Prediction of Drug-Drug Interactions Related to Inhibition or Induction of Drug-Metabolizing Enzymes

Alexander    V.    Dmitriev; Alexey    A.    Lagunin; Dmitry    А.    Karasev; Anastasia    V.    Rudik; Pavel    V.    Pogodin; Dmitry    A.    Filimonov; Vladimir    V.    Poroikov
doi:10.2174/1568026619666190123160406
Abstract

Drug-drug interaction (DDI) is the phenomenon of alteration of the pharmacological activity of a drug(s) when another drug(s) is co-administered in cases of so-called polypharmacy. There are three types of DDIs: pharmacokinetic (PK), pharmacodynamic, and pharmaceutical. PK is the most frequent type of DDI, which often appears as a result of the inhibition or induction of drug-metabolising enzymes (DME). In this review, we summarise in silico methods that may be applied for the prediction of the inhibition or induction of DMEs and describe appropriate computational methods for DDI prediction, showing the current situation and perspectives of these approaches in medicinal and pharmaceutical chemistry. We review sources of information on DDI, which can be used in pharmaceutical investigations and medicinal practice and/or for the creation of computational models. The problem of the inaccuracy and redundancy of these data are discussed. We provide information on the state-of-the-art physiologically- based pharmacokinetic modelling (PBPK) approaches and DME-based in silico methods. In the section on ligand-based methods, we describe pharmacophore models, molecular field analysis, quantitative structure-activity relationships (QSAR), and similarity analysis applied to the prediction of DDI related to the inhibition or induction of DME. In conclusion, we discuss the problems of DDI severity assessment, mention factors that influence severity, and highlight the issues, perspectives and practical using of in silico methods.
Keywords: Adverse drug reactions, ADR, Xenobiotic, Metabolism, Drug interaction, Drug metabolism, P450.
« Previous Next »
Graphical Abstract

[1] 
Borda, I.T.; Slone, D.; Jick, H. Assessment of adverse reactions within a drug surveillance program. JAMA,  1968, 205, 645-647.
[http://dx.doi.org/10.1001/jama.1968.03140350055016] 
[2] 
Kennedy, C.; Brewer, L.; Williams, D. Drug interactions. Medicine (Baltimore),  2016, 44, 422-426.
[http://dx.doi.org/10.1016/j.mpmed.2016.04.015] 
[3] 
Zakharov, A.V.; Varlamova, E.V.; Lagunin, A.A.; Dmitriev, A.V.; Muratov, E.N.; Fourches, D.; Kuz’min, V.E.; Poroikov, V.V.; Tropsha, A.; Nicklaus, M.C. QSAR modeling and prediction of drug-drug interactions. Mol. Pharm.,  2016, 13, 545-556.
[http://dx.doi.org/10.1021/acs.molpharmaceut.5b00762] 
[4] 
Fulton, M.M.; Allen, E.R. Polypharmacy in the Elderly: A literature review. J. Am. Acad. Nurse Pract.,  2005, 17, 123-132.
[http://dx.doi.org/10.1111/j.1041-2972.2005.0020.x] 
[5] 
Studdert, D.M.; Mello, M.M.; Sage, W.M.; DesRoches, C.M.; Peugh, J.; Zapert, K.; Brennan, T.A. Defensive medicine among high-risk specialist physicians in a volatile malpractice environment. JAMA,  2005, 293, 2609-2617.
[http://dx.doi.org/ 10.1001/jama.293.21.2609] 
[6] 
Austad, B.; Hetlevik, I.; Mjølstad, B.P.; Helvik, A-S. Applying clinical guidelines in general practice: A qualitative study of potential complications. BMC Fam. Pract.,  2016, 17, 92.
[http://dx.doi.org/10.1186/s12875-016-0490-3] 
[7] 
Lyubimov, A.V. Encyclopedia of drug metabolism and interactions; , 2012. 
[8] 
Hansten, P.D.; Horn, J.R. The Top 100 drug interactions: A guide to patient management, 18th ed; , 2017. 
[9] 
Aronov, A.M. Common pharmacophores for uncharged human ether-a-go-go-related gene (HERG) blockers. J. Med. Chem.,  2006, 49, 6917-6921.
[http://dx.doi.org/10.1021/jm060500o] 
[10] 
Waring, M.J.; Johnstone, C. A Quantitative assessment of HERG liability as a function of lipophilicity. Bioorg. Med. Chem. Lett.,  2007, 17, 1759-1764.
[http://dx.doi.org/10.1016/j.bmcl.2006. 12.061] 
[11] 
Wandel, C.; Kim, R.B.; Guengerich, F.P.; Wood, A.J. Mibefradil is a P-glycoprotein substrate and a potent inhibitor of both P-glycoprotein and CYP3A in vitro. Drug Metab. Dispos.,  2000, 28, 895-898.
[12] 
Honkakoski, P.; Negishi, M. Regulation of cytochrome P450 (CYP) genes by nuclear receptors. Biochem. J.,  2000, 347, 321-337.
[http://dx.doi.org/10.1042/bj3470321] 
[13] 
Forbes, H.L.; Polasek, T.M. Potential drug-drug interactions with direct oral anticoagulants in elderly hospitalized patients. Ther. Adv. Drug Saf.,  2017, 8, 319-328.
[http://dx.doi.org/ 10.1177/2042098617719815] 
[14] 
Zhang, L.; Zhang, Y.D.; Zhao, P.; Huang, S-M. Predicting drug-drug interactions: An FDA perspective. AAPS J.,  2009, 11, 300-306.
[http://dx.doi.org/10.1208/s12248-009-9106-3] 
[15] 
Marroum, P.J.; Uppoor, R.S.; Parmelee, T.; Ajayi, F.; Burnett, A.; Yuan, R.; Svadjian, R.; Lesko, L.J.; Balian, J.D. in vivo drug-drug interaction studies--A survey of all new molecular entities approved from 1987 to 1997. Clin. Pharmacol. Ther.,  2000, 68, 280-285.
[http://dx.doi.org/10.1067/mcp.2000.109366] 
[16] 
https://www.fda.gov/downloads/Drugs/Guidances/UCM581-965.pdf
[17] 
Clinical drug interaction studies-study design, data analysis, and
clinical implications. Guidance for Industry., https://www.fda.gov/downloads/drugs/guidances/ucm292362.pdf
[18] 
Guengerich, F.P. Cytochrome P450 and chemical toxicology. Chem. Res. Toxicol.,  2008, 21, 70-83.
[http://dx.doi.org/ 10.1021/tx700079z] 
[19] 
Wan, H.; Holmén, A.G. High throughput screening of physicochemical properties and in vitro ADME profiling in drug discovery. Comb. Chem. High Throughput Screen.,  2009, 12, 315-329.
[http://dx.doi.org/10.2174/138620709787581701] 
[20] 
Ai, N.; Fan, X.; Ekins, S. In Silico Methods for Predicting drug-drug interactions with cytochrome P-450s, transporters and beyond. Adv. Drug Deliv. Rev.,  2015, 86, 46-60.
[http://dx.doi.org/10.1016/j.addr.2015.03.006] 
[21] 
Murphy, S.N.; Weber, G.; Mendis, M.; Gainer, V.; Chueh, H.C.; Churchill, S.; Kohane, I. Serving the enterprise and beyond with informatics for integrating biology and the bedside (I2b2). J. Am. Med. Inform. Assoc.,  2010, 17, 124-130.
[http://dx.doi.org/10.1136/jamia.2009.000893] 
[22] 
Baxter, K. Stockley, I. Stockley’s drug interactions., (7th ed. ) , 2005. 
[23] 
Mozayani, A.; Raymon, L. Handbook of drug interactions: A clinical and forensic guide, 2nd ed; , 2011. 
[24] 
Hansten, P.D.; Horn, J.R. Drug interactions analysis and management, 9th ed; , 2014. 
[25] 
Tatro, D.S. Drug Interaction Facts 2015. 2014.
[26] 
Editorial: Drug interactions. Lancet (London, England),  1975, 1, 904-905.
[27] 
Hull, J.H.; Murray, W.J.; Brown, H.S.; Williams, B.O.; Chi, S.L.; Koch, G.G. Potential anticoagulant drug interactions in ambulatory patients. Clin. Pharmacol. Ther.,  1978, 24, 644-649.
[http://dx.doi.org/10.1002/cpt1978246644] 
[28] 
Tatro, D.S.; Moore, T.N.; Cohen, S.N. Computer-based system for adverse drug reaction detection and prevention. Am. J. Hosp. Pharm.,  1979, 36, 198-201.
[29] 
Lucente, F.E. Computerized database of drug interactions: A paradigm for resolving a communication gap in otolaryngology. Laryngoscope,  1985, 95, 1367-1373.
[http://dx.doi.org/10.1288/00005537-198511000-00015] 
[30] 
Smithburger, P.L.; Buckley, M.S.; Bejian, S.; Burenheide, K.; Kane-Gill, S.L. A critical evaluation of clinical decision support for the detection of drug-drug interactions. Expert Opin. Drug Saf.,  2011, 10, 871-882.
[http://dx.doi.org/10.1517/14740338. 2011.583916] 
[31] 
Hazlet, T.K.; Lee, T.A.; Hansten, P.D.; Horn, J.R. Performance of community pharmacy drug interaction software. J. Am. Pharm. Assoc. (Wash),  2001, 41, 200-204.
[http://dx.doi.org/10.1016/S1086-5802(16)31230-X] 
[32] 
Smithburger, P.L.; Kane-Gill, S.L.; Benedict, N.J.; Falcione, B.A.; Seybert, A.L. Grading the severity of drug-drug interactions in the intensive care unit: a comparison between clinician assessment and proprietary database severity rankings. Ann. Pharmacother.,  2010, 44, 1718-1724.
[http://dx.doi.org/ 10.1345/aph.1P377] 
[33] 
Smithburger, P.L.; Kane-Gill, S.L.; Seybert, A.L. Drug-Drug interactions in the medical intensive care unit: An assessment of frequency, severity and the medications involved. Int. J. Pharm. Pract.,  2012, 20, 402-408.
[http://dx.doi.org/10.1111/j.2042-7174.2012.00221.x] 
[34] 
Kheshti, R.; Aalipour, M.; Namazi, S. A comparison of five common drug-drug interaction software programs regarding accuracy and comprehensiveness. J. Res. Pharm. Pract.,  2016, 5, 257-263.
[http://dx.doi.org/10.4103/2279-042X.192461] 
[35] 
Bezhentsev, V.M.; Tarasova, O.A.; Dmitriev, A.V.; Rudik, A.V.; Lagunin, A.A.; Filimonov, D.A.; Poroikov, V.V. Computer-aided prediction of xenobiotic metabolism in the human body. Russ. Chem. Rev.,  2016, 85, 854-879.
[http://dx.doi.org/ 10.1070/RCR4614] 
[36] 
Berman, H.M.; Westbrook, J.; Feng, Z.; Gilliland, G.; Bhat, T.N.; Weissig, H.; Shindyalov, I.N.; Bourne, P.E. The protein data bank. Nucleic Acids Res.,  2000, 28, 235-242.
[http://dx.doi.org/ 10.1093/nar/28.1.235] 
[37] 
Papadatos, G.; Overington, J.P. The ChEMBL Database: A taster for medicinal chemists. Future Med. Chem.,  2014, 6, 361-364.
[http://dx.doi.org/10.4155/fmc.14.8] 
[38] 
NCBI Resource Coordinators. Database resources of the national center for biotechnology information. Nucleic Acids Res.,  2015, 43, D6-D17.
[http://dx.doi.org/10.1093/nar/gku1130] 
[39] 
Law, V.; Knox, C.; Djoumbou, Y.; Jewison, T.; Guo, A.C.; Liu, Y.; Maciejewski, A.; Arndt, D.; Wilson, M.; Neveu, V.; Tang, A.; Gabriel, G.; Ly, C.; Adamjee, S.; Dame, Z.T.; Han, B.; Zhou, Y.; Wishart, D.S. DrugBank 4.0: Shedding new light on drug metabolism. Nucleic Acids Res.,  2014, 42, D1091-D1097.
[http://dx.doi.org/10.1093/nar/gkt1068] 
[40] 
Liu, T.; Lin, Y.; Wen, X.; Jorissen, R.N.; Gilson, M.K.; Binding, D.B. A web-accessible database of experimentally determined protein-ligand binding affinities. Nucleic Acids Res.,  2007, 35, D198-D201.
[http://dx.doi.org/10.1093/nar/gkl999] 
[41] 
Wishart, D.S.; Feunang, Y.D.; Marcu, A.; Guo, A.C.; Liang, K.; Vázquez-Fresno, R.; Sajed, T.; Johnson, D.; Li, C.; Karu, N.; Sayeeda, Z.; Lo, E.; Assempour, N.; Berjanskii, M.; Singhal, S.; Arndt, D.; Liang, Y.; Badran, H.; Grant, J.; Serra-Cayuela, A.; Liu, Y.; Mandal, R.; Neveu, V.; Pon, A.; Knox, C.; Wilson, M.; Manach, C.; Scalbert, A. HMDB 4.0: The human metabolome database for 2018. Nucleic Acids Res.,  2018, 46, D608-D617.
[http://dx.doi.org/10.1093/nar/gkx1089] 
[42] 
https://life-science.kyushu.fujitsu.com/admedb/
[43] 
https://integrity.thomson-pharma.com/integrity/xmlxsl/
[44] 
Preissner, S.; Kroll, K.; Dunkel, M.; Senger, C.; Goldsobel, G.; Kuzman, D.; Guenther, S.; Winnenburg, R.; Schroeder, M.; Preissner, R. SuperCYP: A comprehensive database on cytochrome P450 enzymes including a tool for analysis of CYP-drug interactions. Nucleic Acids Res.,  2010, 38, D237-D243.
[http://dx.doi.org/10.1093/nar/gkp970] 
[45] 
Hoffmann, M.F.; Preissner, S.C.; Nickel, J.; Dunkel, M.; Preissner, R.; Preissner, S. The transformer database: biotransformation of xenobiotics. Nucleic Acids Res.,  2014, 42, D1113-D1117.
[http://dx.doi.org/10.1093/nar/gkt1246] 
[46] 
Cao, D.; Wang, J.; Zhou, R.; Li, Y.; Yu, H.; Hou, T. ADMET evaluation in drug discovery. 11. PharmacoKinetics Knowledge Base (PKKB): A comprehensive database of pharmacokinetic and toxic properties for drugs. J. Chem. Inf. Model.,  2012, 52, 1132-1137.
[http://dx.doi.org/10.1021/ci300112j] 
[47] 
Yap, C.W.; Xue, Y.; Li, H.; Li, Z.R.; Ung, C.Y.; Han, L.Y.; Zheng, C.J.; Cao, Z.W.; Chen, Y.Z. Prediction of compounds with specific pharmacodynamic, pharmacokinetic or toxicological property by statistical learning methods. Mini Rev. Med. Chem.,  2006, 6, 449-459.
[http://dx.doi.org/10.2174/138955706776361501] 
[48] 
Ekins, S.; Polli, J.E.; Swaan, P.W.; Wright, S.H. Computational modeling to accelerate the identification of substrates and inhibitors for transporters that affect drug disposition. Clin. Pharmacol. Ther.,  2012, 92, 661-665.
[http://dx.doi.org/10.1038/clpt. 2012.164] 
[49] 
Guidance for Industry. (Accessed at https://www.fda.gov/ucm/groups/fdagov-public/@fdagov-drugs-gen/documents/document/ucm531207)
[50] 
Yanni, S.B. Translational ADMET for drug therapy: Principles,
methods, and pharmaceutical applications. 2015.
[http://dx.doi.org/10.1002/9781118838440] 
[51] 
Varma, M.V.S.; Lin, J.; Bi, Y-A.; Rotter, C.J.; Fahmi, O.A.; Lam, J.L.; El-Kattan, A.F.; Goosen, T.C.; Lai, Y. Quantitative prediction of repaglinide-rifampicin complex drug interactions using dynamic and static mechanistic models: delineating differential CYP3A4 induction and OATp1B1 inhibition potential of rifampicin. Drug Metab. Dispos.,  2013, 41, 966-974.
[http://dx.doi.org/ 10.1124/dmd.112.050583] 
[52] 
Zientek, M.A.; Youdim, K. Reaction phenotyping: Advances in the experimental strategies used to characterize the contribution of drug-metabolizing enzymes. Drug Metab. Dispos.,  2015, 43, 163-181.
[http://dx.doi.org/10.1124/dmd.114.058750] 
[53] 
Fowler, S.; Kletzl, H.; Finel, M.; Manevski, N.; Schmid, P.; Tuerck, D.; Norcross, R.D.; Hoener, M.C.; Spleiss, O.; Iglesias, V.A.A. UGT2B10 splicing polymorphism common in african populations may greatly increase drug exposure. J. Pharmacol. Exp. Ther.,  2015, 352, 358-367.
[http://dx.doi.org/ 10.1124/jpet.114.220194] 
[54] 
Vieira, M.D.L.T.; Kim, M-J.; Apparaju, S.; Sinha, V.; Zineh, I.; Huang, S-M.; Zhao, P. PBPK model describes the effects of comedication and genetic polymorphism on systemic exposure of drugs that undergo multiple clearance pathways. Clin. Pharmacol. Ther.,  2014, 95, 550-557.
[http://dx.doi.org/ 10.1038/clpt.2014.43] 
[55] 
Wagner, C.; Zhao, P.; Pan, Y.; Hsu, V.; Grillo, J.; Huang, S.M.; Sinha, V. Application of physiologically based pharmacokinetic (PBPK) modeling to support dose selection: Report of an FDA public workshop on PBPK. CPT Pharmacometrics Syst. Pharmacol.,  2015, 4, 226-230.
[http://dx.doi.org/10.1002/psp4.33] 
[56] 
Wagner, C.; Pan, Y.; Hsu, V.; Sinha, V.; Zhao, P. Predicting the effect of CYP3A inducers on the pharmacokinetics of substrate drugs using physiologically based pharmacokinetic (PBPK) modeling: An analysis of PBPK submissions to the US FDA. Clin. Pharmacokinet.,  2016, 55, 475-483.
[http://dx.doi.org/ 10.1007/s40262-015-0330-y] 
[57] 
Boetsch, C.; Parrott, N.; Fowler, S.; Poirier, A.; Hainzl, D.; Banken, L.; Martin-Facklam, M.; Hofmann, C. Effects of cytochrome P450 3A4 inhibitors-ketoconazole and erythromycin-on bitopertin pharmacokinetics and comparison with physiologically based modelling predictions. Clin. Pharmacokinet.,  2016, 55, 237-247.
[http://dx.doi.org/10.1007/s40262-015-0312-0] 
[58] 
de Jong, J.; Skee, D.; Murphy, J.; Sukbuntherng, J.; Hellemans, P.; Smit, J.; de Vries, R.; Jiao, J.J.; Snoeys, J.; Mannaert, E. Effect of CYP3A perpetrators on Ibrutinib exposure in healthy participants. Pharmacol. Res. Perspect.,  2015, 3, e00156.
[http://dx.doi.org/ 10.1002/prp2.156] 
[59] 
Cleary, Y.; Gertz, M. Physiologically based pharmacokinetic
(PBPK) modeling to assess the risk of drug-drug interaction (DDI)
alectinib, 2017.
[60] 
Park, M-H.; Shin, S-H.; Byeon, J-J.; Lee, G-H.; Yu, B-Y.; Shin, Y.G. Prediction of pharmacokinetics and drug-drug interaction potential using physiologically based pharmacokinetic (PBPK) modeling approach: A case study of caffeine and ciprofloxacin. Korean J. Physiol. Pharmacol.,  2017, 21, 107-115.
[http://dx.doi.org/ 10.4196/kjpp.2017.21.1.107] 
[61] 
Tod, M.; Goutelle, S.; Bleyzac, N.; Bourguignon, L. A generic model for quantitative prediction of interactions mediated by efflux transporters and cytochromes: Application to p-glycoprotein and cytochrome 3A4. Clin. Pharmacokinet.,  2018, 58(4), 503-523.
[62] 
Fermier, N.; Bourguignon, L.; Goutelle, S.; Bleyzac, N.; Tod, M. Identification of cytochrome P450-mediated drug-drug interactions at risk in cases of gene polymorphisms by using a quantitative prediction model. clin. Pharmacokinet., 2018.
[http://dx.doi.org/10.1007/s40262-018-0651-8] 
[63] 
Veselovskiĭ, A.V.; Sobolev, B.N.; Zharkova, M.S.; Archakov, A.I. Computer-based substrate specifity prediction for cytochrome P450. Biomed. Khim.,  2010, 56, 90-100.
[http://dx.doi.org/ 10.18097/pbmc20105601090] 
[64] 
Gay, S.C.; Roberts, A.G.; Halpert, J.R. Structural Features of Cytochromes P450 and ligands that affect drug metabolism as revealed by X-Ray crystallography and NMR. Future Med. Chem.,  2010, 2, 1451-1468.
[http://dx.doi.org/10.4155/fmc.10.229] 
[65] 
Shen, Z.; Cheng, F.; Xu, Y.; Fu, J.; Xiao, W.; Shen, J.; Liu, G.; Li, W.; Tang, Y. Investigation of indazole unbinding pathways in CYP2E1 by molecular dynamics simulations. PLoS One,  2012, 7, e33500.
[http://dx.doi.org/10.1371/journal.pone.0033500] 
[66] 
Li, W.; Shen, J.; Liu, G.; Tang, Y.; Hoshino, T. Exploring coumarin egress channels in human cytochrome P450 2A6 by random acceleration and steered molecular dynamics simulations. Proteins,  2011, 79, 271-281.
[http://dx.doi.org/10.1002/prot.22880] 
[67] 
Schwede, T.; Kopp, J.; Guex, N.; Peitsch, M.C. SWISS-MODEL: An automated protein homology-modeling server. Nucleic Acids Res.,  2003, 31, 3381-3385.
[http://dx.doi.org/10.1093/nar/gkg520] 
[68] 
Yao, Y.; Han, W-W.; Zhou, Y-H.; Li, Z-S.; Li, Q.; Chen, X-Y.; Zhong, D-F. The metabolism of CYP2C9 and CYP2C19 for gliclazide by homology modeling and docking study. Eur. J. Med. Chem.,  2009, 44, 854-861.
[http://dx.doi.org/10.1016/j.ejmech.2008.04.015] 
[69] 
Vermeulen, N.P.E. Prediction of drug metabolism: The case of cytochrome P450 2D6. Curr. Top. Med. Chem.,  2003, 3, 1227-1239.
[http://dx.doi.org/10.2174/1568026033451998] 
[70] 
de Groot, M.J.; Ackland, M.J.; Horne, V.A.; Alex, A.A.; Jones, B.C. Novel approach to predicting P450-mediated drug metabolism: Development of a combined protein and pharmacophore model for CYP2D6. J. Med. Chem.,  1999, 42, 1515-1524.
[http://dx.doi.org/10.1021/jm981118h] 
[71] 
Belkina, N.V.; Skvortsov, V.S.; Ivanov, A.S.; Archakov, A.I. Modeling of a Three-Dimensional Structure of Cytochrome P-450 1A2 and Search for Its New Ligands. Vopr. Med. Khim.,  1998, 44, 464-473.
[72] 
Liu, X.; Chen, D-W.; Wu, X.; Zhao, Z.; Fu, Z-W.; Huang, C-T.; Ye, L-X.; Du, Z.; Yu, Y.; Fang, Z-Z.; Sun, H-Z. The inhibition of UDP-Glucuronosyltransferase (UGT) isoforms by praeruptorin A and B. Phytother. Res.,  2016, 30, 1872-1878.
[http://dx.doi.org/ 10.1002/ptr.5697] 
[73] 
Meech, R.; Mackenzie, P.I. Determinants of UDP glucuronosyltransferase membrane association and residency in the endoplasmic reticulum. Arch. Biochem. Biophys.,  1998, 356, 77-85.
[http://dx.doi.org/10.1006/abbi.1998.0750] 
[74] 
Ouzzine, M.; Magdalou, J.; Burchell, B.; Fournel-Gigleux, S. An internal signal sequence mediates the targeting and retention of the human UDP-Glucuronosyltransferase 1A6 to the endoplasmic reticulum. J. Biol. Chem.,  1999, 274, 31401-31409.
[http://dx.doi.org/10.1074/jbc.274.44.31401] 
[75] 
Miley, M.J.; Zielinska, A.K.; Keenan, J.E.; Bratton, S.M.; Radominska-Pandya, A.; Redinbo, M.R. Crystal structure of the cofactor-binding domain of the human phase II drug-metabolism enzyme UDP-Glucuronosyltransferase 2B7. J. Mol. Biol.,  2007, 369, 498-511.
[http://dx.doi.org/10.1016/j.jmb.2007.03.066] 
[76] 
Sliwoski, G.; Kothiwale, S.; Meiler, J.; Lowe, E.W. Computational methods in drug discovery. Pharmacol. Rev.,  2014, 66, 334-395.
[http://dx.doi.org/10.1124/pr.112.007336] 
[77] 
Leach, A.R. Molecular modelling: Principles and applications.,
(2nd ed.) , 2001. 
[78] 
Shaitan, K.V. Stochastic Dynamics Responding to Biomolecules.,  2003, 285.
[http://dx.doi.org/10.1142/9789812795434_0010] 
[79] 
Shan, Y.; Kim, E.T.; Eastwood, M.P.; Dror, R.O.; Seeliger, M.A.; Shaw, D.E. How does a drug molecule find its target binding site? J. Am. Chem. Soc.,  2011, 133, 9181-9183.
[http://dx.doi.org/ 10.1021/ja202726y] 
[80] 
Kitchen, D.B.; Decornez, H.; Furr, J.R.; Bajorath, J. Docking and scoring in virtual screening for drug discovery: methods and applications. Nat. Rev. Drug Discov.,  2004, 3, 935-949.
[http://dx.doi.org/10.1038/nrd1549] 
[81] 
Nandekar, P.P.; Sangamwar, A.T. Cytochrome P450 1A1-mediated anticancer drug discovery: In Silico findings. Expert Opin. Drug Discov.,  2012, 7, 771-789.
[http://dx.doi.org/ 10.1517/17460441. 2012.698260] 
[82] 
Yu, J.; Paine, M.J.I.; Maréchal, J-D.; Kemp, C.A.; Ward, C.J.; Brown, S.; Sutcliffe, M.J.; Roberts, G.C.K.; Rankin, E.M.; Wolf, C.R. In Silico prediction of drug binding to CYP2D6: Identification of a new metabolite of metoclopramide. Drug Metab. Dispos.,  2006, 34, 1386-1392.
[http://dx.doi.org/ 10.1124/dmd.106.009852] 
[83] 
Seifert, A.; Tatzel, S.; Schmid, R.D.; Pleiss, J. Multiple molecular dynamics simulations of human P450 Monooxygenase CYP2C9: The molecular basis of substrate binding and regioselectivity toward warfarin. Proteins,  2006, 64, 147-155.
[http://dx.doi.org/ 10.1002/prot.20951] 
[84] 
Santos, R.; Hritz, J.; Oostenbrink, C. Role of water in molecular docking simulations of cytochrome P450 2D6. J. Chem. Inf. Model.,  2010, 50, 146-154.
[http://dx.doi.org/10.1021/ci900293e] 
[85] 
Vasanthanathan, P.; Hritz, J.; Taboureau, O.; Olsen, L.; Jørgensen, F.S.; Vermeulen, N.P.E.; Oostenbrink, C. Virtual screening and prediction of site of metabolism for cytochrome P450 1A2 Ligands. J. Chem. Inf. Model.,  2009, 49, 43-52.
[http://dx.doi.org/ 10.1021/ci800371f] 
[86] 
Vasanthanathan, P.; Olsen, L.; Jørgensen, F.S.; Vermeulen, N.P.E.; Oostenbrink, C. Computational prediction of binding affinity for CYP1A2-ligand complexes using empirical free energy calculations. Drug Metab. Dispos.,  2010, 38, 1347-1354.
[http://dx.doi.org/10.1124/dmd.110.032946] 
[87] 
Stjernschantz, E.; Oostenbrink, C. Improved ligand-protein binding affinity predictions using multiple binding modes. Biophys. J.,  2010, 98, 2682-2691.
[http://dx.doi.org/10.1016/j.bpj.2010.02.034] 
[88] 
Perić-Hassler, L.; Stjernschantz, E.; Oostenbrink, C.; Geerke, D.P. CYP 2D6 binding affinity predictions using multiple ligand and protein conformations. Int. J. Mol. Sci.,  2013, 14, 24514-24530.
[http://dx.doi.org/10.3390/ijms141224514] 
[89] 
Bren, U.; Oostenbrink, C. Cytochrome P450 3A4 inhibition by ketoconazole: Tackling the problem of ligand cooperativity using molecular dynamics simulations and free-energy calculations. J. Chem. Inf. Model.,  2012, 52, 1573-1582.
[http://dx.doi.org/ 10.1021/ci300118x] 
[90] 
Schuster, D.; Steindl, T.M.; Langer, T. Predicting drug metabolism induction in silico. Curr. Top. Med. Chem.,  2006, 6, 1627-1640.
[http://dx.doi.org/10.2174/156802606778108924] 
[91] 
Ekins, S.; Kortagere, S.; Iyer, M.; Reschly, E.J.; Lill, M.A.; Redinbo, M.R.; Krasowski, M.D. Challenges predicting ligand-receptor interactions of promiscuous proteins: The nuclear receptor PXR. PLOS Comput. Biol.,  2009, 5, e1000594.
[http://dx.doi.org/10.1371/journal.pcbi.1000594] 
[92] 
Kortagere, S.; Chekmarev, D.; Welsh, W.J.; Ekins, S. Hybrid scoring and classification approaches to predict human pregnane X receptor activators. Pharm. Res.,  2009, 26, 1001-1011.
[http://dx.doi.org/10.1007/s11095-008-9809-7] 
[93] 
Khandelwal, A.; Krasowski, M.D.; Reschly, E.J.; Sinz, M.W.; Swaan, P.W.; Ekins, S. Machine learning methods and docking for predicting human pregnane X receptor activation. Chem. Res. Toxicol.,  2008, 21, 1457-1467.
[http://dx.doi.org/10.1021/tx800102e] 
[94] 
Moscovitz, J.E.; Lin, Z.; Johnson, N.; Tu, M.; Goosen, T.C.; Weng, Y.; Kalgutkar, A.S. Induction of human cytochrome p450 3a4 by the irreversible myeloperoxidase inactivator PF-06282999 is mediated by the Pregnane X receptor. Xenobiotica,  2018, 48, 647-655.
[http://dx.doi.org/10.1080/00498254.2017.1353163] 
[95] 
Knebel, C.; Kebben, J.; Eberini, I.; Palazzolo, L.; Hammer, H.S.; Süssmuth, R.D.; Heise, T.; Hessel-Pras, S.; Lampen, A.; Braeuning, A.; Marx-Stoelting, P. Propiconazole is an activator of AHR and causes concentration additive effects with an established AHR ligand. Arch. Toxicol., 2018.
[http://dx.doi.org/10.1007/s00204-018-2321-x] 
[96] 
Zhao, J-C.; Luan, Z-L.; Liang, J-H.; Cheng, Z-B.; Sun, C-P.; Wang, Y-L.; Zhang, M-Y.; Zhang, T-Y.; Wang, Y.; Yang, T-M.; Wu, Y-Y.; Zhang, Y-X.; Zhao, X-Y.; Ma, X-C. Drechmerin H, a Novel 1(2), 2(18)-Diseco indole diterpenoid from the Fungus Drechmeria Sp. as a natural agonist of human pregnane X Receptor. Bioorg. Chem.,  2018, 79, 250-256.
[http://dx.doi.org/10.1016/j.bioorg.2018.05.001] 
[97] 
Burk, O.; Kuzikov, M.; Kronenberger, T.; Jeske, J.; Keminer, O.; Thasler, W.E.; Schwab, M.; Wrenger, C.; Windshügel, B. Identification of approved drugs as potent inhibitors of pregnane X receptor activation with differential receptor interaction profiles. Arch. Toxicol.,  2018, 92, 1435-1451.
[http://dx.doi.org/ 10.1007/s00204-018-2165-4] 
[98] 
Wermuth, C.G.; Ganellin, C.R.; Lindberg, P.; Mitscher, L.A. Glossary of terms used in medicinal chemistry (IUPAC Recommendations 1998). Pure Appl. Chem.,  1998, 70, 1129-1143.
[http://dx.doi.org/10.1351/pac199870051129] 
[99] 
Jones, B.C.; Hawksworth, G.; Horne, V.A.; Newlands, A.; Morsman, J.; Tute, M.S.; Smith, D.A. Putative active site template model for cytochrome P4502C9 (Tolbutamide Hydroxylase). Drug Metab. Dispos.,  1996, 24, 260-266.
[100] 
Dong, D.; Wu, B.; Chow, D.; Hu, M. Substrate selectivity of drug-metabolizing cytochrome P450s predicted from crystal structures and in silico modeling. Drug Metab. Rev.,  2012, 44, 192-208.
[http://dx.doi.org/10.3109/03602532.2011.645580] 
[101] 
Mo, S-L.; Liu, W-F.; Chen, Y.; Luo, H-B.; Sun, L-B.; Chen, X-W.; Zhou, Z-W.; Sneed, K.B.; Li, C.G.; Du, Y-M.; Liang, J.; Zhou, S-F. Ligand- and protein-based modeling studies of the inhibitors of human cytochrome p450 2d6 and a virtual screening for potential inhibitors from the chinese herbal medicine, Scutellaria Baicalensis (Huangqin,Baikal Skullcap). Comb. Chem. High Throughput Screen.,  2012, 15, 36-80.
[http://dx.doi.org/10.2174/138620712798280826] 
[102] 
Kaur, P.; Chamberlin, A.R.; Poulos, T.L.; Sevrioukova, I.F. Structure-based inhibitor design for evaluation of a CYP3A4 pharmacophore model. J. Med. Chem.,  2016, 59, 4210-4220.
[http://dx.doi.org/10.1021/acs.jmedchem.5b01146] 
[103] 
Sorich, M.J.; Miners, J.O.; McKinnon, R.A.; Smith, P.A. Multiple pharmacophores for the investigation of human UDP-Glucuronosyltransferase isoform substrate selectivity. Mol. Pharmacol.,  2004, 65, 301-308.
[http://dx.doi.org/ 10.1124/mol.65.2.301] 
[104] 
Koymans, L.; Vermeulen, N.P.; van Acker, S.A.; te Koppele, J.M.; Heykants, J.J.; Lavrijsen, K.; Meuldermans, W.; Donné-Op den Kelder, G.M. A predictive model for substrates of cytochrome P450-Debrisoquine (2D6). Chem. Res. Toxicol.,  1992, 5, 211-219.
[http://dx.doi.org/10.1021/tx00026a010] 
[105] 
Strobl, G.R.; von Kruedener, S.; Stöckigt, J.; Guengerich, F.P.; Wolff, T. Development of a pharmacophore for inhibition of human liver cytochrome P-450 2D6: Molecular modeling and inhibition studies. J. Med. Chem.,  1993, 36, 1136-1145.
[http://dx.doi.org/10.1021/jm00061a004] 
[106] 
Hochleitner, J.; Akram, M.; Ueberall, M.; Davis, R.A.; Waltenberger, B.; Stuppner, H.; Sturm, S.; Ueberall, F.; Gostner, J.M.; Schuster, D. A combinatorial approach for the discovery of cytochrome P450 2D6 inhibitors from nature. Sci. Rep.,  2017, 7, 8071.
[http://dx.doi.org/10.1038/s41598-017-08404-0] 
[107] 
Kaserer, T.; Höferl, M.; Müller, K.; Elmer, S.; Ganzera, M.; Jäger, W.; Schuster, D. In silico predictions of drug - drug interactions caused by CYP1A2, 2C9 and 3A4 inhibition-A comparative study of virtual screening performance. Mol. Inform.,  2015, 34, 431-457.
[http://dx.doi.org/10.1002/minf.201400192] 
[108] 
Ekins, S.; Erickson, J.A. A pharmacophore for human pregnane X receptor ligands. Drug Metab. Dispos.,  2002, 30, 96-99.
[http://dx.doi.org/10.1124/dmd.30.1.96] 
[109] 
Torimoto-Katori, N.; Huang, R.; Kato, H.; Ohashi, R.; Xia, M. In Silico prediction of HPXR activators using structure-based pharmacophore modeling. J. Pharm. Sci.,  2017, 106, 1752-1759.
[http://dx.doi.org/10.1016/j.xphs.2017.03.004] 
[110] 
Vilar, S.; Uriarte, E.; Santana, L.; Friedman, C.; Tatonetti, N.P. State of the art and development of a drug-drug interaction large scale predictor based on 3D pharmacophoric similarity. Curr. Drug Metab.,  2014, 15, 490-501.
[http://dx.doi.org/10.2174/138920021505141126102223] 
[111] 
Bordás, B.; Komíves, T.; Lopata, A. Ligand-based computer-aided pesticide design. A review of applications of the CoMFA and CoMSIA Methodologies. Pest Manag. Sci.,  2003, 59, 393-400.
[http://dx.doi.org/10.1002/ps.614] 
[112] 
Lukacova, V.; Balaz, S. Multimode ligand binding in receptor site modeling: implementation in CoMFA. J. Chem. Inf. Comput. Sci.,  2003, 43, 2093-2105.
[http://dx.doi.org/10.1021/ci034100a] 
[113] 
Korhonen, L.E.; Rahnasto, M.; Mähönen, N.J.; Wittekindt, C.; Poso, A.; Juvonen, R.O.; Raunio, H. Predictive three-dimensional quantitative structure-activity relationship of cytochrome P450 1A2 Inhibitors. J. Med. Chem.,  2005, 48, 3808-3815.
[http://dx.doi.org/10.1021/jm0489713] 
[114] 
Klebe, G. 3D QSAR in drug design: Comparative molecular similarity indices analysis: CoMSIA 2002.
[115] 
Locuson, C.W.; Suzuki, H.; Rettie, A.E.; Jones, J.P. Charge and substituent effects on affinity and metabolism of benzbromarone-based CYP2C19 Inhibitors. J. Med. Chem.,  2004, 47, 6768-6776.
[http://dx.doi.org/10.1021/jm049605m] 
[116] 
Palyulin; Radchenko; Zefirov. Molecular Field Topology Analysis Method in QSAR studies of organic compounds. J. Chem. Inf. Comput. Sci.,  2000, 40, 659-667.
[http://dx.doi.org/ 10.1021/ci980114i] 
[117] 
Makhaeva, G.F.; Radchenko, E.V.; Baskin, I.I.; Palyulin, V.A.; Richardson, R.J.; Zefirov, N.S. Combined QSAR studies of inhibitor properties of o-phosphorylated oximes toward serine esterases involved in neurotoxicity, drug metabolism and alzheimer’s disease. SAR QSAR Environ. Res.,  2012, 23, 627-647.
[http://dx.doi.org/10.1080/1062936X.2012.679690] 
[118] 
Makhaeva, G.F.; Radchenko, E.V.; Palyulin, V.A.; Rudakova, E.V.; Aksinenko, A.Y.; Sokolov, V.B.; Zefirov, N.S.; Richardson, R.J. Organophosphorus compound esterase profiles as predictors of therapeutic and toxic effects. Chem. Biol. Interact.,  2013, 203, 231-237.
[http://dx.doi.org/10.1016/j.cbi.2012.10.012] 
[119] 
Kim, K.H.; Greco, G.; Novellino, E. 3D QSAR in Drug Design. Recent Advances.,  2002, Vol. 3, 257.
[http://dx.doi.org/10.1007/0-306-46858-1_16] 
[120] 
Kato, H.; Yamaotsu, N.; Iwazaki, N.; Okamura, S.; Kume, T.; Hirono, S. Precise prediction of activators for the human constitutive androstane receptor using structure-based three-dimensional quantitative structure-activity relationship methods. Drug Metab. Pharmacokinet.,  2017, 32, 179-188.
[http://dx.doi.org/10.1016/j.dmpk.2017.02.001] 
[121] 
Bulusu, K.C.; Guha, R.; Mason, D.J.; Lewis, R.P.I.; Muratov, E.; Kalantar Motamedi, Y.; Cokol, M.; Bender, A. modelling of compound combination effects and applications to efficacy and toxicity: state-of-the-art, challenges and perspectives. Drug Discov. Today,  2016, 21, 225-238.
[http://dx.doi.org/ 10.1016/j.drudis. 2015.09.003] 
[122] 
Tarasova, O.A.; Urusova, A.F.; Filimonov, D.A.; Nicklaus, M.C.; Zakharov, A.V.; Poroikov, V.V. QSAR modeling using large-scale databases: case study for hiv-1 reverse transcriptase inhibitors. J. Chem. Inf. Model.,  2015, 55, 1388-1399.
[http://dx.doi.org/ 10.1021/acs.jcim.5b00019] 
[123] 
Todeschini, R.; Consonni, V. Handbook of molecular descriptors; , 2000. 
[http://dx.doi.org/10.1002/9783527613106] 
[124] 
Hawkins, D.M.; Basak, S.C.; Shi, X. QSAR with few compounds and many features. J. Chem. Inf. Comput. Sci.,  2001, 41, 663-670.
[http://dx.doi.org/10.1021/ci0001177] 
[125] 
Yamashita, F.; Hashida, M. In Silico approaches for predicting ADME properties of drugs. Drug Metab. Pharmacokinet.,  2004, 19, 327-338.
[http://dx.doi.org/10.2133/dmpk.19.327] 
[126] 
Weaver, S.; Gleeson, M.P. The importance of the domain of applicability in QSAR modeling. J. Mol. Graph. Model.,  2008, 26, 1315-1326.
[http://dx.doi.org/10.1016/j.jmgm.2008.01.002] 
[127] 
Long, A.; Walker, J.D. Quantitative structure-activity relationships for predicting metabolism and modeling cytochrome P450 enzyme activities. Environ. Toxicol. Chem.,  2003, 22, 1894-1899.
[http://dx.doi.org/10.1897/01-480] 
[128] 
Vasanthanathan, P.; Taboureau, O.; Oostenbrink, C.; Vermeulen, N.P.E.; Olsen, L.; Jørgensen, F.S. Classification of cytochrome P450 1A2 inhibitors and noninhibitors by machine learning techniques. Drug Metab. Dispos.,  2009, 37, 658-664.
[http://dx.doi.org/10.1124/dmd.108.023507] 
[129] 
Lewis, D.F.V. Quantitative structure-activity relationships (QSARs) within the cytochrome P450 System: QSARs describing substrate binding, inhibition and induction of P450s. Inflammopharmacology,  2003, 11, 43-73.
[http://dx.doi.org/10.1163/156856003321547112] 
[130] 
Lewis, D.F.; Modi, S.; Dickins, M. Quantitative structure-activity relationships (QSARs) within substrates of human cytochromes P450 involved in drug metabolism. Drug Metabol. Drug Interact.,  2001, 18, 221-242.
[http://dx.doi.org/10.1515/DMDI.2001.18.3-4.221] 
[131] 
Lewis, D.F.V.; Modi, S.; Dickins, M. Structure-activity relationship for human cytochrome P450 substrates and inhibitors. Drug Metab. Rev.,  2002, 34, 69-82.
[http://dx.doi.org/10.1081/DMR-120001391] 
[132] 
Terfloth, L.; Bienfait, B.; Gasteiger, J. Ligand-based models for the isoform specificity of cytochrome P450 3A4, 2D6, and 2C9 substrates. J. Chem. Inf. Model.,  2007, 47, 1688-1701.
[http://dx.doi.org/10.1021/ci700010t] 
[133] 
Mishra, N.K.; Agarwal, S.; Raghava, G.P. Prediction of cytochrome P450 isoform responsible for metabolizing a drug molecule. BMC Pharmacol.,  2010, 10, 8.
[http://dx.doi.org/10.1186/1471-2210-10-8] 
[134] 
Sorich, M.J.; Miners, J.O.; McKinnon, R.A.; Winkler, D.A.; Burden, F.R.; Smith, P.A. Comparison of linear and nonlinear classification algorithms for the prediction of drug and chemical metabolism by human UDP-Glucuronosyltransferase isoforms. J. Chem. Inf. Comput. Sci.,  2003, 43, 2019-2024.
[http://dx.doi.org/ 10.1021/ci034108k] 
[135] 
Peach, M.L.; Zakharov, A.V.; Liu, R.; Pugliese, A.; Tawa, G.; Wallqvist, A.; Nicklaus, M.C. Computational tools and resources for metabolism-related property predictions. 1. Overview of publicly available (Free and Commercial) databases and software. Future Med. Chem.,  2012, 4, 1907-1932.
[http://dx.doi.org/ 10.4155/fmc.12.150] 
[136] 
Poroikov, V.V.; Filimonov, D.A.; Borodina, Y.V.; Lagunin, A.A.; Kos, A. Robustness of biological activity spectra predicting by computer program pass for noncongeneric sets of chemical compounds. J. Chem. Inf. Comput. Sci.,  2000, 40, 1349-1355.
[http://dx.doi.org/10.1021/ci000383k] 
[137] 
Zakharov, A.V.; Lagunin, A.A.; Filimonov, D.A.; Poroikov, V.V. Quantitative prediction of antitarget interaction profiles for chemical compounds. Chem. Res. Toxicol.,  2012, 25, 2378-2385.
[http://dx.doi.org/10.1021/tx300247r] 
[138] 
Ivashchenko, D.V.; Rudik, A.V.; Poloznikov, A.A.; Nikulin, S.V.; Smirnov, V.V.; Tonevitsky, A.G.; Bryun, E.A.; Sychev, D.A. Which cytochrome P450 metabolizes phenazepam? step by step in Silico, in Vitro, and in Vivo studies. Drug Metab. Pers. Ther.,  2018, 33, 65-73.
[http://dx.doi.org/10.1515/dmpt-2017-0036] 
[139] 
Lagunin, A.A.; Gloriozova, T.A.; Dmitriev, A.V.; Volgina, N.E.; Poroikov, V.V. Computer evaluation of drug interactions with P-Glycoprotein. Bull. Exp. Biol. Med.,  2013, 154, 521-524.
[http://dx.doi.org/10.1007/s10517-013-1992-9] 
[140] 
Borodina, Y.; Sadym, A.; Filimonov, D.; Blinova, V.; Dmitriev, A.; Poroikov, V. Predicting biotransformation potential from molecular structure. J. Chem. Inf. Comput. Sci.,  2003, 43, 1636-1646.
[http://dx.doi.org/10.1021/ci034078l] 
[141] 
Borodina, Y.; Rudik, A.; Filimonov, D.; Kharchevnikova, N.; Dmitriev, A.; Blinova, V.; Poroikov, V. A new statistical approach to predicting aromatic hydroxylation sites. comparison with model-based approaches. J. Chem. Inf. Comput. Sci.,  2004, 44, 1998-2009.
[http://dx.doi.org/10.1021/ci049834h] 
[142] 
Rudik, A.V.; Dmitriev, A.V.; Lagunin, A.A.; Filimonov, D.A.; Poroikov, V.V. Prediction of reacting atoms for the major biotransformation reactions of organic xenobiotics. J. Cheminform.,  2016, 8, 68.
[http://dx.doi.org/10.1186/s13321-016-0183-x] 
[143] 
Tarasova, O.; Rudik, A.; Dmitriev, A.; Lagunin, A.; Filimonov, D.; Poroikov, V. QNA-Based prediction of sites of metabolism. Molecules,  2017, 22(12), E2123.
[144] 
Rudik, A.; Dmitriev, A.; Lagunin, A.; Filimonov, D.; Poroikov, V. SOMP: Web server for in silico prediction of sites of metabolism for drug-like compounds. Bioinformatics,  2015, 31, 2046-2048.
[http://dx.doi.org/10.1093/bioinformatics/btv087] 
[145] 
Rudik, A.V.; Bezhentsev, V.M.; Dmitriev, A.V.; Druzhilovskiy, D.S.; Lagunin, A.A.; Filimonov, D.A.; Poroikov, V.V. MetaTox: Web application for predicting structure and toxicity of xenobiotics’ metabolites. J. Chem. Inf. Model.,  2017, 57, 638-642.
[http://dx.doi.org/10.1021/acs.jcim.6b00662] 
[146] 
Rudik, A.V.; Dmitriev, A.V.; Bezhentsev, V.M.; Lagunin, A.A.; Filimonov, D.A.; Poroikov, V.V. Prediction of metabolites of epoxidation reaction in MetaTox. SAR QSAR Environ. Res.,  2017, 28, 833-842.
[http://dx.doi.org/10.1080/1062936X.2017.1399165] 
[147] 
Dmitriev, A.; Rudik, A.; Filimonov, D.; Lagunin, A.; Pogodin, P.; Dubovskaja, V.; Bezhentsev, V.; Ivanov, S.; Druzhilovsky, D.; Tarasova, O.; Poroikov, V. Integral estimation of xenobiotics’ toxicity with regard to their metabolism in human organism. Pure Appl. Chem.,  2017, 89(10)
[148] 
Yap, C.W.; Chen, Y.Z. Prediction of cytochrome P450 3A4, 2D6, and 2C9 inhibitors and substrates by using support vector machines. J. Chem. Inf. Model.,  2005, 45, 982-992.
[http://dx.doi.org/ 10.1021/ci0500536] 
[149] 
Kriegl, J.M.; Eriksson, L.; Arnhold, T.; Beck, B.; Johansson, E.; Fox, T. Multivariate modeling of cytochrome P450 3A4 inhibition. Eur. J. Pharm. Sci.,  2005, 24, 451-463.
[http://dx.doi.org/ 10.1016/j.ejps.2004.12.009] 
[150] 
Hudelson, M.G.; Ketkar, N.S.; Holder, L.B.; Carlson, T.J.; Peng, C-C.; Waldher, B.J.; Jones, J.P. High Confidence predictions of drug-drug interactions: Predicting affinities for cytochrome P450 2C9 with multiple computational methods. J. Med. Chem.,  2008, 51, 648-654.
[http://dx.doi.org/10.1021/jm701130z] 
[151] 
Hamon, V.; Horvath, D.; Gaudin, C.; Desrivot, J.; Junges, C.; Arrault, A.; Bertrand, M.; Vayer, P. QSAR modelling of CYP3A4 inhibition as a screening tool in the context of drug-drug interaction studies. Mol. Inform.,  2012, 31, 669-677.
[http://dx.doi.org/ 10.1002/minf.201200004] 
[152] 
Ung, C.Y.; Li, H.; Yap, C.W.; Chen, Y.Z. In Silico prediction of pregnane X receptor activators by machine learning approaches. Mol. Pharmacol.,  2007, 71, 158-168.
[http://dx.doi.org/ 10.1124/mol.106.027623] 
[153] 
Dmitriev, A.; Filimonov, D.; Lagunin, A.; Rudik, A.; Karasev, D.; Murtazalieva, K.; Poroikov, V. Computer prediction of the drugdrug interactions severity. FEBS Lett., 2018.
[154] 
Hansten, P.D.; Horn, J.R.; Hazlet, T.K. ORCA: OpeRational classification of drug interactions. J. Am. Pharm. Assoc. (Wash),  2001, 41, 161-165.
[http://dx.doi.org/10.1016/S1086-5802(16)-31244-X] 
[155] 
Vilar, S.; Harpaz, R.; Uriarte, E.; Santana, L.; Rabadan, R.; Friedman, C. Drug-Drug interaction through molecular structure similarity analysis. J. Am. Med. Inform. Assoc.,  2012, 19, 1066-1074.
[http://dx.doi.org/10.1136/amiajnl-2012-000935] 
[156] 
Vilar, S.; Uriarte, E.; Santana, L.; Tatonetti, N.P.; Friedman, C. Detection of drug-drug interactions by modeling interaction profile fingerprints. PLoS One,  2013, 8, e58321.
[http://dx.doi.org/10.1371/journal.pone.0058321] 
[157] 
Vilar, S.; Uriarte, E.; Santana, L.; Lorberbaum, T.; Hripcsak, G.; Friedman, C.; Tatonetti, N.P. Similarity-Based modeling in large-scale prediction of drug-drug interactions. Nat. Protoc.,  2014, 9, 2147-2163.
[http://dx.doi.org/10.1038/nprot.2014.151] 
[158] 
Cheng, F.; Zhao, Z. Machine learning-based prediction of drug-drug interactions by integrating drug phenotypic, therapeutic, chemical, and genomic properties. J. Am. Med. Inform. Assoc.,  2014, 21, e278-e286.
[http://dx.doi.org/10.1136/amiajnl-2013-002512] 
[159] 
Takeda, T.; Hao, M.; Cheng, T.; Bryant, S.H.; Wang, Y. Predicting drug-drug interactions through drug structural similarities and interaction networks incorporating pharmacokinetics and pharmacodynamics knowledge. J. Cheminform.,  2017, 9, 16.
[http://dx.doi.org/10.1186/s13321-017-0200-8] 
[160] 
Zhang, P.; Wang, F.; Hu, J.; Sorrentino, R. Label propagation prediction of drug-drug interactions based on clinical side effects. Sci. Rep.,  2015, 5, 12339.
[http://dx.doi.org/ 10.1038/srep12339] 
[161] 
Sridhar, D.; Fakhraei, S.; Getoor, L. A Probabilistic approach for collective similarity-based drug-drug interaction prediction. Bioinformatics,  2016, 32, 3175-3182.
[http://dx.doi.org/ 10.1093/bioinformatics/btw342] 
[162] 
Gottlieb, A.; Stein, G.Y.; Oron, Y.; Ruppin, E.; Sharan, R. INDI: A computational framework for inferring drug interactions and their associated recommendations. Mol. Syst. Biol.,  2012, 8, 592-603.
[http://dx.doi.org/10.1038/msb.2012.26] 
[163] 
Perlman, L.; Gottlieb, A.; Atias, N.; Ruppin, E.; Sharan, R. Combining drug and gene similarity measures for drug-target elucidation. J. Comput. Biol.,  2011, 18(2), 133-145.
[http://dx.doi.org/ 10.1089/cmb.2010.0213] 
[164] 
Ferdousi, R.; Safdari, R.; Omidi, Y. Computational prediction of drug-drug interactions based on drugs functional similarities. J. Biomed. Inform.,  2017, 70, 54-64.
[http://dx.doi.org/ 10.1016/j.jbi.2017.04.021] 
[165] 
Fowler, S.; Morcos, P.N.; Cleary, Y.; Martin-Facklam, M.; Parrott, N.; Gertz, M.; Yu, L. Progress in prediction and interpretation of clinically relevant metabolic drug-drug interactions: A minireview illustrating recent developments and current opportunities. Curr. Pharmacol. Rep.,  2017, 3, 36-49.
[http://dx.doi.org/10.1007/s40495-017-0082-5] 
Rights & Permissions Print Cite
Article Metrics
91
8
1
Journal Information
For Authors
For Editors
For Reviewers
Explore Articles
Open Access
Open Access Articles
For Visitors
DOI https://dx.doi.org/10.2174/1568026619666190123160406	Print ISSN 1568-0266
Publisher Name Bentham Science Publisher	Online ISSN 1873-4294
Current Topics in Medicinal Chemistry

Prediction of Drug-Drug Interactions Related to Inhibition or Induction of Drug-Metabolizing Enzymes

Abstract Play Pause

Graphical Abstract

Related Journals

Related Books

Related Articles

Abstract
