Combining label-free cell phenotypic profiling with computational approaches for novel drug discovery.

Expert Opin. Drug Discov. Downloaded from informahealthcare.com by University of Ulster at Jordanstown on 04/04/15 For personal use only.

Review

1.

Introduction

2.

Label-free cell phenotypic drug discovery

3.

Computational drug discovery

4.

Conclusion

5.

Expert opinion

Combining label-free cell phenotypic profiling with computational approaches for novel drug discovery Ye Fang Corning Inc., Biochemical Technologies, Science and Technology Division, Corning, NY, USA

Introduction: Drug discovery is a long and costly process. Innovations and paradigm shifts are essential for continuous improvement in the productivity of pharmaceutical R&D. Areas covered: The author reviews the progress of label-free cell phenotypic and computational approaches in early drug discovery since 2004 and proposes a novel paradigm, which combines both approaches. Expert opinion: Label-free cell phenotypic profiling techniques offer an unprecedented and integrated approach to comprehend drug--target interactions in their native environments. However, these approaches have disadvantages associated with the lack of molecular details. Computational approaches, including ligand-, structure- and phenotype-based virtual screens, have become versatile tools in the early drug discovery process. However, these approaches mostly predict the binding of drug molecules to targets of interest and are limited to targets that are either well annotated for ligands or that are structurally resolved. Thus, combining label-free cell phenotypic profiling with computational approaches can provide a potential paradigm to accelerate novel drug discovery by taking advantages of the best of both approaches. Keywords: chemical similarity, drug discovery, label-free cell phenotypic approach, ligand-based virtual screen, polypharmacology, structure-based virtual screen, text mining Expert Opin. Drug Discov. (2015) 10(4):331-343

1.

Introduction

Drug discovery often starts with a hypothetical target for a disease, or a diseaserelevant phenotype of a chemical (e.g., a natural product). Accordingly, early drug discovery is achieved mainly through two distinct strategies, target-based and phenotypic approaches [1,2]. Target-based approaches have been dominating drug discovery over the past three decades; however, in recent years, there has been a renewed interest in phenotypic screens [3,4]. This paradigm shift is in part due to advances in both high-throughput phenotypic assay techniques and target identification methodologies. Label-free cell phenotypic techniques have been emerging as an attractive approach for screening and profiling drugs in native cells, owing to their ability to provide rich information content, real-time kinetics, flexible assays and high throughput, beside wide pathway coverage and ability in multi-target profiling and screening that are common to all phenotypic assays [5-7]. Besides empirical screening, computational approaches have also been integrated in drug discovery. Ligand-based virtual screening (LBVS) begins with the known pharmacophores to compute and prioritize compounds based on their chemical similarity to ligands annotated for the target of interest [8-13]. A pharmacophore is an abstract description of molecular features, which are necessary for molecular 10.1517/17460441.2015.1020788 © 2015 Informa UK, Ltd. ISSN 1746-0441, e-ISSN 1746-045X All rights reserved: reproduction in whole or in part not permitted

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Innovations and paradigm shifts are critical to improve pharmaceutical R&D. Label-free cell phenotypic techniques permit interrogation of drug--target interactions in their native environments with high-throughput, rich texture and wide coverage in pathways, targets, cells and cell phenotypes. Computational approaches, including ligand-, structureand phenotype-based virtual screening, have been maturing into an effective hit identification technique. Both label-free and computational approaches have their own limitations. Integration of both label-free cell phenotypic and computational approaches takes advantages of the best of both techniques, and enables complementary and cost-effective ways for early drug discovery. A strategy to effectively combine both approaches is proposed with an aim to accelerate novel drug discovery.

This box summarizes key points contained in the article.

recognition of a ligand by a target protein [14,15]. Structurebased virtual screening (SBVS) starts with the known structures or homologues of protein targets to compute the binding of tens of millions of compounds to the target structures [16-22]. Phenotype-based virtual screening begins with text mining of published information to extract clinical or experimental phenotypes (e.g., side-effect labels) of drug molecules, and then to predict their target(s) based on phenotype similarity [23,24]. However, label-free cell phenotypic profiling techniques often lack molecular details, so it may be difficult to deconvolute target(s) and pathways and to govern lead optimization process [5]. On the other hand, virtual screen techniques are not only limited to targets that are either well annotated for ligands or structurally resolved, but also only predict binding of drug molecules [10]. Herein, I first review the progress of label-free cell phenotypic and virtual screen approaches used in early drug discovery process since 2004, and then present a strategy to accelerate novel drug discovery by taking advantages of the best of both approaches. 2.

Label-free cell phenotypic drug discovery

Recent advances in instrumentation and assays have made label-free biosensors versatile platforms for cell phenotypebased drug discovery with high throughput and rich texture [25-27]. Label-free biosensors generally consist of three components -- a biological element (e.g., living cells), a detector system and a transducer between them (Figure 1A). These biosensors employ the transducer to translate a drug-induced cellular response into a kinetic and integrated cell phenotypic response, also termed label-free profile or label-free cell phenotypic profile, which is recorded in real time using the 332

detector system [25]. In general, label-free cell phenotypic approaches consist of four critical components including biosensors, cells, cell phenotypes and assays (Figure 1B). Biosensors that have been used for label-free cell phenotypic profiling include electrical impedance biosensor, resonant waveguide grating (RWG), surface acoustic wave (SAW), surface plasmon resonance (SPR) and quartz crystal microbalance with dissipation (QCM-D). These biosensors differ in detection principle (reviewed in [28]), throughput and the origin of biosensor output signals (Table 1). For instance, both RWG and SPR measure dynamic mass redistribution (DMR) arising from receptor signaling, which is often associated with protein trafficking, and remodeling of adhesion, cytoskeleton and morphology [29]. In contrast, QCM-D measures energy dissipation that is mostly sensitive to remodeling of cell adhesion complexes [30]. Furthermore, RWG and SPR are non-invasive, while the others use an electric input signal for detection and are minimally invasive. Non-invasiveness is important for studying ion channels and electrogenic transporters that are sensitive to the membrane potential [31,32]. In addition, RWG, SAW and electric biosensors all are made readily in automation-friendly microplate formats and thus amenable to profiling and screening. Almost all types of cells can be studied using label-free techniques. These include physiologically and/or clinically relevant cells, such as primary [33,34] and stem cells [35,36]. Compared with recombinant lines, primary or stem cells retain many functions seen in vivo and express endogenous targets in their native signaling circuitry, leading to better predictive power. Label-free biosensors can be used to study both adherent and suspension cells [37], as well as heterogeneous samples including tissue cells, co-culture and differentiated products of stem cells [36,38]. The use of cell panels for labelfree profiling can cast a wide net to systematically project the biological activities of compounds, given that each cell line has unique expression pattern of functional receptors and signaling circuitry [32,39]. Compared with a single cell line, the cell panel not only increases hit rates, but also allows for fine classification of active compounds. For a typical highthroughput screen (HTS), a hit refers to a compound that yields a desired size of inhibition or activation effects in the screen, while hit rate describes the percentage of hits over the total number of compounds screened. Large panels of disease-relevant cell lines annotated for both genetic and pharmacological data, as exemplified by NCI60 which consists of 60 (now 59) human cancer cell lines from nine different tissues [40], are powerful tools for drug discovery [41,42]. Compared with traditional molecular assays (e.g., second messengers, enzymes, kinases), label-free biosensors have higher sensitivity, permitting studying endogenous receptors with wide coverage in targets and pathways. For instance, we had profiled endogenous G protein-coupled receptors (GPCRs) in ReNcell VM human neural progenitor stem cell and its differentiated products using RWG biosensorenabled DMR agonist assay with a library of 47 agonists

Expert Opin. Drug Discov. (2015) 10(4)

Combining label-free cell phenotypic profiling with computational approaches for novel drug discovery

A.

B. Cells Transformed, primary, stem, tissue, and engineered cells

Assays Agonism, antagonism, pathway modulation, acute/chronic, temporal stimulation

Cell phenotypes Adhesion, cycle, division, proliferation, death, signaling, infection, migration, invasion, cell-cell communication ..

Biosensor RWG SAW SPR QCM-D Electric impedance

Drug Cell

Transducer


Detector system

Figure 1. Key components of label-free cell phenotypic profiling and screening techniques. (A) A schematic drawing showing the principle of a label-free biosensor for whole cell sensing. (B) Four common critical components of label-free cell assays; namely, biosensors, cells, cell phenotypes and assays. QCM-D: Quartz crystal microbalance with dissipation; RWG: Resonant waveguide grating; SAW: Surface acoustic wave; SPR: Surface plasmon resonance.

Table 1. The characteristics of different label-free biosensors used for cell phenotypic profiling and screening. Biosensor Electric biosensor Resonant waveguide grating Surface plasmon resonance Quartz crystal microbalance with dissipation Surface acoustic wave

Detection principle

Origin of signal

Impedance between cells and microelectrode array excited with sinusoidal voltages Local refractive index-sensitive surface bound electromagnetic wave generated with diffractive grating-based coupling of broadband light Local refractive index-sensitive surface bound electromagnetic wave generated with incident light with variable angles on a gold thin film Frequency and energy dissipation of a quartz disc, sandwiched between two electrodes, excited with an alternating current Surface bound mechanical acoustic wave of a piezoelectric substrate excited with a sinusoidal electrical current between two electrodes

Throughput

Dynamic ionic redistribution recorded as kinetic changes in cell index DMR recorded as kinetic shifts in resonant wavelength

Up to 384-well Up to 1536-well

DMR recorded as kinetic shifts in resonant angle

Up to four channels

Dynamic viscoelasticity alteration recorded as kinetic changes in resonant frequency and energy dissipation Dynamic viscoelasticity/mass alteration recorded as kinetic changes in amplitude/ shift of the acoustic wave

Singular

Up to 96-well

DMR: Dynamic mass redistribution.

that theoretically activate over 110 known GPCRs [36]. We had found that 17 agonists are active in the undifferentiated ReN cells, while 22 are active in the differentiated cells. According to gene expression patterns before and after differentiation, we had found that for a group of GPCRs whose mRNA levels were barely changed during differentiation, their cognate agonists (e.g., acetylcholine for CHRM3, adenosine for ADORA1, glucagon for GCGR and endothelin-1 for EDNRB) triggered quite distinct DMR profiles in both undifferentiated and differentiated cells. Interestingly, for adrenergic receptor (AR) family, only ADRA2B was expressed in ReN cells, and consistent with its decreased expression after differentiation was that its cognate agonist epinephrine resulted in a decreased DMR signal. Similar trend was also observed for neurotensin, an agonist for NTS1. In addition, g-aminobutyric acid triggered a robust DMR only in the differentiated cells, consistent with the up-regulation of GABBR1 and GABBR2 after differentiation. On the other hand, dopamine triggered a DMR with altered characteristics

after differentiation, consistent with the fact that the native ReN cells only expresses the Gi-coupled DRD4, but the differentiated cells also express the Gs-coupled DRD1, beside DRD4. These results suggest that label-free cell phenotypic approach not only has high sensitivity to discriminate between targets, pathways and cell type, but also enable multi-target profiling. A wide range of cell phenotypes can be studied using labelfree techniques. These phenotypes include cell adhesion, cycle, division, growth, death, receptor signaling, pathogen infection, cell migration and invasion and cell--cell communication [6,7,43]. Label-free biosensors often employ twodimensional (2D) cell models for drug profiling. Our recent study showed that the invasion of 3D spheroidal colon cancer cells through 3D Matrigel can also be monitored in real-time at single cell level using a spatially resolved RWG imager [44]. 3D cell models have been postulated to span the gap between 2D culture and animal models for early drug discovery [45]. With innovative assay design, label-free techniques have


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been be used to examine many other possible cell phenotypes including T-cell activation [46], type I allergy response [47], pathogen infection [48] and cardiac rhythmic beating [49,50]. A diverse range of assays can be realized using label-free techniques, owing to their non-invasiveness in measurement as well as sensitivity to cell numbers [51] and states [35,52]. The pharmacological action of drugs can be characterized with fine details by combining multiple assays [53]. First, agonism assay is useful to identify activators that trigger detectable label-free profiles in a cell line, when the cells are directly stimulated with each compound. Second, competitive antagonism assay is useful to differentiate competitive antagonists from allosteric modulators, when the cells are stimulated with each compound together with a known agonist for the receptor of interest. Third, antagonism/ desensitization assay is useful to separate different ligands based on their specificity, potency and modes of action (agonist vs antagonist), when each ligand is introduced before stimulation with a known agonist for the receptor of interest. Fourth, ligand washout assay is useful to separate different ligands mostly based on their binding kinetics, when the cells are first treated with a ligand, followed by the removal of the ligand through washing, and finally the stimulation with a known agonist for the receptor of interest. The binding kinetics, in particular drug residence time (the reciprocal of Koff), is known to have a direct impact on drug pharmacology [54], and label-free profiles of drugs [37,55,56]. Fifth, ligand reverse assay is useful to examine the ability of a ligand to reverse the label-free profile of a known agonist for the receptor of interest, when the cells are first stimulated with the agonist, followed by the treatment with a ligand for the same receptor [53]. Sixth, pathway deconvolution assay is useful to examine pathways that contribute to the label-free profile of an agonist, when the cells are first treated with different pathway modulators, followed by the agonist for the receptor of interest [52]. Seventh, label-free integrative pharmacology on target (iPOT) approach is useful to classify a panel of ligands for a receptor, when all ligands are assayed using a panel of probe molecule-modulated cells [57-59], or a panel of different cell lines, including different recombinant clones [60]. These probe molecules (e.g., RNAi, pathway inhibitors) are used to alter specific pathways downstream receptor signaling, so that the pathway-biased activity, if any, of drugs can be systematically studied. The iPOT approach uses similarity analysis to segregate different ligands for a receptor into different clusters, based on the label-free profiles of ligands obtained under different conditions. The resultant heat map is considered to be a self-referencing pharmacological activity map, wherein ligands within each subcluster shall share similar mechanism of action (MOA). Similarity analysis is commonly used to determine the relationships (i.e., similarity or distances) among different biological responses, particularly for large sets of biological data, such as gene expression data [61]. Eighth, both acute and chronic effects of drugs on cells can be studied. For 334

instance, the acute and chronic toxicity of compounds on cardiomyocytes have been examined using electric biosensor [49]. Ninth, the label-free profiling of compounds can be performed in the context of a specific cellular process such as cell adhesion [62], growth [63,64], signaling [25] or viral infection-induced cell death [65]. Since label-free biosensors monitor in real-time the cellular response upon stimulation, the label-free profile of a drug consists of both background and drug-induced responses [25]. The background signal is dependent on the life cycle of cells on the biosensor surface. During the adhesion and growth phases of cells, the background signal can be greater than the compound-induced responses. For this reason, for receptor signaling and viral infection studies, assays are mostly performed after the cells form a monolayer with a minimal background signal. Nonetheless, the distinct effects of compounds on different cellular processes can be examined using label-free biosensors during different phases of the life cycle of cells on the biosensor surface, so the impact of drugs on the entire life cycle of cells can be fully comprehended. Tenth, combining biosensors with microfluidics may offer extra advantages associated with spatial and temporal controls of chemicals exposed to cells. A biosensor equipped with a microfluidic device is also known as a microfluidic biosensor system [66,67]. Such microfluidic biosensor systems have been used to differentiate short- and long-acting agonists and to elucidate the pathways governing the ligand-directed desensitization for b2-AR [68], detect ligand-directed functional selectivity on trafficking of thrombin receptor [69], and examine the function of binding kinetics-driven antagonist occupancy at the endogenous muscarinic M3 receptor in HT-29 cells [55]. Microfluidic biosensor systems have also been used to identify biased antagonists that selectively inhibit the cell plasma membrane-initiated signaling waves but not intracellular signaling wave mediated by b2-AR [53], and to dissect distinct mechanisms (e.g., binding kinetics, cellular uptake and retention) governing the efficacy of kinase inhibitors [70]. Of note, besides real-time kinetic measurements that can provide rich textures for elucidating the MOA of drugs, label-free techniques can also be used to screen drugs with high throughput by using end point or multipoint measurements [6,7,71]. 3.

Computational drug discovery

Current drug discovery still heavily relies on the use of empirical and trial-and-error approaches. However, computational approaches have become a more prominent and ubiquitous tool in the early phases of drug discovery process since 1980s, with an aim to maximally exploit the available chemical space at minimum cost to identify virtual hits that can be experimentally confirmed and followed-up [72]. In this review, I limit my discussion to three computational approaches, namely ligand-, structure- and phenotype-based virtual screen techniques.




3.1

Ligand-based virtual screen

LBVS predicts the probability of compounds binding to a specific target through comparing their chemical similarity to a set of known ligands for the target (direct drug-target association approach), or neighboring targets that are related to an indication but are strongly associated by chemoinformatics (target-hopping approach) [73]. LBVS assumes the similarity principle, that is, similar compounds will likely have similar properties and bind to the same group of proteins [74,75]. Central to the LBVS is the pharmacophore model, which describes the molecular framework carrying the ensemble of steric and electronic features responsible for the biological activity of drugs [76,77]. A pharmacophore synopses the largest common denominator of the molecular interaction features or fingerprints shared by a set of molecules [14,15]. Pharmacophores can represent and identify molecules on a 2D or 3D level by schematically depicting the key elements for molecular recognition. For LBVS, compounds are represented by a unique data (bit) string, such as SMILES string. The most common method to annotate compounds is to use 2D topological fingerprints such as Daylight and Scitegic extended connectivity fingerprints [78-80]. Target prediction of a chemical compound can be made by querying existing databases for the known biological activity map of structurally similar compounds [81]. A pharmacophore query can be constructed to extract the correct molecular organization for the required interaction pattern using a set of known multiple active ligands (and inactive derivatives). After validation of the query, different molecules can then be systematically compared at the pharmacophore level using a distance measure so all molecules tested can be ranked by similarity. The Tanimoto coefficient, although it is not a true distance measurement, is the most common way to compare molecular fingerprints for similarity. Tanimoto coefficient is the ratio of the number of features common to both molecules (the intersection of the data strings) to the total number of features (the union of the data strings), and gives values in the range of zero (no bits in common) to unity (all bits the same) [79]. Hits with the highest similarity are predicted as the most likely candidate to bind and are then tested experimentally. LBVS allows for organizing biological targets based on chemical patterns among the ligands that bind to them, instead of protein structures, sequences or functions [10-12,82,83]. Paolini et al. applied the simplest form of chemical similarity -- full chemical identity among ligands shared by two or more receptors -- to construct a polypharmacology interaction network [8]. Such a chemical view of drug pharmacology is useful to explore the global relationships between chemical structure and biological targets, and thus fosters probabilistic modeling to predict polypharmacology and assess the druggability of protein targets. Similarly, RaskAndersen et al. also constructed a topographical drug--target network from their curated data set consisting of 989 marketed

drugs and 435 effect-mediating protein targets [84]. They found that the drug--target network consists of many clusters of connected drug targets. Among them, the largest cluster constitutes of 50% of all drugs and is mostly biased towards a limited number of receptor and transporter targets. These drugs also have relatively long history with a median approval year before 1982, suggesting that the main innovations within this cluster of successful drugs occurred before the ‘one drug, one target’ focus of recent times. Notably, the drugs in this cluster have relatively high number of target interactions with an average of three targets, suggesting that polypharmacology may indeed contribute to the success of some of these drugs [85]. LBVS can be used to predict primary and off-targets of drug and probe molecules. Shoichet and collaborators had developed a similarity ensemble approach (SEA), combining statistical models derived from the BLAST algorithms with chemical similarity, for target prediction [11-13]. The SEA predicts a likely target for a drug when the known ligands for a target resemble the ‘bait’ drug much more closely than would be expected at random. Using the SEA, they had discovered primary and off-targets for over 30 drugs against over 40 targets. For instance, they investigated the probability of total 65,241 drugs binding to 246 receptors, with a median and mean of 124 and 289 ligands per target, in the MDL Drug Data Report [11]. They predicted and confirmed the antagonistic effects of the µ-opioid methadone on muscarinic M3 receptor, the activity of the antiparasitic emetine on a2-ARs, and the activity of the gut µ-opioid loperamide on neurokinin 2 receptor, which may be related to their respective clinical side effects. In another study, they had identified the primary targets of seven orphan drugs [73]. These include s-receptor for three antitussives, clemastine, cloperastine and nepinalone; dopamine D2 receptor for the antiemetic benzquinamide; muscarinic M1, M2 and M3 receptors for the muscle relaxant cyclobenzaprine; three aminergic transporters (SERT, NET and DAT) and 5HT2 receptors for the analgesic nefopam and cyclooxygenase-2 for the immunomodulator lobenzarit. Structure-based virtual screening SBVS starts with the promise that the structures of a target will provide a template to predict the binding of compounds within a database. As the structures of more and more proteins become available, SBVS is increasingly used in screening large libraries for compounds that complement targets of known structure. SBVS can predict new ligands for a known 3D structure, in some cases with hit rates significantly greater than those using empirical HTS [18,19,86,87]. In a seminar study, Feng et al. compared the hits identified using SBVS with those obtained using a quantitative HTS of > 70,000 compounds against AmpC b-lactamase [88,89]. Unexpectedly, the empirical screen yielded 1274 initial hits, > 95% of which are false positives due to colloidal aggregation-mediated inhibition of the enzymatic activity. Besides 25 known covalent binders to 3.2


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b-lactamase and 9 promiscuous covalent inhibitors, no reversible, specific, competitive inhibitors were found by HTS. On the other hand, SBVS of the same compound library predicted 16 high-ranking docking hits, 2 of which were confirmed as mid-micromolar competitive inhibitors. Of note, for HTS, a false positive is a truly inactive compound that yields a positive effect, while a false negative is a truly active compound that yields no effect. SBVS can be used as a complementary technique to empirical screen. In fact, hits that are unique to each technique have been discovered for the same target, suggesting that both approaches can be complementary [89]. Compared with typical HTS, SBVS can sample a much larger number of possible new ligands at much lower cost. Although SBVS are also plagued by false positives and negatives, the accuracy of SBVS-based prediction can be improved by using probebound receptor structures. Using the X-ray structure of the b2-AR in complex with the inverse agonist carazolol, Kolb et al. used molecular docking to virtually screen a library of close to one million commercially available, ‘lead-like’ molecules [90]. Based on calculated complementarity to the receptor and their novelty, 25 molecules were selected from the top-ranking 0.05% of the library. Follow-up binding assays showed that six were active with binding affinities < 4 µM, the best of which has a Ki of 9 nM, and five of which were inverse agonists. This suggests that SBVS is effective in discovering high-quality hits, although the docking hits may bias towards the conformation stabilized by the bound probe ligand [90,91]. An increasing number of proteins have been crystallized in the presence of a potent ligand with a unique biological function. In the past decade, > 110 structures of 26 different GPCRs have been determined in complex with ligands of varied pharmacology, peptides, antibodies and a G protein [92,93]. Given the importance of GPCRs in drug discovery [94], these structures have spawned great interests in in silico screening of small molecules for these receptors. Molecular docking studies using X-ray and homology models has led to identification of novel chemical ligands for adenosine A2A receptor [95], D3 dopamine receptors [96] and CXCR4 receptor [97], demonstrating the great potential of X-ray structure-based drug discovery for GPCRs.

unrelated drugs may be caused by their common primary or off-targets [101]. In a seminar work, Campillos et al. used phenotypic side-effect similarities to infer whether two drugs share a target [24]. They extracted the side effects of marketed drugs from drug labels, which are the regulated recording of side effects summarized in the package inserts of these drugs. Drug labels represent a set of clinical phenotypes that are linked to a number of molecular scenarios, such as direct interactions with the primary or off-targets. They then developed a measure for side-effect similarity and analyzed the likelihood of sharing protein targets for 746 marketed drugs for which side-effect information is available. They predicted unexpected, shared targets for 754 drug pairs, and constructed a network of the 424 drugs that are predicted to have at least 25% probability of sharing a target. In vitro binding assays validated 13 drug--target interactions, of which 11 give rise to binding affinities strong enough to cause side effects, and 7 have Ki values within one order of magnitude of the measured average drug plasma concentrations and thus appear relevant in vivo.

Phenotype-based virtual screen with text mining Text and data mining of available biomedical data and information [98,99] are useful for the identification of diseaseassociated networks, therapeutic targets and diagnostic or prognostic markers [100]. For instance, many molecules have been tested in animal models and human beings, and their phenotypes, such as undesired side effects, may have been reported in literature, such as papers and patent applications. Mining and similarity analysis of the phenotypes of drug and probe molecules reported in literature represents an alternative means to discover the primary and off-targets of these molecules. This approach assumes that similar side effects of

Central to early drug discovery is to identify lead molecules for druggable targets. Empirical HTS approaches come not only with high cost, but also often with high false-positive and -negative rates [102]. Essential to HTS is to minimize falsepositive and false-negative rates. Besides the positional effects of wells within plates that are one of the common causes of false results [102], the false negatives are often due to the fact that these screens are mostly biased towards a single molecular event (e.g., binding, a signaling molecule or a gene reporter) [2]. Such a molecular view is contradictory to the behavior of receptors, that is, receptor signaling is mostly encoded in a series of temporal and spatial events [53,103], and ligands often

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4.

Conclusion

Label-free biosensors enable cell phenotypic profiling and screening of drug molecules with a wide coverage in targets, pathways, cellular processes and cell types. Coupled with high sensitivity, throughput and information content, labelfree cell phenotypic techniques hold great potential in early drug discovery, ranging from target identification to hit discovery to lead prioritization and selection. LBVS is useful for not only predicting primary and off-targets of drug molecules, but also allowing for rapid library expansion through chemical similarity search. SBVS can predict the binding of almost unlimited number of compounds for the structures of protein targets. Phenotype-based text mining and similarity analysis is also useful to predict off-targets of drug molecules. Computational approaches including ligand-, structure- and phenotype-based virtual screens offer a complementary means to empirical approaches, such as label-free profiling techniques, to facilitate early drug discovery. 5.

Expert opinion




have distinct operational bias to modulate the different behavior of the receptor, such as GPCRs [104] and nuclear receptors [105]. On the other hand, the false positives are often related to the chemo-physical properties (e.g., aggregation [88,89], colors or fluorescence [106,107]) of compounds, and the presence of compensatory targets/pathways [25]. Label-free cell phenotypic assays measure the complexity of receptor signaling and drug pharmacology [5-7,108]. These assays offer many advantages, for instance, they enable multi-target-based screens [39], manifest polypharmacology of ligands when active in a cell or cell panel [60] and characterize the molecular and biochemical MOAs of lead-like molecules [5,55-59]. For instance, we had used RWG biosensor-enabled DMR assays to investigate the subclonesensitive cell phenotypic pharmacology of a library of 69 AR ligands at the b2-AR stably expressed in HEK-293 cells [60]. The library consists of known ligands for all nine family members of the ARs. The parental HEK293 cell line was transfected with green fluorescent protein-tagged b2-AR, and four stable subclones were established. Results showed that HEK293 endogenously expresses functional Gi-coupled a2-AR and Gs-coupled b2-AR, and epinephrine, norepinephrine and methylnorepinephrine all activated both receptors. Interestingly, pathway deconvolution revealed that distinct ligands have different abilities to activate the cAMP-Epac pathway through the b2-AR. However, these assays also come with several disadvantages, which have been discussed in detail previously [5]. Briefly, first, like many phenotypic screens, targets may be difficult to be determined solely based on the label-free profiles of drugs. Second, structure--activity relationship (SAR) among active hits may be difficult to be quantified, when some of these compounds display polypharmacology. Third, label-free alone may not be effective in guiding lead optimization process, given that the label-free profiles often lack molecular details. Computational approaches have become versatile tools in early drug discovery in recent years. Nowadays, these approaches are often integrated into drug discovery campaigns. However, these approaches have their own limitations. LBVS is only limited to targets that are well annotated for ligands. Although it has been estimated that LBVS is applicable to over 2500 targets, many LBVS campaigns did not result in positive predictions when targets are poorly annotated for ligands, or when the drug does not share chemotypes with the ensemble of annotated ligands for the target(s) [73]. On the other hand, SBVS is constrained by the 3D structure of proteins in crystal form for docking. Protein targets are known to be dynamic in solution and in cells, and often sample a vast ensemble of conformations, some of which are dominant for the binding of distinct classes of ligands [109,110]. Ligands often select the most favored conformation for binding [111]. The rigid structure used for docking only represents one possible conformation of a protein target, so SBVS may also only discover one class of ligands, but not all possible classes of ligands that can bind to the target [90,91].

Furthermore, these approaches only predict compound binding, but not functional activity; and the virtual hits still need to be confirmed experimentally. I here propose a strategy to combine label-free cell phenotypic profiling with computational approaches for novel drug discovery (Figure 2). This strategy consists of multiple iterative steps: i) identifying hits for a novel target; ii) identifying probe and endogenous ligands; iii) predicting biological functions and iv) discovering lead-like molecules and assessing druggability. First, for hit identification, compounds are first screened in a cell line expressing the target of interest using label-free cell phenotypic assays. Similar to typical HTS campaigns, hits are selected from the library based on a desired size of response, and then confirmed through dose--responses and/or counterscreen using an engineered cell line wherein the target receptor is deleted. Only hits that are selective to the target receptor are followed-up. Alternatively, hits can be identified based on similarity (dissimilarity) of their cell phenotypic profiles in a native cell line expressing the target of interest, or a panel of cell lines [5]. The classification based on the profiles in a single cell line may not be sufficient to identify specific hits for the target, because there is abundance in cell signaling pathways, the activation of which could lead to similar, or even identical label-free profiles [25,36]. A panel of cell lines can be used to increase the differentiation power of label-free profiles, and reference compounds that are known to bind to the target of interest are included as the bait to fish out hits for the target. Alternatively, LBVS may be used to sort out hits that activate other compensatory targets so hits for the target of interest can be enriched. SBVS based on known or homology structures may also be used to virtually confirm the specificity of hits to the target of interest. The fusion of multiple VS methods, such as similarity searches and docking to targets and their next neighbors, can further increase the success rate [112]. Second, probe and endogenous agonists are identified by combining both approaches. One or more representative hits identified in label-free screen are used as the bait(s) to search similar compounds from several public databases (Table 2). Alternatively, active fragments or substructures derived from SAR analysis of the hits can also be used to search similar novel structures. Both methods can quickly expand the compound library for follow-up with label-free profiling, so potent probes, and even endogenous agonist(s), for the novel target can be quickly discovered. For instance, we started with a small library of compounds that were synthesized as intermediates of either non-linear optical or organic field-effect transistor materials, and identified two chemical series, 2-(4-methylfuran-2(5H)-ylidene)malononitrile and thieno[3,2-b]-thiophene-2-carboxylic acid derivatives, as GPR35 agonists [113]. SAR analysis pointed to the importance of malononitrile group for activating GPR35 (Figure 3A). This led us to search malononitrile-containing known compounds from public databases, and identified


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Hit ID

Counter screen with label-free Similarity analysis of label-free profiles Enrichment of target-specific hits with LBVS Structures or homology model-based docking

Probe ID

Expand library using LBVS and text mining Iterative screen with label-free

Target function prediction

Label-free probing of disease relevant cells Text mining of the phenotypes of probes Target hopping using LBVS

Lead ID

Label-free on-target pharmacology assessment Label-free toxicity assessment Drug-like property analysis Ligand efficiency analysis

Figure 2. A strategy combining label-free cell phenotypic profiling with computational approaches for novel drug discovery. This strategy consists of four steps. First, once identified in a native cell line, hits are confirmed by counter screen using an engineered counterpart with label-free cell phenotypic assays. Alternatively, similarity analysis based on label-free profiles in one or multiple cell lines can be used to sort out the potential target for the active hits using reference compounds. LBVS and molecular docking can also be used to enrich target-specific hits. Second, probes with interesting biological functions or desired potency can be identified through expanding and profiling compound library using LBVS and text mining, followed by label-free screening and profiling. Third, the biological functions of target can be predicted based on label-free studies of disease-relevant cells that express the target of interest, followed by text mining of the phenotypes of probe molecules and target hopping using LBVS. Fourth, lead-like molecules can be identified or selected from a group of ligands active at the target of interest using label-free on-target pharmacology approach and label-free toxicity assays, in combination with in silico analysis of drug-like properties and ligand efficiency. LBVS: Ligand-based virtual screening.

tyrphostins, a family of tyrosine analogs designed and developed for inhibiting tyrosine kinases [114] that were enriched in commercially available kinase inhibitor libraries. Therefore, we screened these libraries (total 240 inhibitors) and found that 11 out of the 25 hits are tyrphostins, and tyrphostin-51 is the most potent ligand with an EC50 of 0.19 µM, and tyrphostin-25 the second with an EC50 of 0.94 µM (Figure 3B) [115]. Profiling of tyrosine metabolites further led us to discover that multiple tyrosine metabolites including 5,6-dihydroxyindole-2-carboxylic acid and 3-nitro-L-tyrosine (Figure 3C) are GPR35 agonists, suggesting that these metabolites may be endogenous ligands of GPR35, and GPR35 may represent a druggable target for treating certain 338

diseases associated with abnormality of tyrosine metabolism [116]. The agonistic activity of 3-nitro-L-tyrosine further led us to search nitrophenol compounds from databases and commercial sources. Follow-up profiling of nitrophenols led to the identification of 4,4¢-(2,2-dichloroethene-1,1-diyl)bis-(2,6-dinitrophenol) (Figure 3D) as a potent GPR35 agonist with EC50 of 6nM [117]. Although this compound is ‘ugly’ from medicinal chemists’ view, its high potency suggests that it may be a reasonable probe molecule for elucidating the biology of GPR35. Third, the biological functions of novel targets may be able to be predicted by combining label-free profiling with computational approaches. Once probes or endogenous agonists were identified, label-free biosensors can be used to explore the biological significance of the target by using disease-relevant cell models. Text mining of the in vivo effects or phenotypes of probe and endogenous molecules is also useful. For instance, the discovery of cromolyn disodium and nedocromil sodium, the two clinically used anti-asthma chromones, as GPR35 agonists suggests that GPR35 may be a potential target for the treatment of asthma [118]. This led us to search for phytochemicals that have been reported to suppress allergic reaction-related cellular responses in vitro and/ or allergic symptoms in vivo. We hypothesized that some of these anti-inflammatory natural products may modulate allergic responses via their agonistic activity at the GPR35. Results showed gallic acid, caffeic acid, wedelolactone and ellagic acid are indeed GPR35 agonists [119]. Insights could also be made by using target-hopping approach with LBVS [73]. The targethopping approach relies on ligand similarity to predict the in vivo effects of drug molecules or the biological functions of a novel target. Here, a polypharmacology interaction network of different proteins based on a set of probe molecules for a novel target is first established, the in vivo effects or phenotypes of the set of probe ligands are then mined from public databases. After eliminating established target connections of specific phenotypes or effects, the biological function of the novel target can be predicted based on the remaining phenotype(s) or effect(s) that are common to the set of probe molecules. The polypharmacology network enables a deeper understanding of compound and target cross-reactivity (promiscuity) and provides rational approaches to lead hopping and target hopping. For instance, a LBVS-based target-hopping strategy was used to predict the target for the muscle relaxant cyclobenzaprine [73]. Cyclobenzaprine is known to bind to multiple targets, but none of its known targets, such as transporters, is consistent with the muscle relaxation indication. Ligand similarity analysis first predicted that cyclobenzaprine binds to histamine H1 receptor, which, by ligand similarity, is associated with muscarinic receptors, wellaccepted targets for muscle relaxation. Experimental data confirmed that cyclobenzaprine indeed binds to H1, M1, M2 and M3 receptors, all with a Ki value < 100 nM. Given that cyclobenzaprine has maximum plasma concentrations of 16 -- 31 nM, muscarinic receptors are the target linked to its




Table 2. Public ligand--target databases. Database

Website

Key information

ChEMBL PubChem DrugBank

https://www.ebi.ac.uk/chembl/ https://pubchem.ncbi.nlm.nih.gov/ http://www.drugbank.ca/

PDSP KiDB

http://pdsp.med.unc.edu/kidb.php

ZINC

http://zinc.docking.org/

A.

Ref.

Information on the biological activities of ~ 1.5 million small molecules Information on the biological activities of small molecules A bioinformatics and chemoinformatics resource for marketed and experimental drugs A database for Ki, or affinity, values for drugs and probes at G proteincoupled receptors, ion channels, transporters and enzymes A free database of commercially available compounds for virtual screening

[125] [126] [127] [128] [129]

B.

NC

CI

CN

HO

CN

CN

O HO

CN

CI

OH Tyrphostin 25 EC50 = 940 nM

YE120 EC50 = 32 nM C. HO

NH2

O2N

COOH COOH

N H

HO

HO 3-Nitro-L-tryosine

EC50 = 142 µM

DHICA EC50 = 24 µM

D. O2N

CI

CI

NO2

HO

OH

DC-Bi-DNP EC50 = 6 nM

NO2

O2N

Figure 3. The representative GPR35 agonists identified by combining label-free profiling with chemical similarity search and text mining. YE120 (A) was first identified and confirmed to be a full agonist for GPR35 in HT29 cells from our label-free screen. Structure--activity analysis led us to search for and profile known malononitrile-containing compounds from public database, leading to identification of a family of tyrphostins, including tyrphostin-25 (B) as GPR35 agonists. This, in turn, led us to postulate and confirm that multiple tyrosine metabolites, including DHICA (C), are GPR35 agonists. In particular, the agonistic activity of 3-nitro-L-tyrosine (C) led us to search for and profile nitrophenols from public database, yielding DC-Bi-DNP (D) as one of the most potent agonist known for GPR35. EC50 values reported were obtained using dynamic mass redistribution assays in HT29 cells. DC-Bi-DNP: 4,4¢-(2,2-dichloroethene-1,1-diyl)-bis-(2,6-dinitrophenol); DHICA: 5,6-dihydroxyindole-2-carboxylic acid.

muscle relaxation effect. In another study, based on the fact that lithocholic acid is an agonist for farnesoid X receptor (FXR, a nuclear receptor) and TGR5 (a GPCR) but also an antagonist for the EphA2 receptor, Tognolini et al. selected

and tested a set of commercially available FXR or TGR5 ligands for their ability to inhibit EphA2 [120]. Results showed that the stilbene carboxylic acid GW4064 was an effective antagonist of EphA2 in the micromolar range.


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Y. Fang

Fourth, lead-like molecules can be identified by combining both label-free cell phenotypic profiling and computational approaches. Label-free profiling, owing to their unbiased nature for detection, can differentiate multiple types of ligands, including antagonists, agonists, biased agonists and allosteric modulators, for a target, as well as polypharmacology. Labelfree integrative pharmacology on-target approach can be used to select and prioritize leads [57-59]. This approach uses different label-free assays in combination with different cell backgrounds to characterize these ligands with fine details, so lead-like molecules can be divided into different clusters [5-7,25,57-60]. Leading candidates from each cluster can then be picked and used for in vitro testing. Leads can also be selected using computational approaches, based on drug-like properties, ligand diversity and efficiency [121-124]. Furthermore, label-free can also be used to investigate the potential toxicity, such as cardiotoxicity, of hit compounds [49,50]. Bibliography Papers of special note have been highlighted as either of interest () or of considerable interest () to readers. 1.

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Y Fang is a research director and fellow in the Science and Technology Division of Corning Incorporated. He is also a stockholder of Corning Incorporated. The author has no other relevant affiliations or financial involvement with any organization or entity with a financial interest in or financial conflict with the subject matter or materials discussed in the manuscript apart from those disclosed.

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Affiliation Ye Fang Corning Inc., Biochemical Technologies, Science and Technology Division, Corning, NY 14831, USA E-mail: [email protected]

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Computational approaches for drug discovery.

Computational approaches in target identification and drug discovery.

A novel strategy for mechanism based computational drug discovery.

Cell and small animal models for phenotypic drug discovery.

Maximizing computational tools for successful drug discovery.

Biophysical Approaches Facilitate Computational Drug Discovery for ATP-Binding Cassette Proteins.

Computational approaches to natural product discovery.

Novel approaches to pulmonary arterial hypertension drug discovery.

Epigenetic approaches for bipolar disorder drug discovery.

Computational methods in drug discovery.

Computational approaches for rational design of proteins with novel functionalities.

Integrating phenotypic small-molecule profiling and human genetics: the next phase in drug discovery.

Phenotypic Screening Approaches to Develop Aurora Kinase Inhibitors: Drug Discovery Perspectives.

Phenotypic Screens in Antimalarial Drug Discovery.

WITHDRAWN: Novel opportunities for computational biology and sociology in drug discovery.

Bond-based bilinear indices for computational discovery of novel trypanosomicidal drug-like compounds through virtual screening.

Engineering Xenopus embryos for phenotypic drug discovery screening.

A unified approach to computational drug discovery.

Computational cell fate modelling for discovery of rewiring in apoptotic network for enhanced cancer drug sensitivity.

Improving drug discovery with high-content phenotypic screens by systematic selection of reporter cell lines.

Novel approaches to lupus drug discovery using stem cell therapy. Role of mesenchymal-stem-cell-secreted factors.

Emerging approaches for histone deacetylase inhibitor drug discovery.

Latest approaches for efficient protein production in drug discovery.

Combining Metabolite-Based Pharmacophores with Bayesian Machine Learning Models for Mycobacterium tuberculosis Drug Discovery.