In-Silico Modeling in Drug Metabolism and Interaction: Current Strategies of Lead Discovery

Author(s): Harekrishna Roy*, Sisir Nandi*.

Journal Name: Current Pharmaceutical Design

Volume 25 , Issue 31 , 2019

Abstract:

Background: Drug metabolism is a complex mechanism of human body systems to detoxify foreign particles, chemicals, and drugs through bio alterations. It involves many biochemical reactions carried out by invivo enzyme systems present in the liver, kidney, intestine, lungs, and plasma. After drug administration, it crosses several biological membranes to reach into the target site for binding and produces the therapeutic response. After that, it may undergo detoxification and excretion to get rid of the biological systems. Most of the drugs and its metabolites are excreted through kidney via urination. Some drugs and their metabolites enter into intestinal mucosa and excrete through feces. Few of the drugs enter into hepatic circulation where they go into the intestinal tract. The drug leaves the liver via the bile duct and is excreted through feces. Therefore, the study of total methodology of drug biotransformation and interactions with various targets is costly.

Methods: To minimize time and cost, in-silico algorithms have been utilized for lead-like drug discovery. Insilico modeling is the process where a computer model with a suitable algorithm is developed to perform a controlled experiment. It involves the combination of both in-vivo and in-vitro experimentation with virtual trials, eliminating the non-significant variables from a large number of variable parameters. Whereas, the major challenge for the experimenter is the selection and validation of the preferred model, as well as precise simulation in real physiological status.

Results: The present review discussed the application of in-silico models to predict absorption, distribution, metabolism, and excretion (ADME) properties of drug molecules and also access the net rate of metabolism of a compound.

Conclusion: It helps with the identification of enzyme isoforms; which are likely to metabolize a compound, as well as the concentration dependence of metabolism and the identification of expected metabolites. In terms of drug-drug interactions (DDIs), models have been described for the inhibition of metabolism of one compound by another, and for the compound–dependent induction of drug-metabolizing enzymes.

Keywords: Drug metabolism, detoxification, biological systems, enzyme isoforms, drug-drug interactions (DDIs), in-silico models, lead discovery.

[1]
Scotchmer S. Standing on the shoulders of giants: cumulative research and the patent law. J Econ Perspect 1991; 5: 29-41.
[http://dx.doi.org/10.1257/jep.5.1.29]
[2]
Drews J. Drug discovery: a historical perspective. Science 2000; 287(5460): 1960-4.
[http://dx.doi.org/10.1126/science.287.5460.1960] [PMID: 10720314]
[3]
Ruano-Ordás D, Yevseyeva I, Fernandes VB, Méndez JR, Emmerich MT. Improving the drug discovery process by using multiple classifier systems. Expert Syst Appl 2019; 121: 292-303.
[http://dx.doi.org/10.1016/j.eswa.2018.12.032]
[4]
Hejaz HA, Karaman R. Drug overview Commonly used drug 2015; 1-40.
[5]
Abel R, Mondal S, Masse C, et al. Accelerating drug discovery through tight integration of expert molecular design and predictive scoring. Curr Opin Struct Biol 2017; 43: 38-44.
[http://dx.doi.org/10.1016/j.sbi.2016.10.007] [PMID: 27816785]
[6]
The tufts center for the study of drug development [cited 2019 Jan 11] Available from:. http://csdd.tufts.edu
[7]
Kazmi SR, Jun R, Yu MS, Jung C, Na D. In silico approaches and tools for the prediction of drug metabolism and fate: a review. Comput Biol Med 2019; 106: 54-64.
[http://dx.doi.org/10.1016/j.compbiomed.2019.01.008] [PMID: 30682640]
[8]
Campbell IB, Macdonald SJF, Procopiou PA. Medicinal chemistry in drug discovery in big pharma: past, present and future. Drug Discov Today 2018; 23(2): 219-34.
[http://dx.doi.org/10.1016/j.drudis.2017.10.007] [PMID: 29031621]
[9]
Shamsi M, Mohammadi A, Manshadi MKD, Sanati-Nezhad A. Mathematical and computational modeling of nano-engineered drug delivery systems. J Control Release 2019; 307: 150-65.
[http://dx.doi.org/10.1016/j.jconrel.2019.06.014] [PMID: 31229474]
[10]
Vijayakumar S, Prabhu S, Rajalakhsmi S, Manogar P. Review on potential phytocompounds in drug development for Parkinson disease: a pharmacoinformatic approach. IMU 2016; 5: 15-25.
[http://dx.doi.org/10.1016/j.imu.2016.09.002]
[11]
Ooms F. Molecular modeling and computer aided drug design. Examples of their applications in medicinal chemistry. Curr Med Chem 2000; 7(2): 141-58.
[http://dx.doi.org/10.2174/0929867003375317] [PMID: 10637360]
[12]
Maréchal JD, Kemp CA, Roberts GC, Paine MJ, Wolf CR, Sutcliffe MJ. Insights into drug metabolism by cytochromes P450 from modelling studies of CYP2D6-drug interactions. Br J Pharmacol 2008; 153(S1): S82-9.
[13]
Saini N, Bakshi S, Sharma S. In-silico approach for drug induced liver injury prediction: recent advances. Toxicol Lett 2018; 295: 288-95.
[http://dx.doi.org/10.1016/j.toxlet.2018.06.1216] [PMID: 29981923]
[14]
Munir A, Elahi S, Masood N. Clustering based drug-drug interaction networks for possible repositioning of drugs against EGFR mutations: clustering based DDI networks for EGFR mutations. Comput Biol Chem 2018; 75: 24-31.
[http://dx.doi.org/10.1016/j.compbiolchem.2018.04.011] [PMID: 29730365]
[15]
Thomas S, Dimelow RJ. Prediction of phosphoglycoprotein (P-gp)- mediated disposition in early drug discovery. Ed., Johannes Kirchmair 2014; p. 290.
[http://dx.doi.org/10.1002/9783527673261.ch15]
[16]
Yanni SB. PBPK modeling and in silico prediction for ADME and drug–drug interaction. In: Translational ADMET for Drug Therapy. S. B. Yanni (Ed.). John Wiley & Sons, Inc 2015; pp. 221-40.
[http://dx.doi.org/10.1002/9781118838440.ch8]
[17]
Harrington JA, Hernandez-Guerrero TC, Basu B. Early phase clinical trial designs-state of play and adapting for the future. Clin Oncol (R Coll Radiol) 2017; 29(12): 770-7.
[http://dx.doi.org/10.1016/j.clon.2017.10.005] [PMID: 29108786]
[18]
Schytz HW, Hargreaves R, Ashina M. Challenges in developing drugs for primary headaches. Prog Neurobiol 2017; 152: 70-88.
[http://dx.doi.org/10.1016/j.pneurobio.2015.12.005] [PMID: 26751129]
[19]
Garralda E, Dienstmann R, Tabernero J. Pharmacokinetic/pharmacodynamic modeling for drug development in oncology. Am Soc Clin Oncol Educ Book 2017; 37: 210-5.
[http://dx.doi.org/10.14694/EDBK_180460] [PMID: 28561730]
[20]
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] [PMID: 26196247]
[21]
Basu D, Gillman PK, Gnanadesigan N, et al. The serotonin syndrome. N Engl J Med 2005; 352(23): 2454-6.
[http://dx.doi.org/10.1056/NEJM200506093522320] [PMID: 15944434]
[22]
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(6): 1066-74.
[http://dx.doi.org/10.1136/amiajnl-2012-000935] [PMID: 22647690]
[23]
Pedersen JK, Lydolph MC, Somnier F, Junker P. Myopathy in a patient during simvastatin and fluconazole treatment. Ugeskr Laeger 2016; 178(39)V04160257
[PMID: 27697125]
[24]
DRUGDEX Detailed Drug Information.. Truven Health Analytics Inc c2014 [Cited 2018 Dec 7] https://www.micromedexsolutions. com/micromedex2/4.85.0/WebHelp/Document_help/Drug_Eval_document.htm
[25]
Lin L, Wong H. Predicting oral drug absorption: mini review on physiologically-based pharmacokinetic models. Pharmaceutics 2017; 9(4): 41.
[http://dx.doi.org/10.3390/pharmaceutics9040041] [PMID: 28954416]
[26]
Yu XQ, Wilson AG. The role of pharmacokinetic and pharmacokinetic/pharmacodynamic modeling in drug discovery and development. Future Med Chem 2010; 2(6): 923-8.
[http://dx.doi.org/10.4155/fmc.10.181] [PMID: 21426110]
[27]
Guimerà R, Sales-Pardo M. A network inference method for large-scale unsupervised identification of novel drug-drug interactions. PLOS Comput Biol 2013; 9(12)e1003374
[http://dx.doi.org/10.1371/journal.pcbi.1003374] [PMID: 24339767]
[28]
Dinies Omicx inc c2015-2019 [Cited 2019 May 12] Available from:. https://omictools.com/dinies-tool
[29]
Yamanishi Y, Kotera M, Moriya Y, Sawada R, Kanehisa M, Goto S. DINIES: drug-target interaction network inference engine based on supervised analysis. Nucleic Acids Res 2014; 42(Web Server issue): W39-45.
[http://dx.doi.org/10.1093/nar/gku337] [PMID: 24838565]
[30]
Nembri S, Grisoni F, Consonni V, Todeschini R. In silico prediction of cytochrome P450-drug interaction: QSARs for CYP3A4 and CYP2C9. Int J Mol Sci 2016; 17(6): 914.
[http://dx.doi.org/10.3390/ijms17060914] [PMID: 27294921]
[31]
Tyzack JD, Kirchmair J. Computational methods and tools to predict cytochrome P450 metabolism for drug discovery. Chem Biol Drug Des 2019; 93(4): 377-86.
[http://dx.doi.org/10.1111/cbdd.13445] [PMID: 30471192]
[32]
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] [PMID: 25796619]
[33]
Balani SK, Miwa GT, Gan LS, Wu JT, Lee FW. Strategy of utilizing in vitro and in vivo ADME tools for lead optimization and drug candidate selection. Curr Top Med Chem 2005; 5(11): 1033-8.
[http://dx.doi.org/10.2174/156802605774297038] [PMID: 16181128]
[34]
Tetko IV, Bruneau P, Mewes HW, Rohrer DC, Poda GI. Can we estimate the accuracy of ADME-Tox predictions? Drug Discov Today 2006; 11(15-16): 700-7.
[http://dx.doi.org/10.1016/j.drudis.2006.06.013] [PMID: 16846797]
[35]
Deng J, Jhandey A, Zhu X, et al. In silico drug absorption tract: an agent-based biomimetic model for human oral drug absorption. PLoS One 2018; 13(8) e0203361
[http://dx.doi.org/10.1371/journal.pone.0203361] [PMID: 30169515]
[36]
Dokoumetzidis A, Kalantzi L, Fotaki N. Predictive models for oral drug absorption: from in silico methods to integrated dynamical models. Expert Opin Drug Metab Toxicol 2007; 3(4): 491-505.
[http://dx.doi.org/10.1517/17425255.3.4.491] [PMID: 17696801]
[37]
Yamashita S, Furubayashi T, Kataoka M, Sakane T, Sezaki H, Tokuda H. Optimized conditions for prediction of intestinal drug permeability using Caco-2 cells. Eur J Pharm Sci 2000; 10(3): 195-204.
[http://dx.doi.org/10.1016/S0928-0987(00)00076-2] [PMID: 10767597]
[38]
Abd E, Yousef SA, Pastore MN, et al. Skin models for the testing of transdermal drugs. Clin Pharmacol 2016; 8: 163-76.
[http://dx.doi.org/10.2147/CPAA.S64788] [PMID: 27799831]
[39]
Zhao YH, Le J, Abraham MH, et al. Evaluation of human intestinal absorption data and subsequent derivation of a quantitative structure-activity relationship (QSAR) with the Abraham descriptors. J Pharm Sci 2001; 90(6): 749-84.
[http://dx.doi.org/10.1002/jps.1031] [PMID: 11357178]
[40]
Singh S, Singh J. Transdermal drug delivery by passive diffusion and iontophoresis: a review. Med Res Rev 1993; 13(5): 569-621.
[http://dx.doi.org/10.1002/med.2610130504] [PMID: 8412408]
[41]
Yunta MJ. It is important to compute intramolecular hydrogen bonding in drug design. Am J Model Optim 2017; 5: 24-57.
[http://dx.doi.org/10.12691/ajmo-5-1-3]
[42]
Dokoumetzidis A, Kalantzi L, Fotaki N. Predictive models for oral drug absorption: from in silico methods to integrated dynamical models. Expert Opin Drug Metab Toxicol 2007; 3(4): 491-505.
[http://dx.doi.org/10.1517/17425255.3.4.491] [PMID: 17696801]
[43]
Blood brain barrier penetration [Monograph on the internet] Yonsei Engineering Research Complex Yonsei University, Seoul, Republic of Korea 2015.[Cited 2019 Feb 14]; Available from:. https://preadmet.bmdrc.kr/2015/01/28/blood-brain-barrier-penetration/
[44]
Kovačević SZ, Jevrić LR, Podunavac Kuzmanović SO, Lončar ES. Prediction of in-silico ADME properties of 1, 2-O-isopropylidene aldohexose derivatives. Iran J Pharm Res 2014; 13(3): 899-907.
[PMID: 25276190]
[45]
Ajay, Bemis GW, Murcko MA. Designing Libraries with CNS Activity. J Med Chem 1999; 42: 4942-51.
[http://dx.doi.org/10.1021/jm990017w]
[46]
Shahraki S, Shiri F, Saeidifar M. Evaluation of in silico ADMET analysis and human serum albumin interactions of a new lanthanum (III) complex by spectroscopic and molecular modeling studies. Inorg Chim Acta 2017; 463: 80-7.
[http://dx.doi.org/10.1016/j.ica.2017.04.023]
[47]
Hou T. In silico ADMET predictions in pharmaceutical research. Adv Drug Deliv Rev 2015; 86: 1.
[http://dx.doi.org/10.1016/j.addr.2015.06.006] [PMID: 26138539]
[48]
Fox T, Kriegl JM. Machine learning techniques for in silico modeling of drug metabolism. Curr Top Med Chem 2006; 6(15): 1579-91.
[http://dx.doi.org/10.2174/156802606778108915] [PMID: 16918470]
[49]
de Graaf C, Vermeulen NP, Feenstra KA. Cytochrome p450 in silico: an integrative modeling approach. J Med Chem 2005; 48(8): 2725-55.
[http://dx.doi.org/10.1021/jm040180d] [PMID: 15828810]
[50]
J Chem. Metabolizer Software Module 2011.
[51]
Find chemistry in unstructured data [cited 2019 Feb 12] Available from:. https://chemaxon.com/products/chemlocator
[52]
Guijas C, Montenegro-Burke JR, Warth B, Spilker ME, Siuzdak G. Metabolomics activity screening for identifying metabolites that modulate phenotype. Nat Biotechnol 2018; 36(4): 316-20.
[http://dx.doi.org/10.1038/nbt.4101] [PMID: 29621222]
[53]
van der Hooft JJ, Padmanabhan S, Burgess KE, Barrett MP. Urinary antihypertensive drug metabolite screening using molecular networking coupled to high-resolution mass spectrometry fragmentation. Metabolomics 2016; 12: 125.
[http://dx.doi.org/10.1007/s11306-016-1064-z] [PMID: 27471437]
[54]
Ridder L, Wagener M. SyGMa: combining expert knowledge and empirical scoring in the prediction of metabolites. ChemMedChem 2008; 3(5): 821-32.
[http://dx.doi.org/10.1002/cmdc.200700312] [PMID: 18311745]
[55]
Ekins S, Andreyev S, Ryabov A, et al. A combined approach to drug metabolism and toxicity assessment. Drug Metab Dispos 2006; 34(3): 495-503.
[PMID: 16381662]
[56]
Ekins S, Bugrim A, Brovold L, et al. Algorithms for network analysis in systems-ADME/Tox using the MetaCore and MetaDrug platforms. Xenobiotica 2006; 36(10-11): 877-901.
[http://dx.doi.org/10.1080/00498250600861660] [PMID: 17118913]
[57]
Ruiz P, Perlina A, Mumtaz M, Fowler BA. A systems biology approach reveals converging molecular mechanisms that link different POPs to common metabolic diseases. Environ Health Perspect 2016; 124(7): 1034-41.
[http://dx.doi.org/10.1289/ehp.1510308] [PMID: 26685285]
[58]
Glaab E. Building a virtual ligand screening pipeline using free software: a survey. Brief Bioinform 2016; 17(2): 352-66.
[http://dx.doi.org/10.1093/bib/bbv037] [PMID: 26094053]
[59]
Ekins S, Nikolsky Y, Nikolskaya T. Techniques: application of systems biology to absorption, distribution, metabolism, excretion and toxicity. Trends Pharmacol Sci 2005; 26(4): 202-9.
[http://dx.doi.org/10.1016/j.tips.2005.02.006] [PMID: 15808345]
[60]
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.
[http://dx.doi.org/10.1016/S1359-6446(03)02971-4] [PMID: 14960390]
[61]
Xu P, Xu C, Li X, et al. Rapid Identification of berberine metabolites in rat plasma by UHPLC-Q-TOF-MS. Molecules 2019; 24(10): 1994.
[http://dx.doi.org/10.3390/molecules24101994] [PMID: 31137649]
[62]
Gandhi AS, Wohlfarth A, Zhu M, et al. High-resolution mass spectrometric metabolite profiling of a novel synthetic designer drug, N-(adamantan-1-yl)-1-(5-fluoropentyl)-1H-indole-3-carboxamide (STS-135), using cryopreserved human hepatocytes and assessment of metabolic stability with human liver microsomes. Drug Test Anal 2015; 7(3): 187-98.
[http://dx.doi.org/10.1002/dta.1662] [PMID: 24827428]
[63]
Carmai Seto. Tanya Gamble and Hesham Ghobarah Rapid Metabolite Identification using MetabolitePilot™ Software and TripleTOF™ 5600 System [monograph on the internet] AB SCIEX, Concord, Ontario, Canada [cited 2019 Feb 19]; Available from:. https://sciex.com/Documents/Downloads/Literature/Rapid_Metabolite_Identification_using_MetabolitePilot_Software.pdf
[64]
Breakthrough Productivity for ADME Studies Using the AB SCIEX TripleTOF™ 5600 System AB SCIEX Technical Note Publication 0480110-01.
[65]
MetaPrint2D [Home page on the internet Pharmaceutical bioinformatics research group, Uppsala university, Sweden [Cited 2019 Feb 10]; Available from:. https://pharmb.io/tool/metaprint2d/
[66]
Adams SE. Molecular similarity and xenobiotic metabolism 2010.
[67]
Fujitsu BioFrontier/P450: major functions [Cited 2019 Mar 10] Available from:. https://www.fqs.pl/en/chemistry/products
[68]
Thorn CF, Klein TE, Altman RB. PharmGKB: the pharmacogenetics and pharmacogenomics knowledge base. Methods Mol Biol 2005; 311: 179-91.
[http://dx.doi.org/10.1385/1-59259-957-5:179] [PMID: 16100408]
[69]
Schuetz EG, Relling MV, Kishi S, et al. PharmGKB update: II. CYP3A5, cytochrome P450, family 3, subfamily A, polypeptide 5. Pharmacol Rev 2004; 56(2): 159.
[http://dx.doi.org/10.1124/pr.56.2.1] [PMID: 15169924]
[70]
Klopman G, Dimayuga M, Talafous J. META. 1. A program for the evaluation of metabolic transformation of chemicals. J Chem Inf Comput Sci 1994; 34(6): 1320-5.
[http://dx.doi.org/10.1021/ci00022a014] [PMID: 7989397]
[71]
Sedykh A, Saiakhov R, Klopman G. META V. A model of photodegradation for the prediction of photoproducts of chemicals under natural-like conditions. Chemosphere 2001; 45(6-7): 971-81.
[http://dx.doi.org/10.1016/S0045-6535(01)00007-8] [PMID: 11695620]
[72]
Langowski J, Long A. Computer systems for the prediction of xenobiotic metabolism. Adv Drug Deliv Rev 2002; 54(3): 407-15.
[http://dx.doi.org/10.1016/S0169-409X(02)00011-X] [PMID: 11922955]
[73]
Marchant CA, Briggs KA, Long A. In silico tools for sharing data and knowledge on toxicity and metabolism: derek for windows, meteor, and vitic. Toxicol Mech Methods 2008; 18(2-3): 177-87.
[http://dx.doi.org/10.1080/15376510701857320] [PMID: 20020913]
[74]
de Bruyn Kops C, Stork C, Šícho M, et al. GLORY: generator of the structures of likely cytochrome P450 Metabolites based on predicted sites of metabolism. Front Chem 2019; 7: 402.
[http://dx.doi.org/10.3389/fchem.2019.00402] [PMID: 31249827]
[75]
CompuDrug-Your Expert in Chemical Informatics [Cited 2019 Jan 13] Available from:. https://www.compudrug.com/metabolexpert
[76]
Darvas F. Predicting metabolic pathways by logic programming. J Mol Graph 1988; 6: 80-6.
[http://dx.doi.org/10.1016/0263-7855(88)85004-5]
[77]
Discovery M. Molecular Discovery MetaSite [Cited 2019 Mar 24] Available from:. http://www.moldiscovery.com/software/metasite/
[78]
Cruciani G, Carosati E, De Boeck B, et al. MetaSite: understanding metabolism in human cytochromes from the perspective of the chemist. J Med Chem 2005; 48(22): 6970-9.
[PMID: 16250655]
[79]
Zhou D, Afzelius L, Grimm SW, Andersson TB, Zauhar RJ, Zamora I. Comparison of methods for the prediction of the metabolic sites for CYP3A4-mediated metabolic reactions. Drug Metab Dispos 2006; 34(6): 976-83.
[http://dx.doi.org/10.1124/dmd.105.008631] [PMID: 16540587]
[80]
Hughes TB, Miller GP, Swamidass SJ. Modeling epoxidation of drug-like molecules with a deep machine learning network. ACS Cent Sci 2015; 1(4): 168-80.
[http://dx.doi.org/10.1021/acscentsci.5b00131] [PMID: 27162970]
[81]
Mekenyan OG, Dimitrov SD, Pavlov TS, Veith GD. A systematic approach to simulating metabolism in computational toxicology. I. The TIMES heuristic modelling framework. Curr Pharm Des 2004; 10(11): 1273-93.
[http://dx.doi.org/10.2174/1381612043452596] [PMID: 15078141]
[82]
Smith J, Stein V. SPORCalc: A development of a database analysis that provides putative metabolic enzyme reactions for ligand-based drug design. Comput Biol Chem 2009; 33(2): 149-59.
[http://dx.doi.org/10.1016/j.compbiolchem.2008.11.002] [PMID: 19157988]
[83]
Korzekwa KR, Jones JP, Gillette JR. Theoretical studies on cytochrome P-450 mediated hydroxylation: a predictive model for hydrogen atom abstractions. J Am Chem Soc 1990; 112: 7042-6.
[http://dx.doi.org/10.1021/ja00175a040]
[84]
Hubatsch I, Ragnarsson EG, Artursson P. Determination of drug permeability and prediction of drug absorption in Caco-2 monolayers. Nat Protoc 2007; 2(9): 2111-9.
[http://dx.doi.org/10.1038/nprot.2007.303] [PMID: 17853866]
[85]
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(4): 1688-701.
[http://dx.doi.org/10.1021/ci700010t] [PMID: 17608404]
[86]
Nandi S, Bagchi MC. QSAR modeling of 4-anilinofuro [2, 3-b] quinolines: an approach to anticancer drug design. Med Chem Res 2014; 23: 1672-82.
[http://dx.doi.org/10.1007/s00044-013-0759-1]
[87]
Olsen L, Oostenbrink C, Jørgensen FS. Prediction of cytochrome P450 mediated metabolism. Adv Drug Deliv Rev 2015; 86: 61-71.
[http://dx.doi.org/10.1016/j.addr.2015.04.020] [PMID: 25958010]
[88]
Tyzack JD, Kirchmair J. Computational methods and tools to predict cytochrome P450 metabolism for drug discovery. Chem Biol Drug Des 2019; 93(4): 377-86.
[http://dx.doi.org/10.1111/cbdd.13445] [PMID: 30471192]
[89]
Pathania S, Bhatia R, Baldi A, Singh R, Rawal RK. Drug metabolizing enzymes and their inhibitors’ role in cancer resistance. Biomed Pharmacother 2018; 105: 53-65.
[http://dx.doi.org/10.1016/j.biopha.2018.05.117] [PMID: 29843045]
[90]
Litterst CL, Mimnaugh EG, Reagan RL, Gram TE. Comparison of in vitro drug metabolism by lung, liver, and kidney of several common laboratory species. Drug Metab Dispos 1975; 3(4): 259-65.
[PMID: 240655]
[91]
Iwatsubo T, Hirota N, Ooie T, et al. Prediction of in vivo drug metabolism in the human liver from in vitro metabolism data. Pharmacol Ther 1997; 73(2): 147-71.
[http://dx.doi.org/10.1016/S0163-7258(96)00184-2] [PMID: 9131722]
[92]
Vander Heiden MG, DeBerardinis RJ. Understanding the intersections between metabolism and cancer biology. Cell 2017; 168(4): 657-69.
[http://dx.doi.org/10.1016/j.cell.2016.12.039] [PMID: 28187287]
[93]
Çubuk C, Hidalgo MR, Amadoz A, et al. Differential metabolic activity and discovery of therapeutic targets using summarized metabolic pathway models. NPJ Syst Biol Appl 2019; 5: 7.
[http://dx.doi.org/10.1038/s41540-019-0087-2] [PMID: 30854222]
[94]
Kahan BD, Napoli KL, Kelly PA, et al. Therapeutic drug monitoring of sirolimus: correlations with efficacy and toxicity. Clin Transplant 2000; 14(2): 97-109.
[http://dx.doi.org/10.1034/j.1399-0012.2000.140201.x] [PMID: 10770413]
[95]
Ansede JH, Thakker DR. High-throughput screening for stability and inhibitory activity of compounds toward cytochrome P450-mediated metabolism. J Pharm Sci 2004; 93(2): 239-55.
[http://dx.doi.org/10.1002/jps.10545] [PMID: 14705182]
[96]
Koop DR. Oxidative and reductive metabolism by cytochrome P450 2E1. FASEB J 1992; 6(2): 724-30.
[http://dx.doi.org/10.1096/fasebj.6.2.1537462] [PMID: 1537462]
[97]
Williams RT. Introduction: pathways of drug metabolism. In: Concepts in Biochemical Pharmacology. Springer Berlin, Heidelberg 1971; pp. 226-42.
[http://dx.doi.org/10.1007/978-3-642-65177-9_14]
[98]
Wu CY, Benet LZ. Predicting drug disposition via application of BCS: transport/absorption/elimination interplay and development of a biopharmaceutics drug disposition classification system. Pharm Res 2005; 22(1): 11-23.
[http://dx.doi.org/10.1007/s11095-004-9004-4] [PMID: 15771225]
[99]
Salahudeen MS, Nishtala PS. An overview of pharmacodynamic modelling, ligand-binding approach and its application in clinical practice. Saudi Pharm J 2017; 25(2): 165-75.
[http://dx.doi.org/10.1016/j.jsps.2016.07.002] [PMID: 28344466]
[100]
Isbell J, Yuan D, Torrao L, Gatlik E, Hoffmann L, Wipfli P. Plasma protein binding of highly bound drugs determined with equilibrium gel filtration of nonradiolabeled compounds and LC-MS/MS detection. J Pharm Sci 2019; 108(2): 1053-60.
[http://dx.doi.org/10.1016/j.xphs.2018.10.004] [PMID: 30336155]
[101]
Du X, Li Y, Xia YL, et al. Insights into protein-ligand interactions: mechanisms, models, and methods. Int J Mol Sci 2016; 17(2): 144.
[http://dx.doi.org/10.3390/ijms17020144] [PMID: 26821017]
[102]
Pontremoli C, Barbero N, Viscardi G, Visentin S. Insight into the interaction of inhaled corticosteroids with human serum albumin: A spectroscopic-based study. J Pharm Anal 2018; 8(1): 37-44.
[http://dx.doi.org/10.1016/j.jpha.2017.07.003] [PMID: 29568666]
[103]
Johnstone RW, Ruefli AA, Smyth MJ. Multiple physiological functions for multidrug transporter P-glycoprotein? Trends Biochem Sci 2000; 25(1): 1-6.
[http://dx.doi.org/10.1016/S0968-0004(99)01493-0] [PMID: 10637601]
[104]
Hamada H, Tsuruo T. Purification of the 170- to 180-kilodalton membrane glycoprotein associated with multidrug resistance. 170- to 180-kilodalton membrane glycoprotein is an ATPase. J Biol Chem 1988; 263(3): 1454-8.
[PMID: 2891711]
[105]
Custodio JM, Wu CY, Benet LZ. Predicting drug disposition, absorption/elimination/transporter interplay and the role of food on drug absorption. Adv Drug Deliv Rev 2008; 60(6): 717-33.
[http://dx.doi.org/10.1016/j.addr.2007.08.043] [PMID: 18199522]
[106]
Dimelow RJ, Metcalfe PD, Thomas S. In silico models of drug metabolism and drug interactions. Encyclop Drug Metabol Interact 2011; pp. 1-55.
[107]
Gillman PK. Tricyclic antidepressant pharmacology and therapeutic drug interactions updated. Br J Pharmacol 2007; 151(6): 737-48.
[http://dx.doi.org/10.1038/sj.bjp.0707253] [PMID: 17471183]
[108]
Ogilvie BW, Zhang D, Li W, et al. Glucuronidation converts gemfibrozil to a potent, metabolism-dependent inhibitor of CYP2C8: implications for drug-drug interactions. Drug Metab Dispos 2006; 34(1): 191-7.
[http://dx.doi.org/10.1124/dmd.105.007633] [PMID: 16299161]
[109]
Lavecchia A. Machine-learning approaches in drug discovery: methods and applications. Drug Discov Today 2015; 20(3): 318-31.
[http://dx.doi.org/10.1016/j.drudis.2014.10.012] [PMID: 25448759]
[110]
Chen H, Engkvist O, Wang Y, Olivecrona M, Blaschke T. The rise of deep learning in drug discovery. Drug Discov Today 2018; 23(6): 1241-50.
[http://dx.doi.org/10.1016/j.drudis.2018.01.039] [PMID: 29366762]
[111]
Lima AN, Philot EA, Trossini GH, Scott LP, Maltarollo VG, Honorio KM. Use of machine learning approaches for novel drug discovery. Expert Opin Drug Discov 2016; 11(3): 225-39.
[http://dx.doi.org/10.1517/17460441.2016.1146250] [PMID: 26814169]
[112]
Harrington RD, Woodward JA, Hooton TM, Horn JR. Life-threatening interactions between HIV-1 protease inhibitors and the illicit drugs MDMA and γ-hydroxybutyrate. Arch Intern Med 1999; 159(18): 2221-4.
[http://dx.doi.org/10.1001/archinte.159.18.2221] [PMID: 10527300]
[113]
Dymond AW, So K, Martin P, et al. Effects of cytochrome P450 (CYP3A4 and CYP2C19) inhibition and induction on the exposure of selumetinib, a MEK1/2 inhibitor, in healthy subjects: results from two clinical trials. Eur J Clin Pharmacol 2017; 73(2): 175-84.
[http://dx.doi.org/10.1007/s00228-016-2153-7] [PMID: 27889832]
[114]
Zhou S, Chan E, Li X, Huang M. Clinical outcomes and management of mechanism-based inhibition of cytochrome P450 3A4. Ther Clin Risk Manag 2005; 1(1): 3-13.
[http://dx.doi.org/10.2147/tcrm.1.1.3.53600] [PMID: 18360537]
[115]
Neuvonen PJ, Niemi M, Backman JT. Drug interactions with lipid-lowering drugs: mechanisms and clinical relevance. Clin Pharmacol Ther 2006; 80(6): 565-81.
[http://dx.doi.org/10.1016/j.clpt.2006.09.003] [PMID: 17178259]
[116]
Suchard J, Orange CA, Suchard JR. Wherefore withdrawal? The science behind recent drug withdrawals and war. Int J Med Toxicol 2001; 4: 15.
[117]
Watkins RE, Wisely GB, Moore LB, et al. The human nuclear xenobiotic receptor PXR: structural determinants of directed promiscuity. Science 2001; 292(5525): 2329-33.
[http://dx.doi.org/10.1126/science.1060762] [PMID: 11408620]
[118]
Ratajewski M, Walczak-Drzewiecka A, Sałkowska A, Dastych J. Aflatoxins upregulate CYP3A4 mRNA expression in a process that involves the PXR transcription factor. Toxicol Lett 2011; 205(2): 146-53.
[http://dx.doi.org/10.1016/j.toxlet.2011.05.1034] [PMID: 21641981]
[119]
Teo YL, Saetaew M, Chanthawong S, et al. Effect of CYP3A4 inducer dexamethasone on hepatotoxicity of lapatinib: clinical and in vitro evidence. Breast Cancer Res Treat 2012; 133(2): 703-11.
[http://dx.doi.org/10.1007/s10549-012-1995-7] [PMID: 22370628]
[120]
Jones BC, Tyman CA, Smith DA. Identification of the cytochrome P450 isoforms involved in the O-demethylation of 4-nitroanisole in human liver microsomes. Xenobiotica 1997; 27(10): 1025-37.
[http://dx.doi.org/10.1080/004982597240000] [PMID: 9364740]
[121]
Jones G, Willett P, Glen RC, Leach AR, Taylor R. Development and validation of a genetic algorithm for flexible docking. J Mol Biol 1997; 267(3): 727-48.
[http://dx.doi.org/10.1006/jmbi.1996.0897] [PMID: 9126849]
[122]
Maréchal JD, Kemp CA, Roberts GC, Paine MJ, Wolf CR, Sutcliffe MJ. Insights into drug metabolism by cytochromes P450 from modelling studies of CYP2D6-drug interactions. Br J Pharmacol 2008; 153(S1): S82-9.
[123]
Kemp CA, Flanagan JU, van Eldik AJ, et al. Validation of model of cytochrome P450 2D6: an in silico tool for predicting metabolism and inhibition. J Med Chem 2004; 47(22): 5340-6.
[http://dx.doi.org/10.1021/jm049934e] [PMID: 15481972]
[124]
Szymański P, Markowicz M, Mikiciuk-Olasik E. Adaptation of high-throughput screening in drug discovery-toxicological screening tests. Int J Mol Sci 2012; 13(1): 427-52.
[http://dx.doi.org/10.3390/ijms13010427] [PMID: 22312262]
[125]
Decker SR, Harman-Ware AE, Happs RM, et al. High throughput screening technologies in biomass characterization. Front Energy Res 2018; 6: 120.
[http://dx.doi.org/10.3389/fenrg.2018.00120]
[126]
Wold S, Ruhe A, Wold H, Dunn WJ. The collinearity problem in linear regression. The partial least squares (PLS) approach to generalized inverses. J Scientific Stat Comput 1984; 5: 735-43.
[http://dx.doi.org/10.1137/0905052]
[127]
Bishop CM. Neural networks for pattern recognition 1995.
[128]
Burges CJ. A tutorial on support vector machines for pattern recognition. Data Min Knowl Discov 1998; 2: 121-67.
[http://dx.doi.org/10.1023/A:1009715923555]
[129]
Sorich MJ, Miners JO, McKinnon RA, Winkler DA, Burden FR, Smith PA. 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(6): 2019-24.
[http://dx.doi.org/10.1021/ci034108k] [PMID: 14632453]
[130]
Radominska-Pandya A, Czernik PJ, Little JM, Battaglia E, Mackenzie PI. Structural and functional studies of UDP-glucuronosyltransferases. Drug Metab Rev 1999; 31(4): 817-99.
[http://dx.doi.org/10.1081/DMR-100101944] [PMID: 10575553]
[131]
Tukey RH, Strassburg CP. Human UDP-glucuronosyltransferases: metabolism, expression, and disease. Annu Rev Pharmacol Toxicol 2000; 40: 581-616.
[http://dx.doi.org/10.1146/annurev.pharmtox.40.1.581] [PMID: 10836148]
[132]
Miners JO, Mackenzie PI. Drug glucuronidation in humans. Pharmacol Ther 1991; 51(3): 347-69.
[http://dx.doi.org/10.1016/0163-7258(91)90065-T] [PMID: 1792239]
[133]
Sorich MJ, Miners JO, McKinnon RA, Smith PA. Multiple pharmacophores for the investigation of human UDP-glucuronosyltransferase isoform substrate selectivity. Mol Pharmacol 2004; 65(2): 301-8.
[http://dx.doi.org/10.1124/mol.65.2.301] [PMID: 14742671]
[134]
Yu J, Paine MJ, Maréchal JD, et al. In silico prediction of drug binding to CYP2D6: identification of a new metabolite of metoclopramide. Drug Metab Dispos 2006; 34(8): 1386-92.
[http://dx.doi.org/10.1124/dmd.106.009852] [PMID: 16698891]
[135]
Kirton SB, Kemp CA, Tomkinson NP, St-Gallay S, Sutcliffe MJ. Impact of incorporating the 2C5 crystal structure into comparative models of cytochrome P450 2D6. Proteins 2002; 49(2): 216-31.
[http://dx.doi.org/10.1002/prot.10192] [PMID: 12211002]
[136]
Paine MJ, McLaughlin LA, Flanagan JU, et al. Residues glutamate 216 and aspartate 301 are key determinants of substrate specificity and product regioselectivity in cytochrome P450 2D6. J Biol Chem 2003; 278(6): 4021-7.
[http://dx.doi.org/10.1074/jbc.M209519200] [PMID: 12446689]
[137]
Jones G, Willett P, Glen RC, Leach AR, Taylor R. Development and validation of a genetic algorithm for flexible docking. J Mol Biol 1997; 267(3): 727-48.
[http://dx.doi.org/10.1006/jmbi.1996.0897] [PMID: 9126849]
[138]
Eldridge MD, Murray CW, Auton TR, Paolini GV, Mee RP. Empirical scoring functions: I. The development of a fast empirical scoring function to estimate the binding affinity of ligands in receptor complexes. J Comput Aided Mol Des 1997; 11(5): 425-45.
[http://dx.doi.org/10.1023/A:1007996124545] [PMID: 9385547]
[139]
Verdonk ML, Cole JC, Hartshorn MJ, Murray CW, Taylor RD. Improved protein-ligand docking using GOLD. Proteins 2003; 52(4): 609-23.
[http://dx.doi.org/10.1002/prot.10465] [PMID: 12910460]
[140]
Hlavica P, Lewis DF. Allosteric phenomena in cytochrome P450-catalyzed monooxygenations. Eur J Biochem 2001; 268(18): 4817-32.
[http://dx.doi.org/10.1046/j.1432-1327.2001.02412.x] [PMID: 11559350]
[141]
Williams PA, Cosme J, Ward A, Angove HC, Matak Vinković D, Jhoti H. Crystal structure of human cytochrome P450 2C9 with bound warfarin. Nature 2003; 424(6947): 464-8.
[http://dx.doi.org/10.1038/nature01862] [PMID: 12861225]
[142]
Wester MR, Yano JK, Schoch GA, et al. The structure of human cytochrome P450 2C9 complexed with flurbiprofen at 2.0-A resolution. J Biol Chem 2004; 279(34): 35630-7.
[http://dx.doi.org/10.1074/jbc.M405427200] [PMID: 15181000]
[143]
Wade RC, Winn PJ, Schlichting I. Sudarko. A survey of active site access channels in cytochromes P450. J Inorg Biochem 2004; 98(7): 1175-82.
[http://dx.doi.org/10.1016/j.jinorgbio.2004.02.007] [PMID: 15219983]
[144]
Yano JK, Wester MR, Schoch GA, Griffin KJ, Stout CD, Johnson EF. The structure of human microsomal cytochrome P450 3A4 determined by X-ray crystallography to 2.05-A resolution. J Biol Chem 2004; 279(37): 38091-4.
[http://dx.doi.org/10.1074/jbc.C400293200] [PMID: 15258162]
[145]
Seifert A, Tatzel S, Schmid RD, Pleiss J. Multiple molecular dynamics simulations of human p450 monooxygenase CYP2C9: the molecular basis of substrate binding and regioselectivity toward warfarin. Proteins 2006; 64(1): 147-55.
[http://dx.doi.org/10.1002/prot.20951] [PMID: 16639745]
[146]
Santos R, Hritz J, Oostenbrink C. Role of water in molecular docking simulations of cytochrome P450 2D6. J Chem Inf Model 2010; 50(1): 146-54.
[http://dx.doi.org/10.1021/ci900293e] [PMID: 19899781]
[147]
Faber MS, Jetter A, Fuhr U. Assessment of CYP1A2 activity in clinical practice: why, how, and when? Basic Clin Pharmacol Toxicol 2005; 97(3): 125-34.
[http://dx.doi.org/10.1111/j.1742-7843.2005.pto_973160.x] [PMID: 16128905]
[148]
Bapiro TE, Sayi J, Hasler JA, et al. Artemisinin and thiabendazole are potent inhibitors of cytochrome P450 1A2 (CYP1A2) activity in humans. Eur J Clin Pharmacol 2005; 61(10): 755-61.
[http://dx.doi.org/10.1007/s00228-005-0037-3] [PMID: 16261361]
[149]
Peterson S, Lampe JW, Bammler TK, Gross-Steinmeyer K, Eaton DL. Apiaceous vegetable constituents inhibit human cytochrome P-450 1A2 (hCYP1A2) activity and hCYP1A2-mediated mutagenicity of aflatoxin B1. Food Chem Toxicol 2006; 44(9): 1474-84.
[http://dx.doi.org/10.1016/j.fct.2006.04.010] [PMID: 16762476]
[150]
Zhu R, Hu L, Li H, Su J, Cao Z, Zhang W. Novel natural inhibitors of CYP1A2 identified by in silico and in vitro screening. Int J Mol Sci 2011; 12(5): 3250-62.
[http://dx.doi.org/10.3390/ijms12053250] [PMID: 21686183]
[151]
Moon T, Chi MH, Kim DH, Yoon CN, Choi YS. Quantitative Structure-Activity Relationships (QSAR) Study of flavonoid derivatives for inhibition of cytochrome P450 1A2. Quant Struct-Act Relat 2000; 19: 257-63.
[http://dx.doi.org/10.1002/1521-3838(200006)19:3<257:AID-QSAR257>3.0.CO;2-2]
[152]
Lee H, Yeom H, Kim YG, et al. Structure-related inhibition of human hepatic caffeine N3-demethylation by naturally occurring flavonoids. Biochem Pharmacol 1998; 55(9): 1369-75.
[http://dx.doi.org/10.1016/S0006-2952(97)00644-8] [PMID: 10076527]
[153]
Kless A, Eitrich T. Cytochrome P450 classification of drugs with support vector machines implementing the nearest point algorithm.Lect Notes Comput Sci. 2004; 3303: pp. 191-205.
[http://dx.doi.org/10.1007/978-3-540-30478-4_17]
[154]
Zuegge J, Fechner U, Roche O, Parrott NJ, Engkvist O, Schneider G. A fast virtual screening filter for cytochrome P450 3A4 inhibition liability of compound libraries. Quant Struct-Act Relat 2002; 21: 249-56.
[http://dx.doi.org/10.1002/1521-3838(200208)21:3<249:AID-QSAR249>3.0.CO;2-S]
[155]
Molnár L, Keserű GM. A neural network based virtual screening of cytochrome P450 3A4 inhibitors. Bioorg Med Chem Lett 2002; 12(3): 419-21.
[http://dx.doi.org/10.1016/S0960-894X(01)00771-5] [PMID: 11814811]
[156]
Yap CW, Chen YZ. Prediction of cytochrome P450 3A4, 2D6, and 2C9 inhibitors and substrates by using support vector machines. J Chem Inf Model 2005; 45(4): 982-92.
[http://dx.doi.org/10.1021/ci0500536] [PMID: 16045292]
[157]
O’Brien SE, de Groot MJ. Greater than the sum of its parts: combining models for useful ADMET prediction. J Med Chem 2005; 48(4): 1287-91.
[http://dx.doi.org/10.1021/jm049254b] [PMID: 15715500]
[158]
Super computer education and research centre Accelrys Suite Accelrys Inc, Cerius2 [Cited 2019 Jan 19] Available from:. http://www.serc.iisc.ac.in/software/accelrys-suite/
[159]
Scitegic Inc Pipeline Pilot; version 3060, San Diego, CA [Cited 2019 Jan 11] Available from:. https://www.3dsbiovia.com/about/news-pr/pipeline-pilot-80.html
[160]
Kriegl JM, Arnhold T, Beck B, Fox T. Prediction of human cytochrome P450 inhibition using support vector machines. QSAR Comb Sci 2005; 24: 491-502.
[http://dx.doi.org/10.1002/qsar.200430925]
[161]
Kriegl JM, Arnhold T, Beck B, Fox T. A support vector machine approach to classify human cytochrome P450 3A4 inhibitors. J Comput Aided Mol Des 2005; 19(3): 189-201.
[http://dx.doi.org/10.1007/s10822-005-3785-3] [PMID: 16059671]
[162]
Molecular operating environment MOE release 2003 [Cited 2019 Jan 29] Available from:. https://www.chemcomp.com/
[163]
VolSurf+. VolSurf version 3011, Molecular Discovery Ltd London, UK [Cited 2019 Mar 15]; Available from:. http://www.moldiscovery.com/software/vsplus/
[164]
Clark T, Alex A, Beck B, et al. University of Erlangen, Erlangen, Germany (This version is provided as part of Materials Studio 221 by Accelrys, Inc)
[165]
Korolev D, Balakin KV, Nikolsky Y, et al. Modeling of human cytochrome p450-mediated drug metabolism using unsupervised machine learning approach. J Med Chem 2003; 46(17): 3631-43.
[http://dx.doi.org/10.1021/jm030102a] [PMID: 12904067]
[166]
Libraries C, Compounds S. Compound Libraries and Screening Compounds Chemical Diversity Labs, Inc 2002.[Cited 2019 Jun 23]; Available from:. http://www.chemdiv.com/services-menu/screening-libraries/
[167]
BIOVIA Data analysis Accelrys, Inc 2000.[Cited 2019 Apr 24]; Available from:. https://www.3dsbiovia.com/products/process-production-operations/biovia-discoverant/data-analysis.html
[168]
Balakin KV, Ekins S, Bugrim A, et al. Quantitative structure-metabolism relationship modeling of metabolic N-dealkylation reaction rates. Drug Metab Dispos 2004; 32(10): 1111-20.
[http://dx.doi.org/10.1124/dmd.104.000364] [PMID: 15269187]
[169]
Sammon JW. A nonlinear mapping for data structure analysis. IEEE Trans Comput 1969; 100: 401-9.
[http://dx.doi.org/10.1109/T-C.1969.222678]
[170]
Susnow RG, Dixon SL. Use of robust classification techniques for the prediction of human cytochrome P450 2D6 inhibition. J Chem Inf Comput Sci 2003; 43(4): 1308-15.
[http://dx.doi.org/10.1021/ci030283p] [PMID: 12870924]
[171]
Hamelin BA, Bouayad A, Drolet B, Gravel A, Turgeon J. In vitro characterization of cytochrome P450 2D6 inhibition by classic histamine H1 receptor antagonists. Drug Metab Dispos 1998; 26(6): 536-9.
[PMID: 9616188]
[172]
Strobl GR, von Kruedener S, Stöckigt J, Guengerich FP, 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(9): 1136-45.
[http://dx.doi.org/10.1021/jm00061a004] [PMID: 8487254]
[173]
Arimoto R, Prasad MA, Gifford EM. Development of CYP3A4 inhibition models: comparisons of machine-learning techniques and molecular descriptors. J Biomol Screen 2005; 10(3): 197-205.
[http://dx.doi.org/10.1177/1087057104274091] [PMID: 15809315]
[174]
Bertz RJ, Granneman GR. Use of in vitro and in vivo data to estimate the likelihood of metabolic pharmacokinetic interactions. Clin Pharmacokinet 1997; 32(3): 210-58.
[http://dx.doi.org/10.2165/00003088-199732030-00004] [PMID: 9084960]
[175]
Manga N, Duffy JC, Rowe PH, Cronin MT. Structure-based methods for the prediction of the dominant P450 enzyme in human drug biotransformation: consideration of CYP3A4, CYP2C9, CYP2D6. SAR QSAR Environ Res 2005; 16(1-2): 43-61.
[http://dx.doi.org/10.1080/10629360412331319871] [PMID: 15844442]
[176]
Genetest’s Human P450 Metabolism Database [Cited 2019 Mar 18] Available from:. http://www.gentest.com/human/p450_database/index.html
[177]
Stuttgart Neural Network Simulator, University of Stuttgart 1995.[Cited 2019 Mar 22]; Available from:. http://www.ra.cs.uni-tuebingen.de/SNNS/UserManual/UserManual.html
[178]
Sorich MJ, McKinnon RA, Miners JO, Winkler DA, Smith PA. Rapid prediction of chemical metabolism by human UDP-glucuronosyltransferase isoforms using quantum chemical descriptors derived with the electronegativity equalization method. J Med Chem 2004; 47(21): 5311-7.
[http://dx.doi.org/10.1021/jm0495529] [PMID: 15456275]
[179]
Bultinck P, Langenaeker W, Lahorte P, et al. The electronegativity equalization method I: Parametrization and validation for atomic charge calculations. J Phys Chem A 2002; 106: 7887-94.
[http://dx.doi.org/10.1021/jp0205463]
[180]
Bursi R, de Gooyer ME, Grootenhuis A, Jacobs PL, van der Louw J, Leysen D. (Q) SAR study on the metabolic stability of steroidal androgens. J Mol Graph Model 2001; 19(6): 552-556, 607-608.
[http://dx.doi.org/10.1016/S1093-3263(01)00089-4] [PMID: 11552683]


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