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Mini-Reviews in Medicinal Chemistry

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ISSN (Print): 1389-5575
ISSN (Online): 1875-5607

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Review Article

Alternative Methods for Pulmonary-Administered Drugs Metabolism: A Breath of Change

Author(s): Érika Yoko Suzuki, Alice Simon, Thaisa Francielle Souza Domingos, Bárbara de Azevedo Abrahim Vieira, Alessandra Mendonça Teles de Souza, Carlos Rangel Rodrigues, Valeria Pereira de Sousa, Flávia Almada do Carmo and Lucio Mendes Cabral*

Volume 23, Issue 2, 2023

Published on: 24 August, 2022

Page: [170 - 186] Pages: 17

DOI: 10.2174/1389557522666220620125623

Price: $65

Abstract

Prediction of pulmonary metabolites following inhalation of a locally acting pulmonary drug is essential to the successful development of novel inhaled medicines. The lungs present metabolic enzymes, therefore they influence drug disposal and toxicity. The present review provides an overview of alternative methods to evaluate the pulmonary metabolism for the safety and efficacy of pulmonary delivery systems. In vitro approaches for investigating pulmonary drug metabolism were described, including subcellular fractions, cell culture models and lung slices as the main available in vitro methods. In addition, in silico studies are promising alternatives that use specific software to predict pulmonary drug metabolism, determine whether a molecule will react with a metabolic enzyme, the site of metabolism (SoM) and the result of this interaction. They can be used in an integrated approach to delineate the major cytochrome P450 (CYP) isoforms to rationalize the use of in vivo methods. A case study about a combination of experimental and computational approaches was done using fluticasone propionate as an example. The results of three tested software, RSWebPredictor, SMARTCyp and XenoSite, demonstrated greater probability of the fluticasone propionate being metabolized by CYPs 3A4 at the S1 atom of 5-S-fluoromethyl carbothioate group. As the in vitro studies were not able to directly detect pulmonary metabolites, those alternatives in silico methods may reduce animal testing efforts, following the principle of 3Rs (Replacement, Reduction and Refinement), and contribute to the evaluation of pharmacological efficacy and safety profiles of new drugs in development.

Keywords: Pulmonary metabolism, in silico, in vitro, site of metabolism, CYP450, experimental and computational approaches.

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[1]
Eddershaw, P.J.; Beresford, A.P.; Bayliss, M.K. ADME/PK as part of a rational approach to drug discovery. Drug Discov. Today, 2000, 5(9), 409-414.
[http://dx.doi.org/10.1016/S1359-6446(00)01540-3] [PMID: 10931658]
[2]
Russell, W.M.S.; Burch, R.L. The Principles of Humane Experimental Technique; Methuen & Co Ltd: London, 1959.
[3]
Tronde, A. Pulmonary drug absorption: In vitro and in vivo investigations of drug absorption across the lung barrier and its relation to drug physicochemical properties; PhD Thesis, Acta Universitatis Upsaliensis: Uppsala, 2002. p. ISBN 91-554-5373-2
[4]
Bäckman, P.; Arora, S.; Couet, W.; Forbes, B.; de Kruijf, W.; Paudel, A. Advances in experimental and mechanistic computational models to understand pulmonary exposure to inhaled drugs. Eur. J. Pharm. Sci., 2018, 113, 41-52.
[http://dx.doi.org/10.1016/j.ejps.2017.10.030] [PMID: 29079338]
[5]
Ibrahim, M.; Garcia-Contreras, L. Mechanisms of absorption and elimination of drugs administered by inhalation. Ther. Deliv., 2013, 4(8), 1027-1045.
[http://dx.doi.org/10.4155/tde.13.67] [PMID: 23919477]
[6]
Lin, J.; Sahakian, D.C.; de Morais, S.M.; Xu, J.J.; Polzer, R.J.; Winter, S.M. The role of absorption, distribution, metabolism, excretion and toxicity in drug discovery. Curr. Top. Med. Chem., 2003, 3(10), 1125-1154.
[http://dx.doi.org/10.2174/1568026033452096] [PMID: 12769713]
[7]
Peach, M.L.; Zakharov, A.V.; Liu, R.; Pugliese, A.; Tawa, G.; Wallqvist, A.; Nicklaus, M.C. Computational tools and resources for meta-bolism-related property predictions. 1. Overview of publicly available (free and commercial) databases and software. Future Med. Chem., 2012, 4(15), 1907-1932.
[http://dx.doi.org/10.4155/fmc.12.150] [PMID: 23088273]
[8]
Costa, A.; Sarmento, B.; Seabra, V. An evaluation of the latest in vitro tools for drug metabolism studies. Expert Opin. Drug Metab. Toxicol., 2014, 10(1), 103-119.
[http://dx.doi.org/10.1517/17425255.2014.857402] [PMID: 24205859]
[9]
Sasaki, E.; Yokoi, T. Role of cytochrome P450-mediated metabolism and involvement of reactive metabolite formations on antiepileptic drug-induced liver injuries. J. Toxicol. Sci., 2018, 43(2), 75-87.
[http://dx.doi.org/10.2131/jts.43.75] [PMID: 29479037]
[10]
Almazroo, O.A.; Miah, M.K.; Venkataramanan, R. Drug metabolism in the liver. Clin. Liver Dis., 2017, 21(1), 1-20.
[http://dx.doi.org/10.1016/j.cld.2016.08.001] [PMID: 27842765]
[11]
Donato, M.T.; Lahoz, A.; Castell, J.V.; Gómez-Lechón, M.J. Cell lines: A tool for in vitro drug metabolism studies. Curr. Drug Metab., 2008, 9(1), 1-11.
[http://dx.doi.org/10.2174/138920008783331086] [PMID: 18220566]
[12]
Braga, R.C.; Andrade, C.H. QSAR and QM/MM approaches applied to drug metabolism prediction. Mini Rev. Med. Chem., 2012, 12(6), 573-582.
[http://dx.doi.org/10.2174/138955712800493807] [PMID: 22587770]
[13]
Zhou, S-F. Drugs behave as substrates, inhibitors and inducers of human cytochrome P450 3A4. Curr. Drug Metab., 2008, 9(4), 310-322.
[http://dx.doi.org/10.2174/138920008784220664] [PMID: 18473749]
[14]
Castell, J.V.; Donato, M.T.; Gómez-Lechón, M.J. Metabolism and bioactivation of toxicants in the lung. The in vitro cellular approach. Exp. Toxicol. Pathol., 2005, 57(Suppl. 1), 189-204.
[http://dx.doi.org/10.1016/j.etp.2005.05.008] [PMID: 16092727]
[15]
Enlo-Scott, Z.; Bäckström, E.; Mudway, I.; Forbes, B. Drug metabolism in the lungs: Opportunities for optimising inhaled medicines. Expert Opin. Drug Metab. Toxicol., 2021, 17(5), 611-625.
[http://dx.doi.org/10.1080/17425255.2021.1908262] [PMID: 33759677]
[16]
Oesch, F.; Fabian, E.; Landsiedel, R. Xenobiotica-metabolizing enzymes in the lung of experimental animals, man and in human lung mo-dels. Arch. Toxicol., 2019, 93(12), 3419-3489.
[http://dx.doi.org/10.1007/s00204-019-02602-7]
[17]
Fröhlich, E. Toxicity of orally inhaled drug formulations at the alveolar barrier: Parameters for initial biological screening. Drug Deliv., 2017, 24(1), 891-905.
[http://dx.doi.org/10.1080/10717544.2017.1333172] [PMID: 28574335]
[18]
Sun, H.; Scott, D.O. Structure-based drug metabolism predictions for drug design. Chem. Biol. Drug Des., 2010, 75(1), 3-17.
[http://dx.doi.org/10.1111/j.1747-0285.2009.00899.x] [PMID: 19878193]
[19]
Newland, N.; Baxter, A.; Hewitt, K.; Minet, E. CYP1A1/1B1 and CYP2A6/2A13 activity is conserved in cultures of differentiated primary human tracheobronchial epithelial cells. Toxicol. In Vitro, 2011, 25(4), 922-929.
[http://dx.doi.org/10.1016/j.tiv.2011.02.014] [PMID: 21376804]
[20]
Ong, H.X.; Traini, D.; Young, P.M. Pharmaceutical applications of the Calu-3 lung epithelia cell line. Expert Opin. Drug Deliv., 2013, 10(9), 1287-1302.
[http://dx.doi.org/10.1517/17425247.2013.805743] [PMID: 23730924]
[21]
Salar-Behzadi, S.; Wu, S.; Mercuri, A.; Meindl, C.; Stranzinger, S.; Fröhlich, E. Effect of the pulmonary deposition and in vitro permeabili-ty on the prediction of plasma levels of inhaled budesonide formulation. Int. J. Pharm., 2017, 532(1), 337-344.
[http://dx.doi.org/10.1016/j.ijpharm.2017.08.124] [PMID: 28890175]
[22]
Forbes, I. Human airway epithelial cell lines for in vitro drug transport and metabolism studies. Pharm. Sci. Technol. Today, 2000, 3(1), 18-27.
[http://dx.doi.org/10.1016/S1461-5347(99)00231-X] [PMID: 10637597]
[23]
Boei, J.J.W.A.; Vermeulen, S.; Klein, B.; Hiemstra, P.S.; Verhoosel, R.M.; Jennen, D.G.J.; Lahoz, A.; Gmuender, H.; Vrieling, H. Xenobio-tic metabolism in differentiated human bronchial epithelial cells. Arch. Toxicol., 2017, 91(5), 2093-2105.
[http://dx.doi.org/10.1007/s00204-016-1868-7] [PMID: 27738743]
[24]
Wienkers, L.C.; Heath, T.G. Predicting in vivo drug interactions from in vitro drug discovery data. Nat. Rev. Drug Discov., 2005, 4(10), 825-833.
[http://dx.doi.org/10.1038/nrd1851] [PMID: 16224454]
[25]
Somers, G.I.; Lindsay, N.; Lowdon, B.M.; Jones, A.E.; Freathy, C.; Ho, S.; Woodrooffe, A.J.; Bayliss, M.K.; Manchee, G.R. A comparison of the expression and metabolizing activities of phase I and II enzymes in freshly isolated human lung parenchymal cells and cryopreser-ved human hepatocytes. Drug Metab. Dispos., 2007, 35(10), 1797-1805.
[http://dx.doi.org/10.1124/dmd.107.015966] [PMID: 17627976]
[26]
Hansen, T.; Blickwede, M.; Borlak, J. Primary rat alveolar epithelial cells for use in biotransformation and toxicity studies. Toxicol. In Vitro, 2006, 20(5), 757-766.
[http://dx.doi.org/10.1016/j.tiv.2005.10.011] [PMID: 16326067]
[27]
Garcia-Canton, C.; Minet, E.; Anadon, A.; Meredith, C. Metabolic characterization of cell systems used in in vitro toxicology testing: Lung cell system BEAS-2B as a working example. Toxicol. In Vitro, 2013, 27(6), 1719-1727.
[http://dx.doi.org/10.1016/j.tiv.2013.05.001] [PMID: 23669205]
[28]
Sharan, S.; Nagar, S. Pulmonary metabolism of resveratrol: In vitro and in vivo evidence. Drug Metab. Dispos., 2013, 41(5), 1163-1169.
[http://dx.doi.org/10.1124/dmd.113.051326] [PMID: 23474649]
[29]
Zhang, D.; Luo, G.; Ding, X.; Lu, C. Preclinical experimental models of drug metabolism and disposition in drug discovery and develop-ment. Acta Pharm. Sin. B, 2012, 2(6), 549-561.
[http://dx.doi.org/10.1016/j.apsb.2012.10.004]
[30]
Foster, K.A.; Oster, C.G.; Mayer, M.M.; Avery, M.L.; Audus, K.L. Characterization of the A549 cell line as a type II pulmonary epithelial cell model for drug metabolism. Exp. Cell Res., 1998, 243(2), 359-366.
[http://dx.doi.org/10.1006/excr.1998.4172] [PMID: 9743595]
[31]
Jia, L.; Liu, X. The conduct of drug metabolism studies considered good practice (II): In vitro experiments. Curr. Drug Metab., 2007, 8(8), 822-829.
[http://dx.doi.org/10.2174/138920007782798207] [PMID: 18220563]
[32]
Aoki, M.; Okudaira, K.; Haga, M.; Nishigaki, R.; Hayashi, M. Contribution of rat pulmonary metabolism to the elimination of lidocaine, midazolam, and nifedipine. Drug Metab. Dispos., 2010, 38(7), 1183-1188.
[http://dx.doi.org/10.1124/dmd.110.032227] [PMID: 20371639]
[33]
Neal, R.A. A comparison of the in vitro metabolism of parathion in the lung and liver of the rabbit. Toxicol. Appl. Pharmacol., 1972, 23(1), 123-130.
[http://dx.doi.org/10.1016/0041-008X(72)90211-6] [PMID: 5071035]
[34]
Powley, M.W.; Carlson, G.P. Cytochromes P450 involved with benzene metabolism in hepatic and pulmonary microsomes. J. Biochem. Mol. Toxicol., 2000, 14(6), 303-309.
[http://dx.doi.org/10.1002/1099-0461(2000)14:6<303::AIDJBT2>3.0.CO;2-8] [PMID: 11083083]
[35]
Elsherbiny, M.E.; El-Kadi, A.O.S.; Brocks, D.R. The effect of β-naphthoflavone on the metabolism of amiodarone by hepatic and extra-hepatic microsomes. Toxicol. Lett., 2010, 195(2-3), 147-154.
[http://dx.doi.org/10.1016/j.toxlet.2010.03.019] [PMID: 20362646]
[36]
Devereux, T.R.; Domin, B.A.; Philpot, R.M. Xenobiotic metabolism by isolated pulmonary cells. Pharmacol. Ther., 1989, 41(1-2), 243-256.
[http://dx.doi.org/10.1016/0163-7258(89)90109-5] [PMID: 2652151]
[37]
Hansen, T.; Chougule, A.; Borlak, J. Isolation and cultivation of metabolically competent alveolar epithelial cells from A/J mice. Toxicol. In Vitro, 2014, 28(5), 812-821.
[http://dx.doi.org/10.1016/j.tiv.2014.03.009] [PMID: 24681204]
[38]
Nayak, D.K.; Mendez, O.; Bowen, S.; Mohanakumar, T. Isolation and in vitro culture of murine and human alveolar macrophages. J. Vis. Exp., 2018, 2018(134), 1-8.
[http://dx.doi.org/10.3791/57287] [PMID: 29733312]
[39]
Oreffo, V.I.C.; Morgan, A.; Richards, R.J. Isolation of Clara cells from the mouse lung. Environ. Health Perspect., 1990, 85, 51-64.
[http://dx.doi.org/10.2307/3430665] [PMID: 2200669]
[40]
Tǎbǎran, A.F.; O’Sullivan, M.G.; Seabloom, D.E.; Vevang, K.R.; Smith, W.E.; Wiedmann, T.S.; Peterson, L.A. Inhaled furan selectively damages club cells in lungs of A/J mice. Toxicol. Pathol., 2019, 47(7), 842-850.
[http://dx.doi.org/10.1177/0192623319869306] [PMID: 31426723]
[41]
Forbes, B.; Ehrhardt, C. Human respiratory epithelial cell culture for drug delivery applications. Eur. J. Pharm. Biopharm., 2005, 60(2), 193-205.
[http://dx.doi.org/10.1016/j.ejpb.2005.02.010] [PMID: 15939233]
[42]
Sheets, P.L.; Yost, G.S.; Carlson, G.P. Benzene metabolism in human lung cell lines BEAS-2B and A549 and cells overexpressing CYP2F1. J. Biochem. Mol. Toxicol., 2004, 18(2), 92-99.
[http://dx.doi.org/10.1002/jbt.20010] [PMID: 15122651]
[43]
Kuzuya, Y.; Adachi, T.; Hara, H.; Anan, A.; Izuhara, K.; Nagai, H. Induction of drug-metabolizing enzymes and transporters in human bronchial epithelial cells by beclomethasone dipropionate. IUBMB Life, 2004, 56(6), 355-359.
[http://dx.doi.org/10.1080/10258140412331286946] [PMID: 15370884]
[44]
Foster, K.A.; Avery, M.L.; Yazdanian, M.; Audus, K.L. Characterization of the Calu-3 cell line as a tool to screen pulmonary drug deli-very. Int. J. Pharm., 2000, 208(1-2), 1-11.
[http://dx.doi.org/10.1016/S0378-5173(00)00452-X] [PMID: 11064206]
[45]
De Kanter, R.; Monshouwer, M.; Draaisma, A.L.; De Jager, M.H.; de Graaf, I.A.M.; Proost, J.H.; Meijer, D.K.; Groothuis, G.M. Prediction of whole-body metabolic clearance of drugs through the combined use of slices from rat liver, lung, kidney, small intestine and colon. Xenobiotica, 2004, 34(3), 229-241.
[http://dx.doi.org/10.1080/004982502000196758] [PMID: 15204696]
[46]
Lake, B.G.; Meredith, C.; Scott, M.P.; Renwick, A.B.; Price, R.J. Use of cultured precision-cut rat lung slices to study the in vitro induction of pulmonary cytochrome P450 forms. Xenobiotica, 2003, 33(7), 691-702.
[http://dx.doi.org/10.1080/0049825031000108306] [PMID: 12893519]
[47]
Parrish, A.R.; Gandolfi, A.J.; Brendel, K. Precision-cut tissue slices: Applications in pharmacology and toxicology. Life Sci., 1995, 57(21), 1887-1901.
[http://dx.doi.org/10.1016/0024-3205(95)02176-J] [PMID: 7475939]
[48]
Liberati, T.A.; Randle, M.R.; Toth, L.A. In vitro lung slices: A powerful approach for assessment of lung pathophysiology. Expert Rev. Mol. Diagn., 2010, 10(4), 501-508.
[http://dx.doi.org/10.1586/erm.10.21] [PMID: 20465504]
[49]
Morin, J.P.; Baste, J.M.; Gay, A.; Crochemore, C.; Corbière, C.; Monteil, C. Precision cut lung slices as an efficient tool for in vitro lung physio-pharmacotoxicology studies. Xenobiotica, 2013, 43(1), 63-72.
[http://dx.doi.org/10.3109/00498254.2012.727043] [PMID: 23030793]
[50]
de Graaf, I.A.M.; Koster, H.J. Cryopreservation of precision-cut tissue slices for application in drug metabolism research. Toxicol. In Vitro, 2003, 17(1), 1-17.
[http://dx.doi.org/10.1016/S0887-2333(02)00117-0] [PMID: 12537957]
[51]
Umachandran, M.; Ioannides, C. Stability of cytochromes P450 and phase II conjugation systems in precision-cut rat lung slices cultured up to 72 h. Toxicology, 2006, 224(1-2), 14-21.
[http://dx.doi.org/10.1016/j.tox.2006.03.020] [PMID: 16701934]
[52]
Yilmaz, Y.; Umehara, K.; Williams, G.; Faller, T.; Schiller, H.; Walles, M.; Kraehenbuehl, S.; Camenisch, G.; Manevski, N. Assessment of the pulmonary CYP1A1 metabolism of mavoglurant (AFQ056) in rat. Xenobiotica, 2018, 48(8), 793-803.
[http://dx.doi.org/10.1080/00498254.2017.1373311] [PMID: 28879796]
[53]
Sturton, R.G.; Trifilieff, A.; Nicholson, A.G.; Barnes, P.J. Pharmacological characterization of indacaterol, a novel once daily inhaled 2 adrenoceptor agonist, on small airways in human and rat precision-cut lung slices. J. Pharmacol. Exp. Ther., 2008, 324(1), 270-275.
[http://dx.doi.org/10.1124/jpet.107.129296] [PMID: 17916760]
[54]
Nave, R.; Fisher, R.; McCracken, N. in vitro metabolism of beclomethasone dipropionate, budesonide, ciclesonide, and fluticasone pro-pionate in human lung precision-cut tissue slices. Respir. Res., 2007, 8(1), 65.
[http://dx.doi.org/10.1186/1465-9921-8-65] [PMID: 17883839]
[55]
Vickers, A.E.M.; Jimenez, R.M.; Spaans, M.C.; Pflimlin, V.; Fisher, R.L.; Brendel, K. Human and rat lung biotransformation of cyclospo-rin A and its derivatives using slices and bronchial epithelial cells. Drug Metab. Dispos., 1997, 25(7), 873-880.
[PMID: 9224782]
[56]
Price, R.J.; Renwick, A.B.; Walters, D.G.; Young, P.J.; Lake, B.G. Metabolism of nicotine and induction of CYP1A forms in precision-cut rat liver and lung slices. Toxicol. In Vitro, 2004, 18(2), 179-185.
[http://dx.doi.org/10.1016/j.tiv.2003.08.012] [PMID: 14757108]
[57]
Bäckström, E.; Lundqvist, A.; Boger, E.; Svanberg, P.; Ewing, P.; Hammarlund-Udenaes, M.; Fridén, M. Development of a novel lung slice methodology for profiling of inhaled compounds. J. Pharm. Sci., 2016, 105(2), 838-845.
[http://dx.doi.org/10.1002/jps.24575] [PMID: 26178700]
[58]
Musuamba, F.T.; Skottheim Rusten, I.; Lesage, R.; Russo, G.; Bursi, R.; Emili, L.; Wangorsch, G.; Manolis, E.; Karlsson, K.E.; Kulesza, A.; Courcelles, E.; Boissel, J.P.; Rousseau, C.F.; Voisin, E.M.; Alessandrello, R.; Curado, N.; Dall’ara, E.; Rodriguez, B.; Pappalardo, F.; Geris, L. Scientific and regulatory evaluation of mechanistic in silico drug and disease models in drug development: Building model credi-bility. CPT Pharmacometrics Syst. Pharmacol., 2021, 10(8), 804-825.
[http://dx.doi.org/10.1002/psp4.12669] [PMID: 34102034]
[59]
Abrahim-Vieira, B.A.; De Souza, A.M.T.; Barros, R.C.; Do Carmo, F.A.; De Abreu, L.C.L.; Moreira, R.S.S. HonOrio, T.S.; Rodriguez, C.R.; De Sousa, V.P.; Cabral, M.M. In silico studies of novel sildenafil self-emulsifying drug delivery system absorption improvement for pulmonary arterial hypertension. An Acad. Bras. Cienc., 2020, 92(2), e20191445.
[60]
de Mello, M.V.P.; Abrahim-Vieira, B.A.; Domingos, T.F.S.; de Jesus, J.B.; de Sousa, A.C.C.; Rodrigues, C.R.; Souza, A.M.T. A com-prehensive review of chalcone derivatives as antileishmanial agents. Eur. J. Med. Chem., 2018, 150, 920-929.
[http://dx.doi.org/10.1016/j.ejmech.2018.03.047] [PMID: 29602038]
[61]
European Medicines Agency. EMA Regulatory Science to 2025; The Netherlands , 2020.
[62]
Food and Drug Administration. 2021: Advancing Regulatory Science at FDA: Focus Areas of Regulatory Science (FARS). 2021. Available from: https://www.fda.gov/media/145001/download (Accessed on: October 1, 2021).
[63]
Hosea, N.A.; Jones, H.M. Predicting pharmacokinetic profiles using in silico derived parameters. Mol. Pharm., 2013, 10(4), 1207-1215.
[http://dx.doi.org/10.1021/mp300482w] [PMID: 23427934]
[64]
de Bruyn Kops, C.; Šícho, M.; Mazzolari, A.; Kirchmair, J. GLORYx: Prediction of the metabolites resulting from phase 1 and phase 2 biotransformations of xenobiotics. Chem. Res. Toxicol., 2021, 34(2), 286-299.
[http://dx.doi.org/10.1021/acs.chemrestox.0c00224] [PMID: 32786543]
[65]
Kingsley, L.J.; Wilson, G.L.; Essex, M.E.; Lill, M.A. Combining structure- and ligand-based approaches to improve site of metabolism prediction in CYP2C9 substrates. Pharm. Res., 2015, 32(3), 986-1001.
[http://dx.doi.org/10.1007/s11095-014-1511-3] [PMID: 25208877]
[66]
He, S.B.; Li, M.M.; Zhang, B.X.; Ye, X.T.; Du, R.F.; Wang, Y.; Qiao, Y.J. Construction of metabolism prediction models for CYP450 3A4, 2D6, and 2C9 based on microsomal metabolic reaction system. Int. J. Mol. Sci., 2016, 17(10), E1686.
[http://dx.doi.org/10.3390/ijms17101686] [PMID: 27735849]
[67]
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), E914.
[http://dx.doi.org/10.3390/ijms17060914] [PMID: 27294921]
[68]
Kirchmair, J.; Williamson, M.J.; Tyzack, J.D.; Tan, L.; Bond, P.J.; Bender, A.; Glen, R.C. Computational prediction of metabolism: Sites, products, SAR, P450 enzyme dynamics, and mechanisms. J. Chem. Inf. Model., 2012, 52(3), 617-648.
[http://dx.doi.org/10.1021/ci200542m] [PMID: 22339582]
[69]
Kazmi, S.R.; Jun, R.; Yu, M.S.; 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]
[70]
Wexler, P., Ed.; Encyclopedia of Toxicology, Third Edit.; Academic Press: Bethesda, MD, USA, 2014.
[71]
OECD Guidance Document on the Validation of (Quantitative) Structure-Activity Relationship; [(Q)SAR] Models, 2014. Available from:. (Accessed on: October 1, 2021).
[http://dx.doi.org/10.1787/9789264085442-en]
[72]
Chapman, R.W.; House, A.; Jones, H.; Richard, J.; Celly, C.; Prelusky, D.; Ting, P.; Hunter, J.C.; Lamca, J.; Phillips, J.E. Effect of inhaled roflumilast on the prevention and resolution of allergen-induced late phase airflow obstruction in Brown Norway rats. Eur. J. Pharmacol., 2007, 571(2-3), 215-221.
[http://dx.doi.org/10.1016/j.ejphar.2007.05.074] [PMID: 17610865]
[73]
Crivori, P.; Poggesi, I. Computational approaches for predicting CYP-related metabolism properties in the screening of new drugs. Eur. J. Med. Chem., 2006, 41(7), 795-808.
[http://dx.doi.org/10.1016/j.ejmech.2006.03.003] [PMID: 16644065]
[74]
Tyzack, J.D.; Hunt, P.A.; Segall, M.D. Predicting regioselectivity and lability of cytochrome P450 metabolism using quantum mechanical simulations. J. Chem. Inf. Model., 2016, 56(11), 2180-2193.
[http://dx.doi.org/10.1021/acs.jcim.6b00233] [PMID: 27753488]
[75]
Seger, S.T.; Rydberg, P.; Olsen, L. Mechanism of the N-hydroxylation of primary and secondary amines by cytochrome P450. Chem. Res. Toxicol., 2015, 28(4), 597-603.
[http://dx.doi.org/10.1021/tx500371a] [PMID: 25651340]
[76]
Segall, M.D.; Payne, M.C.; Ellis, S.W.; Tucker, G.T.; Boyes, R.N. Ab initio molecular modeling in the study of drug metabolism. Eur. J. Drug Metab. Pharmacokinet., 1997, 22, 283-289.
[77]
Hennemann, M.; Friedl, A.; Lobell, M.; Keldenich, J.; Hillisch, A.; Clark, T.; Göller, A.H. CypScore: Quantitative prediction of reactivity toward cytochromes P450 based on semiempirical molecular orbital theory. ChemMedChem, 2009, 4(4), 657-669.
[http://dx.doi.org/10.1002/cmdc.200800384] [PMID: 19243088]
[78]
Cuperlovic-Culf, M. Machine learning methods for analysis of metabolic data and metabolic pathway modeling. Metabolites, 2018, 8(1), E4.
[http://dx.doi.org/10.3390/metabo8010004] [PMID: 29324649]
[79]
Yang, X.; Wang, Y.; Byrne, R.; Schneider, G.; Yang, S. Concepts of artificial intelligence for computer-assisted drug discovery. Chem. Rev., 2019, 119(18), 10520-10594.
[http://dx.doi.org/10.1021/acs.chemrev.8b00728] [PMID: 31294972]
[80]
Ekins, S.; Waller, C.L.; Swaan, P.W.; Cruciani, G.; Wrighton, S.A.; Wikel, J.H. Progress in predicting human ADME parameters in silico. J. Pharmacol. Toxicol. Methods, 2000, 44(1), 251-272.
[http://dx.doi.org/10.1016/S1056-8719(00)00109-X] [PMID: 11274894]
[81]
Ekins, S.; Rose, J. In silico ADME/Tox: The state of the art. J. Mol. Graph. Model., 2002, 20(4), 305-309.
[http://dx.doi.org/10.1016/S1093-3263(01)00127-9] [PMID: 11858639]
[82]
Rydberg, P.; Gloriam, D.E.; Zaretzki, J.; Breneman, C.; Olsen, L. SMARTCyp: A 2D method for prediction of cytochrome P450-mediated drug metabolism. ACS Med. Chem. Lett., 2010, 1(3), 96-100.
[http://dx.doi.org/10.1021/ml100016x] [PMID: 24936230]
[83]
Kirchmair, J.; Williamson, M.J.; Afzal, A.M.; Tyzack, J.D.; Choy, A.P.K.; Howlett, A.; Rydberg, P.; Glen, R.C. FAst MEtabolizer (FAME): A rapid and accurate predictor of sites of metabolism in multiple species by endogenous enzymes. J. Chem. Inf. Model., 2013, 53(11), 2896-2907.
[http://dx.doi.org/10.1021/ci400503s] [PMID: 24219364]
[84]
Kirchmair, J.; Göller, A.H.; Lang, D.; Kunze, J.; Testa, B.; Wilson, I.D.; Glen, R.C.; Schneider, G. Predicting drug metabolism: Experiment and/or computation? Nat. Rev. Drug Discov., 2015, 14(6), 387-404.
[http://dx.doi.org/10.1038/nrd4581] [PMID: 25907346]
[85]
Dapkunas, J.; Sazonovas, A.; Japertas, P. Probabilistic prediction of the human CYP3A4 and CYP2D6 metabolism sites. Chem. Biodivers., 2009, 6(11), 2101-2106.
[http://dx.doi.org/10.1002/cbdv.200900078] [PMID: 19937844]
[86]
Zakharov, A.V.; Peach, M.L.; Sitzmann, M.; Filippov, I.V.; McCartney, H.J.; Smith, L.H.; Pugliese, A.; Nicklaus, M.C. Computational tools and resources for metabolism-related property predictions. 2. Application to prediction of half-life time in human liver microsomes. Future Med. Chem., 2012, 4(15), 1933-1944.
[http://dx.doi.org/10.4155/fmc.12.152] [PMID: 23088274]
[87]
Fujitsu Limited. ADMEWORKS Predictor. Tokyo, Japan, 2021. 2021 Available from: https://www.fujitsu.com/global/solutions/business-technology/tc/sol/admeworks/predictor/index.html (Accessed on: June 1, 2021).
[88]
Tian, S.; Djoumbou-Feunang, Y.; Greiner, R.; Wishart, D.S. CypReact: A software tool for in silico reactant prediction for human cytoch-rome P450 enzymes. J. Chem. Inf. Model., 2018, 58(6), 1282-1291.
[http://dx.doi.org/10.1021/acs.jcim.8b00035] [PMID: 29738669]
[89]
Terfloth, L.; Bienfait, B.; Gasteiger, J. Ligand-based models for the isoform specificity of cytochrome P450 3A4, 2D6, and 2C9 substra-tes. J. Chem. Inf. Model., 2007, 47(4), 1688-1701.
[http://dx.doi.org/10.1021/ci700010t] [PMID: 17608404]
[90]
Chemaxon Limited. Metabolizer Software Module, version 5.7.1. Budapest, Hungary 2011. Available from: https://docs.chemaxon.com/display/docs/metabolizer.md (Accessed on: March 10, 2019).
[91]
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), 1320-1325.
[http://dx.doi.org/10.1021/ci00022a014] [PMID: 7989397]
[92]
Darvas, F. Predicting metabolic pathways by logic programming. J. Mol. Graph., 1988, 6(2), 80-86.
[http://dx.doi.org/10.1016/0263-7855(88)85004-5]
[93]
Çubuk, C.; Hidalgo, M.R.; Amadoz, A.; Rian, K.; Salavert, F.; Pujana, M.A.; Mateo, F.; Herranz, C.; Carbonell-Caballero, J.; Dopazo, J. Differential metabolic activity and discovery of therapeutic targets using summarized metabolic pathway models. NPJ Syst. Biol. Appl., 2019, 5(1), 7.
[http://dx.doi.org/10.1038/s41540-019-0087-2] [PMID: 30854222]
[94]
Progreso y Salud, F. Metabolizer: Metabolic module analysis suite; , 2017. Available from: http://metabolizer.babelomics.org/
[95]
Ekins, S.; Andreyev, S.; Ryabov, A.; Kirillov, E.; Rakhmatulin, E.A.; Bugrim, A.; Nikolskaya, T. Computational prediction of human drug metabolism. Expert Opin. Drug Metab. Toxicol., 2005, 1(2), 303-324.
[http://dx.doi.org/10.1517/17425255.1.2.303] [PMID: 16922645]
[96]
Adams, S.E. Molecular Similarity and Xenobiotic Metabolism;; PhD Thesis, Trinity College, University of Cambridge, 2010.
[http://dx.doi.org/10.17863/CAM.16274]
[97]
Pelkonen, O.; Tolonen, A.; Korjamo, T.; Turpeinen, M.; Raunio, H. From known knowns to known unknowns: Predicting in vivo drug metabolites. Bioanalysis, 2009, 1(2), 393-414.
[http://dx.doi.org/10.4155/bio.09.32] [PMID: 21083174]
[98]
Testa, B.; Balmat, A.L.; Long, A.; Judson, P. Predicting drug metabolism-an evaluation of the expert system METEOR. Chem. Biodivers., 2005, 2(7), 872-885.
[http://dx.doi.org/10.1002/cbdv.200590064] [PMID: 17193178]
[99]
CompuDrug International Inc. MEXAlert. 2001. Available from: https://www.compudrug.com/mexalert (Accessed on: June 1, 2021).
[100]
Filimonov, D.; Poroikov, V. Probabilistic approaches in activity prediction.In: Varnek, A.; Tropsha, A.; Eds.; Chemoinformatics Approa-ches to Virtual Screen; RSC: Cambridge, UK, 2008, pp. 182-216.
[http://dx.doi.org/10.1039/9781847558879-00182]
[101]
Laoui, A.; Polyakov, V.R. Web services as applications’ integration tool: QikProp case study. J. Comput. Chem., 2011, 32(9), 1944-1951.
[http://dx.doi.org/10.1002/jcc.21778] [PMID: 21455963]
[102]
Ridder, L.; Wagener, M. SyGMa: Combining expert knowledge and empirical scoring in the prediction of metabolites. ChemMedChem, 2008, 3(5), 821-832.
[http://dx.doi.org/10.1002/cmdc.200700312] [PMID: 18311745]
[103]
Zaretzki, J.; Bergeron, C.; Rydberg, P.; Huang, T.W.; Bennett, K.P.; Breneman, C.M. RS-predictor: A new tool for predicting sites of cyto-chrome P450-mediated metabolism applied to CYP 3A4. J. Chem. Inf. Model., 2011, 51(7), 1667-1689.
[http://dx.doi.org/10.1021/ci2000488] [PMID: 21528931]
[104]
Li, J.; Schneebeli, S.T.; Bylund, J.; Farid, R.; Friesner, R.A. IDSite: An accurate approach to predict P450-mediated drug metabolism. J. Chem. Theory Comput., 2011, 7(11), 3829-3845.
[http://dx.doi.org/10.1021/ct200462q] [PMID: 22247702]
[105]
Zaretzki, J.; Matlock, M.; Swamidass, S.J. XenoSite: Accurately predicting CYP-mediated sites of metabolism with neural networks. J. Chem. Inf. Model., 2013, 53(12), 3373-3383.
[http://dx.doi.org/10.1021/ci400518g] [PMID: 24224933]
[106]
Kulkarni, S.A.; Zhu, J.; Blechinger, S. In silico techniques for the study and prediction of xenobiotic metabolism: A review. Xenobiotica, 2005, 35(10-11), 955-973.
[http://dx.doi.org/10.1080/00498250500354402] [PMID: 16393855]
[107]
Tarcsay, A.; Kiss, R.; Keserű, G.M. Site of metabolism prediction on cytochrome P450 2C9: A knowledge-based docking approach. J. Comput. Aided Mol. Des., 2010, 24(5), 399-408.
[http://dx.doi.org/10.1007/s10822-010-9347-3] [PMID: 20361237]
[108]
Wohlfarth, A.; Scheidweiler, K.B.; Pang, S.; Zhu, M.; Castaneto, M.; Kronstrand, R.; Huestis, M.A. Metabolic characterization of AH-7921, a synthetic opioid designer drug: In vitro metabolic stability assessment and metabolite identification, evaluation of in silico predic-tion, and in vivo confirmation. Drug Test. Anal., 2016, 8(8), 779-791.
[http://dx.doi.org/10.1002/dta.1856] [PMID: 26331297]
[109]
T’jollyn, H.; Boussery, K.; Mortishire-Smith, R.J.; Coe, K.; De Boeck, B.; Van Bocxlaer, J.F.; Mannens, G. Evaluation of three state-of-the-art metabolite prediction software packages (Meteor, MetaSite, and StarDrop) through independent and synergistic use. Drug Metab. Dispos., 2011, 39(11), 2066-2075.
[http://dx.doi.org/10.1124/dmd.111.039982] [PMID: 21832003]
[110]
Shoombuatong, W.; Prathipati, P.; Prachayasittikul, V.; Schaduangrat, N.; Malik, A.A.; Pratiwi, R.; Wanwimolruk, S.; Wikberg, J.E.S.; Gleeson, M.P.; Spjuth, O.; Nantasenamat, C. Towards predicting the cytochrome P450 modulation: From QSAR to proteochemometric modeling. Curr. Drug Metab., 2017, 18(6), 540-555.
[http://dx.doi.org/10.2174/1389200218666170320121932] [PMID: 28322159]
[111]
Ekins, S.; Mestres, J.; Testa, B. In silico pharmacology for drug discovery: Methods for virtual ligand screening and profiling. Br. J. Pharmacol., 2007, 152(1), 9-20.
[http://dx.doi.org/10.1038/sj.bjp.0707305] [PMID: 17549047]
[112]
Marchant, C.A.; Briggs, K.A.; 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), 177-187.
[http://dx.doi.org/10.1080/15376510701857320] [PMID: 20020913]
[113]
Alqahtani, S. In silico ADME-Tox modeling: Progress and prospects. Expert Opin. Drug Metab. Toxicol., 2017, 13(11), 1147-1158.
[http://dx.doi.org/10.1080/17425255.2017.1389897] [PMID: 28988506]
[114]
Šícho, M.; de Bruyn Kops, C.; Stork, C.; Svozil, D.; Kirchmair, J. FAME 2: Simple and effective machine learning model of cytochrome P450 regioselectivity. J. Chem. Inf. Model., 2017, 57(8), 1832-1846.
[http://dx.doi.org/10.1021/acs.jcim.7b00250] [PMID: 28782945]
[115]
Rydberg, P.; Gloriam, D.E.; Olsen, L. The SMARTCyp cytochrome P450 metabolism prediction server. Bioinformatics, 2010, 26(23), 2988-2989.
[http://dx.doi.org/10.1093/bioinformatics/btq584] [PMID: 20947523]
[116]
Schrödinger Release Inc. QikProp, New York, USA 2021. Available from: https://www.schrodinger.com/products/qikprop (Accessed on: June 18, 2021).
[117]
Attia, K.A-S.M.; Nassar, M.W.I.; El-Olemy, A.; Elsayed, A.O. Smart signal processing versus zero order spectrophotometric techniques for the analysis of salmeterol xinafoate and fluticasone propionate in their pharmaceutical dosage form: A comparative study. Anal. Chem. Lett., 2018, 8(1), 104-121.
[http://dx.doi.org/10.1080/22297928.2017.1398679]
[118]
GlaxoSmithKline. FLONASE® Full Prescribing Information. 2019. Available from: https://www.accessdata.fda.gov/drugsatfda_docs/label/2019/020121s045lbl.pdf (Accessed on: November 11, 2020).
[119]
Murai, T.; Reilly, C.A.; Ward, R.M.; Yost, G.S. The inhaled glucocorticoid fluticasone propionate efficiently inactivates cytochrome P450 3A5, a predominant lung P450 enzyme. 2010, 23(8), 1356-1364.
[120]
Pearce, R.E.; Leeder, J.S.; Kearns, G.L. Biotransformation of fluticasone: In vitro characterization. Drug Metab. Dispos., 2006, 34(6), 1035-1040.
[http://dx.doi.org/10.1124/dmd.105.009043] [PMID: 16565171]
[121]
Fu, X.; He, S.; Du, L.; Lv, Z.; Zhang, Y.; Zhang, Q.; Wang, Y. Using chemical bond-based method to predict site of metabolism for five biotransformations mediated by CYP 3A4, 2D6, and 2C9. Biochem. Pharmacol., 2018, 152, 302-314.
[http://dx.doi.org/10.1016/j.bcp.2018.03.024] [PMID: 29588194]
[122]
Wakayama, N.; Toshimoto, K.; Maeda, K.; Hotta, S.; Ishida, T.; Akiyama, Y.; Sugiyama, Y. In silico prediction of major clearance pathways of drugs among 9 routes with two-step support vector machines. Pharm. Res., 2018, 35(10), 197.
[http://dx.doi.org/10.1007/s11095-018-2479-1] [PMID: 30143865]

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