In Silico Repurposing of Cell Cycle Modulators for Cancer Treatment

C H A P T E R

9 In Silico Repurposing of Cell Cycle Modulators for Cancer Treatment Yu-Chen Lo*, Jorge Z. Torres† *

†

Department of Bioengineering, Stanford University, Stanford, CA, United States Department of Chemistry and Biochemistry, University of California-Los Angeles, Los Angeles, CA, United States

1 TARGETING THE CELL CYCLE IN THE TREATMENT OF CANCER The cell-division cycle plays an important role in the development and survival of multicellular organisms. However, aberrations in the mechanisms controlling the celldivision cycle can lead to abnormal cell growth and often tumorigenesis. In eukaryotes, the cell-division cycle consists of four phases: G1 (growth phase), S (DNA synthesis), G2 (growth phase), and M (mitosis) (Schwartz & Shah, 2005). During the G1 phase, the cell responds to extracellular growth signals and increases its biosynthetic activities through the activation of transcriptional and translational programs that lead to an increased production of ribosomes and cellular proteins critical for synthesizing DNA, which is unwounded and replicated during S phase. The G2 phase marks the stage in which cells grow substantially in regard to total protein mass and cell size and prepare for cell division. The M phase is a very dynamic and highly regulated event that culminates in the division of one cell into two cells. The M phase can be further subdivided into five subphases: the prophase, where DNA undergoes condensation and compaction; the prometaphase, where chromosomes are congressed towards the cell midplane; the metaphase, where chromosomes align along the metaphase plate; the anaphase, where sister chromatids are separated and move to opposite ends of the cell; and the telophase, where the nuclear lamina reforms around each mass of the separated DNA and the cells cytoplasm is physically bisected to generate two cells. In normal cells, cell-cycle checkpoints ensure that the progression of the cell cycle from one phase to another is tightly regulated and occurs with high fidelity. The G1 checkpoint, also known as the “restriction point,” checks for DNA damage present in cells through an ATM/ATR-dependent pathway, which leads to a slowed progression from the G1 to the

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S phase and the accumulation of a G1 cell population to allow time for DNA repair (Sancar, Lindsey-Boltz, Unsal-Kacmaz, & Linn, 2004). Similarly, damaged DNA following the S phase will activate the G2/M checkpoint, which arrests cells in order to repair DNA using a similar mechanism to the G1/S transition and is dependent on the phosphorylation state of the Chk1 kinase (Sancar et al., 2004). During the M phase, the spindle assembly checkpoint (SAC) ensures the fidelity of chromosome segregation by arresting cells at the metaphase to anaphase transition in response to unattached kinetochores and/or kinetochores that lack proper tension (Rudner & Murray, 1996). Finally, during cytokinesis the abscission checkpoint inhibits the abscission of the cytokinetic bridge in response to cell stress and chromatin that traverses the cleavage plane (Nahse, Christ, Stenmark, & Campsteijn, 2017). Since uncontrolled cell proliferation is an important hallmark of cancer, one strategy for the treatment of cancer has been the use of cell-cycle modulators, which are small molecules (drugs) that can activate cell-cycle checkpoints, which leads to a subsequent cell-cycle arrest and apoptosis (cell death) (Manchado, Guillamot, & Malumbres, 2012). G1 modulators consist mostly of kinase inhibitors, like saturosporine and tryphostin, or ion channel blockers, like thapsigargin (sarcoendoplasmic reticulum Ca2+ ATPase inhibitor), quabain (Na+/K+ ATPase inhibitor), and ionophore antibiotics that inhibit the MAPK signaling pathway, which is essential for DNA synthesis and critical for cancer proliferation (Blenis, 1993; Goekjian & Jirousek, 2001; Osherov, Gazit, Gilon, & Levitzki, 1993; Ruegg & Burgess, 1989; Takeuchi, Nakamura, Takeuchi, Hashimoto, & Yamamura, 1992; Tanramluk, Schreyer, Pitt, & Blundell, 2009). S phase modulators consist of DNA-damaging agents and include well-known drugs such as 5-fluorouracil and doxorubicin (Clifford, Beljin, Stark, & Taylor, 2003). These compounds interfere with DNA replication by binding to the DNA directly or by coupling with topoisomerase complexes. Another major class of S phase inhibitors are antimetabolites, such as motexafin gadolinium, which inhibit ribonucleotide reductase, a key enzyme that catalyzes the reduction of ribonucleotides to deoxyribonucleotides, the building blocks of DNA (Zahedi Avval, Berndt, Pramanik, & Holmgren, 2009). G2 modulators, such as etoposide and amascarine also cause DNA damage similar to G1 modulators that intercalate into DNA and form topoisomerase 2-DNA covalent complexes (Clifford et al., 2003). M phase inhibitors, commonly known as antimitotics, consist of a broad class of anticancer drugs in current clinical use that inhibit cell division by targeting the dynamic instability of microtubules (Dumontet & Jordan, 2010). Well-known microtubule modulators include the taxanes and vinca alkaloids, which bind to beta-tubulin and perturb microtubule instability by stabilizing or destabilizing microtubule formation leading to cell death. M-phase inhibitors that target mitotic kinases (AurKA/B and Plk1) and kinesins (Kif11 and CENP-E) have also been developed recently (Manchado et al., 2012). Nevertheless, many of these compounds have issues with regards to their toxicity and side effect and show limited efficacy in vivo. Therefore the identification of novel cell-cycle modulators as probes for defining the mechanisms of cell division and with potential to be developed as therapeutics are critical to improving the current state of cancer treatments. Experimental approaches for discovering cell-cycle modulators can be broadly divided into target-based or phenotype-based screening (Swinney & Anthony, 2011; Zheng, Thorne, & McKew, 2013). Target-based screening focuses on the discovery of high-affinity compounds against a prevalidated target (Hoelder, Clarke, & Workman, 2012). With the advancement of genome wide studies, human genes can be selectively depleted to determine their roles in cell-cycle progression and provide a wealth of information on the enzymatic machinery 2. THEORETICAL BACKGROUND AND METHODOLOGIES

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required for cell division. For example, using a small interfering RNA (siRNA) screen targeting 600 mitotic microtubule-associated proteins, Torres et al. identified a novel kinesin, STARD9, which played an important role in regulating spindle pole cohesion during mitosis and demonstrated fast apoptotic kinetics when depleted (Torres et al., 2011). The identification of these putative mitotic targets then becomes the focus of target-based, drug-screening campaigns aimed at identifying high-affinity binders capable of modulating their enzymatic activities. For example, an in vitro chemical screen against Plk1 identified the inhibitor BI2536, which not only elucidated the mechanisms of Plk1 function during cell division, but also lead to the development of new cancer drugs in clinical trials (Lenart et al., 2007; Steegmaier et al., 2007). The main limitation of target-based screening is the requirement for preselection of the drug target without any guarantee of their druggability and in vivo performance. Thus phenotype-based chemical screening has been an attractive approach for the discovery of cell-cycle modulators regardless of their mechanism of action (MOA) (Mayer et al., 1999; Murphey, Stern, Straub, & Zon, 2006). Phenotype-based chemical screening can be accomplished by cytometry (flow-based and microscope-based) to study how compounds from a chemical library perturb cell-population distributions in distinct cell-cycle phases due to the activation of cell-cycle checkpoints (Fig. 1). To discover novel cell-cycle modulators Senese et al. screened 80,000 drug-like compounds in Hela cells using Vybrant DyeCycle Green, which binds to DNA and emits a fluorescent signal that is proportional to DNA mass (Senese et al., 2014) (Fig. 1). Using highthroughput microscope-based cytometry, a cell-cycle histogram was generated for each compound treated well and the extent of cell-cycle arrest in G1, S, and G2/M was quantified. Ranking of compounds based on their degree of M phase arrest identified a novel M phase inhibitor, MI-181, which destabilized microtubules and was demonstrated to contain potent activities against a broad array of cancer cells, especially melanomas. The study also successfully repurposed Fatostatin, a specific inhibitor of sterol regulatory element-binding protein (SREBP) activation with an additional MOA that targeted tubulin polymerization, which caused an M-phase cell-cycle arrest, SAC activation, and mitotic catastrophe that reduced cell viability (Gholkar et al., 2016). Fatostatin’s ability to inhibit both SREBP activity and cell division could prove beneficial in treating aggressive types of cancers, such as glioblastomas, which have elevated lipid metabolism and fast proliferation rates and often develop resistance to current anticancer therapies.

2 DRUG REPURPOSING IN CANCER DRUG DISCOVERY Despite the discovery of a wide range of de novo cell-cycle modulators for developing cancer treatments, the major limitations of existing therapies include severe dose-limiting toxicity and side effects, which hamper their tolerability in patients. In fact, the high attrition rate in modern anticancer drug discovery can often be attributed to low efficacy or high toxicity in the late stages of clinical trials (Pammolli, Magazzini, & Riccaboni, 2011). Another important consideration is the lengthy drug-discovery process for a cancer drug to be approved from bench to bedside. It has been shown that the time required for developing a new drug has increased from an average 7.9to 13.9 years, with an average expenditure of US$1.8 billion to introduce a drug to the market (Gupta, Sung, Prasad, Webb, & Aggarwal, 2013). The side effects and toxicity of drugs can often be explained by drug polypharmacology, where the compound interacts with 2. THEORETICAL BACKGROUND AND METHODOLOGIES

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FIG. 1 Cell-cycle, small-molecule screening for drug repurposing. (A) HeLa cancer cells were plated into 384 well plates and a diverse 80,000 drug-like compound library encompassing broad chemical space was used to place one compound per well to screen for cell-cycle, phase-specific inhibitors. The cells were fixed and stained with the DNAselective stain Vybrant DyeCycle Green, which binds to DNA and emits a fluorescent signal that is proportional to DNA mass when excited at 488 nm. Plates were scanned with a fluorescence micro-plate cytometer and a cell-cycle histogram profile was generated for each compound-treated well. This cell-based and microscope-based, highthroughput, chemical-screening approach identified compounds targeting different phases of the cell cycle, leading to a cell-cycle arrest in G1, S, or G2/M phases. (B) Compound in silico, cell-cycle profiling. The drug-induced, cellcycle profiles of 1200 selected FDA-approved drugs were expressed as cell-cycle fingerprints consisting of G1, S, G2/M and subG1 phases relative to the dimethyl sulfoxide (DMSO) control profile. The diagram shows that FDAapproved drugs induced a variety of cell cycle-arresting patterns, which likely correlate with different anticancer (Continued)

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a myriad of off-targets with comparable potency and causes unwanted physiological effects. Many of these off-targets are unknown a priori and are often discovered unexpectedly from an in vitro or in silico counter screen. Furthermore, there is no systematic way to test all potential off-targets exhaustively. Therefore an effective strategy that allows rapid discovery and development of safer anticancer drugs for clinical use is urgently needed. Recently, drug repurposing, also known as “drug repositioning,” “therapeutic switch,” or “activity reversal,” has gained popularity as an emerging approach for the discovery of safer and more effective medicines. In a typical drug-repurposing program, an approved drug is usually selected as a lead and by gaining additional knowledge about its bioactivities or MOA, one can reuse the same molecule to treat new diseases with minimal or no structural modification. Furthermore, since approved drugs have good safety profiles, reusing a known drug for a new indication can potentially accelerate drug development and their approval for clinical use. Although drug repurposing is a relatively new concept, the approach has been applied extensively in the history of drug discovery. A notable example is the discovery of sildenafil (Viagra), a blockbuster drug for the treatment of erectile dysfunction that was originally intended for use in heart conditions (Boolell et al., 1996). Many similar discoveries have resulted from serendipity. Only recently has the drug repurposing approach been formalized in a more systematic way and it encompasses diverse experimental and in silico approaches.

3 IN SILICO CELL CYCLE MODULATOR REPURPOSING Experimental drug repositioning for anticancer agents is typically conducted via a highthroughput activity-based screen with a preselected target or disease phenotype against a chemical library of approved drugs. For example, the screening of 2200 approved drugs for antiproliferative activity in endothelial and prostate cancer cell lines successfully identified the antifungal drug itraconazole and the cardiac glycoside digoxin as potential anticancer drugs (Chong et al., 2007). Nonetheless, a high-throughput screening (HTS) campaign can be time consuming and costly because of the number of compounds required to assay. Furthermore, the hit rate from an HTS is typically low and the false-positive rate is high, which often necessitates an extensive postvalidation process. Therefore in silico methods capable of identifying known drugs with novel off-target activities to support repurposing efforts have increasingly been applied in drug-discovery campaigns. Recent in silico methods for drug repurposing are ligand-, target-, and system-based methods (the latter comprising expression- and phenotype-based methods), and each has their own advantages and limitations. Therefore selecting the most appropriate approach requires thoughtful consideration and evaluation of different scenarios to maximize the success of a drug-repositioning program (Fig. 2). FIG. 1, CONT’D mechanisms. Note that the y-axis represents the cell cycle-phase change relative to the DMSO control and each cell cycle phase change is indicated by RG1, RS, RM, or RSG1 respectively. ((A) Modified from Senese, S., Lo, Y. C., Huang, D., Zangle, T. A., Gholkar, A. A., Robert, L., . . . Torres, J. Z. (2014). Chemical dissection of the cell cycle: probes for cell biology and anti-cancer drug development. Cell Death & Disease, 5, e1462. doi:https://doi.org/ 10.1038/cddis.2014.420. (B) Adapted from Lo, Y. C., Senese, S., France, B., Gholkar, A. A., Damoiseaux, R., & Torres, J. Z. (2017). Computational cell sycle profiling of cancer cells for prioritizing FDA-approved drugs with repurposing potential. Scientific Reports, 7(1), 11261. doi:https://doi.org/10.1038/s41598-017-11508-2).

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FIG. 2 In silico approaches for drug repositioning of cell-cycle modulators. Computational repositioning of cellcycle modulating drugs can be classified as ligand-based, target-based, and system-based approaches. (A) The ligand-based approach infers off-target mechanisms based on information derived from the ligand; large-scale, (Continued)

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3.1 Ligand-Based Drug Repurposing Ligand-based drug repurposing aims to identify novel indications based on the information of the ligand itself. A common theme among these approaches is chemical similarity comparisons where the degree of similarity between two ligands often indicates their likelihood of sharing similar properties, such as off-targets, side effects, and other bioactivities. This “chemical similarity principle” assumption can therefore allow a known drug to be repurposed for a new indication that is shared with structurally similar compounds from annotated databases, such as PubChem, ChEMBL or ChemProt (Bajorath, 2004; Gaulton et al., 2012; Kringelum et al., 2016; Nickel et al., 2014). Many ligand-based target-prediction approaches, including similarity ensemble approach (SEA), SuperPred, TargetHunter, HitPick, PASS, and ChemMapper have been developed and can be utilized for in silico drug repositioning (Dunkel, Gunther, Ahmed, Wittig, & Preissner, 2008; Gfeller et al., 2014; Gong et al., 2013; Keiser et al., 2007; Lagunin, Stepanchikova, Filimonov, & Poroikov, 2000; Liu, Vogt, Haque, & Campillos, 2013; Wang et al., 2013) (Table 1). To compare two chemical structures, the ligands are first converted into specific mathematical representations, called “chemical fingerprints,” characterized by the presence and absence of structural patterns extracted from the chemical graph (Awale & Reymond, 2017). Diverse chemical fingerprints have been developed over the years, which include the wellknown Daylight fingerprints that characterize a ligand based on path information extracted from a chemical graph or Molecular ACCess System (MACCS) fingerprint based on predefined substructural fragments (O’Boyle et al., 2011). Next, the similarity between fingerprints can be evaluated by metrics, such as the Tanimoto coefficient (Tc), which determine the shared bits between two comparing vectors. Regardless of representation, a high Tc value often indicates that the two compounds are structurally similar and may share similar bioactivities based on the chemical similarity principle. Other approaches like SEA derive a statistical score by evaluating chemical similarity relative to a random background similar to that used in BLAST sequence searches (Keiser et al., 2007). Although the chemical similarity principle is valid most of the time, it can still be violated by the “activity cliff,” where structural changes in compounds lead to significant changes in their bioactivities and they do not obey the linear assumption of a quantitative structure-activity relationship (QSAR) Hu, Stumpfe, & Bajorath, 2013; Stumpfe, de la Vega de Leon, Dimova, & Bajorath, 2014). The concept of ligand similarity has prompted the development of a network pharmacology concept that attempts to determine the multibody relationships between ligands and

FIG. 2, CONT’D ligand-based promiscuity prediction can be used to identify unexpected off-target interactions for drug repositioning. (B) The target-based approach can be used to identify approved drugs that interact with a preselected off-target if the structure information is available. The reverse structure screen can be accomplished by binding site similarity comparisons to identify putative off-target structures followed by molecular docking to confirm protein-ligand interaction. (C) The system-based approach infers novel drug-target mechanisms by bioactivity similarity comparison. The biological behavior of a known drug can be compared to those of landmark compounds to infer off-target mechanisms of known drugs. (D) The ligand-based, target-based, and system-based approaches can all be used for in silico repositioning of cell-cycle modulating drugs. (Modified from Lo, Y. C., Senese, S., France, B., Gholkar, A. A., Damoiseaux, R., & Torres, J. Z. (2017). Computational cell sycle profiling of cancer cells for prioritizing FDA-approved drugs with repurposing potential. Scientific Reports, 7(1), 11261. doi:https://doi.org/10.1038/s41598-017-11508-2).

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TABLE 1 In Silico Drug Repositioning Tools Name

Description

Reference

ChemProt

A resource of annotated and predicted chemical-protein interactions with a compilation of over 1,100,000 unique chemicals with biological activity for more than 15,000 proteins.

Taboureau et al. (2011)

CSNAP

Chemical similarity network analysis pulldown (CSNAP) is a computational approach for compound target identification based on network-similarity graphs.

Lo et al. (2015)

DASPFind

Predicts new drug-target interactions from a network that encodes information about the known drug-target interactions, similarities between the drugs and similarities between targets.

Ba-Alawi et al. (2016)

DMC

DMC enables users to integrate, query, visualize, interrogate, and download multilevel data of known drugs or compounds for drug-repositioning studies all within one system.

Fu et al. (2013)

DPDR-CPI

Drug candidate positioning and drug repositioning via chemicalprotein interactome. Server provides indication predictions with probability values grouped by ICD-9-CM disease family.

Luo et al. (2016)

ElectroShape Polypharmacology Server

Estimates polypharmacology profiles and side effects of compounds based on the molecular similarity concept.

Armstrong et al. (2010)

GoPredict

GoPredict uses comprehensive integration of genomics data, signaling-pathway information, drug-target databases, and curated knowledge in databases.

Louhimo et al. (2016)

HitPick

An approach that combines two 2D molecular similarity-based methods: a simple 1-nearest-neighbor similarity searching and a machine-learning method based on Laplacian-modified naive Bayesian models.

Liu et al. (2013)

MANTRA

Computational tool for the analysis of the mode of action (MOA) of novel drugs and the identification of known and approved candidates for drug repositioning. It is based on network theory and nonparametric statistics on gene expression data.

Carrella et al. (2014)

MeSHDD

A framework for computational drug repositioning using literature-derived, drug-drug similarity.

Brown and Patel (2017)

NFFinder

NFFinder uses transcriptomic data to find relationships between drugs, diseases, and a phenotype of interest. It also identifies experts that have published in that domain.

Setoain et al. (2015)

PassOnline

Based on the comparison of the user’s compound to a database of 260,000 drug-like biologically active compounds using the multilevel neighborhoods of atoms (MNA) structure descriptors.

Poroikov, Filimonov, Lagunin, Gloriozova, and Zakharov (2007)

PPB

PPB (polypharmacology browser) searches through 4613 groups of at least 10 bioactive molecules with documented activity against a biological target, as listed in ChEMBL.

Awale and Reymond (2017)

Prosmicuous

Exhaustive resource of protein-protein and drug-protein interactions with the aim of providing a uniform data set for drug repositioning and further analysis.

von Eichborn et al. (2011)

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TABLE 1

In Silico Drug Repositioning Tools—cont’d

Name

Description

Reference

SEA

Similarity ensemble approach (SEA) relates proteins based on the set-wise chemical similarity among their ligands.

Keiser et al. (2007)

SuperPred

Translates a molecule into a structural fingerprint by comparing it to 6300 drugs, which are enriched by 7300 links to molecular targets by text mining.

Nickel et al. (2014)

SwissTargetPrediction Online tool to predict the targets of bioactive small molecules in humans and other vertebrates.

Gfeller et al. (2014)

TargetHunter

Wang et al. (2013)

Web portal for predicting the therapeutic potential of small organic molecules based on a chemogenomic database.

uncover novel bioactivities of compounds for drug repurposing (Zhao & Iyengar, 2012). To this end, we recently proposed a novel network-based approach for large-scale compound profiling called chemical similarity network analysis pull-down (CSNAP) (Lo et al., 2015) (Fig. 3). CSNAP is an instance-based unsupervised learning method that simultaneously clusters query compounds and drugs retrieved from the ChEMBL database (a compound database with annotated bioactivities) into a network similarity graph using a predefined Tc threshold. Next, a consensus statistics score of targets is assigned to each query compound in the network based on the observed target frequency from its first-order neighbors similar to that used for functional predictions in a protein-protein interaction network. CSNAP enabled compound expletory analysis based on their chemical diversity and has the ability to carry out large-scale off-target profiling of massive compound sets. CSNAP analysis of 200 drug-like compounds from a cell-cycle screen correctly predicted several known cancer targets as well as novel compounds that clustered with known drugs for potential drug repurposing in multiple applications (Aretz & Meierhofer, 2016; de Anda-Ja´uregui, Guo, McGregor, & Hur, 2018; Ravikumar & Aittokallio, 2018). Another network-based drugrepurposing approach comprises drug-target networks (Yıldırım, Goh, Cusick, Baraba´si, & Vidal, 2007). Unlike a chemical similarity network, a drug-target network is a bipartite network that explicitly considers the relationship between the drug and target through their binary associations. Based on this formulation, a supervised learning algorithm was subsequently developed to predict drug-target interactions (DTIs) by integrating chemical and genomic space with high accuracy (Yamanishi, Araki, Gutteridge, Honda, & Kanehisa, 2008). Using a similar network-based inference approach, Cheng et al. successfully repositioned two known drugs, simvastatin and ketoconazole, by demonstrating their potent antiproliferative activities on a human MDA-MB-231 breast cancer cell line (Cheng, Liu, et al., 2012). A major limitation of the ligand-based, drug-repurposing approach is the inability to identify targets for newly synthesized drugs that represent novel chemotypes distinct from the existing drugs. Therefore a simple chemical search of the bioactivity database will fail to retrieve any hits. Recently, several new approaches have been proposed to tackle this challenge. One approach is to consider 3D pharmacophore and/or shape similarity between compounds to determine if two compared ligands are a scaffold hopping pair, indicating that

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FIG. 3 Description of chemical similarity network analysis pull-down algorithm. (A) Diverse hit compounds from a cell-based chemical screen are used as inputs for the chemical similarity network analysis pull-down (CSNAP) program to predict their drug targets. (B) In CSNAP, the Obabel FP2 fingerprints, which characterize molecules by a series of structural motifs as binary numbers (0 and 1), are utilized for structural comparison and compound retrieval from the ChEMBL database (version 16) containing more than 1 million annotated molecules with reported bioactivities. The target annotations of the selected ChEMBL compounds (baits) most similar to input ligands are subsequently retrieved from ChEMBL and PubChem databases. (C) Based on the output of ligand similarity comparisons, a chemical similarity network is constructed by connecting pairs of ligands with similarity above a Tc threshold according to a weighted adjacency matrix. This results in weighted graphs (networks) in which nodes represent compounds and edges represent chemical similarity. (D) To infer drug targets from the networks, a target consensus statistic score, Schwikowski (Continued)

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the two compounds may interact with a common drug-binding site through consensus functional groups despite their apparent differences in chemical structures (Armstrong et al., 2010). Based on this concept, Lo et al. recently developed the CSNAP3D program, a 3D upgrade to the original CSNAP framework, by incorporating 2D and 3D similarity metrics for automated scaffold recognition (Lo, Senese, Damoiseaux, & Torres, 2016). CSNAP3D was able to improve target identification for challenging drug classes such as HIVreverse transcriptase (HIV-RT), which had previously resulted in poor performance when evaluated by many 2D similarity-based compound target profiling programs. CSNAP3D analysis of an HIV-RT drug set retrieved from the directory of useful decoys (DUDs) recognized consensus chemical features of three structurally distinct HIV-RT inhibitors, efavirenz, nevirapine, and tivirapine, and was applied to the discovery of a novel scaffold capable of promoting microtubule formation similar to paclitaxel. Another approach for discerning chemical scaffold hopping is through 2D substructure searches by identify consensus fragments co-existing in the structures of two drugs. Along this line, Wu et al. developed the substructure-drug-target network-based inference (SDTNBI) for large-scale drug-target interaction (DTI) prediction and drug repositioning and applied this approach to the reposition of nonsteroidal antiinflammatory drugs (NSIDs) as novel anticancer agents targeting AKR1C3, CA9, CA12, or CDK2 (Wu et al., 2017).

3.2 Target-Based Drug Repurposing The growing number of protein crystal structures in the Protein Data Bank (PDB) accompanied by the maturation of in silico structural bioinformatics analysis makes the target-based drug repurposing approach a top choice if the 3D structure of the target is available (Moriaud et al., 2011). Target-based drug repurposing utilizes 3D structures of proteins to identify additional off-targets that may share similar bioactivities to cognate ligands. In one approach, binding-site similarity comparison between on- and off-protein targets can be used to suggest new off-targets of the co-crystal ligands that may potentially interact. In the second approach, direct docking of a compound library to a target or docking a target ligand to a panel of proteins represent alternative strategies to predict novel protein-ligand interactions for drug repositioning. Similar to the ligand-based, drug-repurposing approach, the binding site of a target protein can be used to search a database of drug binding pockets to identify off-targets that may potentially interact with the cognate ligands. Several pocket similarity comparison methods have been developed that provide potential opportunities for structure-based drug repositioning

FIG. 3, CONT’D score (S-score), is calculated by ranking the most common targets shared among neighboring annotated ligands of each query compound within the network. For example, compound alpha is predicted to have an S-score of 3 for target A and a S-score of 1 for target C. Additionally, a Hishigaki score (H-score), a chi-square like test based on the mean target annotation frequency distributed within the whole network, is also implemented to compute a significance value for each drug-target assignment. (E) Target profiling of analyzed compounds using target-ligand interaction fingerprints and target enrichment analysis based on S-scores. (Modified from Lo, Y. C., Senese, S., Li, C. M., Hu, Q., Huang, Y., Damoiseaux, R., & Torres, J. Z. (2015). Large-scale chemical similarity networks for target profiling of compounds identified in cell-based chemical screens. PLoS Computational Biology, 11(3), e1004153).

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(Haupt & Schroeder, 2011). The simplest way for pocket comparison is through a structural alignment by superposing two sites. One such approach is implemented by ProBis-Ligands, which uses local structure alignment to transport ligands between homologous protein structures and has potential applicability for drug repositioning (Konc & Janezic, 2014). Using a similar approach, Franchi et al. investigated the cross-reaction of protein kinase inhibitors with synapsin I, an ATP-binding protein regulating neurotransmitter release in the synapse (Defranchi et al., 2010). By systematic pair-wise comparison of the staurosporine-binding site of the proto-oncogene Pim-1 kinase with 6412 druggable protein-ligand binding sites retrieved from the scPDB database, they showed that the ATP-binding site of synapsin I may recognize the pan-kinase inhibitor staurosporine. A limitation of this approach is that chemical features, which are essential for ligand binding, are not considered and therefore this can lead to a high false-positive rate. More effective site-comparison methods are molecular field analyses, such as the GRID method, that use different energetic probes to provide a more precise evaluation of protein pocket similarity (Goodford, 1985). A different pocket similarity comparison approach based on protein microenvironments called PocketFEATURE has been developed by the Altman group at Stanford (Liu & Altman, 2011). To measure the binding chemistry of a protein binding site, a micro˚ concentric spheres consisting of diverse physicochemical environment, which consisted of 6A properties were evaluated at each residue of the binding pocket. Next, an exhaustive combinatorial search was then performed between the multiple microenvironments of two comparing sites to identify the best matching pockets. Since comparison is performed based on discrete probes independent of the overall protein structure, the PocketFEATURE algorithm does not necessitate strong geometric requirements and can therefore accommodate structural factors resulting from protein structural flexibility. To identify novel antimitotics for the treatment of cancer, Lo et al. recently applied the PocketFEATURE program to repositioning Food and Drug Administration (FDA)-approved drugs capable of interacting with the taxane binding site of tubulin. A binding-site similarity comparison between the taxane binding pocket and a database of >14,000 drug-like pockets retrieved from the PDB identified several selective estrogen-receptor modulators (SERMs) as novel antimitotics, which were originally indicated for the treatment of osteoporosis in menopausal woman. In vitro and cell-based evaluation showed that SERMs stabilize microtubule formation, compete directly for the taxane binding site, and display distinct cell death dynamics from the traditional taxanes (Lo, Cormier, Liu, Nettles, Katzenellenbogen, Stearns, & Altman, manuscript in prep). While binding-site similarity searches provide an efficient first-order estimate for ligand off-target binding, there is no guarantee that the co-crystal ligand will interact with the predicted pockets for a successful repositioning. Therefore in silico approaches that directly model the ligand position and orientation, known as “pose” or “binding modes,” in the binding site of interest have been developed (Taylor, Jewsbury, & Essex, 2002). In contrast to experimental HTS, the possibility of simulating the binding modes of multiple ligands from a large database using molecular docking algorithms resulted in a new computer-aided drugscreening approach called virtual screening (VS) or structure-based VS (SBVS) (Cheng, Li, Zhou, Wang, & Bryant, 2012). In the forward VS, a database of compounds is docked into a protein of interest and the docking scores can be used to prioritize the ligands for synthesis and experimental evaluation. For structure-based drug repurposing, docking of approved

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drugs to a protein target of interest can be used to explore secondary activities of known drugs. To identify nonsteroidal antagonists against the human androgen receptor (AR), a therapeutic target for prostate cancer, Bisson et al. docked a library of marketed oral drugs into multiple structure models of antagonist-bound AR receptor. By evaluating 11 selected compounds with the highest docking scores using an in vitro assay for antagonism of dihydrotestosterone-induced AR transactivation, they identified the phenothiazine derivatives acetophenazine, fluphenazine, and periciazine, which were used clinically as antipsychotic drugs, as leads for AR antagonists (Bisson et al., 2007). In another study, Ma et al. applied a similar approach to discover methylene blue (MB) as a novel scaffold for DNA intercalating agents (Balasubramanian, Hurley, & Neidle, 2011). They showed that the compounds can be optimized to stabilize an alternative form of a DNA complex, c-myc G-quadruplex, a potential cancer target that has been reported to repress c-myc oncogenic expression and inhibit cancer cell growth. In contrast to forward SBVS, reverse VS docking, also known as “structure-based target fishing,” docks a single ligand to a panel of drug-binding pockets to identify the most probable off-targets ranked by docking scores (Rognan, 2010). In this case, a target promiscuity profile can be established for a given drug to identify novel bioactivities for drug repositioning purposes (Kharkar, Warrier, & Gaud, 2014; Lee, Lee, & Kim, 2016). One major challenge of this approach is the requirement to develop an automated procedure for binding-site preparation from PDB files. However, this problem has recently been addressed by the development of several databases containing ready-to-dock protein pockets, such as the scPDB database, as well as servers for performing reverse target fishing like the TarFisDock server (Desaphy, Bret, Rognan, & Kellenberger, 2015; Li et al., 2006; Paul, Kellenberger, Bret, Muller, & Rognan, 2004). One success story of reverse VS drug repositioning has been reported by Zahler et al. (2007). A structure-based docking study of indirubin derivatives against the scPDB database identified novel kinase targets including phosphoinositide-dependent kinase 1 (PDK1) as a target of one derivative (6BIO). In another study, Zheng et al. used reverse VS to investigate several functional components in green tea including epigallocatechin gallate (EGCG), epigallocatechin (EGC), epicatechin gallate (ECG) and epicatechin (EC), which were found to have broad antineoplastic activity (Zheng, Chen, & Lu, 2011). Using TarFisDock along with the Potential Drug Target Database (PDTD) and selecting targets with high docking scores and their relevance to human disease, the binding mode of EGCG, with its potential target protein leukotriene A4 hydrolase, was determined. As reported from many large-scale validation studies, the limitations of current docking algorithms include the inability to recapitulate the ligand-binding pose from the crystal structures, to enrich true positives by docking scores in certain cases and to accurately predict ligand-binding affinity (Schneider, 2010). Other considerations include structure flexibility due to “induced fit,” where the protein binding site undergoes a conformational change upon ligand binding that deviates from the presumed rigid structure (Lin, 2011). An emerging docking approach to tackle target flexibility is ensemble docking, which docks a ligand to multiple protein conformers as a partial solution to account for protein flexibility (Okamoto et al., 2009; Sperandio et al., 2010). For more challenging cases, molecular dynamics (MD) can be used, which applies classical mechanics to simulate the dynamics trajectory of the protein-ligand complex (Kerrigan, 2013; Sledz & Caflisch, 2018). In one study, Singh et al. applied structure- and ligand-based VS methods to narrow down the number of possible

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drugs from the DrugBank database that could target the allosteric site of the Abl kinase. Based on the docking results, selected drugs were simulated in the allosteric site of the Abl kinase, using molecular dynamics and steered molecular dynamics simulations (Singh et al., 2017). They showed that gefitinib, an EGFR inhibitor approved for the treatment of lung cancer, could bind effectively to the allosteric site of Bcr-Abl and had synergistic antiproliferative activity with imatinib in the chronic myeloid leukemia cell line K562.

3.3 Expression-Based Drug Repurposing The importance of polypharmacology in understanding multitarget MOA has prompted the investigation of drug responses at a systems-wide level (Li et al., 2016). The wide availability of high-throughput experimental platforms enables large-scale measurements of cellular responses under pharmacological perturbation. Gene expression analysis is the earliest and most commonly used approach for differentiating drug MOA at a systems level (Setoain et al., 2015). In typical gene-expression profiling, the expression of thousands of genes is measured to obtain a snapshot of the cellular state under the treatment of a drug. The analysis is usually performed by comparing the treatment and control profiles. The difference in the mRNA expression level for a set of genes reveals a potential molecular target or pathway targeted by the test compounds. Several highthroughput transcriptome platforms have been developed including DNA microarray and RNA-seq to measure the relative expression of known genes. With the advancement of transcriptome technology, several data-driven approaches to mine gene expression data for drug repositioning have also been developed (Iorio, Rittman, Ge, Menden, & SaezRodriguez, 2013). A notable example is the connectivity map (CMap), which contains gene expression profiles for 1400 FDA-approved drugs across multiple cell lines (Lamb et al., 2006). A direct extension of this effort is the Library of Integrated Network-based Cellular Signatures (LINCS), which contains gene-expression profile information for cancer cell lines treated with different compounds at multiple doses (Vidovic, Koleti, & Schurer, 2014). In one approach called “signature reversion,” Jahchan et al. systematically compared drug and disease gene expression to uncover novel drug-disease relationships and identified tricyclic antidepressant drugs as potential novel treatments for lung and neuroendocrine tumors ( Jahchan et al., 2013). In another study, Shigemizu et al. compared gene expression profiles between CMap and Gene Expression Omnibus (GEO) by looking for inverse correlations between the most perturbed gene-expression levels in human cancer tissue and the most perturbed expression levels induced by bioactive compounds to reposition known drugs against breast cancer, myelogenous leukemia, and prostate cancer (Shigemizu et al., 2012). The application of proteomic approaches for drug repositioning aims to address several limitations of gene expression profiling. Protein abundance may not necessarily correlate with that of RNA abundance due to mechanisms such as alternative splicing and posttranslational modifications (Rogers et al., 2008). Therefore measurement of cellular responses at the protein level may provide a more direct and accurate assessment of drug effects. Several experimental platforms have been developed for proteomic analysis of drug-induced effects including enzyme-linked immunosorbent assay (ELISA), 2D-polyacrylamide gel electrophoresis (PAGE), and mass spectrometry (MS). Large-scale proteomic data also prompted the

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development of several in silico tools and databases for proteomic level drug repositioning. In silico methods like protein-protein interaction (PPI) networks connect proteins based on their physical or functional association to identify critical drug targets, disease-causing proteins, using descriptive network statistics (Ba-Alawi, Soufan, Essack, Kalnis, & Bajic, 2016; Carrella et al., 2014). For example, Fukuoka et al. proposed a two-step drug repositioning approach based on a PPI network of two diseases and the similarity of the drugs (Fukuoka, Takei, & Ogawa, 2013). In the proposed method, the first step involved a similarity evaluation between two disease-shared genes, from which a PPI network was generated. In the second step, drugs prescribed for the diseases were obtained from a drug database. If the target gene(s) of each of the obtained drugs was involved in the PPI network, the drugs were selected as repositioning candidates. In another study to identify novel targets for the treatment of nasopharyngeal carcinoma (NPC), Lan et al. used PPI networks to study 558 upregulated and 993 downregulated genes from microarray data (Lan et al., 2010). By analyzing cliquebased hubs in the NPC network subgraph, they successfully identified 24 upregulated and six downregulated bottleneck genes for predicting NPC oncogenesis. Pathway analysis is another important network tool that analyzes proteomic data where directionality between functional components in a biological network is important. In particular, pathway enrichment analysis can be used to identify consensus pathways with significant gene-expression changes due to drug-treatment (Smith, Dampier, Tozeren, Brown, & Magid-Slav, 2012). Recently, Pan et al. used pathway enrichment analysis to study the MOA of sixteen FDA-approved drugs based on retrieved drug targets interacting with or affected by the investigated drugs (Pan, Cheng, Wang, & Bryant, 2014). Using this approach they found Celecoxib, a well-known antiinflammatory agent as a selective inhibitor of prostaglandin-endoperoxide synthase 2 (PTGS2 or COX2) and identified new pathways responsible for pancreatic and lung cancers. Chen et al. developed another approach for pathway-guided drug repositioning of FDA drugs by mining a human functional linkage network for inversely correlated modules of drug and disease gene targets (Chen, Sherr, Hu, & DeLisi, 2016). By considering gene mutation, gene expression, and functional connectivity as well as proximity within module genes, they correctly predicted five approved drugs against breast and prostate cancers: clotrimazole, triprolidine, thioridazine, mefloquine, and fluphenazine.

3.4 Phenotype-Based Drug Repurposing Another important approach for drug repurposing concerns the use of “phenome” information retrieved from whole organisms as novel signatures for drug repositioning. The phenome, by definition, represents the comprehensive collection of phenotype information regarding the whole organism. This includes cell line drug sensitivity data, side effects, toxicity, dosing, pharmacokinetic (PK)/pharmacodynamic (PD) properties, and other pathological conditions retrieved from clinical and electronic medical records. The approach is also aided by the development of many publicly available clinical databases and servers such as the Sider database for drug-side effect relationships and pharmGKB server for drugdisease relationships from clinical trials (Altman, 2007; Kuhn, Letunic, Jensen, & Bork, 2016). The basic assumption of phenome-based drug repositioning is often based on

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“guilt-by-association,” where compounds with the same MOA will generally exhibit similar biological behavior across assays. Disregarding the source of measurement, successful repurposing using this multidimensional screening approach often necessitates the establishment of pharmacological profiles based on a set of landmark compounds (“seed”) from which the query compounds can compare for mechanism inference (Kim et al., 2004). Due to the wide availability of diverse cancer cell lines for in vitro testing of compound activities, phenotype-based drug repositioning that relies on cell line-sensitivity data is commonly used. A seminal example for drug repositioning using cell line-sensitivity data is the US National Cancer Institute’s NCI-60 dataset, which contains cell viability data for a panel of cancer cell lines exposed to a large number of natural products and drugs (Paull et al., 1989). Using the COMPARE algorithm, Zaharevitz et al. applied mean graphs generated from the compound activity profiles across cancer cell lines for compound bioactivity comparison and inferred cyclin-dependent kinases as novel targets for the small molecule inhibitor class of paullones capable of delaying cell-cycle progression (Zaharevitz et al., 1999). A more recent study using this approach is reported by Yamori et al., whom coupled the COMPARE algorithm with chemosensitivity analysis of 39 human cancer cell lines, gene-expression profiles, and target-based assays to discover a novel DNA minor-groove binder against topoisomerases I and II, MS-247, and a potent novel telomerase inhibitor, FJ5002, with potent in vivo antitumor activity against various human cancer xenografts (Yamori, 2003). In addition to cell viability, cell-cycle profiling has recently been proposed as a novel biosignature for bioactivity similarity profiling. By generating cell-cycle profiles for a library of 884 FDA-approved drugs using microscopy-based ctyometry, Lo et al. converted the cellcycle profiles of each test compound into a cell cycle-phase fingerprint and predicted compound cytotoxicity based on the fingerprint distance called the cell-cycle index (CCI), which measured the degree of cell-cycle profile changes from the DMSO control profile quantified using a Euclidean distance metric (Lo et al., 2017). Compound prioritization based on the CCI led to the identification of six FDA-approved drugs with novel cytotoxic effects against cancer cell lines including methiazole, medrysone, nicergoline, tribenoside, primaquine, and GBR 12909. Further unsupervised clustering of the cell-cycle fingerprints and 3D chemical structural similarity analyses repositioned several clinically relevant microtubule stabilizers with unexpected DNA damaging properties (Lo et al., 2016). While cell-line sensitivity data provide valuable insight into a drug’s potency against in vitro cancer models, this observation rarely translates into their effectiveness against human tumors in vivo and in clinical trials. The missing link can often be attributed to complex drug-drug interactions, metabolism, toxicity, dosing, and bioavailability. However, two drugs sharing similar PK/KD properties can often result in similar side effects or other adverse events. Although it is not possible to accurately deduce a detailed molecular mechanism from a shared side effect, it is understood that there might be some underlying MOA linking disease to clinical phenotype between the two drugs. Campillos et al. analyzed a network of 746 FDA-approved drugs based on side effect similarities where 261 linkages were formed by connected drugs with dissimilar chemical structures. The network connectivity allowed them to experimentally test the predicted target binding of 20 compounds and validate 13 implied drug-target relations as low micromolar inhibitors (Campillos, Kuhn, Gavin, Jensen, & Bork, 2008). A similar approach called Drug Repositioning based on the Side-Effectome (DRoSEf) was developed by Yang et al. They constructed a database of disease-side effect relationships

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to predict new indications for marketed drugs using a Naive Bayes model and subsequently analyzed 4200 ligands with no readily available side effect information using QSAR modeling (Yang & Agarwal, 2011). Another approach was recently proposed by Ye et al. who used nearest neighbor inference for drug repositioning from the drug-drug side effect similarity network (Ye, Liu, & Wei, 2014). Besides clinical side effects, the disease indication of known drugs can likewise be utilized for drug repositioning. One assumption is that if two diseases share a similar set of therapeutics, then drug treatment of one indication can be repurposed for a novel indication. This idea has been proposed by Chiang et al. who compiled a DrugDisease Knowledge Base (DrDKB) consisting of 726 diseases and 2022 drugs. By evaluating 5549 disease pairs that shared at least one FDA-approved drug, they identified 156,279 unique drug use suggestions for marketed drugs.

4 CHALLENGES AND FUTURE DIRECTION Despite the advancements of HTS, combinatorial synthesis and structure-based de novo drug discovery, the decline of R&D productivity in generating safe and effective new chemical entities (NCE) for clinical trials and the treatment of patients continues to challenge existing approaches to drug discovery. The traditional “one drug, one target” paradigm focusing on optimizing target binding affinity or potency of a validated target has been replaced by the concept of polypharmacology. It is now recognized that the off-target binding of a drug lead is critical if not more important than their interaction with the original intended target. In fact, many adverse events are caused by drug promiscuity. Interestingly, this realization also prompts the development of more effective drug discovery approaches called drug repurposing, where the off-target binding of a known drug can be reconsidered for a new indication. Although drug repurposing is not new, it has become an emerging area of drug research due to the advancement of several high-throughput experimental platforms. The wealth of data generated also prompts the development of a multitude of in silico, drugrepurposing approaches with various levels of dependency on wet lab validation and each has their own strengths and limitations. Among the in silico methods discussed, the ligand-based approaches, such as the similarity-based activity profiling, are some of the most effective approaches for drug repurposing due to their low dependency on experimental data and high computational efficiency. Although the ligand-based method cannot usually be used to repurpose a ligand for a preselected target, comprehensive off-target profiling of large compound collections can sometimes be used to discover unexpected off-target activities for the screened ligand, which provides potential opportunities for target-based repositioning. Limitations associated with chemical similarity searches include the presence of activity cliffs that challenge the QSAR model as well as domain predictability for novel compounds not represented in existing databases. Nevertheless, several machine-learning techniques such as the kernel method allow mapping of ligand similarity to high dimensional space to model nonlinear QSAR. Likewise, novel compounds sharing low structural similarity can be evaluated for their scaffold hopping potential based on 3D molecular descriptors comparison or coupled with systematic analysis using expression, cell-line sensitivity, or cell-cycle profiling.

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While the ligand-based repurposing approach is useful for the repositioning of existing drugs, in many cases, these compounds usually display lower affinity than their original target. Therefore structural modification of the original scaffold is often necessary to achieve activity reversal from primary to secondary target. However, ligand-based design based on a repurposed compound may be challenging as there is no SAR data available to guide chemical modifications. Although a randomized chemical screen against a focused library is potentially feasible, this approach is usually time consuming and does not provide direct insight into the drug-binding mechanism. Furthermore, the ligand-based method is not applicable to situations where off-target ligands for a specific target are sought. In this case, the structure-based, drug-repurposing approach can be used. A typical structure-based, drugrepurposing approach consists of two steps. First, starting with the co-crystal structure, a binding site-similarity comparison can be performed to identify potential off-target structures from a binding pocket database such as scPDB. In the second step, the target-ligand structure is docked into repurposed targets using molecular docking algorithms. The postvalidation step can be coupled with pharmacophore modeling to identify consensus protein-ligand interactions or MD simulations to determine complex stability. Although it is possible to perform drug repurposing using reverse docking, it is usually not advisable due to the high computational demand required for the docking procedure as well as the lack of secondary information to support the repurposing hypothesis. Major limitations of target-based drug repurposing include the availability and the quality of structural data. However, this problem has been greatly alleviated due to the increased output of protein structural data from structural genomic initiatives as well as the development of homology modeling and de novo protein structure-prediction techniques if the target structure is not readily presented (Baker & Sali, 2001; Cavasotto & Phatak, 2009). In comparison, phenotype-based drug repurposing based on biological behaviors derived from cell lines, whole organisms, or clinical data has less translational issues than other in silico approaches. Furthermore, since MOA is generally not a prerequisite for drug approval, this approach generally provides the most efficient way to repurpose a marketed drug. However, should clinical issues arise regarding their efficacy, toxicity and adverse events, accurate determination of MOA then becomes essential. The challenges of determining MOA from phenotypic profiling can often result from redundancy of the biological system. For example, diverse signaling pathways modulated by a wide range of enzymatic activities can result in similar phenotypes. Thus, phenotype similarity does not necessarily indicate molecular similarity. In such instances, complementary approaches that incorporate ligand and structure based-similarity searches as well as expression profiling will aid in the process of target identification. In fact, an emerging direction for future drug repurposing will be the seamless integration of in silico and experimental approaches to maximize the success rate of a drug-repurposing campaign. On the one hand, large-scale experimental measurements will often be accompanied by diverse chemoinformatic and bioinformatic machine-learning platforms to explore potential repurposing opportunities. On the other hand, in silico methods can also lead the hypothesis-generation step, which can then be validated experimentally. For instance, to determine the drug targets from a cell-cycle modulator small molecule screen, Lo et al. recently applied the CSNAP algorithm to cluster hits with a set of reference compounds retrieved from the ChEMBL database using chemical similarity networks (Lo et al., 2015).

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This combined approach led to the identification of novel and validated targets for a set of M-phase inhibitors. Furthermore, analysis of a M-phase inhibitor network subgraph followed by molecular docking validation enabled the discovery of a novel chemical series targeting microtubule assembly. Recently, knowledge-based drug repurposing has grown in popularity due to the accelerated availability of biological “big data,” as well as advances in machine-learning techniques (Luo et al., 2016). One characteristic of these approaches is the incorporation of heterogeneous data sources via data-fusion techniques to dissect complex relationships between genes, drugs, and diseases (Fu et al., 2013; Louhimo et al., 2016; von Eichborn et al., 2011). For example, Napolitano et al. recently combined gene-expression data, chemical similarity, and a PPI network to train a multi-class support vector machine (SVM) to classify the compound anatomical therapeutic chemical (ATC) code (Napolitano et al., 2013). Using this approach, they showed the repositioning of anthelmintics to antineoplastic agents and of antineoplastic agents to antibacterials were the most frequent drug reclassifications from their prediction, which agrees with previous drug-repurposing efforts. In one approach called sematic network, Mullen et al. created a drug interaction network integrated from 11 sources to quantify drug-drug similarity (Mullen, Cockell, Tipney, Woollard, & Wipat, 2016). By mining the occurrence of network subgraphs using the algorithm DReSMin, they identified and ranked 9,643,061 putative drug-target interactions and demonstrated a strong correlation between top scoring associations and those reported by literature. With the recent development of natural language-processing technology, drug repositioning can be accomplished by mining of unstructured text known as literature-based discovery through information extraction, sematic-similarity comparison, and sematic-relationship inference (Andronis, Sharma, Virvilis, Deftereos, & Persidis, 2011; Brown & Patel, 2017). For example, Frijters et al. developed a text mining tool called CoPub Discovery for mining drug, gene, pathway, and disease relationships from Medline abstracts based on their mutual co-occurrence (Frijters et al., 2010). The study led to the identification of dephostatin, a tyrosine phosphatase inhibitor, and damnacanthal, a tyrosine kinase inhibitor, being relevant to cell proliferation at low micromolar concentrations. Despite increasing popularity, knowledge-based drug repurposing has faced several challenges, including problems with the integration of heterogeneous data sources with diverse formats, the quality assurance of the data used, as well as the lack of mechanistic interpretation for the predicted drug mechanism.

5 CONCLUSION In this chapter, we provided an in-depth discussion of in silico, drug-repurposing approaches for the discovery of cell-cycle modulators for the treatment of cancer. First, we provided an overview of the basic biology of cell-cycle regulation and the drug-targeting mechanisms for cancer treatment. This was followed by an overview of drug-repurposing principles and experimental approaches for cell-cycle modulator discovery. Next, we discussed four major types of in silico approaches for drug repositioning based on ligand structure (ligand-based), protein structure (target-based), high-throughput experimentation (expression-based), and high-level drug responses and effects (phenotype-based). Finally, we

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discussed challenges and future developments in this emerging field. The examples provided in this chapter demonstrate the importance of in silico approaches for drug repurposing by fully utilizing information through effective and systematic means as well as understanding their strengths and limitations. These case studies also suggest that integration of computational and experimental tools as well as the ability to integrate disparate data from heterogeneous sources are key to the success of future drug repositioning efforts.

Acknowledgments We thank members of the Helix group at Stanford University and the Torres group at UCLA for their helpful feedback and suggestions. This project was supported by a Stanford Dean’s Postdoctoral Fellowship to Y.C. Lo and a Cottrell Scholar Award from the Research Corporation for Science Advancement and a National Science Foundation Grant NSF-MCDB1243645 to J.Z. Torres, any opinions, findings, and conclusions or recommendations expressed in this material are those of the authors and do not necessarily reflect the views of the National Science Foundation.

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