Abstract
Drug promiscuity refers to multitarget activity of a drug and can elicit two distinct or opposing actions: adverse side effects and improved therapeutic efficacy. On one hand, drug promiscuity is the source and mechanism of off-target effects; on the other hand, it forms the basis for polypharmacology-based drug repurposing, thereby a source of drug rediscovery. These opposing effects reflect two sides of a coin: positive/good/desirable drug promiscuity and negative/bad/undesirable drug promiscuity. In Chap. 13, the “good” side of drug promiscuity for drug repurposing and how the “good” side should be used for drug rediscovery have been discussed. The topic in this chapter focuses on the “bad” side of drug promiscuity, specifically the application of polypharmacology principles to predicting the drug toxicity induced by drug promiscuity, which is a critical issue in drug development.
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References
Hopkins AL. Drug discovery: predicting promiscuity. Nature. 2009;462(7270):167–8.
Feldmann C, Miljković F, Yonchev D, et al. Identifying promiscuous compounds with activity against different target classes. Molecules. 2019;24(22):4185.
Gupta MN, Alam A, Hasnain SE. Protein promiscuity in drug discovery, drug-repurposing and antibiotic resistance. Biochimie. 2020;175:50–7.
Scheiber J, Chen B, Milik M, et al. Gaining insight into off-target mediated effects of drug candidates with a comprehensive systems chemical biology analysis. J Chem Inf Model. 2009;49:308–17.
Fosnocht D, Taylor JR, Caravati EM. Emergency department patient knowledge concerning acetaminophen (paracetamol) in over-the-counter and prescription analgesics. Emerg Med J. 2008;25:213–6.
Huang T, Cui W, Hu L, et al. Prediction of pharmacological and xenobiotic responses to drugs based on time course gene expression profiles. PLoS ONE. 2009;4(12):e8126.
Kola I, Landis J. Can the pharmaceutical industry reduce attrition rates? Nat Rev Drug Discov. 2004;3:711–6.
FitzGerald GA. Coxibs and cardiovascular disease. N Engl J Med. 2004;351:1709–11.
O. of R. Affairs, Primus Announces a Voluntary Nationwide Recall of All Lots Within Expiry of Prescription Medical Food Limbrel® Due to Rare But Serious and Reversible Adverse Events While Seeking FDA’s Cooperation to Restore Access for Patients with Medical Necessity, U.S. Food and Drug Administration. 2019. http://www.fda.gov/safety/recalls-market-withdrawals-safety-alerts/primus-announces-voluntary-nationwide-recall-all-lots-within-expiry-prescription-medical-food. Accessed 10 Mar 2020.
Waring MJ, Arrowsmith J, Leach AR, et al. An analysis of the attrition of drug candidates from four major pharmaceutical companies. Nat Rev Drug Discov. 2015;14:475–86.
Thomas D, Clinical pharmacy education, practice and research: clinical pharmacy, drug information, pharmacovigilance, pharmacoeconomics and clinical research. Elsevier; 2018.
Hrdlicka M, Beranova I, Zamecnikova R, et al. Mirtazapine in the treatment of adolescent anorexia nervosa. Eur Child Adolesc Psychiatry. 2008;17:187–9.
Schatz SN, Weber RJ. Adverse drug reactions. In: Pharmacotherapy Self-Assessment Program (PSAP); 2015. p. 5–21.
Hopkins AL. Network pharmacology: the next paradigm in drug discovery. Nat Chem Biol. 2008;4:682–90.
Plant N. Can systems toxicology identify common biomarkers of non-genotoxic carcinogenesis? Toxicology. 2008;254:164–9.
Baell JB, Walters MA. Chemistry: chemical con artists foil drug discovery. Nature. 2014;513:481–3.
Aldrich C, Bertozzi C, Georg GI, et al. The ecstasy and agony of assay interference compounds. J Chem Inf Model. 2017;57:387–90.
Irwin JJ, Duan D, Torosyan H, et al. An aggregation advisor for ligand discovery. J Med Chem. 2015;58:7076–87.
Gaulton A, Hersey A, Nowotka M, et al. The ChEMBL database in 2017. Nucleic Acids Res. 2017;45(D1):D945–54.
Gilberg E, Gütschow M, Bajorath J. Promiscuous ligands from experimentally determined structures, binding conformations, and protein family-dependent interaction hotspots. ACS Omega. 2019;4(1):1729–37.
Sturm N, Desaphy J, Quinn RJ, et al. Structural insights into the molecular basis of the ligand promiscuity. J Chem Inf Model. 2012;52:2410–21.
Haupt VJ, Daminelli S, Schroeder M. Drug promiscuity in PDB: protein binding site similarity is key. PLoS One. 2013;8:e65894.
Pinzi L, Caporuscio F, Rastelli G. Selection of protein conformations for structure-based polypharmacology studies. Drug Discov Today. 2018;23:1889–96.
Feldmann C, Bajorath J. X-ray structure-based chemoinformatic analysis identifies promiscuous ligands binding to proteins from different classes with varying shapes. Int J Mol Sci. 2020;21(11):3782.
Bugrim A, Nikolskaya T, Nikolsky Y. Early prediction of drug metabolism and toxicity: systems biology approach and modeling. Drug Discov Today. 2004;9(3):127–35.
Wu Q, Taboureau O, Audouze K. Development of an adverse drug event network to predict drug toxicity. Curr Res Toxicol. 2020;1:48–55.
Stathias V, Turner J, Koleti A, et al. LINCS Data Portal 2.0: next generation access point for perturbation-response signatures. Nucleic Acids Res. 2020;48:D431–9.
Dix DJ, Houck KA, Martin MT, et al. The ToxCast program for prioritizing toxicity testing of environmental chemicals. Toxicol Sci. 2007;95:5–12.
Kuhn M, Letunic I, Jensen LJ, et al. The SIDER database of drugs and side effects. Nucleic Acids Res. 2016;44:D1075–9.
Taboureau O, Nielsen SK, Audouze K, et al. ChemProt: a disease chemical biology database. Nucleic Acids Res. 2011;39:D367–72.
Ciallella HL, Zhu H. Advancing computational toxicology in the big data era by artificial intelligence: data-driven and mechanism-driven modeling for chemical toxicity. Chem Res Toxicol. 2019;32:536–47.
Audouze K, Juncker AS, Roque FJ, et al. Deciphering diseases and biological targets for environmental chemicals using toxicogenomics networks. PLoS Comput Biol. 2010;6(5):e1000788.
Taboureau O, Audouze K. Human environmental disease network: a computational model to assess toxicology of contaminants. ALTEX. 2017;34:289–300.
Hodos RA, Kidd BA, Khader S, et al. Computational approaches to drug repurposing and pharmacology. Wiley Interdiscip Rev Syst Biol Med. 2016;8:186–210.
Peters JU, Schnider P, Mattei P, et al. Pharmacological promiscuity: dependence on compound properties and target specificity in a set of recent Roche compounds. ChemMedChem. 2009;4(4):680–6.
Peters JU, Hert J, Bissantz C, et al. Can we discover pharmacological promiscuity early in the drug discovery process? Drug Discov Today. 2012;17(7-8):325–35.
Klekota J, Brauner E, Roth FP, et al. Using high-throughput screening data to discriminate compounds with single-target effects from those with side effects. J Chem Inf Model. 2006;46(4):1549–62.
McGovern SL, Caselli E, Grigorieff N, et al. A common mechanism underlying promiscuous inhibitors from virtual and high-throughput screening. J Med Chem. 2002;45(8):1712–22.
Feng BY, Shelat A, Doman TN, et al. High-throughput assays for promiscuous inhibitors. Nat Chem Biol. 2005;1(3):146–8.
Uesawa Y. Quantitative structure–activity relationship analysis using deep learning based on a novel molecular image input technique. Bioorg Med Chem Lett. 2018;28:3400–3.
Mateen R, Ali MM, Hoare T. A printable hydro-gel microarray for drug screening avoids false positives associated with promiscuous aggregating inhibitors. Nat Commun. 2018;9(1):602.
Bloomingdale P, Housand C, Apgar JF, et al. Quantitative systems toxicology. Curr Opin Toxicol. 2017;4:79–87.
Ghasemi P-S, Mehri P-G. Neural network and deep-learning algorithms used in QSAR studies: merits and drawbacks. Drug Discov Today. 2018;23(10):1784–90.
Nantasenamat C, Isarankura-Na-Ayudhya C, Prachayasittikul V. Advances in computational methods to predict the biological activity of compounds. Expert Opin Drug Discov. 2010;5(7):633–54.
Yousefinejad S, Hemmateenejad B. Chemometrics tools in QSAR/QSPR studies: a historical perspective. Chemom Intell Lab Syst. 2015;149(B):177–204.
Perkins R, Fang H, Tong W, et al. Quantitative structure-activity relationship methods: perspectives on drug discovery and toxicology. Environ Toxicol Chem. 2003;22(8):1666–79.
Kwon S, Bae H, Jo J, et al. Comprehensive ensemble in QSAR prediction for drug discovery. BMC Bioinform. 2019;20(1):521.
Freyhult EK, Andersson K, Gustafsson MG. Structural modeling extends QSAR analysis of antibody-lysozyme interactions to 3D-QSAR. Biophysical J. 2003;84(4):2264–72.
Mauri A, Consonni V, Todeschini R. Molecular descriptors. In: Handbook of computational chemistry. Springer; 2017. p. 2065–93.
Zhu X, Kruhlak NL. Construction and analysis of a human hepatotoxicity database suitable for QSAR modeling using post-market safety data. Toxicology. 2014;321:62–72.
Atias N, Sharan R. An algorithmic framework for predicting side effects of drugs. J Comput Biol. 2011;18:207–18.
Hammann F, Gutmann H, Vogt N, et al. Prediction of adverse drug reactions using decision tree modeling. Clin Pharmacol Ther. 2010;88:52–9.
Azzaoui K, Hamon J, Faller B, et al. Modeling promiscuity based on in vitro safety pharmacology profiling data. ChemMedChem. 2007;2(6):874–80.
Dimova D, Hu Y, Bajorath J. Matched molecular pair analysis of small molecule microarray data identifies promiscuity cliffs and reveals molecular origins of extreme compound promiscuity. J Med Chem. 2012;55(22):10220–8.
Bender A, Scheiber J, Glick M, et al. Analysis of pharmacology data and the prediction of adverse drug reactions and off-target effects from chemical structure. ChemMedChem. 2007;2(6):861–73.
Ferreira LG, Dos Santos RN, Oliva G, et al. Molecular docking and structure-based drug design strategies. Molecules. 2015;20(7):13384–421.
Batool M, Ahmad B, Choi S. A Structure-based drug discovery paradigm. Int J Mol Sci. 2019;20(11):2783.
Hu Y, Bajorath J. Activity profile relationships between structurally similar promiscuous compounds. Eur J Med Chem. 2013;69:393–8.
Fukuzaki M, Seki M, Kashima H, et al. Side effect prediction using cooperative pathways. In: Proceedings of the IEEE international conference on bioinformatics and biomedicine (BIBM ‘09); 2009. p. 42–147.
Scheiber J, Jenkins JL, Sukuru SCK, et al. Mapping adverse drug reactions in chemical space. J Med Chem. 2009;52(9):3103–7.
Pauwels E, Stoven V, Yamanishi Y. Predicting drug side-effect profiles: a chemical fragment-based approach. BMC Bioinformatics. 2011;12:169.
Chen L, Huang T, Zhang J, et al. Predicting drugs side effects based on chemical-chemical interactions and protein-chemical interactions. Biomed Res Int. 2013;2013:485034.
Kanehisa M, Goto S. KEGG: Kyoto encyclopedia of genes and genomes. Nucleic Acids Res. 2000;28(1):27–30.
Kuhn M, von Mering C, Campillos M, et al. STITCH: interaction networks of chemicals and proteins. Nucleic Acids Res. 2008;36(1):D684–8.
Hu L, Chen C, Huang T, et al. Predicting biological functions of compounds based on chemical-chemical interactions. PLoS ONE. 2011;6(12):e29491.
Yamanishi Y, Araki M, Gutteridge A, et al. Prediction of drug-target interaction networks from the integration of chemical and genomic spaces. Bioinformatics. 2008;24(13):i232–40.
Chen L, He Z, Huang T, et al. Using compound similarity and functional domain composition for prediction of drug-target interaction networks. Medicinal Chem. 2010;6(6):388–95.
Chen L, Zeng W, Cai Y, et al. Predicting anatomical therapeutic chemical (ATC) classification of drugs by integrating chemical-chemical interactions and similarities. PLoS ONE. 2012;7(4):e35254.
Sharan R, Ulitsky I, Shamir R. Network-based prediction of protein function. Mol Syst Biol. 2007;3:88.
Bogdanov P, Singh AK. Molecular function prediction using neighborhood features. IEEE/ACM Trans Comput Biol Bioinform. 2010;7(2):208–17.
Kourmpetis YAI, van Dijk ADJ, Bink MCAM, et al. Bayesian Markov random field analysis for protein function prediction based on network data. PLoS ONE. 2010;5(2):e9293.
Hu L, Huang T, Shi X, et al. Predicting functions of proteins in mouse based on weighted protein-protein interaction network and protein hybrid properties. PLoS ONE. 2011;6(1):e14556.
Kuhn M, Campillos M, Letunic I, et al. A side effect resource to capture phenotypic effects of drugs. Mol Syst Biol. 2010;6:343.
Weininger DSMILES. a chemical language and information system. 1. Introduction to methodology and encoding rules. J Chem Inf Computer Sci. 1988;28:31–6.
Chen X, Liu X, Jia X, et al. Network characteristic analysis of ADR-related proteins and identification of ADR-ADR associations. Sci Rep. 2013;3:1–7.
Campillos M, Kuhn M, Gavin A-C, et al. Drug target identification using side-effect similarity. Science. 2008;321:263–6.
Oprea TI, Nielsen SK, Ursu O, et al. Associating drugs, targets and clinical outcomes into an integrated network affords a new platform for computer-aided drug repurposing. Mol Inform. 2011;30:100–11.
Hu Y, Bajorath J. Target family-directed exploration of scaffolds with different SAR profiles. J Chem Inf Model. 2011;51(12):3138–48.
Hu Y, Bajorath J. Polypharmacology directed compound data mining: identification of promiscuous chemotypes with different activity profiles and comparison to approved drugs. J Chem Inf Model. 2010;50(12):2112–8.
Yang JJ, Ursu O, Lipinski CA, et al. Badapple: promiscuity patterns from noisy evidence. J Cheminform. 2016;8:29.
Baell JB, Holloway GA. New substructure filters for removal of pan assay interference compounds (PAINS) from screening libraries and for their exclusion in bioassays. J Med Chem. 2010;53:2719–40.
Yang Y, Chen H, Nilsson I, et al. Investigation of the relationship between topology and selectivity for druglike molecules. J Med Chem. 2010;53:7709–14.
Peterson RT. Chemical biology and the limits of reductionism. Nat Chem Biol. 2008;4:635–8.
Lee JM, Gianchandani EP, Papin JA. Flux balance analysis in the era of metabolomics. Brief Bioinformatics. 2006;7:140–50.
McAdams HH, Shapiro L. Circuit simulation of genetic networks. Science. 1995;269:650–6.
Peleg M, Rubin D, Altman RB. Using Petri Net tools to study properties and dynamics of biological systems. J Am Med Inform Assoc. 2005;12:181–99.
Shannon P, Markiel A, Ozier O, et al. Cytoscape: a software environment for integrated models of biomolecular interaction networks. Genome Res. 2003;13:2498–504.
Clément-Ziza M, Malabat C, Weber C, et al. Genoscape: a cytoscape plug-in to automate the retrieval and integration of gene expression data and molecular networks. Bioinformatics. 2009;25(19):2617–8.
Brown JB, Okuno Y. Systems biology and systems chemistry: new directions for drug discovery. Chem Biol. 2012;19(1):23–8.
Schneider G, Fechner U. Computer-based de novo design of drug-like molecules. Nat Rev Drug Discov. 2005;4:649–63.
Jacoby E. Computational chemogenomics. Comput Mol Sci. 2011;1:57–67.
Maggiora GM. The reductionist paradox: Are the laws of chemistry and physics sufficient for the discovery of new drugs? J Comput Aided Mol Des. 2011;25:699–708.
Duran-Frigola M, Siragusa L, Ruppin E, et al. Detecting similar binding pockets to enable systems polypharmacology. PLoS Comput Biol. 2017;13(6):e1005522.
Siragusa L, Cross S, Baroni M, et al. BioGPS: navigating biological space to predict polypharmacology, off-targeting, and selectivity. Proteins. 2015;83(3):517–32.
Siragusa L, Luciani R, Borsari C, et al. Comparing drug images and repurposing drugs with BioGPS and FLAPdock: the thymidylate synthase case. ChemMedChem. 2016;11(15):1653–66.
Konc J, Janezic D. Binding site comparison for function prediction and pharmaceutical discovery. Curr Opin Struct Biol. 2014;25:34–9.
Xie L, Xie L, Bourne PE. Structure-based systems biology for analyzing off-target binding. Curr Opin Struct Biol. 2011;21(2):189–99.
Wong MT, Choi SB, Kuan CS, et al. Structural modeling and biochemical characterization of recombinant KPN_02809, a zinc-dependent metalloprotease from Klebsiella pneumoniae MGH 78578. Int J Mol Sci. 2012;13(1):901–17.
Lin H, Sassano MF, Roth BL, et al. A pharmacological organization of G protein-coupled receptors. Nat Methods. 2013;10(2):140–6.
Rubio-Perez C, Tamborero D, Schroeder MP, et al. In silico prescription of anticancer drugs to cohorts of 28 tumor types reveals targeting opportunities. Cancer Cell. 2015;27(3):382–96.
Yizhak K, Gaude E, Le Devedec S, et al. Phenotype-based cell-specific metabolic modeling reveals metabolic liabilities of cancer. Elife. 2014;3:e03641.
Tatonetti NP, Liu T, Altman RB. Predicting drug side-effects by chemical systems biology. Genome Biol. 2009;10(9):238.
Networking chemical biology. Nat Chem Biol. 2008;4:633.
Russell RB, Aloy P. Targeting and tinkering with interaction networks. Nat Chem Biol. 2008;4:666–73.
Bonneau R. Learning biological networks: from modules to dynamics. Nat Chem Biol. 2008;4:658–64.
Research highlights. Nat Chem Biol. 2008;4:657.
Enoksson M, Salvesen GS. Proteolytic needles in the cellular haystack. Nat Chem Biol. 2008;4:651–2.
Simon GM, Cravatt BF. Challenges for the ‘chemical-systems’ biologist. Nat Chem Biol. 2008;4:639–42.
Zamir E, Bastiaens PIH. Reverse engineering intracellular biochemical networks. Nat Chem Biol. 2008;4:643–7.
Lehár J, Stockwell BR, Giaever G, et al. Combination chemical genetics. Nat Chem Biol. 2008;4:674–81.
Seelig B. An autocatalytic network for ribozyme self-construction. Nat Chem Biol. 2008;4:654–5.
Cipriano A, Sbardella G, Ciulli A. Targeting epigenetic reader domains by chemical biology. Curr Opin Chem Biol. 2020;57:82–94.
Apsel B, Blair JA, Gonzalez B, et al. Targeted polypharmacology: discovery of dual inhibitors of tyrosine and phosphoinositide kinases. Nat Chem Biol. 2008;4:691–9.
Bilanges B, Torbett N, Vanhaesebroeck B. Killing two kinase families with one stone. Nat Chem Biol. 2008;4:648–9.
Yabuuchi H, Niijima S, Takematsu H, et al. Analysis of multiple compound-protein interactions reveals novel bioactive molecules. Mol Syst Biol. 2011;7:472.
Dobson CM. Chemical space and biology. Nature. 2004;432:824–8.
Lipinski C, Hopkins A. Navigating chemical space for biology and medicine. Nature. 2004;432:855–61.
Renner S, van Otterlo WA, Dominguez Seoane M, et al. Bioactivity-guided mapping and navigation of chemical space. Nat Chem Biol. 2009;5:585–92.
Wang Y, Xiao J, Suzek TO, et al. PubChem: a public information system for analyzing bioactivities of small molecules. Nucleic Acids Res. 2009;37:W623–33.
Eckert H, Bajorath J. Molecular similarity analysis in virtual screening: foundations, limitations and novel approaches. Drug Discov Today. 2007;12:225–3.
Young DW, Bender A, Hoyt J, et al. Integrating high-content screening and ligand-target prediction to identify mechanism of action. Nat Chem Biol. 2008;4:59–68.
MacDonald ML, Lamerdin J, Owens S, et al. Identifying off-target effects and hidden phenotypes of drugs in human cells. Nat Chem Biol. 2006;2:329–33.
Paolini GV, Shapland RH, van Hoorn WP, et al. Global mapping of pharmacological space. Nat Biotechnol. 2006;24:805–15.
Oprea TI, Tropsha A, Faulon JL, et al. Systems chemical biology. Nat Chem Biol. 2007;3:447–50.
Keiser MJ, Setola V, Irwin JJ, et al. Predicting new molecular targets for known drugs. Nature. 2009;462:175–81.
Oprea TI, Matter H. Integrating virtual screening in lead discovery. Curr Opin Chem Biol. 2004;8:349–58.
Muegge I, Oloff S. Advances in virtual screening. Drug Discov Today Technol. 2006;3:405–11.
McInnes C. Virtual screening strategies in drug discovery. Curr Opin Chem Biol. 2007;11:494–502.
Xie L, Li J, Xie L, Bourne PE. Drug discovery using chemical systems biology: identification of the protein-ligand binding network to explain the side effects of CETP inhibitors. PLoS Comput Biol. 2009;5:e1000387.
Hert J, Keiser MJ, Irwin JJ, et al. Quantifying the relationships among drug classes. J Chem Inf Model. 2008;48:755–65.
Pathguide: the pathway resource list http://www.pathguide.org
Blow N. Systems biology: untangling the protein web. Nature. 2009;460:415–8.
Hansen N, Brunak S, Altman R. Generating genome-scale candidate gene lists for pharmacogenomics. Clin Pharmacol Ther. 2009;86:183–9.
Dimova D, Gilberg E, Bajorath J. Identification and analysis of promiscuity cliffs formed by bioactive compounds and experimental implications. RSC Adv. 2017;7(1):58–66.
Dimova D, Bajorath J. Rationalizing promiscuity cliffs. ChemMedChem. 2018;13(6):490–4.
Miljković F, Vogt M, Bajorath J. Systematic computational identification of promiscuity cliff pathways formed by inhibitors of the human kinome. J Comput Aided Mol Des. 2019;33(6):559–72.
Miljković F, Bajorath J. Data structures for compound promiscuity analysis: promiscuity cliffs, pathways and promiscuity hubs formed by inhibitors of the human kinome. Future Sci OA. 2019;5(7):FSO404.
Hu H, Bajorath J. Exploring structure-promiscuity relationships using dual-site promiscuity cliffs and corresponding single-site analogs. Bioorg Med Chem. 2020;28(1):115238.
Peltason L, Hu Y, Bajorath J. From structure-activity to structure-selectivity relationships: quantitative assessment, selectivity cliffs, and key compounds. ChemMedChem. 2009;4(11):1864–73.
Hu Y, Bajorath J. Compound promiscuity: what can we learn from current data? Drug Discov Today. 2013;18(13–14):644–50.
Hu Y, Bajorath J. Systematic assessment of molecular selectivity at the level of targets, bioactive compounds, and structural analogues. ChemMedChem. 2016;11(12):1362–70.
Maggiora GM. On outliers and activity cliffs--why QSAR often disappoints. J Chem Inf Model. 2006;46(4):1535.
Stumpfe D, Bajorath J. Exploring activity cliffs in medicinal chemistry. J Med Chem. 2012;55(7):2932–42.
Stumpfe D, Hu Y, Dimova D, et al. Recent progress in understanding activity cliffs and their utility in medicinal chemistry. J Med Chem. 2014;57(1):18–28.
Blaschke T, Feldmann C, Bajorath J. Prediction of promiscuity cliffs using machine learning. Mol Inform. 2021;40(1):e2000196.
Kenny PW, Sadowski J. Chemoinformatics in drug discovery. Weinheim: Wiley-VCH; 2004. p. 271–85.
Hussain J, Rea C. Computationally efficient algorithm to identify matched molecular pairs (MMPs) in large data sets. J Chem Inf Model. 2010;50(3):339–48.
Hu Y, Hu Y, Vogt M, et al. MMP-Cliffs: systematic identification of activity cliffs on the basis of matched molecular pairs. J Chem Inf Model. 2012;52(5):1138–45.
Wang Y, Suzek T, Zhang J, et al. PubChem BioAssay: 2014 update. Nucleic Acids Res. 2014;42(Database issue):D1075–82.
Shoichet BK. Screening in a spirit haunted world. Drug Discov Today. 2006;11(13-14):607–15.
Hu Y, Jasial S, Gilberg E. Structure-promiscuity relationship puzzles-extensively assayed analogs with large differences in target annotations. AAPS J. 2017;19(3):856–64.
Blaschke T, Miljković F, Bajorath J. Prediction of different classes of promiscuous and nonpromiscuous compounds using machine learning and nearest neighbor analysis. ACS Omega. 2019;4:6883–90.
Hu Y, Bajorath J. Extending the activity cliff concept: structural categorization of activity cliffs and systematic identification of different types of cliffs in the ChEMBL database. J Chem Inf Model. 2012;52(7):1806–11.
Heikamp K, Hu X, Yan A, et al. Chem Inf Model. 2012;52:2354–65.
Gandomi A, Haider M. Beyond the hype: big data concepts, methods, and analytics. Int J Inf Manage. 2015;35(2):137–44.
Schadt EE, Linderman MD, Sorenson J, et al. Cloud and heterogeneous computing solutions exist today for the emerging big data problems in biology. Nat Rev Genet. 2011;12:224.
Liu W, Schmidt B, Voss G, et al. Streaming algorithms for biological sequence alignment on GPUs. IEEE Trans Parallel Distrib Syst. 2007;18(9):1270–81.
Charikar M, O’Callaghan L, Panigrahy R. Better streaming algorithms for clustering problems. Proc thirty-fifth ACM Symp Theory Comput – STOC. 2003;03:30–9.
Zhu H, Bouhifd M, Donley E, et al. Supporting read-across using biological data. ALTEX. 2016;33(2):167–82.
Hartung T. Making big sense from big data in toxicology by read-across. ALTEX. 2016;33(2):83–93.
Hu Y, Bajorath J. Entering the ‘big data’ era in medicinal chemistry: molecular promiscuity analysis revisited. Future Sci OA. 2017;3:FSO179.
Lynch C. Big data: how do your data grow? Nature. 2008;455(7209):28–9.
Marx V. The big challenges of big data. Nature. 2013;498(7453):255–60.
Al-Lazikani B, Workman P. Minimizing bias in target selection by exploiting multidisciplinary Big Data and the protein interactome. Future Med Chem. 2016;8(14):1711–6.
Bajorath J, Jenkins J, Overington J, et al. Drug discovery and development in the era of big data. Future Med Chem. 2016;8(15):1807–13.
Hu Y, Bajorath J. Learning from ‘big data’: compounds and targets. Drug Discov Today. 2014;19(4):357–60.
Lusher SJ, McGuire R, van Schaik RC, et al. Data-driven medicinal chemistry in the era of big data. Drug Discov Today. 2014;19(7):859–68.
Boran AD, Iyengar R. Systems approaches to polypharmacology and drug discovery. Curr Opin Drug Discov Devel. 2010;13(3):297–309.
Jalencas X, Mestres J. On the origins of drug polypharmacology. Med Chem Commun. 2013;4(1):80–7.
Anighoro A, Bajorath J, Rastelli G. Polypharmacology: challenges and opportunities in drug discovery: miniperspective. J Med Chem. 2014;57(19):7874–87.
Hu Y, Bajorath J. Promiscuity profiles of bioactive compounds: potency range and difference distributions and the relation to target numbers and families. Med Chem Commun. 2013;4:1196–201.
Schneider G, Neidhart W, Giller T, et al. “Scaffold-hopping” by topological pharmacophore search: a contribution to virtual screening. Angew Chem Int Ed Engl. 1999;38(19):2894–6.
Müller G. Medicinal chemistry of target family-directed masterkeys. Drug Discov Today. 2003;8(15):681–91.
Hu Y, Bajorath J. How promiscuous are pharmaceutically relevant compounds? A data-driven assessment. AAPS J. 2013;15(1):104–11.
National Institutes of Health. Big Data to Knowledge; National Institutes of Health. 2018. https://commonfund.nih.gov/bd2k. Accessed 10 Nov 2018.
Margolis R, Derr L, Dunn M, et al. The National Institutes of Health’s Big Data to Knowledge (BD2K) initiative: capitalizing on biomedical big data. J Am Med Informatics Assoc. 2014;21(6):957–8.
Judson RS, Houck KA, Kavlock RJ, et al. In Vitro screening of environmental chemicals for targeted testing prioritization: the ToxCast project. Environ Health Perspect. 2010;118(4):485–92.
Kavlock R, Chandler K, Houck K, et al. Update on EPA’s ToxCast program: providing high throughput decision support tools for chemical risk management. Chem Res Toxicol. 2012;25(7):1287–302.
Attene-Ramos MS, Miller N, Huang R, et al. The Tox21 robotic platform for the assessment of environ-mental chemicals – from vision to reality. Drug Discov Today. 2013;18(15–16):716–23.
Thomas RS, Paules RS, Simeonov A, et al. The US federal Tox21 program: a strategic and operational plan for continued leadership. ALTEX. 2018;35(2):163–8.
Shukla SJ, Huang R, Austin CP, et al. The future of toxicity testing: a focus on in vitro methods using a quantitative high-throughput screening platform. Drug Discov Today. 2010;15(23–24):997–1007.
Hsu C-W, Huang R, Attene-Ramos MS, et al. Advances in high-throughput screening technology for toxicology. Int J Risk Assess Manage. 2017;20:109.
Zhao L, Zhu H. Big data in computational toxicology: challenges and opportunities. In: Ekins S, editor. Computational toxicology. Hoboken: Wiley; 2018. p. 291–312.
Judson RS, Martin MT, Egeghy P, et al. Aggregating data for computational toxicology applications: the U.S. Environmental Protection Agency (EPA) Aggregated Computational Toxicology Resource (ACToR) system. Int J Mol Sci. 2012;13(2):1805–31.
Judson R, Richard A, Dix D, et al. ACToR – aggregated computational toxicology resource. Toxicol Appl Pharmacol. 2008;233(1):7–13.
Luechtefeld T, Maertens A, Russo DP, et al. Analysis of publically available skin sensitization data from REACH registrations 2008–2014. ALTEX. 2016;33(2):135–48.
Luechtefeld T, Maertens A, Russo DP, et al. Analysis of public oral toxicity data from REACH registrations 2008–2014. ALTEX. 2016;33(2):111–22.
Luechtefeld T, Maertens A, Russo DP, et al. Global analysis of publicly available safety data for 9,801 substances registered under REACH from 2008–2014. ALTEX. 2016;33(2):95–109.
Luechtefeld T, Maertens A, Russo DP, et al. Analysis of draize eye irritation testing and its prediction by mining publicly available 2008–2014 REACH data. ALTEX. 2016;33(2):123–34.
Bitsch A, Jacobi S, Melber C, et al. REPDOSE: a database on repeated dose toxicity studies of commercial chemicals – a multifunctional tool. Regul Toxicol Pharmacol. 2006;46(3):202–10.
Vinken M, Pauwels M, Ates G, et al. Screening of repeated dose toxicity data present in SCC(NF)P/SCCS safety evaluations of cosmetic ingredients. Arch Toxicol. 2012;86(3):405–12.
Fonger GC, Stroup D, Thomas PL, et al. TOXNET: a computerized collection of toxicological and environ-mental health information. Toxicol Ind Health. 2000;16(1):4–6.
Lea IA, Gong H, Paleja A, et al. CEBS: a comprehensive annotated database of toxicological data. Nucleic Acids Res. 2017;45(D1):D964–71.
Gaulton A, Bellis LJ, Bento AP, et al. ChEMBL: a large-scale bioactivity database for drug discovery. Nucleic Acids Res. 2012;40(D1):1100–7.
Subramanian A, Narayan R, Corsello SM, et al. A next generation connectivity map: L1000 platform and the first 1,000,000 profiles. Cell. 2017;171(6):1437–52.
Lamb J, Crawford ED, Peck D, et al. The connectivity map: using gene-expression signatures to connect small molecules, genes, and disease. Science. 2006;313(5795):1929–35.
Davis AP, Grondin CJ, Johnson RJ, et al. The comparative toxicogenomics database: update 2017. Nucleic Acids Res. 2017;45(D1):D972–8.
Ganter B, Tugendreich S, Pearson CI, et al. Development of a large-scale chemogenomics database to improve drug candidate selection and to understand mechanisms of chemical toxicity and action. J Biotechnol. 2005;119(3):219–44.
Barrett T, Wilhite SE, Ledoux P, et al. NCBI GEO: archive for functional genomics data sets – update. Nucleic Acids Res. 2012;41(D1):D991–5.
Edgar R, Domrachev M, Lash AE. Gene expression omnibus: NCBI gene expression and hybridization array data repository. Nucleic Acids Res. 2002;30(1):207–10.
Sayers EW, Barrett T, Benson DA, et al. Database resources of the National Center for Biotechnology Information. Nucleic Acids Res. 2009;37(D1):D5–15.
Sayers EW, Barrett T, Benson DA, et al. Database resources of the National Center for Biotechnology Information. Nucleic Acids Res. 2010;38(D1):D5–16.
Sayers EW, Agarwala R, Bolton EE, et al. Database resources of the National Center for Biotechnology Information. Nucleic Acids Res. 2019;47(D1):D23–8.
Yoon M, Campbell JL, Andersen ME, et al. Quantitative in vitro to in vivo extrapolation of cell-based toxicity assay results. Crit Rev Toxicol. 2012;42(8):633–52.
Ankley GT, Bennett RS, Erickson RJ, et al. Adverse outcome pathways: a conceptual framework to support ecotoxicology research and risk assessment. Environ Toxicol Chem. 2010;29(3):730–41.
Patlewicz G, Simon TW, Rowlands JC, et al. Proposing a scientific confidence framework to help support the application of adverse outcome pathways for regulatory purposes. Regul Toxicol Pharmacol. 2015;71(3):463–77.
Clippinger AJ, Allen D, Behrsing H, et al. Pathway-based predictive approaches for non-animal assessment of acute inhalation toxicity. Toxicol In Vitro. 2018;52:131–45.
Bal-Price A, Lein PJ, Keil KP, et al. Developing and applying the adverse outcome pathway concept for understanding and predicting neurotoxicity. NeuroToxicology. 2017;59:240–55.
Bal-Price A, Meek ME. Adverse outcome pathways: application to enhance mechanistic understanding of neurotoxicity. Pharmacol Ther. 2017;179:84–95.
Sachana M, Rolaki A, Bal-Price A. Development of the adverse outcome pathway (AOP): Chronic binding of antagonist to N-methyl-D-aspartate receptors (NMDARs) during brain development induces impairment of learning and memory abilities of children. Toxicol Appl Pharmacol. 2018;354:153–75.
Maxwell G, MacKay C, Cubberley R, et al. Applying the skin sensitization adverse outcome pathway (AOP) to quantitative risk assessment. Toxicol In Vitro. 2014;28(1):8–12.
Patlewicz G, Kuseva C, Kesova A, et al. Towards AOP application – Implementation of an Integrated Approach to Testing and Assessment (IATA) into a pipeline tool for skin sensitization. Regul Toxicol Pharmacol. 2014;69(3):529–45.
Organization for Economic Co-Operation and Development. The adverse outcome pathway for skin sensitisation initiated by covalent binding to proteins part 1: scientific evidence. Ser Test Assess 2012; 168.
Browne P, Noyes PD, Casey WM, et al. Application of adverse outcome pathways to U.S. EPA’s endocrine disruptor screening program. Environ Health Perspect. 2017;125(9):096001.
Benigni R, Battistelli CL, Bossa C, et al. Endocrine disruptors: data-based survey of in vivo tests, predictive models and the adverse outcome pathway. Regul Toxicol Pharmacol. 2017;86:18–24.
Proctor DM, Suh M, Chappell G, et al. An adverse outcome pathway (AOP) for forestomach tumors induced by non-genotoxic initiating events. Regul Toxicol Pharmacol. 2018;96:30–40.
Vinken M, Landesmann B, Goumenou M, et al. Development of an adverse outcome pathway from drug-mediated bile salt export pump inhibition to cholestatic liver injury. Toxicol Sci. 2013;136(1):97–106.
Xia M, Huang R, Shi Q, et al. Comprehensive analyses and prioritization of tox21 10k chemicals affecting mitochondrial function by in-depth mechanistic studies. Environ Health Perspect. 2018;126(7):077010.
Mellor CL, Steinmetz FP, Cronin MTD. Using molecular initiating events to develop a structural alert based screening workflow for nuclear receptor ligands associated with hepatic steatosis. Chem Res Toxicol. 2016;29(2):203–12.
Gadaleta D, Manganelli S, Roncaglioni A, et al. QSAR modeling of ToxCast assays relevant to the molecular initiating events of AOPs leading to hepatic steatosis. J Chem Inf Model. 2018;58(8):1501–17.
Frid AA, Matthews EJ. Prediction of drug-related cardiac adverse effects in humans-B: use of QSAR programs for early detection of drug-induced cardiac toxicities. Regul Toxicol Pharmacol. 2010;56:276–89.
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Wang, Z., Yang, B. (2022). Polypharmacology in Predicting Drug Toxicity: Drug Promiscuity. In: Polypharmacology. Springer, Cham. https://doi.org/10.1007/978-3-031-04998-9_14
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