Soft Matter. 7 (2011) 1139–1146.
NU
[323] H. Lee, Interparticle dispersion, membrane curvature, and penetration induced by single-walled carbon nanotubes wrapped with lipids and PEGylated lipids, J. Phys. Chem. B. 117 (2013)
MA
1337–1344.
[324] S. Kraszewski, A. Bianco, M. Tarek, C. Ramseyer, Insertion of short amino-functionalized single-walled carbon nanotubes into phospholipid bilayer occurs by passive diffusion, PLoS
D
One. 7 (2012) e40703.
TE
[325] S. Kraszewski, F. Picaud, I. Elhechmi, T. Gharbi, C. Ramseyer, How long a functionalized carbon nanotube can passively penetrate a lipid membrane, Carbon. 50 (2012) 5301–5308.
AC CE P
[326] H. Lee, Dispersion and bilayer interaction of single-walled carbon nanotubes modulated by covalent and noncovalent PEGylation, Mol. Simul. 41 (2014) 1254–1263. [327] A. Di Crescenzo, M. Aschi, A. Fontana, Toward a better understanding of steric stabilization when using block copolymers as stabilizers of single-walled carbon nanotubes (SWCNTs) aqueous dispersions, Macromolecules. 45 (2012) 8043–8050. [328] A. Sridhar, B. Srikanth, A. Kumar, A.K. Dasmahapatra, Coarse-grain molecular dynamics study of fullerene transport across a cell membrane, J. Chem. Phys. 143 (2015) 24907. [329] E. Sarukhanyan, G. Milano, D. Roccatano, Coating mechanisms of single-walled carbon nanotube by linear polyether surfactants: Insights from computer simulations, J. Phys. Chem. C. 118 (2014) 18069–18078. [330] H. Lee, Molecular dynamics studies of PEGylated single-walled carbon nanotubes: The effect of PEG size and grafting density, J. Phys. Chem. C. 117 (2013) 26334–26341. [331] H. Shrestha, R. Bala, S. Arora, Lipid-based drug delivery systems, J. Pharm. 2014 (2014) 66
ACCEPTED MANUSCRIPT
801820.
PT
[332] S. Mashaghi, T. Jadidi, G. Koenderink, A. Mashaghi, Lipid nanotechnology, Int. J. Mol. Sci. 14 (2013) 4242–4282.
RI
[333] A.L. Klibanov, K. Maruyama, V.P. Torchilin, L. Huang, Amphipathic polyethyleneglycols effectively prolong the circulation time of liposomes, FEBS Lett. 268 (1990) 235–237. .
issanen, N.
zydłowska, A. Bunker, M. Kumorek, D. amróz, et al.,
SC
[334] M. Dzieciuch,
PEGylated liposomes as carriers of hydrophobic porphyrins, J. Phys. Chem. B. 119 (2015)
NU
6646–6657.
[335] J. Lehtinen, A. Magarkar, M. Stepniewski, S. Hakola, M. Bergman, T. Róg, et al., Analysis of
MA
cause of failure of new targeting peptide in PEGylated liposome: Molecular modeling as rational design tool for nanomedicine, Eur. J. Pharm. Sci. 46 (2012) 121–130. [336] A. Magarkar, T. Róg, A. Bunker, Molecular dynamics simulation of PEGylated membranes
TE
15541–15549.
D
with cholesterol: Building toward the DOXIL formulation, J. Phys. Chem. C. 118 (2014)
[337] K. Laasonen, M.L. Klein, Molecular dynamics simulations of the structure and ion diffusion in
AC CE P
poly(ethylene oxide), J. Chem. Soc., Faraday Trans. 91 (1995) 2633–2638. [338] F. Müller-Plathe, W.F. van Gunsteren, Computer simulation of a polymer electrolyte: Lithium iodide in amorphous poly(ethylene oxide), J. Chem. Phys. 103 (1995) 4745-4756. [339] C. Özdemir Dinç, G. Kibarer, A. Güner, Solubility profiles of poly(ethylene glycol)/solvent systems. II. comparison of thermodynamic parameters from viscosity measurements, J. Appl. Polym. Sci. 117 (2010) 1100–1119. [340] O. Borodin, G.D. Smith, R.L. Jaffe, Ab initio quantum chemistry and molecular dynamics simulations studies of LiPF6/poly(ethylene oxide) interactions, J. Comput. Chem. 22 (2001) 641–654. [341] M. Nowakowska, K. Nawalany, M. Kepczynski, Z. Krawczyk, Novel nanostructural hybride materials for photodynamic theraphy, Macromol. Symp. 279 (2009) 132–137. [342] I.F. Hakem, J. Lal, M.R. Bockstaller, Binding of monovalent ions to PEO in Solution: Relevant parameters and structural transitions, Macromolecules. 37 (2004) 8431–8440. 67
ACCEPTED MANUSCRIPT
[343] A. Magarkar, E. Karakas, M. Stepniewski, T. Róg, A. Bunker, Molecular dynamics simulation
PT
of PEGylated bilayer interacting with salt ions: A model of the liposome surface in the bloodstream, J. Phys. Chem. B. 116 (2012) 4212–4219.
RI
[344] M. Stepniewski, M. Pasenkiewicz-Gierula, T. Róg, R. Danne, A. Orlowski, M. Karttunen, et al., Study of PEGylated lipid layers as a model for PEGylated liposome surfaces: molecular
SC
dynamics simulation and Langmuir monolayer studies, Langmuir. 27 (2011) 7788–7798. [345] M. Pannuzzo, D.H. De Jong, A. Raudino, S.J. Marrink, Simulation of polyethylene glycol and
NU
calcium-mediated membrane fusion, J. Chem. Phys. 140 (2014) 124905. [346] A. Magarkar, T. Róg, A. Bunker, Molecular dynamics simulation of inverse-phosphocholine [347] N.D. Winter,
.K. . Murphy, T.
MA
lipids, J. Phys. Chem. C. 118 (2014) 19444–19449.
O’Halloran, G. . chatz, Development and modeling of
arsenic-trioxide-loaded thermosensitive liposomes for anticancer drug delivery, J. Liposome
D
Res. 21 (2011) 106–115.
TE
[348] E.K. Perttu, A.G. Kohli, F.C. Szoka, Inverse-phosphocholine lipids: A remix of a common phospholipid, J. Am. Chem. Soc. 134 (2012) 4485–4488.
AC CE P
[349] H. Lee, R.W. Pastor, Coarse-grained model for PEGylated lipids: Effect of PEGylation on the size and shape of self-assembled structures, J. Phys. Chem. B. 115 (2011) 7830–7837. [350] J.C. Shillcock, Spontaneous vesicle self-assembly: A mesoscopic view of membrane dynamics, Langmuir. 28 (2011) 541–547. [351] W. Shinoda, D.E. Discher, M.L. Klein, S.M. Loverde, Probing the structure of PEGylated-lipid assemblies by coarse-grained molecular dynamics, Soft Matter. 9 (2013) 11549-11556. [352] J.J. Janke, W.F.D. Bennett, D.P. Tieleman, Oleic acid phase behavior from molecular dynamics simulations, Langmuir. 30 (2014) 10661–10667. [353] N. Dan, Nanostructured lipid carriers: Effect of solid phase fraction and distribution on the release of encapsulated materials, Langmuir. 30 (2014) 13809–13814. [354] N. Dan, Drug release through liposome pores, Colloids Surf. B 126 (2015) 80–86. [355] C. Vitorino, J. Almeida, L.M. Gonçalves, A J. Almeida, J.J. Sousa,
A.A.C.C. Pais, Co-
encapsulating nanostructured lipid carriers for transdermal application: From experimental 68
ACCEPTED MANUSCRIPT
design to the molecular detail, J. Control. Release. 167 (2013) 301–314.
PT
[356] J.P.M. Jämbeck, E.S.E. Eriksson, A. Laaksonen, A.P. Lyubartsev, L. A Eriksson, Molecular dynamics studies of liposomes as carriers for photosensitizing drugs: Development, validation,
RI
and simulations with a coarse-grained model, J. Chem. Theory Comput. 10 (2014) 5–13. [357] B. van Hoof, A.J. Markvoort, R.A. van Santen, P.A.J. Hilbers, Molecular simulation of protein
SC
encapsulation in vesicle formation, J. Phys. Chem. B. 118 (2014) 3346–3354. [358] C.D. Novina, P.A. Sharp, The RNAi revolution, Nature. 430 (2004) 161–164. Wang,
Optimizing
drug
delivery :
haracterization
NU
[359] S.L.
of
DLin-KC2-DMA
/
distearoylphosphatidylserine by 31P and 2H NMR spectroscopy, Diss. Sci. Dep. Mol. Biol.
MA
Biochem, (2013).
[360] D. Rozmanov, S. Baoukina, D.P. Tieleman, Density based visualization for molecular simulation, Faraday Discuss. 169 (2014) 225–243.
D
[361] A. Martín-Molina, G. Luque-Caballero, J. Faraudo, M. Quesada-Pérez, J. Maldonado-
(2014) 172–185.
TE
Valderrama, Adsorption of DNA onto anionic lipid surfaces, Adv. Colloid Interface Sci. 206
AC CE P
[362] G. Ajnai, A. Chiu, T. Kan, C.C. Cheng, T.H. Tsai, J. Chang, Trends of gold nanoparticle-based drug delivery system in cancer therapy, J. Exp. & Clin. Med. 6 (2014) 172–178. [363] A.C. Yang, C.I. Weng, Structural and dynamic properties of water near monolayer-protected gold clusters with various alkanethiol tail groups, J. Phys. Chem. C. 114 (2010) 8697–8709. [364] G. Milano, G. Santangelo, F. Ragone, L. Cavallo, A. Di Matteo, Gold nanoparticle/polymer interfaces: All atom structures from molecular dynamics simulations, J. Phys. Chem. C. 115 (2011) 15154–15163. [365] J. Yu, M.L. Becker, G.A. Carri, The influence of amino acid sequence and functionality on the binding process of peptides onto gold surfaces, Langmuir. 28 (2011) 1408–1417. [366] K.H. Lee, F.M. Ytreberg, Effect of gold nanoparticle conjugation on peptide dynamics and structure, Entropy. 14 (2012) 630–641. [367] R.C. Van Lehn, M. Ricci, P.H.J. Silva, P. Andreozzi, J. Reguera, K. Voïtchovsky, et al., Lipid tail protrusions mediate the insertion of nanoparticles into model cell membranes, Nat. Commun. 69
ACCEPTED MANUSCRIPT
5 (2014) 4482.
PT
[368] R.C. Van Lehn, A. Alexander-Katz, Free energy change for insertion of charged, monolayerprotected nanoparticles into lipid bilayers, Soft Matter. 10 (2014) 648–658.
RI
[369] R.C. Van Lehn, A. Alexander-Katz, Structure of mixed-monolayer-protected nanoparticles in aqueous salt solution from atomistic molecular dynamics simulations, J. Phys. Chem. C. 117
SC
(2013) 20104–20115.
[370] R.C. Van Lehn, A. Alexander-Katz, Ligand-mediated short-range attraction drives aggregation
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of charged monolayer-protected gold nanoparticles, Langmuir. 29 (2013) 8788–8798. [371] S.J. Marrink, A.H. de Vries, D.P. Tieleman, Lipids on the move: Simulations of membrane
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pores, domains, stalks and curves, Biochim. Biophys. Acta. 1788 (2009) 149–168.
Physicochemical characterization
Properties of Interest
Self-assembly of DDS components Structure and dynamics of drug-DDS complex Drug loading Drug distribution Drug-DDS interactions Functionalization Drug leakage Aggregation
Circulation
Interaction with biomolecules Stability 70
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Interactions with membrane
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Interaction with membrane proteins Intracellular
Drug-DDS dissociation
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Interaction with endogenous molecules Nucleus translocation
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Experimental Technique
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Table 1: Questions relevant to drug delivery system design that can be answered by computational simulations. Properties
Dynamic light scattering
DLS
Particle size, aggregation
Laser diffraction
LD
Particle size, aggregation
Transmission electron microscopy
TEM
Particle size, dispersity, shape, surface morphology
Scanning electron microscopy
SEM
Particle size, dispersity, shape, surface morphology
Atomic force microscopy
AFM
Fluorescence
FLIM
Particle size, dispersity, shape, surface morphology Drug release
Zeta potential
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Surface charge
Gel electrophoresis
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Encapsulation efficiency of nucleic acids Encapsulation efficiency
Ultraviolet-visible light spectroscopy
UV-vis
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X-ray scattering
Repeat spacing, chemical connectivity
SAXS and WAXS -
NMR
Electron spin resonance spectroscopy
ESR
DSC ITC
Carrier fluidity, gadolinium concentration, microviscosity, micropolarity Viscosity, polarity, concentration, drug release Phase transitions Phase transitions
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Crystallinity, molecular mobility, chemical connectivity
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Table 2: Experimental methods used to study drug delivery systems.
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Highlights Nanoparticles have the potential to improve drug delivery and enable precision medicine.
Nanoparticles for drug delivery can be based on many different chemistries.
We review experimental and computational approaches to study the structure and properties of
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