Best Practices of Computer-Aided Drug Discovery: Lessons Learned

Perspective pubs.acs.org/jcim

Best Practices of Computer-Aided Drug Discovery: Lessons Learned from the Development of a Preclinical Candidate for Prostate Cancer with a New Mechanism of Action Fuqiang Ban, Kush Dalal, Huifang Li, Eric LeBlanc, Paul S. Rennie, and Artem Cherkasov* Vancouver Prostate Centre, Department of Urologic Sciences, Faculty of Medicine, The University of British Columbia, 2660 Oak Street, Vancouver, British Columbia, Canada V6H 3Z6

ABSTRACT: Small-molecule drug design is a complex and iterative decision-making process relying on pre-existing knowledge and driven by experimental data. Low-molecular-weight chemicals represent an attractive therapeutic option, as they are readily accessible to organic synthesis and can easily be characterized.1 Their potency as well as pharmacokinetic and pharmacodynamic properties can be systematically and rationally investigated and ultimately optimized via expert science behind medicinal chemistry and methods of computer-aided drug design (CADD). In recent years, significant advances in molecular modeling techniques have afforded a variety of tools to effectively identify potential binding pockets on prospective targets, to map key interactions between ligands and their binding sites, to construct and assess energetics of the resulting complexes, to predict ADMET properties of candidate compounds, and to systematically analyze experimental and computational data to derive meaningful structure−activity relationships leading to the creation of a drug candidate. This Perspective describes a real case of a drug discovery campaign accomplished in a relatively short time with limited resources. The study integrated an arsenal of available molecular modeling techniques with an array of experimental tools to successfully develop a novel class of potent and selective androgen receptor inhibitors with a novel mode of action. It resulted in the largest academic licensing deal in Canadian history, totaling $142M. This project exemplifies the importance of team science, an integrative approach to drug discovery, and the use of best practices in CADD. We posit that the lessons learned and best practices for executing an effective CADD project can be applied, with similar success, to many drug discovery projects in both academia and industry.

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dominated the area of hit identification5 over the last two decades.6,7 On the other hand, in recent years computer-aided drug design (CADD) has become an integral part of an early discovery process.3,8 Notably, molecular docking9 has advanced as the central technology10 in the pipeline of virtual screening (VS) 11−13 to complement HTS and to enhance lead optimization efforts.14 An efficient molecular modeling computational toolbox has also been developed to assist medicinal chemists to prioritize organic synthesis efforts.14 Various well-developed visualization technologies in molecular modeling and numerous algorithms quantifying protein−ligand interactions can facilitate VS and every aspect of lead optimization. Both structure- and ligand-based types of molecular modeling have been comprehensively reviewed in the literature.8,10,15,16 Although precise rank ordering17 of compounds is still beyond current computational capabilities,18−20 many successes of virtual screening have been

INTRODUCTION The development of an effective and safe drug is an increasingly time-consuming and expensive endeavor with an average cost of US$2.6 billion.2 Typically it begins with the generation of fundamental knowledge on a target disease so that various working hypotheses can be generated, such as modulation of a particular biological pathway that could result in a desired therapeutic response.3 This stage of target identification and validation lays a foundation for all following drug discovery and development efforts. The objective of the consequent hit discovery and optimization stages3 is to find a few candidate molecules that demonstrate adequate potency on the target, exhibit suitable in vivo pharmacokinetics (PK) behavior,3 and possess acceptable toxicity characteristics. Such molecules should also lend themselves to proper formulation and synthetic scalability and adhere to Good Manufacturing Practices (GMP)4 to become suitable drug candidates for subsequent clinical trials. The initial drug discovery efforts have traditionally relied on the high-throughput screening (HTS) technique, which has © 2017 American Chemical Society

Received: March 7, 2017 Published: April 25, 2017 1018

DOI: 10.1021/acs.jcim.7b00137 J. Chem. Inf. Model. 2017, 57, 1018−1028

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Journal of Chemical Information and Modeling extensively reported.21 Hence, the intelligent use of a significant number of various CADD techniques that are already readily available to the scientific community should effectively accelerate research efforts.21,22 This Perspective illustrates the integral role that molecular modeling played in our recent drug discovery project and emphasizes its synergetic use with modern experimental techniques. Various structure-based virtual screening tools assisted by in silico absorption, distribution, metabolism, excretion, and toxicity (ADMET) predictions are highlighted with the examples. A concise update on the recent developments of molecular modeling approaches is also provided in the context of the case study.

avenues involving the AR LBD, such as the development of small-molecule interrupters for critical protein−protein interactions at such AR functional sites as activation function-2 (AF2)29 and binding function-3 (BF3).30−34

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EXPLORING MULTIPLE AR BINDING POCKETS The conventional molecular graphics tools (e.g., MOE, Maestro,35 and PyMOL35 among many others) enable detailed inspection of AR structures cocrystallized with various native steroidal ligands and antiandrogen drugs. For example, the Xray structures of hydroxyflutamide and bicalutamide inside the LBD sites of the AR LBD-T877A and AR LBD- W741L mutants, respectively (PDB entries 2AX6 and 1Z95), are available and provide significant insight into critical protein− drug interactions and explain the structural basis for the conversion of hydroxyflutamide and bicalutamide into AR agonists by the corresponding gain-of-function mutations.36,37 The development of next-generation AR ABS binders, such as enzalutamide,38 ARN-509,39 ODM-201,40and galeterone,41 which aim to override the agonistic ABS mutations, is still a major trend in antiandrogen drug discovery. However, these efforts, as well as the development of AF2- and BF3-directed drugs,30−34 became significantly undermined by the emergence of constitutively active AR variants, many of which lack the entire LBD segment (Figure 3). The occurrence of such truncated forms of the AR in castration-resistant prostate cancer samples prompted us to consider targeting the P-box binding site of the AR DBD,42,43 which is conserved in all known forms of the androgen receptor and thus could provide an entirely novel opportunity to overcome the drug resistance that rises both from AR ABS mutations and AR truncation.

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ANDROGEN RECEPTOR: AN OLD TARGET WITH NEW DRUGGABLE POCKETS The human androgen receptor (AR) has been considered as a master regulator for prostate cancer ever since its seminal discovery by Huggins and Hodges in 1941.23 However, all current clinical AR inhibitors (antiandrogens) bind to the same androgen binding site (ABS) of the receptor and encounter significant limitations of drug resistance.24−26 This challenge motivated the members of the Therapeutics Development Group at the Vancouver Prostate Centre (consisting of multidisciplinary scientists and clinicians) to combine efforts to re-evaluate AR targeting and to develop AR-directed drugs with an entirely novel mode of action. For this purpose, we employed molecular modeling techniques that enable the exploration of all feasible small-molecule binding sites on the AR surface (where the corresponding structural information is available). In particular, we used the Molecular Operating Environment (MOE), a software package27 employed for many modeling tasks, and examined Protein Data Bank (PDB)deposited structures of the ligand binding domain (LBD) of the AR (Figure 1; PDB entry 2PIO) as well as the DNA binding domain (DBD) of the receptor28 (Figure 2A; PBD entry 1R4I). The CADD probing of the AR surface uncovered its significant diversity and illustrated the feasibility of multiple drug discovery

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ADDRESSING THE HOMOLOGY PROBLEM

The DNA binding domain is the most conserved part among all nuclear receptors that share a similar modular organization (Figure 3). As such, it has never been considered as a potential drug target, even though it represents an actual active site of the protein (unlike the steroid binding pocket, which simply acts as an allosteric “switch” that only facilitates AR activation). The sequence identity levels of the AR DBD with the corresponding segments of the estrogen receptor (ER), glucocorticoid receptor (GR), and progesterone receptor (PR) proteins (the three most related major nuclear receptors (NRs)) are 58%, 77%, and 56%, respectively (Figure 4). However, the detailed analysis of the conservation of the DBDs in these NRs and exploration of the corresponding three-dimensional (3D) structures with the MOE Site Finder module identified several critical structural differences that could be utilized for selective targeting guided by the CADD methodology. In particular, MOE Site Finder revealed a targetable site in the crystal structure of the AR DBD (PBD entry 1R4I); however, the corresponding residues Gln238, Gly204, Ala207, Asn217, Lys235, Arg211, and Tyr219 in the ER DBD crystal structure (PDB entry 1HCQ) do not form a detectable binding site (Figure 2B). The residues ER-Arg211 and ER-Gln238 make the neighboring region less accessible to small molecules, providing sufficient structural basis for selective targeting of the AR DBD. Thus, the emergence of a repertoire of resistant ABS mutants and various truncated forms of the AR detected in liquid biopsies from CRPC patients44 as well as insight into the druggability of the AR DBD pocket and specifics of its

Figure 1. Three pockets found in the X-ray structure of the AR LDB (PDB entry 2PIO): androgen binding site (ABS, green); AF2 binding site (pink); BF3 binding site (blue). 1019


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Figure 2. (A) MOE Site Finder revealed a targetable DBD binding site (around the gray and red spheres) in the crystal structure of the AR DBD (PDB entry 1R4I). (B) Corresponding ER residues in the ER DBD crystal structure (PDB entry 1HCQ) aligned to the AR DBD sequence: Gln238, Gly204, Ala207, and Asn217, which are different, and Arg211 and Tyr219 do not form a detectable binding site.

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BINDING HYPOTHESIS, VIRTUAL SCREENING, AND CONSENSUS SCORING The rapidly increasing number of X-ray, NMR, and cryogenic electron microscopy (cryoEM)-resolved structures45 of proteins, nucleic acids, and complex assemblies (105 441 at the time of writing)46 serve as a rich and utmost important information resource for all kinds of molecular modeling, including docking. As more 3D structures of target proteins have become available, structure-based virtual screening (SBVS) has gained considerable momentum. The effectiveness of SBVS relies on reasonable predictions of binding pose and free energy of binding between the ligand and protein, by which the interactions of the ligand and the residues around the binding site can be interpreted according to fundamental physical principles. Protein−ligand complexes have served as a basis for the development and refinement of all kinds of existing docking approaches,21,47 which could be drastically different in their underlying scoring functions and search algorithms. We find it the most practical to use several docking programs in parallel (our methods of choice are Glide,48−50 eHits,51 and ICM52). Only when a given hit is confirmed by several docking protocols (as judged, for instance, by the root-mean-square deviation (RMSD) between the highest-ranked docking poses)34 do we deem it as a probable binding scenario to be followed by additional, more elaborate scoring including free energy perturbation calculations.53,54 Such high-confidence hits (and the corresponding binding poses) can also be more consistently used for further rescoring with additional (often higher-level) structure-based computations. In recent years, structure-based approaches have started dominating even those CADD areas that were historically more oriented toward ligand-based methodology. Thus, a large number of 4D and 4D+ descriptors55 have emerged from the analysis of structurally resolved protein−ligand complexes.56 The role of 4D+ quantitative structure−activity relationship (QSAR) models57 in virtual screening is still largely underestimated in our opinion; given an adequate training set, they could represent an excellent choice for the development of docking/scoring functions customized for a particular target or compound series and can significantly enhance any CADD pipeline. Another traditional ligand-based methodology expanded into the structure-based domain is pharmacophore modeling, which

Figure 3. Organization of the full-length AR versus the major AR splice variants. NTD = N-terminal domain; DBD = DNA binding domain; Hinge = hinge region containing the nuclear localization sequence; LBD = C-terminal ligand binding domain; U = unique regions consisting of variant-specific amino acids arising from mRNA splicing. The exons from the AR gene that are transcribed and encode each protein domain are shown for each form of the receptor.

Figure 4. Sequence alignment of the DBDs from related NRs. The sequences of the DBDs and flanking regions from the most-related human NRs (AR, ER, GR, and PR) are shown and numbered according to the AR. Sequences were aligned using the ClustalW algorithm, visualized in Jalview (version 2.10.0b1), and colored according to percent identity. The core sequence comprises residues 559−624, followed immediately by the hinge region containing the nuclear localization signal (RKLKK in the AR). Pairwise sequence alignment was used to calculate the sequence identity between the AR and each related receptor: ER, 57.58%; GR, 77.27%; PR, 56.06%.

conservation among related NRs (provided by the means of CADD) motivated us to consider the development of an AR DBD-directed and -selective prostate cancer drug. The important note here is that before starting any CADD campaign, a computational scientist should develop an extensive knowledge of the target and deep understanding of its biology in order to form a truly successf ul collaboration with experimentalists and effectively synergize wet-lab and dry-lab efforts. 1020


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molecular dynamics motion of the target site to ensure its consistency and conducted energy minimization of the key target residues. The lead-like ZINC database consisted of about 3 million entries at the time and was preprocessed to eliminate reactive compounds, duplicate and broken entries, and inorganic admixtures as well as to assign proper ionization states and correct stereoisomers. The initial round of docking was then conducted using both Glide and eHits, and the initial scoring cutoff values were established. At the next stage, we selected only those qualified molecules that were consistently docked into the AR DBD site by both programs (i.e., for which the RMSD between the best Glide and best eHits docking poses did not exceed 2 Å). We fully appreciated the fact that a lot of potentially suitable molecules could be lost to such filtering, but at the same time, we anticipated that when such different algorithms as Glide (hierarchical search) and eHits (molecular fragment fitting) agree, the corresponding predictions should have a rather high confidence. As a result, a list of 5000 consistently docked molecules was generated and consequently subjected to rescoring using the dock-pKi method implemented within the scoring.svl script of MOE (available from the SVL Exchange62). After consensus evaluation on the basis of the docking score, ligand efficiency, and predicted physicochemical properties, the top-ranked structures were clustered using the fingerprint method implemented in MOE, and a final selected set of 48 chemicals that passed our visual inspection were purchased. To test the selected compounds for their ability to inhibit the AR, an enhanced green fluorescent protein (eGFP) assay was employed that quantifies the AR-driven transcriptional activity in LNCaP cells.42 In addition, to avoid false-positive detection by the eGFP assay, the complementary prostate-specific antigen (PSA) assay was used for more active molecules to confirm their inhibitory effect. The screening identified five hits that demonstrated AR and PSA inhibition with IC50 values below 10 μM. Among these, the compound vpc14203 (Figure 5) demonstrated the most potent inhibition of both AR transcriptional activity (eGFP IC50 = 3.17 μM) and PSA expression (PSA IC50 = 3.91 μM). We considered the resulting ∼10% hit rate as very satisfactory given the challenging and recalcitrant nature of the target site and the significant solvent exposure of the pocket on the AR DBD protein surface. Thereafter, a retrospective consensus ranking scheme was established with three votes from ligand efficiency, docking score, and logP per heavy atom. It is clear that consensus votes by summing of the ranks, such as by two votes from ligand efficiency and logP per heavy atom or by all three votes (the corresponding ROC curves are featured in Figure 6), is more efficient in retrieving actives than each single vote for hit retrieval. We believe that only the use of diverse methods of virtual screening, utilizing both ligand- and structure-based approaches, and the implementation of stringent consensus protocols allowed the identif ication of a number of diverse AR DBD hits using a very low throughput (less than 50 compounds) for wet-lab experiments.

can be used as a tremendously useful tool for virtual screening.32 Both structure- and ligand-based pharmacophores can be applied to narrow down the docking poses of the screened library and to generate and refine binding hypotheses that reflect key protein−ligand interactions. The use of pharmacophore models can greatly contribute to the success of hit identification,58 lead generation, and optimization. In our practice, as much as possible, we try to accommodate both structure- and ligand-based methodologies, and in addition to multiple docking and on-site rescoring, we employ a variety of ligand- and structure-based pharmacophores as well as 2D, 3D, and 4D QSAR models. For example, we have employed a structure-based pharmacophore modeling approach to discover the 1H-indole-2-carboxamide backup series of the AR BF3 inhibitors.32 In addition, we have adopted a 4D QSAR pipeline that accurately describes and quantifies the important structural aspects involved in the mutant−drug interactions for the AR, among others.57 There is an abundance of review literature available on the subject, and we provide yet another evidence that the use of a large number of independent, diverse CADD methodologies and the implementation of elaborate consensus scoring protocols59 enable significant enhancement of the positive predictive power of any VS pipeline. The main purpose of the initial stage of hit discovery (particularly in academia settings) is to eliminate true-negative and minimize false-positive predictions, while the loss of potential true hits is a lesser challenge. Thus, the implementation of multiple-step voting and consensus scoring can significantly improve the screening enrichment rate22 and lead to rapid discovery of efficient hits.60 Another important aspect of the CADD process that warrants specific mention and should not be overlooked is the visual inspection of poses and target interactions of compounds to be selected for further experimental evaluation. An arsenal of viewing tools could be used, some of which could even provide elaborate 3D rendering61 of protein−ligand complexes. This quality-control step is critical for meaningful selection of candidate molecules, and it appeals to the chemical intuition of the project’s scientistsa subjective but ever so important factor in any drug discovery campaign.

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IMPLEMENTATION OF TEAM SCIENCE HIT IDENTIFICATION As an example of a comprehensive CADD campaign, we will discuss our recent discovery of novel AR DBD inhibitors,42 where a broad range of computational technologies were utilized and many experimental assays were adopted and developed to evaluate upstream in silico predictions. This case should exemplify an effective synergy of dry- and wet-lab efforts in cooperative hypothesis generation and testing. After the initial identification of an actionable site on the AR DBD surface, we prepared both a target protein structure and a docking database (Figure 5). In particular, we explored the

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EXPLORATION OF CHEMICAL SPACE AROUND HITS After the initial success of the hit discovery, we expanded the search for AR DBD binders to a full version of the ZINC database, containing about 12 million entries at the time. In order to capitalize on already-confirmed hits, we conducted an in silico search (Figure 5) for analogues of the most active

Figure 5. Strategic virtual screening for AR DBD inhibitors. 1021


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practical CADD recommendation from this stage was to thoroughly analyze the conf irmed hits, search for their analogues, and explore the chemical space around them. A CADD practitioner should also analyze produced inactives, build cheminformatics models distinguishing them f rom hits, and use the generated binary QSAR to increase the stringency of the CADD pipeline.

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CADD-ASSISTED LEAD OPTIMIZATION Lead optimization represents another critical step in a drug discovery process. This phase poses considerable challenges from the perspective of simultaneous optimization of PK and pharmacodynamic (PD) properties of a small molecule while maintaining its selectivity and on-target effect. The main objective of hit-to-lead optimization is to transform a prospective compound into a clinical candidate through iterative cycles of CADD-assisted design, medicinal chemistry (medchem) synthesis, and experimental evaluation. Thus, while the vpc14228 prototype demonstrated the desired in vitro potency against the AR and exhibited no obvious toxicities, its biological stability appeared to be suboptimal. The microsomal half-clearance time (T1/2) of vpc14228 was established to be only 10 min (data not shown). Ultimately, a round of medchem campaign was required to decrease the metabolic susceptibility of prospective AR DBD inhibitors and to reach higher T1/2 values suitable for future oral administration. To assist in developing a proper synthetic strategy, we conducted in silico prediction of possible metabolic transformations of vpc14228 using the ADMET Predictor 7.2 software.64 This program identified that the phenyl and morpholine rings of vpc14228 could be vulnerable to the action of cytochrom P450 (CYP) enzymes (see Figure 7A), suggesting that the corresponding ring replacement with lessCYP-susceptible molecular fragments should improve the overall compound stability. We further utilized ROCS software to design stereoelectronic replacements for the benzene ring and considered various substituted and unsubstituted five- and six-membered heterocyclic rings. The hypothetical derivatives were then docked into the target AR DBD site and evaluated by the earlier-developed consensus scoring protocols. On the basis of the in silico predictions, we then synthesized 34 prioritized analogues of vpc14228 in which the phenyl ring (Figure 7B) was replaced with heterocycles or modified with various substituents. Of the developed compounds, 21 demonstrated sufficient anti-AR activity (eGFP IC50 < 10 μM), with six of them outperforming the parent vpc14228. In particular, the synthetic analogue vpc14449 bearing a 2,4-dibromoimidazole (Figure 7C) displayed a modest improvement in potency over vpc14228, at least in the eGFP reporter expression from LNCaP cells (eGFP IC50 = 0.38 μM, PSA IC50 = 0.34 μM; Figure 8A). Similarly, vpc14449 could suppress the cellular viability of LNCaP cells without any general toxicity against AR-negative PC3 cells, as determined using MTS assays (Figure 8B). Importantly, vpc14449 also exhibited increased microsomal stability (T1/2 ≈ 30 min, as determined by BientaEnamine; data not shown). This compound was subjected to an array of other experimental evaluations to confirm further its suitability as a drug candidate. Thus, we adopted a luciferase reporter assay to test the effect of vpc14449 on transcriptional activity of overexpressed ER, GR, and PR. Little inhibitory activity was detected against the latter two receptors up to 25 μM, with modest ER inhibition by vpc14449 noted at higher

Figure 6. Receiver operating characteristic (ROC) curves (truepositive rate (TPR) vs false-positive rate (FPR)) with area under the curve (AUC) values derived from the rank or sum of ranks of ligand efficiency, docking score, and logP per heavy atom.

compound, vpc14203, using both substructure-search and topology-based (through standard fingerprints) and shapebased (as implemented by the ROCS approach63) similarities. Thus, we used ROCS similarity statistics, Glide and eHits scores, dock_pKi, and ligand efficiency to select a set of 45 second-round chemicals belonging to the 4-(4-phenylthiazol-2yl)morpholine series. Of those, 19 compounds exhibited eGFP IC50 values below 10 μM, resulting in an overall hit rate of 40% for this round of screening. The most active compound, 4-(4-phenylthiazol-2-yl)-morpholine (vpc14228) (Figure 5) demonstrated IC50 valued of ∼0.5 μM in both the GFP and PSA assays. This submicromolar compound was then considered by us as the most promising candidate for more detailed evaluation of an on-target effect. The compound vpc14228 was further subjected to an extensive experimental evaluation that aimed to rule out its potential interactions with known functional sites in the AR, including the ABS, AF2, and BF3, all of which are known to interact with small molecules. To this end, the experimental team conducted androgen displacement experiments with vpc14228 and estimated that it could not displace dihydrotestosterone (DHT), a native ABS ligand, from the purified, recombinant AR LBD in solution.42 Similarly, an in vitro biolayer interferometry (BLI) assay using recombinant AR LBD was carried out and also demonstrated the absence of binding of vpc14228 anywhere on the LBD. The DHT displacement and BLI assays together suggest that at least part of the inhibitory mechanism of vpc14228 acts beyond known binding sites in the C-terminal LBD of the receptor, although formal proof of the direct interaction with the AR DBD is still pending. Thus, the use of a more focused in silico screening approach and additional fine-tuned scoring metrics allowed a satisfying 40% hit rate to be achieved in the second round of drug discovery but also yielded a submicromolar AR DBD inhibitor, vpc14228 (Figure 5), that did not demonstrate any overt toxicities or off-target effects. Notably, this compound and a number of its active analogues were identified without the use of any medicinal chemistry optimization at this stage. Thus, a 1022


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Figure 8. Compound efficacy in PCa cell lines. (A) eGFP (left) and secreted PSA (right) AR transcription assays42 in LNCaP eGFP cells. The value of 100% refers to the transcriptional activation of the AR in response to 0.1 nM R1881 synthetic androgen. Either 14228 and 14449 was administered to cells for 72 h at the indicated concentrations. (B) MTS assays43 (PrestoBlue) were performed in LNCaP (left) or PC3 (right) cells.

approved PCa drug, enzalutamide, demonstrated even more profound ER inhibition at the same concentration levels).43 Hence, a number of independent experimental results characterized vpc14449 as a potent and selective AR inhibitor that exhibits no general toxicities and can potentially address the problem of drug resistance associated with LBD alterations such as mutations or truncations. Compound vpc14449 is the first prototypical inhibitor for the DBD portion of a nuclear receptor, which represents the actual active site of the protein. On the other hand, at the time we lacked direct evidence that vpc14449 binds to the exact target site on the AR DBD surface and were yet to have concrete confirmations of its LBDindependent mode of action (MOA). Hence, a number of additional sophisticated experiments were conceived and implemented to investigate the mechanistic details of vpc14449 inhibition.

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MOA, PK, PD, AND EFFICACY STUDIES In the course of almost any drug discovery campaign, there will be critical checkpoints that require the development of customized biological assays or physicochemical tests that are fine-tuned for the project’s target. Thus, in order to gain more direct insight into the MOA of vpc14449 and to confirm its direct DBD action, we created a transcriptional assay for the V7 form of the AR, its constituently active and truncated version in which the entire LBD section is missing. When tested, vpc14449 demonstrated concentration-dependent inhibition of V7 transcriptional activity, while as expected, enzalutamide was proven to be completely inactive (see Figure 9A). These results characterize vpc14449 as an AR-DBD-specific inhibitor and the first reported case of a CADD-designed molecule capable of V7 suppression.43 In a subsequent stage of testing, we developed several assays with mutated forms of the AR DBD where key ligandinteracting residues were replaced. According to the predicted binding pose of vpc14499 (featured in Figure 7D), AR residues Gln592 and Tyr594 should form critical contacts with the bound ligand (Figure 7D). Thus, the aspartic acid point

Figure 7. (A) Reactive atomic sites (red spheres) predicted by metabolism prediction using ADMET Predictor 7.2. (B) The phenol ring of vpc14228 was identified to be replaced. (C) The structure of vpc14449 synthesized. (D) vpc14449 (pink) was predicted to have a binding pose similar to that of vpc14228 (green).

concentrations that did not represent a problem for its antiandrogen application (it is worthy of note that the latest 1023


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vpc14449 as a prototypical first-in-class AR DBD inhibitor. Subsequent pharmacokinetic evaluation and efficacy studies were conducted using in vivo xenograft models in mice and indicated that vpc14449 is capable of inhibiting conventional forms of prostate cancer (e.g., LNCaP).43

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LICENSING TO INDUSTRY The amount of money spent globally on the diagnosis and treatment of prostate cancer totaled $25 billion in 2010 and is projected to be $50 billion by 2017.65 This drug category (hormone therapy, chemotherapy, and immunotherapy) had sales of $7.7B in 2010, with sales projections of $18.6B in 2017. Hormonal therapies are the dominant drug class, with $4.2B in sales in 2010 and predicted sales of $10.4B in 2017.65 In the course of the development of AR DBD inhibitors, we have maintained a very close cooperation with the UBC University− Industry Liaison Office (UILO), which has the mandate to assist in commercialization of intellectual property (IP) generated by UBC scientists. We filed the initial technology disclosure on the initial hit compounds from the first and second rounds of screening (13 hits at the time) in March 2012, and the UILO immediately conducted a detailed technology assessment. The search of the prior art was performed on each of the 13 originally disclosed molecules, and no intellectual property barriers were identified. Thus, we submitted 84 additional medchem derivatives to the UILO in January 2013 and another 68 compounds in January 2014, bringing the updated total number of potential AR DBD inhibitors to 165, which includes several very strong leads. At the time of disclosure, we had not yet published/disclosed any results. A comprehensive evaluation of the 165 molecules was conducted by the UILO in 2014, and similarity searches were performed to identify closely related structures that may be relevant for patentability. Importantly, the eight top-performing lead compounds, having IC50 values between 0.010 and 0.260 μM, appear to be novel, as no prior art was found. Thus, composition of matter and method of treatment claims were classified by the UILO as likely available, and the IP position was defined as strong and protectable. No freedom-to-operate issues were identified. As the result, the U.S. provisional patent application “Human Androgen Receptor Dimer Binding Domain (DBD) Compounds as Therapeutics and Methods for Their Use” was filed in February 2014 in order to claim rights to all of the disclosed compounds in terms of their structure and method of use to inhibit androgen receptor and treat disease, such as prostate cancer and other indications. It is noteworthy that as of February 2014 the total numbers of compounds being studied that have potential use in prostate cancer stood as follows: 30 in preclinical studies, 24 in Phase I trials, 81 in Phase II trials, and 27 in Phase III trials.66 None of these molecules were intended as AR DBD inhibitors, and the vpc14449 series could be classified as a potential “first in class” type. After the filing of the above-mentioned U.S. provisional patent application, we published the results of the research in the Journal of Medicinal Chemistry43 and Journal of Biological Chemistry,44 which triggered the immediate interest of the industry and started an approximately one-year-long process of licensing negotiation. Thus, we were able to conduct the entire discovery process (starting from generation of the AR DBD targeting concept to the in vivo validation of the lead molecule) in an academic

Figure 9. Luciferase reporter assay of the ARV7 splice variant and point mutations in the full-length AR. (A) PC3 cells were transfected (4 h) with a mammalian expression plasmid encoding for ARV7 and an ARR3tk-Luc reporter plasmid as described in ref 42. vpc14449 or enzalutamide (Enz) was administered for 16 h, followed by cell lysis and luminescence measurement. (B) Same as in (A) but with a plasmid encoding the wild-type or mutant full-length androgen receptor.

mutations were created at these positions, and the corresponding full-length AR plasmid vectors were cotransfected with ARR3tk-luciferase reporter into AR-negative PC3 cells.43 As expected, the inhibitory effect of vpc14449 on the AR was diminished by the Gln592Asp and Tyr594Asp mutations (Figure 9B), confirming that the compounding interactions are maintained between vpc14449 and the proposed AR DBD target site (Figure 7D). The intended MOA of vpc14449 has been evaluated in greater depth and confirmed by a number of independent experimental techniques, including direct binding experiments with isothermal titration calorimetry (ITC), investigation of AR−chromatin interactions in cell-based assays (fractionation, ChIP-PCR, ChIP-Seq), and V7 target-gene validation. Notably, vpc14449 effectively suppresses the transcriptional activity of the full-length receptor bearing point mutations in the LBD (e.g., T877A, F876L, W741C, and others identified in circulating free DNA from patients44) in PC3-based luciferase assays, indicating the ability of the prototype drug to bypass known drug-resistant mutations. These new results will be presented in a specialized publication currently in development (unpublished data). Thus, a broad repertoire of experimental and computational approaches were used to develop and validate the compound 1024


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odds to significantly enhance lead optimization performance. We believe that the future development of computational technology and the incorporation of higher-level electronic structure theory17 into the simulation of macromolecular systems will further enhance the role of molecular modeling in drug discovery. Other emerging areas of CADD are methods for the prediction of synthetic feasibility, ADMET properties,42,43 and PK/PD properties of small molecules.72 The current shortcomings of these predictive methods are partly determined by their limited training data,73−75 and this situation is rapidly changing as the amount of available chemical information is exploding. Data-driven drug discovery has great potential to benefit from big-data analysis in medicinal chemistry and chemogenomics.76−81 It is feasible to anticipate that in the very near future the corresponding CADD methodologies will achieve adequate maturity that will cause a paradigm shift in many areas of therapeutics development. The use of mature CADD technology will also significantly expand the repertoire of drug targets, such as bringing in previously unattended functional and protein−protein interact sites, exposed surface areas, and protein−DNA interfaces (as exemplified by our AR DBD project). Taken together with the recent revolution in methods of structural determination, such as the introduction of single-particle cryoEM, the computational technology will transform drug discovery into a truly integrative team science that embraces parts of informatics, structural biology, genomics, biochemistry, medicinal chemistry, pharmacology, and personalized medicine. Even now, creative use of the methods of computational chemistry and adherence to the best CADD practices (outlined below) can lead to swift success. The presented case of the development of a selective AR DBD inhibitor serves as only one example of that. The implementation of closely integrated cycles of in silico modeling and carefully designed and elaborate experiments resulted in the generation of an intellectual property package.82 We would like to explicitly indicate to our readership that we have not presented novel discoveries in this Perspective per se; rather, we have distilled multiple available CADD approaches to a practically useful pipeline balanced for speed and accuracy. Thus, herein we have shared practical, easy-to-follow recipes as well as our success story with a broader audience in order to encourage our colleagues to rely on properly executed and combined existing technologies to discover more novel molecules of therapeutic significance. To summarize, on the basis of our recent experiences, some of which have been described in the previous sections, we have developed the following set of recommendations and best practices for CADD research: 1. Study your target to gain deep knowledge of its biology and corresponding experimental assays. 2. Validate your screening tools and prepare your molecular databases thoroughly. Use diverse methods of virtual screening, multiple docking programs, both ligand- and structure-based approaches, and consensus scoring. 3. Analyze your inactives, and build cheminformatics models distinguishing them from your hits. Build QSAR models to rank your hits and use them to reiterate your CADD pipeline; adhere to best QSAR practices.

setting over a timeline of approximately 2 years, and it resulted in significant interest from industry to conduct further development of this class of inhibitor and led to a $142M licensing deal. Such interest became possible only because of the close collaboration between CADD scientists and biologists at all stages of the project coupled with the strong support and engagement of the UILO. It is important to note that dry- and wet-lab efforts provide the best results when they are closely integrated. The CADD technology can assist in designing the most effective experimental strategies and can help experimentalists learn from both positive and negative results. Importantly, in the course of the development and commercialization of the vpc14449 series, we formulated a set of rules and observations that we could recommend as a set of best practices of CADD. These are summarized below.

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SUMMARY OF CADD BEST PRACTICES Decision-making in drug discovery is a process of paramount complexity driven by heterogeneous (and often massive) amounts of data constantly generated throughout the entire process. Ultimately, the current methods of computer-aided drug discovery are designed to effectively guide an iterative discovery process rather than to readily pinpoint a perfect drug. The notorious shortcoming of the current methods of virtual screening, such as their inability to accurately estimate free energies of interaction or even to reliably rank-order virtual hits,17 are well-documented.18−20 The success of a CADD campaign still often depends on the expertise of a particular drug designer’s chemical intuition and relies on many subjective factors that could be collectively called a “golden touch”. Modern CADD tools do provide scientists with the fundamental capability to probe 3D structure(s) of a particular target, to map favorable and unfavorable protein−ligand interactions, and to allow the development of validated SAR models with defined applicability domains. Proper application of CADD methodology, such as enforcing certain pharmacophore features or employing consensus scoring for hit selection, may effectively increase the odds of discovering truly active compounds. There is no shortage of CADD tools that can be readily adopted by scientists, such as elaborate drug-like67 and lead-like68 filters, similarity search engines, docking protocols, pharmacophore modeling, and fingerprint matching methods. There is also a vast chemical space, represented by 120 million molecules from the latest ZINC15 database,69 which can be readily narrowed down to experimentally manageable sets of chemicals. The computational power is also no longer a bottleneck, and any academic group can have adequate access to high-performance computing (HPC) facilities these days. All of these factors should make in silico modeling a part of a routine practice for any lab working in the field of drug discovery. New methods for CADD keep advancing, and some major limitations are being addressed. As has already been mentioned, accurate estimation of the binding free energy for a protein− ligand complex is a key factor for understanding molecular recognition events. The latest advancements in algorithms, such as the free energy perturbation (FEP) protocol from Schrödinger,54 may provide a reasonable and suitable solution for the problem. A number of successful hit optimization cases have already been reported with the use of FEP, including the design of BACE1 inhibitors70 and GPCR ligands.71 The algorithm is finding increasing use in our own practice; with proper HPC implementation and support, FEP has all of the 1025


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J. W.; Schopfer, U.; Sittampalam, G. S. Impact of High-throughput Screening in Biomedical Research. Nat. Rev. Drug Discovery 2011, 10, 188−195. (7) Bajorath, J. Integration of Virtual and High-Throughput Screening. Nat. Rev. Drug Discovery 2002, 1, 882−894. (8) Ferreira, L. G.; Dos Santos, R. N.; Oliva, G.; Andricopulo, A. D. Molecular Docking and Structure-Based Drug Design Strategies. Molecules 2015, 20, 13384−13421. (9) Kitchen, D. B.; Decornez, H.; Furr, J. R.; Bajorath, J. Docking and Scoring in Virtual Screening for Drug Discovery: Methods and Applications. Nat. Rev. Drug Discovery 2004, 3, 935−949. (10) Zhu, T.; Cao, S.; Su, P. C.; Patel, R.; Shah, D.; Chokshi, H. B.; Szukala, R.; Johnson, M. E.; Hevener, K. E. Hit Identification and Optimization in Virtual Screening: Practical Recommendations Based on a Critical Literature Analysis. J. Med. Chem. 2013, 56, 6560−6572. (11) Shoichet, B. K. Virtual Screening of Chemical Libraries. Nature 2004, 432, 862−865. (12) Lipinski, C.; Hopkins, A. Navigating Chemical Space for Biology and Medicine. Nature 2004, 432, 855−861. (13) Zhou, Z. Y.; Felts, A. K.; Friesner, R. A.; Levy, R. M. Comparative Performance of Several Flexible Docking Programs and Scoring Functions: Enrichment Studies for a Diverse Set of Pharmaceutically Relevant Targets. J. Chem. Inf. Model. 2007, 47, 1599−1608. (14) Macalino, S. J.; Gosu, V.; Hong, S.; Choi, S. Role of ComputerAided Drug Design in Modern Drug Discovery. Arch. Pharmacal Res. 2015, 38, 1686−1701. (15) Sliwoski, G.; Kothiwale, S.; Meiler, J.; Lowe, E. W., Jr. Computational Methods in Drug Discovery. Pharmacol. Rev. 2014, 66, 334−395. (16) Liao, C.; Sitzmann, M.; Pugliese, A.; Nicklaus, M. C. Software and Resources for Computational Medicinal Chemistry. Future Med. Chem. 2011, 3, 1057−1085. (17) Chaskar, P.; Zoete, V.; Rohrig, U. F. Toward On-The-Fly Quantum Mechanical/Molecular Mechanical (QM/MM) Docking: Development and Benchmark of a Scoring Function. J. Chem. Inf. Model. 2014, 54, 3137−3152. (18) Yuriev, E.; Holien, J.; Ramsland, P. A. Improvements, Trends, and New Ideas in Molecular Docking: 2012−2013 in Review. J. Mol. Recognit. 2015, 28, 581−604. (19) Jorgensen, W. L. Efficient Drug Lead Discovery and Optimization. Acc. Chem. Res. 2009, 42, 724−733. (20) Feher, M.; Williams, C. I. Numerical Errors and Chaotic Behavior in Docking Simulations. J. Chem. Inf. Model. 2012, 52, 724− 738. (21) Irwin, J. J.; Shoichet, B. K. Docking Screens for Novel Ligands Conferring New Biology. J. Med. Chem. 2016, 59, 4103−4120. (22) Charifson, P. S.; Corkery, J. J.; Murcko, M. A.; Walters, W. P. Consensus Scoring: A Method for Obtaining Improved Hit Rates from Docking Databases of Three-Dimensional Structures into Proteins. J. Med. Chem. 1999, 42, 5100−5109. (23) Huggins, C.; Hodges, C. V. The Effect of Castration, of Estrogen and of Androgen Injection on Serum Phosphatases in Metastatic Carcinoma of the Prostate. Cancer Res. 1941, 1, 293−297. (24) Imamura, Y.; Sadar, M. D. Androgen Receptor Targeted Therapies in Castration-Resistant Prostate Cancer: Bench to Clinic. Int. J. Urol. 2016, 23, 654−665. (25) Lorente, D.; Mateo, J.; Zafeiriou, Z.; Smith, A. D.; Sandhu, S.; Ferraldeschi, R.; de Bono, J. S. Switching and Withdrawing Hormonal Agents for Castration-Resistant Prostate Cancer. Nat. Rev. Urol. 2015, 12, 37−47. (26) Romanel, A.; Tandefelt, D. G.; Conteduca, V.; Jayaram, A.; Casiraghi, N.; Wetterskog, D.; Salvi, S.; Amadori, D.; Zafeiriou, Z.; Rescigno, P.; Bianchini, D.; Gurioli, G.; Casadio, V.; Carreira, S.; Goodall, J.; Wingate, A.; Ferraldeschi, R.; Tunariu, N.; Flohr, P.; De Giorgi, U.; de Bono, J. S.; Demichelis, F.; Attard, G. Plasma AR and Abiraterone-Resistant Prostate Cancer. Sci. Transl. Med. 2015, 7, 312re10.

4. Analyze and visually inspect generated docking poses, and use your chemical intuition to create testable binding hypotheses. 5. Work closely with experimentalists during all stages of the project, learn from negative results, and fine-tune your CADD pipeline on the basis of the wet-lab outputs. 6. Analyze your confirmed hits, search for analogues, explore chemical space around them, and assess the synthetic feasibility of possible derivatives. 7. Utilize ADMET predictions and FEP for better lead optimization recommendations.

AUTHOR INFORMATION

Corresponding Author

*Tel.: 604-875-4111, ext. 69628. Fax: 604-875-5654. E-mail: [email protected]. ORCID

Artem Cherkasov: 0000-0002-1599-1439 Notes

The authors declare no competing financial interest.

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ACKNOWLEDGMENTS We are grateful for the generous support from Prostate Cancer Canada through a Canada Safeway Grant (SP2013-02) and an operating grant (272111), a Proof-of-Principle Grant (328186) from the Canadian Institutes of Health Research, a Movember Discovery Program Award, and a grant from the Department of Defense (11496001).

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ABBREVIATIONS CADD, computer-aided drug design and discovery; PK, pharmacokinetics; GMP, Good Manufacturing Practices; HTS, high-throughput screening; VS, virtual screening; ADMET, absorption, distribution, metabolism, excretion, and toxicity; AR, androgen receptor; ABS, androgen binding site; LBD, ligand binding domain; DBD, DNA binding domain; NR, nuclear receptor; SBVS, structure-based virtual screening; RMSD, root-mean-square deviation; 4D, four-dimensional; eGFP, enhanced green fluorescent protein; PSA, prostatespecific antigen; logP, logarithm of the predicted octanol/water partition coefficient; T1/2, half-life; QSAR, quantitative structure−activity relationship

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