Article pubs.acs.org/molecularpharmaceutics
Assessing the Efficiency of Polymeric Excipients by Atomistic Molecular Dynamics Simulations Prateek K. Jha and Ronald G. Larson* Department of Chemical Engineering, University of Michigan, Ann Arbor, Michigan 48109-2136, United States ABSTRACT: We have performed all-atom molecular dynamics simulations of aqueous solutions of model oligomers of hydroxypropyl methylcellulose (HPMC) and hydroxypropyl methylcellulose acetate succinate (HPMCAS) excipients interacting with a representative poorly soluble active pharmaceutical ingredient (API), phenytoin. Simulations reveal formation of excipient-API complexes for some of the oligomers, which results in a reduction of API aggregation. API aggregation and diffusivity decreased with an increase in excipient content. Excipients form a “gel-like” phase spanning the simulation box beyond ∼10 wt %; API diffusivity within this gel phase is much smaller than API diffusivity without excipient, and decreases exponentially, by 5 orders of magnitude, with increased polymer concentration. Substantial differences are observed with variations in methyl, hydroxypropyl, acetate, and succinate substitution levels in the model oligomers and with the deprotonation state of succinate groups, with strongest interactions with hydrophobic phenytoin observed in the case of acetate substitution. These are used to develop quantitative measures of excipient-API interactions and excipient efficiency in the inhibition of API aggregation. We also find that for model oligomers based on Methocel E (manufactured by Dow Pharma & Food Solutions) chemistry, oligomers of length 10 monomers and simulation boxes of size 7 nm give results similar to those for longer oligomers and bigger boxes. The quantitative measures developed in this study are expected to prove useful as computational screening tools in excipient design. KEYWORDS: cellulosic polymers, API, HPMC, HPMCAS, molecular dynamics
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excipients;13 some of the currently marketed products containing HPMC/HPMCAS for solubility enhancement are Certican, Nivadil, Rezulin, Intelence, Zelboraf, Crestor, Prograf, and Sporanox. The presence of three substitution positions on each D-glucose monomeric unit (Figure 1) gives rise to a large design space of cellulosic polymers, both in terms of the substitution patterns and the degrees of substitutions of different substituents. However, the large design space of cellulosic polymers also poses a technological challenge of designing polymers with optimal properties for a given API. Molecular interactions between polymers and APIs in the solid and dissolved states and the mechanisms of precipitation/ crystallization inhibition are not well understood, which hinders the development and commercialization of polymeric excipients. Several physical characteristics of a good excipient have been identified in prior studies. First, an excipient must show affinity for the API via specific interactions (e.g., hydrogen-bonding) or by weak van der Waals interactions. As a rule of thumb, an excipient and an API of similar hydrophobicity are miscible with each other, which imply hydrophobic excipients are suitable for poorly soluble (hydrophobic) APIs.14 Second,
INTRODUCTION Poorly soluble APIs (active pharmaceutical ingredients) have limited bioavailability, which necessitates the use of drug delivery technologies that either enhance API solubility or maintain supersaturation by inhibiting precipitation and crystallization of APIs.1,2 Solid dispersions3−5 fall into the latter category, whereby molecular mixing of the amorphous API and excipient maintains API in amorphous form in final dosage form and results in a supersaturated API solution upon dissolution in the gastrointestinal (GI) tract. Though the earliest studies on solid dispersions used low molecular weight compounds such as urea as excipients,6 high molecular weight polymers are preferred as excipients in newer generations of solid dispersions. Polymers result in increased viscosity on dissolution, thereby slowing the release of APIs and inhibit possible recrystallization. Moreover, the multifunctionality of polymers enables control of polymer-API interactions by varying the substituents on the polymer backbone. Examples of polymers used in commercial excipients are PVP (polyvinylpyrrolidone), PEG (poly(ethylene glycol)), HPC (hydroxypropyl cellulose), HPMC (hydroxypropyl methylcellulose or hypromellose), and HPMCAS (hydroxypropyl methylcellulose acetate succinate).4 Cellulosic polymers are generally superior to alternative excipients in the inhibition of API crystallization/precipitation, as established by many experimental studies over the past decade.7−12 Commercial grades of HPMC and HPMCAS are widely used as © 2014 American Chemical Society
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models is the unreliability of available solubility parameter estimates. One promising route is to determine these parameters by molecular dynamics (MD) simulations, which has been explored in recent studies.20,21 Molecular structures of solid dispersions have also been determined by simulated annealing22 simulations. Theoretical studies of excipient-API interactions in a dissolved state are relatively scarce. Tomassone and colleagues have studied polymer-drug interactions on the surface of drug crystals by MD simulations.23,24 Also, a recent simulation study has employed molecular docking and MD simulations to investigate molecular interactions between bicalutamide and three excipients.25 These simulations have, however, been restricted to short time scales (CO and −NH− groups of phenytoin molecules (red lines). Bold and dashed lines represent 3.3 wt % phenytoin without HPMC and 3.3 wt % phenytoin with 10 wt % HPMC, respectively. Small r region of g(r) is zoomed in the semilogarithmic plot in inset.
specific hydrogen-bonding interactions between API molecules. The average numbers of hydrogen-bonds per API molecule are 0.29(1) and 0.10(2) in the absence and presence of HPMC, 1681
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respectively. All these hydrogen bonds are formed between >CO and −NH− groups of API molecules, resulting in a dominant peak at r ≈ 0.2 nm in the RDF between >CO and −NH− groups. Note that the RDF between >CO and −NH− groups has multiple peaks because two >CO and two −NH− groups are present per phenytoin molecule, and they all participate in hydrogen-bonding. Such features are absent in RDFs between other chemical moieties in the phenytoin molecule (e.g., between phenyl groups; not shown), meaning that they do not interact via specific interactions. However, they can still interact via nonspecific van der Waals (hydrophobic) interactions, which is the main driving force for API aggregation. Similar hydrogen bonding interactions between >CO and −NH− groups have been reported in recent studies of felodipine41 and flutamide.42 Next, we compare the behavior of systems containing HPMC and HPMCAS oligomers to understand the effect of additional acetate or succinate substitution on HPMC. Although HPMCAS usually contains both acetate and succinate substituents, here we consider special cases in which only one of these two groups is present. Here “HPMCAc” represents an oligomer containing additional acetate groups on HPMC; HPMCSu and HPMCSu(−) contain additional protonated and deprotonated succinate groups, respectively, on HPMC. Snapshots in Figure 7 show a substantial difference in the
Figure 8. (a) Excipient-API RDF and (b) API-API RDF of systems containing 3.3 wt % phenytoin and 10 wt % of different HPMCAS oligomers.
HPMCAc appears to be the best excipient for phenytoin because it shows the highest affinity for phenytoin (Figure 8a) and results in maximum inhibition of phenytoin aggregation (Figure 8b). Assuming that the ability of an excipient to inhibit aggregation correlates with its ability to inhibit crystallization, our results are in accord with a previous finding43 that higher acetyl content and lower succinyl content are best for crystallization inhibition of poorly soluble APIs. Note, however, that commercial HPMCAS excipients usually contain both acetate and succinate groups, and the dissolution behavior usually shows nonmonotonic dependence on the acetate-tosuccinate ratio.43 The presence of succinate groups results in an amphiphilic character of HPMCAS; hydrophobic regions facilitate association with API and hydrophilic regions gives rise to the formation of stable hydrated nanostructures with improved dissolution properties.8 In contrast, we have considered extreme cases with no succinate and with no acetate groups in HPMCAc and HPMCSu/HPMCSu(−), respectively. More work is needed, therefore, to explore the effects of combining both acetate and succinate groups in the same excipient. We now discuss results of simulations containing Methocel E oligomers (Table 2) with phenytoin molecules. To mimic the behavior of solid dispersions during dissolution, we simulate a mixture of 10-monomer oligomers and phenytoin molecules at different weight percent of water. For each of these simulations, we begin with the equilibrated state containing 10 wt % oligomer and 3.3 wt % phenytoin and remove appropriate number of water molecules to achieve the desired weight percent of water. The resulting system is then subjected to reequilibration (to relax the system after removal of water) and production steps as described in the Simulations section. This results in substantial reduction in simulation box volume, since water molecules have been removed. An alternative and perhaps more realistic scheme of starting with a water-free system and gradually adding water molecules is expected to result in similar final states but may require longer equilibration times. It is worth noting that unlike other systems considered in this paper, simulation box lengths are less than the contour lengths of oligomers (≈5.4 nm) at the smaller weight percent of water.
Figure 7. Simulation snapshots of systems containing 3.3 wt % phenytoin and 10 wt % of three different HPMCAS oligomers. Simulation box lengths are approximately equal to 7 nm for these systems.
behavior of these oligomers, which is a consequence of differences in their hydrophobicity (Table 1). HPMC and HPMCAc are more hydrophobic and form a gel-like phase, whereas HPMCSu(−) is less hydrophobic and does not gel at this concentration. The order of hydrophobicity of these oligomers (Table 1) correlates positively with their affinity for the API as shown by the excipient-API RDF in Figure 8a. HPMCSu(−) shows the least affinity for phenytoin followed by HPMCSu. HPMCAc and HPMC have similar hydrophobicity and show similar affinity for phenytoin. However, the API-API RDF in Figure 8b shows an interesting trend. Although HPMC has stronger affinity for phenytoin than HPMCSu and HPMCSu(−), this does not translate into a reduction in API aggregation, as indicated by higher peak of the API-API RDF for HPMC. Therefore, the notion that hydrophobic excipients are best suited for hydrophobic APIs14 is not completely valid and specific interactions between the excipient and API must also be considered. It is interesting to note that the behavior of oligomers containing deprotonated succinate groups is substantially different from those containing protonated succinate groups. Therefore, excipients containing succinate groups should respond to pH changes in the stomach and GI tract, as a result of changes in the deprotonation level. 1682
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(Figure 10b).28,44 The actual magnitudes of the diffusion coefficient were found to be sensitive to variations in the system size (e.g., by doubling the number of molecules for the same weight percent of API and excipient), which arise due to the use of periodic boundary conditions.45 However, API diffusion coefficients for the systems containing the same weight percent of 10-monomer and 20-monomer Methocel E oligomers were almost identical for similar system sizes. Finally, we discuss the effects of system size and oligomer length in our simulations. In Figure 11, we compare excipient-
Figure 9 shows simulation snapshots of systems containing varying amounts of water. We did not observe aggregation of
Figure 9. Simulation snapshots of systems containing 10-monomer Methocel E oligomers and phenytoin molecules, with varying weight percent of water. All systems contain the same number of oligomer and phenytoin molecules. Weights of oligomers and phenytoin molecules are in the ratio 3:1. Simulation box lengths are approximately 3.3, 4, and 6 nm, for boxes containing no water, 40 wt % water, and 80 wt % water, respectively.
phenytoin molecules in the water-free state, which is an indication of molecular miscibility of Methocel E and phenytoin in the solid state. An increase in water weight percent results in a solid → gel transition, together with a corresponding increase in API mobility (Figure 10a). However,
Figure 11. Effect of system size and oligomer length on the excipientAPI RDF for 10 wt % excipient and 3.3 wt % API. Red lines are results for simulations containing HPMC oligomer (Figure 2) for different system sizes, with simulation box lengths approximately equal to 6.8 nm (dotted line) and 10.7 nm (solid line). Blue lines are results for simulations containing 10-monomer Methocel E-oligomers, with simulation box lengths approximately equal to 7 nm (dotted line) and 14.9 nm (solid line). The black line is the result for system containing a 20-mer Methocel E-oligomer with simulation box length approximately equal to 14.9 nm.
API RDFs for two systems containing HPMC oligomers, at the same weight percent of both the excipient and API, but with the larger system (red solid line) containing roughly four times the number of molecules of the smaller system (red dotted line). Both systems form a gel-like phase spanning the simulation box, indicating that the gel formation is not an artifact of the finite size of systems. Figure 11 also shows results of simulations containing 10-monomer (blue lines) and 20monomer oligomers of Methocel E (black line). These systems show very similar behavior, as also evident from snapshots in Figure 12. Small differences (up to 12% in the RDFs) between the two systems result from the different numbers of end monomers, which are more hydrophilic due to the presence of an additional −OH group at the 1 or 4 position (Figure 1). In general, excipient-API RDFs show small differences in the peak height and position for both HPMC and Methocel E systems but with a longer tail for the larger system (solid lines) than for
Figure 10. (a) API mean square displacement with time and (b) API diffusion coefficient against water weight percent for systems containing 10-monomer Methocel E oligomers. API diffusion coefficients are obtained using the slope of the linear regions (typically between 10 and 80 ns) of mean square displacement with time plot; dotted lines in panel a show the linear fits for the linear regions. Weights of oligomers and phenytoin molecules are in the ratio of 3:1. Markers represent simulation results (symbols are roughly the size of error bars). Line represents an exponential fit: D ≈ 1.55 × 10−10exp(0.1 × water wt %) cm2/s.
the Methocel E-phenytoin complex did not completely dissolve even at highest water weight percent studied, implying that Methocel E is capable of maintaining supersaturation for long times. We must point out that the time scales of simulation (∼100 ns) are smaller than experimental time scales (minutes to hours) that claims regarding either the molecular miscibility in the solid state or the maintenance of supersaturation warrant further investigation. The API diffusion coefficient increases exponentially with water weight percent for these systems
Figure 12. Simulation snapshots of systems containing 3.3 wt % phenytoin with 10 wt % of 10-monomer and 20-monomer Methocel E oligomers. Simulation box lengths are approximately equal to 14.9 nm. 1683
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faces without (with) excipient molecules. Further, the formation of a “gel-like” sample-spanning structure for HPMC/HPMCAS oligomers beyond ∼10 wt % in our simulations may be consistent with the gelation behavior of methylcellulose and HPMC observed at high polymer concentrations.48−50 However, we also observe apparent “gellike” structures at room temperature, which is much lower than the experimental gelation temperature (≈50 °C for 10 wt % of Methocel E Chemistry44). It is important to note that what appears as a “gel” structure in our simulations is a very smallscale, short-time, structure compare to what counts as a “gel” macroscopically. Thus, the clustering that we observe in our simulations might be consistent with a fluid phase if viewed at much longer length and time scales. One promising route to reach longer length and time scales is coarse-graining, where the parameters of the coarse-grained model can be obtained from atomistic simulations discussed here. Our current research efforts are directed along these lines of investigation.
the smaller system (dotted lines). Although the agreement among these results is encouraging, possible differences in the system behavior in the flexible limit (l > lp) cannot be ruled out, the exploration of which would require simulations with >30 monomers and even longer simulation times.
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CONCLUSION AND FUTURE DIRECTIONS The main objective of this study was to understand the molecular interactions between cellulosic excipients and a representative poorly soluble API (phenytoin) in an aqueous medium, and how they affect dissolution and release of APIs. Our simulations show that excipients complex with APIs resulting in an inhibition of API aggregation. Beyond a certain weight percent, the excipient forms a gel-like phase with API molecules entrapped inside, which considerably slows API diffusion. Therefore, excipients help maintain highly supersaturated concentrations of APIs in water, which can result in higher API adsorption in the intestine. The degree of complexation and the reduction in API aggregation can be quantified by excipient-API RDF and API-API RDFs, respectively. The gel-formation behavior is found to be roughly independent of the system size and length of oligomers. Small differences are observed in the RDFs with variations in system size/oligomer length. We find that hydroxypropyl methylcellulose (HPMC) excipients and hydroxypropyl methylcellulose acetate succinate (HPMCAS) excipients with high acetyl content show greater complexation with phenytoin than with HPMCAS excipients with high succinyl content. This difference can be explained by their difference in hydrophobicity; the more hydrophobic excipients show higher affinity for hydrophobic APIs. However, HPMCAS excipients with high succinyl content results in reduced phenytoin aggregation compared to HPMC excipients, implying that more complexation does not necessarily result in less API aggregation. HPMCAS excipients with higher acetyl content appear to be the best excipients for phenytoin, since it has high excipient-API affinity and results in maximum inhibition of API aggregation. Further, the protonation state of succinate groups in HPMCAS has a substantial influence on system behavior, signaling possible excipient response to pH changes in the stomach and GI tract. We also performed simulations on model oligomers based on Methocel E chemistry at different weight percentages of water to mimic the dissolution behavior of solid dispersions. The diffusion coefficient of phenytoin increases exponentially with an increase in the water weight percent. Although the simulation results look promising, the length and time scales reachable by atomistic simulations were not sufficient to observe rare nucleation events and crystal growth of API. Although aggregation can be thought as a precursor to nucleation, the formation of a crystalline phase also demands specific orientation of API molecules with respect to each other; aggregates may undergo long reorganization times before they form a critical nucleus and subsequently a crystalline phase. These processes are difficult to observe within modest simulation times.46 In addition to crystals formed via nucleation, small crystals present in solid form may function as seed crystals for crystal growth, which is arguably easier to simulate using MD simulations. Seed crystals can be incorporated in our simulations by introducing a crystal slab of API molecules (periodically extended in two-directions).47 Such simulations need to be performed for various crystal faces exposed to a solution of free API molecules (and excipients) in solvent, in order to study crystal growth rates along different
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AUTHOR INFORMATION
Corresponding Author
*E-mail: [email protected]. Notes
The authors declare no competing financial interest.
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ACKNOWLEDGMENTS This work used the Extreme Science and Engineering Discovery Environment (XSEDE), which is supported by National Science Foundation grant number OCI-1053575. The authors thank Alex Albaugh and Andrew Twinning for their help with building molecules in Materials Studio, and Indranil Saha Dalal and Wenjun Huang for useful discussions. The work was funded by The Dow Chemical Company.
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REFERENCES
(1) Williams, H. D.; Trevaskis, N. L.; Charman, S. A.; Shanker, R. M.; Charman, W. N.; Pouton, C. W.; Porter, C. J. H. Strategies to Address Low Drug Solubility in Discovery and Development. Pharmacol. Rev. 2013, 65, 315−499. (2) Water-Insoluble Drug Formulation; Liu, R., Ed.; CRC Press: Boca Raton, FL, 2008. (3) Serajuddin, A. T. M. Solid Dispersion of Poorly Water-Soluble Drugs: Early Promises, Subsequent Problems, and Recent Breakthroughs. J. Pharm. Sci. 1999, 88 (10), 1058−1066. (4) Vo, C. L.-N.; Park, C.; Lee, B.-J. Current trends and future perspectives of solid dispersions containing poorly water-soluble drugs. Eur. J. Pharm. Biopharm. 2013, 85 (3), 799−813. (5) Van den Mooter, G. The use of amorphous solid dispersions: A formulation strategy to overcome poor solubility and dissolution rate. Drug Discovery Today 2012, 9 (2), e79−e85. (6) Sekiguchi, K.; Obi, N. Studies on absorption of eutectic mixture. I. Comparison of behavior of eutectic mixture of sulfathiazole and that of ordinary sulfathiazole in man. Chem. Pharm. Bull. 1961, 9 (11), 866−872. (7) Alonzo, D. E.; Gao, Y.; Zhou, D.; Mo, H.; Zhang, G. G. Z.; Taylor, L. S. Dissolution and precipitation behavior of amorphous solid dispersions. J. Pharm. Sci. 2011, 100 (8), 3316−3331. (8) Curatolo, W.; Nightingale, J. A.; Herbig, S. M. Utility of Hydroxypropylmethylcellulose Acetate Succinate (HPMCAS) for Initiation and Maintenance of Drug Supersaturation in the GI Milieu. Pharm. Res. 2009, 26 (6), 1419−1431. (9) Friesen, D. T.; Shanker, R.; Crew, M.; Smithey, D. T.; Curatolo, W. J.; Nightingale, J. A. S. Hydroxypropyl Methylcellulose Acetate Succinate-Based Spray-Dried Dispersions: An Overview. Mol. Pharmaceutics 2008, 5 (6), 1003−1019. 1684
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(10) Ilevbare, G. A.; Liu, H.; Edgar, K. J.; Taylor, L. S. Understanding Polymer Properties Important for Crystal Growth InhibitionImpact of Chemically Diverse Polymers on Solution Crystal Growth of Ritonavir. Cryst. Growth Des. 2012, 12 (6), 3133−3143. (11) Bevernage, J.; Forier, T.; Brouwers, J.; Tack, J.; Annaert, P.; Augustijns, P. Excipient-Mediated Supersaturation Stabilization in Human Intestinal Fluids. Mol. Pharm. 2011, 8 (2), 564−570. (12) Konno, H.; Handa, T.; Alonzo, D. E.; Taylor, L. S. Effect of polymer type on the dissolution profile of amorphous solid dispersions containing felodipine. Eur. J. Pharm. Biopharm. 2008, 70 (2), 493−499. (13) Warren, D. B.; Benameur, H.; Porter, C. J. H.; Pouton, C. W. Using polymeric precipitation inhibitors to improve the absorption of poorly water-soluble drugs: A mechanistic basis for utility. J. Drug. Target. 2010, 18 (10), 704−731. (14) Ilevbare, G. A.; Liu, H.; Edgar, K. J.; Taylor, L. S. Maintaining Supersaturation in Aqueous Drug Solutions: Impact of Different Polymers on Induction Times. Cryst. Growth Des. 2013, 13, 740−751. (15) Alonzo, D. E.; Raina, S.; Zhou, D.; Gao, Y.; Zhang, G. G. Z.; Taylor, L. S. Characterizing the Impact of Hydroxypropylmethyl Cellulose on the Growth and Nucleation Kinetics of Felodipine from Supersaturated Solutions. Cryst. Growth Des. 2012, 12, 1538−1547. (16) Oucherif, K. A.; Raina, S.; Taylor, L. S.; Litster, J. D. Quantitative analysis of the inhibitory effect of HPMC on felodipine crystallization kinetics using population balance modeling. CrystEngComm 2013, 15, 2197. (17) Marsac, P. J.; Li, T.; Taylor, L. S. Estimation of Drug−Polymer Miscibility and Solubility in Amorphous Solid Dispersions Using Experimentally Determined Interaction Parameters. Pharm. Res. 2008, 26 (1), 139−151. (18) Marsac, P. J.; Shamblin, S. L.; Taylor, L. S. Theoretical and Practical Approaches for Prediction of Drug-Polymer Miscibility and Solubility. Pharm. Res. 2006, 23 (10), 2417−2426. (19) Zhao, Y.; Inbar, P.; Chokshi, H. P.; Malick, A. W.; Choi, D. S. Prediction of the thermal phase diagram of amorphous solid dispersions by flory−huggins theory. J. Pharm. Sci. 2011, 100 (8), 3196−3207. (20) Gupta, J.; Nunes, C.; Vyas, S.; Jonnalagadda, S. Prediction of Solubility Parameters and Miscibility of Pharmaceutical Compounds by Molecular Dynamics Simulations. J. Phys. Chem. B 2011, 115, 2014−2023. (21) Maus, M.; Wagner, K. G.; Kornherr, A.; Zifferer, G. Molecular dynamics simulations for drug dosage form development: thermal and solubility characteristics for hot-melt extrusion. Mol. Simul. 2008, 34 (10−15), 1197−1207. (22) Ouyang, D. Investigating the molecular structures of solid dispersions by the simulated annealing method. Chem. Phys. Lett. 2012, 554, 177−184. (23) Zhu, W.; Romanski, F. S.; Dalvi, S. V.; Dave, R. N.; Tomassone, M. S. Atomistic simulations of aqueous griseofulvin crystals in the presence of individual and multiple additives. Chem. Eng. Sci. 2012, 73, 218−230. (24) Zhu, W.; Romanski, F. S.; Meng, X.; Mitra, S.; Tomassone, M. S. Atomistic simulation study of surfactant and polymer interactions on the surface of a fenofibrate crystal. Eur. J. Pharm. Sci. 2011, 42, 452− 461. (25) Li, C.; Wang, J.-X.; Le, Y.; Chen, J.-F. Studies of Bicalutamide− Excipients Interaction by Combination of Molecular Docking and Molecular Dynamics Simulation. Mol. Pharmacol. 2013, 10 (6), 2362− 2369. (26) Ahmad, S.; Johnston, B. F.; Mackay, S. P.; Schatzlein, A. G.; Gellert, P.; Sengupta, D.; Uchegbu, I. F. In silico modelling of drug− polymer interactions for pharmaceutical formulations. J. R. Soc. Interface 2010, 7, S423−S433. (27) Chen, T.; D’Addio, S. M.; Kennedy, M. T.; Swietlow, A.; Kevrekidis, I. G.; Panagiotopoulos, A. Z.; Prud’homme, R. K. Protected Peptide Nanoparticles: Experiments and Brownian Dynamics Simulations of the Energetics of Assembly. Nano Lett. 2009, 9 (6), 2218−2222.
(28) Siepmann, J.; Peppas, N. A. Modeling of drug release from delivery systems based on hydroxypropyl methylcellulose (HPMC). Adv. Drug. Delivery Rev. 2001, 48 (2−3), 139−157. (29) Pronk, S.; Páll, S.; Schulz, R.; Larsson, P.; Bjelkmar, P.; Apostolov, R.; Shirts, M. R.; Smith, J. C.; Kasson, P. M.; van der Spoel, D.; Hess, B.; Lindahl, E. GROMACS 4.5: a high-throughput and highly parallel open source molecular simulation toolkit. Bioinformatics 2013, 29 (7), 845−854. (30) Bjelkmar, P.; Larsson, P.; Cuendet, M. A.; Hess, B.; Lindahl, E. Implementation of the CHARMM Force Field in GROMACS: Analysis of Protein Stability Effects from Correction Maps, Virtual Interaction Sites, and Water Models. J. Chem. Theory Comput. 2010, 6 (2), 459−466. (31) Zoete, V.; Cuendet, M. A.; Grosdidier, A.; Michielin, O. SwissParam: A Fast Force Field Generation Tool for Small Organic Molecules. J. Comput. Chem. 2011, 32 (11), 2359−2368. (32) Foley, B. L.; Tessier, M. B.; Woods, R. J. Carbohydrate force fields. WIREs Comput. Mol. Sci. 2011, 2 (4), 652−697. (33) Damm, W.; Frontera, A.; Tirado−Rives, J.; Jorgensen, W. L. OPLS all-atom force field for carbohydrates. J. Comput. Chem. 1997, 18 (16), 1955−1970. (34) Jorgensen, W. L.; Maxwell, D. S.; Tirado-Rives, J. Development and testing of the OPLS all-atom force field on conformational energetics and properties of organic liquids. J. Am. Chem. Soc. 1996, 118 (45), 11225−11236. (35) Irwin, J. J.; Sterling, T.; Mysinger, M. M.; Bolstad, E. S.; Coleman, R. G. ZINC: A Free Tool to Discover Chemistry for Biology. J. Chem. Inf. Model. 2012, 52 (7), 1757−1768. (36) Patel, T. R.; Morris, G. A.; Garcia de la Torre, J.; Ortega, A.; Mischnick, P.; Harding, S. E. Molecular Flexibility of Methylcelluloses of Differing Degree of Substitution by Combined Sedimentation and Viscosity Analysis. Macromol. Biosci. 2008, 8, 1108−1115. (37) Pant, P. V. K.; Boyd, R. H. Molecular-dynamics simulation of diffusion of small penetrants in polymers. Macromolecules 1993, 26 ((4)), 679−686. (38) Wohlert, J.; Edholm, O. Dynamics in atomistic simulations of phospholipid membranes: nuclear magnetic resonance relaxation rates and lateral diffusion. J. Chem. Phys. 2006, 125 (20), 204703. (39) Humphrey, W.; Dalke, A.; Schulten, K. VMD: Visual molecular dynamics. J. Mol. Graphics 1996, 14 (1), 33−38. (40) Adden, R.; Muller, R.; Mischnick, P. Analysis of the substituent distribution in the glucosyl units and along the polymer chain of hydroxypropylmethyl celluloses and statistical evaluation. Cellulose 2006, 13 (4), 459−476. (41) Kestur, U. S.; Taylor, L. S. Role of polymer chemistry in influencing crystal growth rates from amorphous felodipine. CrystEngComm 2010, 12, 2390−2397. (42) Trasi, N. S.; Taylor, L. S. Effect of Additives on Crystal Growth and Nucleation of Amorphous Flutamide. Cryst. Growth Des. 2012, 12 (6), 3221−3230. (43) Affinisol: Solving the Insoluble with Dow; Literature − Pharma Solutions; Dow Pharma & Food Solutions: Midland, MI, 2013. (44) Gao, P.; Fagerness, P. E. Diffusion in HPMC gels. I. Determination of drug and water diffusivity by pulsed-field-gradient spin-echo NMR. Pharm. Res. 1995, 12 (7), 955−964. (45) Yeh, I.-C.; Hummer, G. System-Size Dependence of Diffusion Coefficients and Viscosities from Molecular Dynamics Simulations with Periodic Boundary Conditions. J. Phys. Chem. B 2004, 108 (40), 15873−15879. (46) Yi, P.; Locker, C. R.; Rutledge, G. C. Molecular Dynamics Simulation of Homogeneous Crystal Nucleation in Polyethylene. Macromolecules 2013, 46 (11), 4723−4733. (47) Salvalaglio, M.; Vetter, T.; Giberti, F.; Mazzotti, M.; Parrinello, M. Uncovering Molecular Details of Urea Crystal Growth in the Presence of Additives. J. Am. Chem. Soc. 2012, 134 (41), 17221− 17233. (48) Sarkar, N. Thermal Gelation Properties of Methyl and Hydroxypropyl Methylcellulose. J. Appl. Polym. Sci. 1979, 24, 1073− 1087. 1685
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Article
(49) Arvidson, S. A.; Lott, J. R.; McAllister, J. W.; Zhang, J.; Bates, F. S.; Lodge, T. P.; Sammler, R. L.; Li, Y.; Brackhagen, M. Interplay of Phase Separation and Thermoreversible Gelation in Aqueous Methylcellulose Solutions. Macromolecules 2013, 46 (1), 300−309. (50) Fairclough, J. P. A.; Yu, H.; Kelly, O.; Ryan, A. J.; Sammler, R. L.; Radler, M. Interplay between Gelation and Phase Separation in Aqueous Solutions of Methylcellulose and Hydroxypropylmethylcellulose. Langmuir 2012, 28 (28), 10551−10557.
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Assessing the Efficiency of Polymeric Excipients by Atomistic Molecular Dynamics Simulations Prateek K. Jha and Ronald G. Larson* Department of Chemical Engineering, University of Michigan, Ann Arbor, Michigan 48109-2136, United States ABSTRACT: We have performed all-atom molecular dynamics simulations of aqueous solutions of model oligomers of hydroxypropyl methylcellulose (HPMC) and hydroxypropyl methylcellulose acetate succinate (HPMCAS) excipients interacting with a representative poorly soluble active pharmaceutical ingredient (API), phenytoin. Simulations reveal formation of excipient-API complexes for some of the oligomers, which results in a reduction of API aggregation. API aggregation and diffusivity decreased with an increase in excipient content. Excipients form a “gel-like” phase spanning the simulation box beyond ∼10 wt %; API diffusivity within this gel phase is much smaller than API diffusivity without excipient, and decreases exponentially, by 5 orders of magnitude, with increased polymer concentration. Substantial differences are observed with variations in methyl, hydroxypropyl, acetate, and succinate substitution levels in the model oligomers and with the deprotonation state of succinate groups, with strongest interactions with hydrophobic phenytoin observed in the case of acetate substitution. These are used to develop quantitative measures of excipient-API interactions and excipient efficiency in the inhibition of API aggregation. We also find that for model oligomers based on Methocel E (manufactured by Dow Pharma & Food Solutions) chemistry, oligomers of length 10 monomers and simulation boxes of size 7 nm give results similar to those for longer oligomers and bigger boxes. The quantitative measures developed in this study are expected to prove useful as computational screening tools in excipient design. KEYWORDS: cellulosic polymers, API, HPMC, HPMCAS, molecular dynamics
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excipients;13 some of the currently marketed products containing HPMC/HPMCAS for solubility enhancement are Certican, Nivadil, Rezulin, Intelence, Zelboraf, Crestor, Prograf, and Sporanox. The presence of three substitution positions on each D-glucose monomeric unit (Figure 1) gives rise to a large design space of cellulosic polymers, both in terms of the substitution patterns and the degrees of substitutions of different substituents. However, the large design space of cellulosic polymers also poses a technological challenge of designing polymers with optimal properties for a given API. Molecular interactions between polymers and APIs in the solid and dissolved states and the mechanisms of precipitation/ crystallization inhibition are not well understood, which hinders the development and commercialization of polymeric excipients. Several physical characteristics of a good excipient have been identified in prior studies. First, an excipient must show affinity for the API via specific interactions (e.g., hydrogen-bonding) or by weak van der Waals interactions. As a rule of thumb, an excipient and an API of similar hydrophobicity are miscible with each other, which imply hydrophobic excipients are suitable for poorly soluble (hydrophobic) APIs.14 Second,
INTRODUCTION Poorly soluble APIs (active pharmaceutical ingredients) have limited bioavailability, which necessitates the use of drug delivery technologies that either enhance API solubility or maintain supersaturation by inhibiting precipitation and crystallization of APIs.1,2 Solid dispersions3−5 fall into the latter category, whereby molecular mixing of the amorphous API and excipient maintains API in amorphous form in final dosage form and results in a supersaturated API solution upon dissolution in the gastrointestinal (GI) tract. Though the earliest studies on solid dispersions used low molecular weight compounds such as urea as excipients,6 high molecular weight polymers are preferred as excipients in newer generations of solid dispersions. Polymers result in increased viscosity on dissolution, thereby slowing the release of APIs and inhibit possible recrystallization. Moreover, the multifunctionality of polymers enables control of polymer-API interactions by varying the substituents on the polymer backbone. Examples of polymers used in commercial excipients are PVP (polyvinylpyrrolidone), PEG (poly(ethylene glycol)), HPC (hydroxypropyl cellulose), HPMC (hydroxypropyl methylcellulose or hypromellose), and HPMCAS (hydroxypropyl methylcellulose acetate succinate).4 Cellulosic polymers are generally superior to alternative excipients in the inhibition of API crystallization/precipitation, as established by many experimental studies over the past decade.7−12 Commercial grades of HPMC and HPMCAS are widely used as © 2014 American Chemical Society
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models is the unreliability of available solubility parameter estimates. One promising route is to determine these parameters by molecular dynamics (MD) simulations, which has been explored in recent studies.20,21 Molecular structures of solid dispersions have also been determined by simulated annealing22 simulations. Theoretical studies of excipient-API interactions in a dissolved state are relatively scarce. Tomassone and colleagues have studied polymer-drug interactions on the surface of drug crystals by MD simulations.23,24 Also, a recent simulation study has employed molecular docking and MD simulations to investigate molecular interactions between bicalutamide and three excipients.25 These simulations have, however, been restricted to short time scales (CO and −NH− groups of phenytoin molecules (red lines). Bold and dashed lines represent 3.3 wt % phenytoin without HPMC and 3.3 wt % phenytoin with 10 wt % HPMC, respectively. Small r region of g(r) is zoomed in the semilogarithmic plot in inset.
specific hydrogen-bonding interactions between API molecules. The average numbers of hydrogen-bonds per API molecule are 0.29(1) and 0.10(2) in the absence and presence of HPMC, 1681
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respectively. All these hydrogen bonds are formed between >CO and −NH− groups of API molecules, resulting in a dominant peak at r ≈ 0.2 nm in the RDF between >CO and −NH− groups. Note that the RDF between >CO and −NH− groups has multiple peaks because two >CO and two −NH− groups are present per phenytoin molecule, and they all participate in hydrogen-bonding. Such features are absent in RDFs between other chemical moieties in the phenytoin molecule (e.g., between phenyl groups; not shown), meaning that they do not interact via specific interactions. However, they can still interact via nonspecific van der Waals (hydrophobic) interactions, which is the main driving force for API aggregation. Similar hydrogen bonding interactions between >CO and −NH− groups have been reported in recent studies of felodipine41 and flutamide.42 Next, we compare the behavior of systems containing HPMC and HPMCAS oligomers to understand the effect of additional acetate or succinate substitution on HPMC. Although HPMCAS usually contains both acetate and succinate substituents, here we consider special cases in which only one of these two groups is present. Here “HPMCAc” represents an oligomer containing additional acetate groups on HPMC; HPMCSu and HPMCSu(−) contain additional protonated and deprotonated succinate groups, respectively, on HPMC. Snapshots in Figure 7 show a substantial difference in the
Figure 8. (a) Excipient-API RDF and (b) API-API RDF of systems containing 3.3 wt % phenytoin and 10 wt % of different HPMCAS oligomers.
HPMCAc appears to be the best excipient for phenytoin because it shows the highest affinity for phenytoin (Figure 8a) and results in maximum inhibition of phenytoin aggregation (Figure 8b). Assuming that the ability of an excipient to inhibit aggregation correlates with its ability to inhibit crystallization, our results are in accord with a previous finding43 that higher acetyl content and lower succinyl content are best for crystallization inhibition of poorly soluble APIs. Note, however, that commercial HPMCAS excipients usually contain both acetate and succinate groups, and the dissolution behavior usually shows nonmonotonic dependence on the acetate-tosuccinate ratio.43 The presence of succinate groups results in an amphiphilic character of HPMCAS; hydrophobic regions facilitate association with API and hydrophilic regions gives rise to the formation of stable hydrated nanostructures with improved dissolution properties.8 In contrast, we have considered extreme cases with no succinate and with no acetate groups in HPMCAc and HPMCSu/HPMCSu(−), respectively. More work is needed, therefore, to explore the effects of combining both acetate and succinate groups in the same excipient. We now discuss results of simulations containing Methocel E oligomers (Table 2) with phenytoin molecules. To mimic the behavior of solid dispersions during dissolution, we simulate a mixture of 10-monomer oligomers and phenytoin molecules at different weight percent of water. For each of these simulations, we begin with the equilibrated state containing 10 wt % oligomer and 3.3 wt % phenytoin and remove appropriate number of water molecules to achieve the desired weight percent of water. The resulting system is then subjected to reequilibration (to relax the system after removal of water) and production steps as described in the Simulations section. This results in substantial reduction in simulation box volume, since water molecules have been removed. An alternative and perhaps more realistic scheme of starting with a water-free system and gradually adding water molecules is expected to result in similar final states but may require longer equilibration times. It is worth noting that unlike other systems considered in this paper, simulation box lengths are less than the contour lengths of oligomers (≈5.4 nm) at the smaller weight percent of water.
Figure 7. Simulation snapshots of systems containing 3.3 wt % phenytoin and 10 wt % of three different HPMCAS oligomers. Simulation box lengths are approximately equal to 7 nm for these systems.
behavior of these oligomers, which is a consequence of differences in their hydrophobicity (Table 1). HPMC and HPMCAc are more hydrophobic and form a gel-like phase, whereas HPMCSu(−) is less hydrophobic and does not gel at this concentration. The order of hydrophobicity of these oligomers (Table 1) correlates positively with their affinity for the API as shown by the excipient-API RDF in Figure 8a. HPMCSu(−) shows the least affinity for phenytoin followed by HPMCSu. HPMCAc and HPMC have similar hydrophobicity and show similar affinity for phenytoin. However, the API-API RDF in Figure 8b shows an interesting trend. Although HPMC has stronger affinity for phenytoin than HPMCSu and HPMCSu(−), this does not translate into a reduction in API aggregation, as indicated by higher peak of the API-API RDF for HPMC. Therefore, the notion that hydrophobic excipients are best suited for hydrophobic APIs14 is not completely valid and specific interactions between the excipient and API must also be considered. It is interesting to note that the behavior of oligomers containing deprotonated succinate groups is substantially different from those containing protonated succinate groups. Therefore, excipients containing succinate groups should respond to pH changes in the stomach and GI tract, as a result of changes in the deprotonation level. 1682
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(Figure 10b).28,44 The actual magnitudes of the diffusion coefficient were found to be sensitive to variations in the system size (e.g., by doubling the number of molecules for the same weight percent of API and excipient), which arise due to the use of periodic boundary conditions.45 However, API diffusion coefficients for the systems containing the same weight percent of 10-monomer and 20-monomer Methocel E oligomers were almost identical for similar system sizes. Finally, we discuss the effects of system size and oligomer length in our simulations. In Figure 11, we compare excipient-
Figure 9 shows simulation snapshots of systems containing varying amounts of water. We did not observe aggregation of
Figure 9. Simulation snapshots of systems containing 10-monomer Methocel E oligomers and phenytoin molecules, with varying weight percent of water. All systems contain the same number of oligomer and phenytoin molecules. Weights of oligomers and phenytoin molecules are in the ratio 3:1. Simulation box lengths are approximately 3.3, 4, and 6 nm, for boxes containing no water, 40 wt % water, and 80 wt % water, respectively.
phenytoin molecules in the water-free state, which is an indication of molecular miscibility of Methocel E and phenytoin in the solid state. An increase in water weight percent results in a solid → gel transition, together with a corresponding increase in API mobility (Figure 10a). However,
Figure 11. Effect of system size and oligomer length on the excipientAPI RDF for 10 wt % excipient and 3.3 wt % API. Red lines are results for simulations containing HPMC oligomer (Figure 2) for different system sizes, with simulation box lengths approximately equal to 6.8 nm (dotted line) and 10.7 nm (solid line). Blue lines are results for simulations containing 10-monomer Methocel E-oligomers, with simulation box lengths approximately equal to 7 nm (dotted line) and 14.9 nm (solid line). The black line is the result for system containing a 20-mer Methocel E-oligomer with simulation box length approximately equal to 14.9 nm.
API RDFs for two systems containing HPMC oligomers, at the same weight percent of both the excipient and API, but with the larger system (red solid line) containing roughly four times the number of molecules of the smaller system (red dotted line). Both systems form a gel-like phase spanning the simulation box, indicating that the gel formation is not an artifact of the finite size of systems. Figure 11 also shows results of simulations containing 10-monomer (blue lines) and 20monomer oligomers of Methocel E (black line). These systems show very similar behavior, as also evident from snapshots in Figure 12. Small differences (up to 12% in the RDFs) between the two systems result from the different numbers of end monomers, which are more hydrophilic due to the presence of an additional −OH group at the 1 or 4 position (Figure 1). In general, excipient-API RDFs show small differences in the peak height and position for both HPMC and Methocel E systems but with a longer tail for the larger system (solid lines) than for
Figure 10. (a) API mean square displacement with time and (b) API diffusion coefficient against water weight percent for systems containing 10-monomer Methocel E oligomers. API diffusion coefficients are obtained using the slope of the linear regions (typically between 10 and 80 ns) of mean square displacement with time plot; dotted lines in panel a show the linear fits for the linear regions. Weights of oligomers and phenytoin molecules are in the ratio of 3:1. Markers represent simulation results (symbols are roughly the size of error bars). Line represents an exponential fit: D ≈ 1.55 × 10−10exp(0.1 × water wt %) cm2/s.
the Methocel E-phenytoin complex did not completely dissolve even at highest water weight percent studied, implying that Methocel E is capable of maintaining supersaturation for long times. We must point out that the time scales of simulation (∼100 ns) are smaller than experimental time scales (minutes to hours) that claims regarding either the molecular miscibility in the solid state or the maintenance of supersaturation warrant further investigation. The API diffusion coefficient increases exponentially with water weight percent for these systems
Figure 12. Simulation snapshots of systems containing 3.3 wt % phenytoin with 10 wt % of 10-monomer and 20-monomer Methocel E oligomers. Simulation box lengths are approximately equal to 14.9 nm. 1683
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faces without (with) excipient molecules. Further, the formation of a “gel-like” sample-spanning structure for HPMC/HPMCAS oligomers beyond ∼10 wt % in our simulations may be consistent with the gelation behavior of methylcellulose and HPMC observed at high polymer concentrations.48−50 However, we also observe apparent “gellike” structures at room temperature, which is much lower than the experimental gelation temperature (≈50 °C for 10 wt % of Methocel E Chemistry44). It is important to note that what appears as a “gel” structure in our simulations is a very smallscale, short-time, structure compare to what counts as a “gel” macroscopically. Thus, the clustering that we observe in our simulations might be consistent with a fluid phase if viewed at much longer length and time scales. One promising route to reach longer length and time scales is coarse-graining, where the parameters of the coarse-grained model can be obtained from atomistic simulations discussed here. Our current research efforts are directed along these lines of investigation.
the smaller system (dotted lines). Although the agreement among these results is encouraging, possible differences in the system behavior in the flexible limit (l > lp) cannot be ruled out, the exploration of which would require simulations with >30 monomers and even longer simulation times.
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CONCLUSION AND FUTURE DIRECTIONS The main objective of this study was to understand the molecular interactions between cellulosic excipients and a representative poorly soluble API (phenytoin) in an aqueous medium, and how they affect dissolution and release of APIs. Our simulations show that excipients complex with APIs resulting in an inhibition of API aggregation. Beyond a certain weight percent, the excipient forms a gel-like phase with API molecules entrapped inside, which considerably slows API diffusion. Therefore, excipients help maintain highly supersaturated concentrations of APIs in water, which can result in higher API adsorption in the intestine. The degree of complexation and the reduction in API aggregation can be quantified by excipient-API RDF and API-API RDFs, respectively. The gel-formation behavior is found to be roughly independent of the system size and length of oligomers. Small differences are observed in the RDFs with variations in system size/oligomer length. We find that hydroxypropyl methylcellulose (HPMC) excipients and hydroxypropyl methylcellulose acetate succinate (HPMCAS) excipients with high acetyl content show greater complexation with phenytoin than with HPMCAS excipients with high succinyl content. This difference can be explained by their difference in hydrophobicity; the more hydrophobic excipients show higher affinity for hydrophobic APIs. However, HPMCAS excipients with high succinyl content results in reduced phenytoin aggregation compared to HPMC excipients, implying that more complexation does not necessarily result in less API aggregation. HPMCAS excipients with higher acetyl content appear to be the best excipients for phenytoin, since it has high excipient-API affinity and results in maximum inhibition of API aggregation. Further, the protonation state of succinate groups in HPMCAS has a substantial influence on system behavior, signaling possible excipient response to pH changes in the stomach and GI tract. We also performed simulations on model oligomers based on Methocel E chemistry at different weight percentages of water to mimic the dissolution behavior of solid dispersions. The diffusion coefficient of phenytoin increases exponentially with an increase in the water weight percent. Although the simulation results look promising, the length and time scales reachable by atomistic simulations were not sufficient to observe rare nucleation events and crystal growth of API. Although aggregation can be thought as a precursor to nucleation, the formation of a crystalline phase also demands specific orientation of API molecules with respect to each other; aggregates may undergo long reorganization times before they form a critical nucleus and subsequently a crystalline phase. These processes are difficult to observe within modest simulation times.46 In addition to crystals formed via nucleation, small crystals present in solid form may function as seed crystals for crystal growth, which is arguably easier to simulate using MD simulations. Seed crystals can be incorporated in our simulations by introducing a crystal slab of API molecules (periodically extended in two-directions).47 Such simulations need to be performed for various crystal faces exposed to a solution of free API molecules (and excipients) in solvent, in order to study crystal growth rates along different
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AUTHOR INFORMATION
Corresponding Author
*E-mail: [email protected]. Notes
The authors declare no competing financial interest.
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ACKNOWLEDGMENTS This work used the Extreme Science and Engineering Discovery Environment (XSEDE), which is supported by National Science Foundation grant number OCI-1053575. The authors thank Alex Albaugh and Andrew Twinning for their help with building molecules in Materials Studio, and Indranil Saha Dalal and Wenjun Huang for useful discussions. The work was funded by The Dow Chemical Company.
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REFERENCES
(1) Williams, H. D.; Trevaskis, N. L.; Charman, S. A.; Shanker, R. M.; Charman, W. N.; Pouton, C. W.; Porter, C. J. H. Strategies to Address Low Drug Solubility in Discovery and Development. Pharmacol. Rev. 2013, 65, 315−499. (2) Water-Insoluble Drug Formulation; Liu, R., Ed.; CRC Press: Boca Raton, FL, 2008. (3) Serajuddin, A. T. M. Solid Dispersion of Poorly Water-Soluble Drugs: Early Promises, Subsequent Problems, and Recent Breakthroughs. J. Pharm. Sci. 1999, 88 (10), 1058−1066. (4) Vo, C. L.-N.; Park, C.; Lee, B.-J. Current trends and future perspectives of solid dispersions containing poorly water-soluble drugs. Eur. J. Pharm. Biopharm. 2013, 85 (3), 799−813. (5) Van den Mooter, G. The use of amorphous solid dispersions: A formulation strategy to overcome poor solubility and dissolution rate. Drug Discovery Today 2012, 9 (2), e79−e85. (6) Sekiguchi, K.; Obi, N. Studies on absorption of eutectic mixture. I. Comparison of behavior of eutectic mixture of sulfathiazole and that of ordinary sulfathiazole in man. Chem. Pharm. Bull. 1961, 9 (11), 866−872. (7) Alonzo, D. E.; Gao, Y.; Zhou, D.; Mo, H.; Zhang, G. G. Z.; Taylor, L. S. Dissolution and precipitation behavior of amorphous solid dispersions. J. Pharm. Sci. 2011, 100 (8), 3316−3331. (8) Curatolo, W.; Nightingale, J. A.; Herbig, S. M. Utility of Hydroxypropylmethylcellulose Acetate Succinate (HPMCAS) for Initiation and Maintenance of Drug Supersaturation in the GI Milieu. Pharm. Res. 2009, 26 (6), 1419−1431. (9) Friesen, D. T.; Shanker, R.; Crew, M.; Smithey, D. T.; Curatolo, W. J.; Nightingale, J. A. S. Hydroxypropyl Methylcellulose Acetate Succinate-Based Spray-Dried Dispersions: An Overview. Mol. Pharmaceutics 2008, 5 (6), 1003−1019. 1684
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Article
(10) Ilevbare, G. A.; Liu, H.; Edgar, K. J.; Taylor, L. S. Understanding Polymer Properties Important for Crystal Growth InhibitionImpact of Chemically Diverse Polymers on Solution Crystal Growth of Ritonavir. Cryst. Growth Des. 2012, 12 (6), 3133−3143. (11) Bevernage, J.; Forier, T.; Brouwers, J.; Tack, J.; Annaert, P.; Augustijns, P. Excipient-Mediated Supersaturation Stabilization in Human Intestinal Fluids. Mol. Pharm. 2011, 8 (2), 564−570. (12) Konno, H.; Handa, T.; Alonzo, D. E.; Taylor, L. S. Effect of polymer type on the dissolution profile of amorphous solid dispersions containing felodipine. Eur. J. Pharm. Biopharm. 2008, 70 (2), 493−499. (13) Warren, D. B.; Benameur, H.; Porter, C. J. H.; Pouton, C. W. Using polymeric precipitation inhibitors to improve the absorption of poorly water-soluble drugs: A mechanistic basis for utility. J. Drug. Target. 2010, 18 (10), 704−731. (14) Ilevbare, G. A.; Liu, H.; Edgar, K. J.; Taylor, L. S. Maintaining Supersaturation in Aqueous Drug Solutions: Impact of Different Polymers on Induction Times. Cryst. Growth Des. 2013, 13, 740−751. (15) Alonzo, D. E.; Raina, S.; Zhou, D.; Gao, Y.; Zhang, G. G. Z.; Taylor, L. S. Characterizing the Impact of Hydroxypropylmethyl Cellulose on the Growth and Nucleation Kinetics of Felodipine from Supersaturated Solutions. Cryst. Growth Des. 2012, 12, 1538−1547. (16) Oucherif, K. A.; Raina, S.; Taylor, L. S.; Litster, J. D. Quantitative analysis of the inhibitory effect of HPMC on felodipine crystallization kinetics using population balance modeling. CrystEngComm 2013, 15, 2197. (17) Marsac, P. J.; Li, T.; Taylor, L. S. Estimation of Drug−Polymer Miscibility and Solubility in Amorphous Solid Dispersions Using Experimentally Determined Interaction Parameters. Pharm. Res. 2008, 26 (1), 139−151. (18) Marsac, P. J.; Shamblin, S. L.; Taylor, L. S. Theoretical and Practical Approaches for Prediction of Drug-Polymer Miscibility and Solubility. Pharm. Res. 2006, 23 (10), 2417−2426. (19) Zhao, Y.; Inbar, P.; Chokshi, H. P.; Malick, A. W.; Choi, D. S. Prediction of the thermal phase diagram of amorphous solid dispersions by flory−huggins theory. J. Pharm. Sci. 2011, 100 (8), 3196−3207. (20) Gupta, J.; Nunes, C.; Vyas, S.; Jonnalagadda, S. Prediction of Solubility Parameters and Miscibility of Pharmaceutical Compounds by Molecular Dynamics Simulations. J. Phys. Chem. B 2011, 115, 2014−2023. (21) Maus, M.; Wagner, K. G.; Kornherr, A.; Zifferer, G. Molecular dynamics simulations for drug dosage form development: thermal and solubility characteristics for hot-melt extrusion. Mol. Simul. 2008, 34 (10−15), 1197−1207. (22) Ouyang, D. Investigating the molecular structures of solid dispersions by the simulated annealing method. Chem. Phys. Lett. 2012, 554, 177−184. (23) Zhu, W.; Romanski, F. S.; Dalvi, S. V.; Dave, R. N.; Tomassone, M. S. Atomistic simulations of aqueous griseofulvin crystals in the presence of individual and multiple additives. Chem. Eng. Sci. 2012, 73, 218−230. (24) Zhu, W.; Romanski, F. S.; Meng, X.; Mitra, S.; Tomassone, M. S. Atomistic simulation study of surfactant and polymer interactions on the surface of a fenofibrate crystal. Eur. J. Pharm. Sci. 2011, 42, 452− 461. (25) Li, C.; Wang, J.-X.; Le, Y.; Chen, J.-F. Studies of Bicalutamide− Excipients Interaction by Combination of Molecular Docking and Molecular Dynamics Simulation. Mol. Pharmacol. 2013, 10 (6), 2362− 2369. (26) Ahmad, S.; Johnston, B. F.; Mackay, S. P.; Schatzlein, A. G.; Gellert, P.; Sengupta, D.; Uchegbu, I. F. In silico modelling of drug− polymer interactions for pharmaceutical formulations. J. R. Soc. Interface 2010, 7, S423−S433. (27) Chen, T.; D’Addio, S. M.; Kennedy, M. T.; Swietlow, A.; Kevrekidis, I. G.; Panagiotopoulos, A. Z.; Prud’homme, R. K. Protected Peptide Nanoparticles: Experiments and Brownian Dynamics Simulations of the Energetics of Assembly. Nano Lett. 2009, 9 (6), 2218−2222.
(28) Siepmann, J.; Peppas, N. A. Modeling of drug release from delivery systems based on hydroxypropyl methylcellulose (HPMC). Adv. Drug. Delivery Rev. 2001, 48 (2−3), 139−157. (29) Pronk, S.; Páll, S.; Schulz, R.; Larsson, P.; Bjelkmar, P.; Apostolov, R.; Shirts, M. R.; Smith, J. C.; Kasson, P. M.; van der Spoel, D.; Hess, B.; Lindahl, E. GROMACS 4.5: a high-throughput and highly parallel open source molecular simulation toolkit. Bioinformatics 2013, 29 (7), 845−854. (30) Bjelkmar, P.; Larsson, P.; Cuendet, M. A.; Hess, B.; Lindahl, E. Implementation of the CHARMM Force Field in GROMACS: Analysis of Protein Stability Effects from Correction Maps, Virtual Interaction Sites, and Water Models. J. Chem. Theory Comput. 2010, 6 (2), 459−466. (31) Zoete, V.; Cuendet, M. A.; Grosdidier, A.; Michielin, O. SwissParam: A Fast Force Field Generation Tool for Small Organic Molecules. J. Comput. Chem. 2011, 32 (11), 2359−2368. (32) Foley, B. L.; Tessier, M. B.; Woods, R. J. Carbohydrate force fields. WIREs Comput. Mol. Sci. 2011, 2 (4), 652−697. (33) Damm, W.; Frontera, A.; Tirado−Rives, J.; Jorgensen, W. L. OPLS all-atom force field for carbohydrates. J. Comput. Chem. 1997, 18 (16), 1955−1970. (34) Jorgensen, W. L.; Maxwell, D. S.; Tirado-Rives, J. Development and testing of the OPLS all-atom force field on conformational energetics and properties of organic liquids. J. Am. Chem. Soc. 1996, 118 (45), 11225−11236. (35) Irwin, J. J.; Sterling, T.; Mysinger, M. M.; Bolstad, E. S.; Coleman, R. G. ZINC: A Free Tool to Discover Chemistry for Biology. J. Chem. Inf. Model. 2012, 52 (7), 1757−1768. (36) Patel, T. R.; Morris, G. A.; Garcia de la Torre, J.; Ortega, A.; Mischnick, P.; Harding, S. E. Molecular Flexibility of Methylcelluloses of Differing Degree of Substitution by Combined Sedimentation and Viscosity Analysis. Macromol. Biosci. 2008, 8, 1108−1115. (37) Pant, P. V. K.; Boyd, R. H. Molecular-dynamics simulation of diffusion of small penetrants in polymers. Macromolecules 1993, 26 ((4)), 679−686. (38) Wohlert, J.; Edholm, O. Dynamics in atomistic simulations of phospholipid membranes: nuclear magnetic resonance relaxation rates and lateral diffusion. J. Chem. Phys. 2006, 125 (20), 204703. (39) Humphrey, W.; Dalke, A.; Schulten, K. VMD: Visual molecular dynamics. J. Mol. Graphics 1996, 14 (1), 33−38. (40) Adden, R.; Muller, R.; Mischnick, P. Analysis of the substituent distribution in the glucosyl units and along the polymer chain of hydroxypropylmethyl celluloses and statistical evaluation. Cellulose 2006, 13 (4), 459−476. (41) Kestur, U. S.; Taylor, L. S. Role of polymer chemistry in influencing crystal growth rates from amorphous felodipine. CrystEngComm 2010, 12, 2390−2397. (42) Trasi, N. S.; Taylor, L. S. Effect of Additives on Crystal Growth and Nucleation of Amorphous Flutamide. Cryst. Growth Des. 2012, 12 (6), 3221−3230. (43) Affinisol: Solving the Insoluble with Dow; Literature − Pharma Solutions; Dow Pharma & Food Solutions: Midland, MI, 2013. (44) Gao, P.; Fagerness, P. E. Diffusion in HPMC gels. I. Determination of drug and water diffusivity by pulsed-field-gradient spin-echo NMR. Pharm. Res. 1995, 12 (7), 955−964. (45) Yeh, I.-C.; Hummer, G. System-Size Dependence of Diffusion Coefficients and Viscosities from Molecular Dynamics Simulations with Periodic Boundary Conditions. J. Phys. Chem. B 2004, 108 (40), 15873−15879. (46) Yi, P.; Locker, C. R.; Rutledge, G. C. Molecular Dynamics Simulation of Homogeneous Crystal Nucleation in Polyethylene. Macromolecules 2013, 46 (11), 4723−4733. (47) Salvalaglio, M.; Vetter, T.; Giberti, F.; Mazzotti, M.; Parrinello, M. Uncovering Molecular Details of Urea Crystal Growth in the Presence of Additives. J. Am. Chem. Soc. 2012, 134 (41), 17221− 17233. (48) Sarkar, N. Thermal Gelation Properties of Methyl and Hydroxypropyl Methylcellulose. J. Appl. Polym. Sci. 1979, 24, 1073− 1087. 1685
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Article
(49) Arvidson, S. A.; Lott, J. R.; McAllister, J. W.; Zhang, J.; Bates, F. S.; Lodge, T. P.; Sammler, R. L.; Li, Y.; Brackhagen, M. Interplay of Phase Separation and Thermoreversible Gelation in Aqueous Methylcellulose Solutions. Macromolecules 2013, 46 (1), 300−309. (50) Fairclough, J. P. A.; Yu, H.; Kelly, O.; Ryan, A. J.; Sammler, R. L.; Radler, M. Interplay between Gelation and Phase Separation in Aqueous Solutions of Methylcellulose and Hydroxypropylmethylcellulose. Langmuir 2012, 28 (28), 10551−10557.
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