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Drug Metabolism Reviews Volume 53, 2021 - Issue 2: Next Generation Drug Discovery and Development: Rethinking Translational Pharmacology for Accelerated Drug Development
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Review Articles

Recent advances in computational metabolite structure predictions and altered metabolic pathways assessment to inform drug development processes

Mary Alexandra Schleiffa Department of Biochemistry and Molecular Biology, University of Arkansas for Medical Sciences, Little Rock, AR, USACorrespondence[email protected]
ORCID Iconhttps://orcid.org/0000-0003-3594-6835View further author information
ORCID Icon,
Deepika Dhawareb Biotransformation and ADME, Research and Development, Orion Corporation, Espoo, FinlandView further author information
&
Jasleen K. Sodhic Department of Bioengineering and Therapeutic Sciences, Schools of Pharmacy and Medicine, University of California San Francisco, San Francisco, CA, USAView further author information
Pages 173-187 | Received 08 Feb 2021, Accepted 25 Mar 2021, Published online: 11 May 2021

References

  • de Bruyn Kops C, Stork C, Šícho M, Kochev N, Svozil D, Jeliazkova N, Kirchmair J. 2019. GLORY: generator of the structures of likely cytochrome P450 metabolites based on predicted sites of metabolism. Front Chem. 7:402.
  • Djoumbou-Feunang Y, Fiamoncini J, Gil-de-la-Fuente A, Greiner R, Manach C, Wishart DS. 2019. BioTransformer: a comprehensive computational tool for small molecule metabolism prediction and metabolite identification. J Cheminform. 11:2.
  • Lombardo F, Desai PV, Arimoto R, Desino KE, Fischer H, Keefer CE, Petersson C, Winiwarter S, Broccatelli F. 2017. In silico absorption, distribution, metabolism, excretion, and pharmacokinetics (ADME-PK): utility and best practices. An industry perspective from the international consortium for innovation through quality in pharmaceutical development. J Med Chem. 60:9097–9113.
  • Park BK, Boobis A, Clarke S, Goldring CEP, Jones D, Kenna JG, Lambert C, Laverty HG, Naisbitt DJ, Nelson S, et al. 2011. Managing the challenge of chemically reactive metabolites in drug development. Nat Rev Drug Discov. 10:292–306.
  • Park K, Williams DP, Naisbitt DJ, Kitteringham NR, Pirmohamed M. 2005. Investigation of toxic metabolites during drug development. Toxicol Appl Pharmacol. 207:425–434.
  • Ridder L, Wagener M. 2008. SyGMa: combining expert knowledge and empirical scoring in the prediction of metabolites. ChemMedChem. 3:821–832.
  • Schleiff MA, Russell LE, Gonzalez E, Bart AG, Broccatelli F, Hartman JH, Humphreys WG, Lauschke VM, Martin I, Nwabufo C, et al. 2020. Advances in the study of drug metabolism – symposium report of the 12th Meeting of the International Society for the Study of Xenobiotics (ISSX). Drug Metab Rev. 52:395–313.
  • Šícho M, de Bruyn Kops C, Stork C, Svozil D, Kirchmair J. 2017. FAME 2: simple and effective machine learning model of cytochrome P450 regioselectivity. J Chem Inf Model. 57:1832–1846.
  • Wu F, Zhou Y, Li L, Shen X, Chen G, Wang X, Liang X, Tan M, Huang Z. 2020. Computational approaches in preclinical studies on drug discovery and development. Front Chem. 8:726.

References

  • Djoumbou-Feunang Y, Fiamoncini J, Gil-de-la-Fuente A, Greiner R, Manach C, Wishart DS. 2019. BioTransformer: a comprehensive computational tool for small molecule metabolism prediction and metabolite identification. J Cheminform. 11:2.
  • Lombardo F, Desai PV, Arimoto R, Desino KE, Fischer H, Keefer CE, Petersson C, Winiwarter S, Broccatelli F. 2017. In silico absorption, distribution, metabolism, excretion, and pharmacokinetics (ADME-PK): utility and best practices. An industry perspective from the international consortium for innovation through quality in pharmaceutical development. J Med Chem. 60:9097–9113.
  • Marchant CA, Briggs KA, Long A. 2008. In silico tools for sharing data and knowledge on toxicity and metabolism: derek for windows, meteor, and vitic. Toxicol Mech Methods. 18:177–187.
  • Park BK, Boobis A, Clarke S, Goldring CEP, Jones D, Kenna JG, Lambert C, Laverty HG, Naisbitt DJ, Nelson S, et al. 2011. Managing the challenge of chemically reactive metabolites in drug development. Nat Rev Drug Discov. 10:292–306.
  • Park K, Williams DP, Naisbitt DJ, Kitteringham NR, Pirmohamed M. 2005. Investigation of toxic metabolites during drug development. Toxicol Appl Pharmacol. 207:425–434.
  • Ridder L, Wagener M. 2008. SyGMa: combining expert knowledge and empirical scoring in the prediction of metabolites. ChemMedChem. 3:821–832.
  • Wicker J, Fenner K, Ellis L, Wackett L, Kramer S. 2010. Predicting biodegradation products and pathways: a hybrid knowledge- and machine learning-based approach. Bioinformatics. 26:814–821.
  • Wicker J, Lorsbach T, Gütlein M, Schmid E, Latino D, Kramer S, Fenner K. 2016. enviPath – the environmental contaminant biotransformation pathway resource. Nucleic Acids Res. 44:D502–508.
  • Wu F, Zhou Y, Li L, Shen X, Chen G, Wang X, Liang X, Tan M, Huang Z. 2020. Computational approaches in preclinical studies on drug discovery and development. Front Chem. 8:726.

References

  • Dang NL, Matlock MK, Hughes TB, Swamidass SJ. 2020. The metabolic rainbow: deep learning phase I metabolism in five colors. J Chem Inf Model. 60:1146–1164.
  • Flynn NR, Dang NL, Ward MD, Swamidass SJ. 2020. XenoNet: inference and likelihood of intermediate metabolite formation. J Chem Inf Model. 60:3431–3449.
  • Hughes TB, Dang NL, Kumar A, Flynn NR, Swamidass SJ. 2020. Metabolic forest: predicting the diverse structures of drug metabolites. J Chem Inf Model. 60:4702–4716.
  • Hughes TB, Dang NL, Miller GP, Swamidass SJ. 2016. Modeling reactivity to biological macromolecules with a deep multitask network. ACS Cent Sci. 2:529–537.
  • Hughes TB, Miller GP, Swamidass SJ. 2015. Site of reactivity models predict molecular reactivity of diverse chemicals with glutathione. Chem Res Toxicol. 28:797–809.
  • Hughes TB, Swamidass SJ. 2017. Deep learning to predict the formation of quinone species in drug metabolism. Chem Res Toxicol. 30:642–656.
  • Park BK, Boobis A, Clarke S, Goldring CEP, Jones D, Kenna JG, Lambert C, Laverty HG, Naisbitt DJ, Nelson S, et al. 2011. Managing the challenge of chemically reactive metabolites in drug development. Nat Rev Drug Discov. 10:292–306.
  • Park K, Williams DP, Naisbitt DJ, Kitteringham NR, Pirmohamed M. 2005. Investigation of toxic metabolites during drug development. Toxicol Appl Pharmacol. 207:425–434.
  • Wu F, Zhou Y, Li L, Shen X, Chen G, Wang X, Liang X, Tan M, Huang Z. 2020. Computational approaches in preclinical studies on drug discovery and development. Front Chem. 8:726.

References

  • Dutreix C, Peng B, Mehring G, Hayes M, Capdeville R, Pokorny R, Seiberling M. 2004. Pharmacokinetic interaction between ketoconazole and imatinib mesylate (Glivec) in healthysubjects. Cancer Chemother Pharmacol. 54:290–294.
  • Filppula AM, Laitila J, Neuvonen PJ, Backman JT. 2012. Potent mechanism-based inhibition of CYP3A4 by imatinib explains its liability to interact with CYP3A4 substrates. Br J Pharmacol. 165:2787–2798.
  • Honkalammi J, Niemi M, Neuvonen PJ, and, Backman JT. 2011. Mechanism-based inactivation of CYP2C8 by gemfibrozil occurs rapidly in humans. Clin Pharmacol Ther. 89:579–586.
  • Larson RA, Druker BJ, Guilhot F, O’Brien SG, Riviere GJ, Krahnke T, Gathmann I, Wang Y, IRIS (International Randomized Interferon vs STI571) Study Group. 2008. Imatinib pharmacokinetics and its correlation with response and safety in chronic-phase chronic myeloid leukemia: a subanalysis of the IRIS study. Blood. 111:4022–4028.
  • Nebot N, Crettol S, D’Esposito F, Tattam B, Hibbs DE, Murray M. 2010. Participation ofCYP2C8 and CYP3A4 in the N-demethylation of imatinib in human hepatic microsomes. Br JPharmacol. 161:1059–1069.
  • Peng B, Hayes M, Resta D, Racine-Poon A, Druker BJ, Talpaz M, Sawyers CL, Rosamilia M, Ford J, Lloyd P, et al. 2004. Pharmacokinetics and pharmacodynamics of imatinib in a phase I trial with chronic myeloid leukemia patients. J Clin Oncol. 22:935–942.
  • Picard S, Titier K, Etienne G, Teilhet E, Ducint D, Bernard M-A, Lassalle R, Marit G, Reiffers J, Begaud B, et al. 2006. Trough imatinib plasma levels are associated with both cytogenetic and molecular responses to standard-dose imatinib in chronic myeloid leukemia. Blood. 109:3496–3499.
  • Rowland Yeo K, Walsky RL, Jamei M, Rostami-Hodjegan A, Tucker GT. 2011. Prediction of time-dependent CYP3A4 drug-drug interactions by physiologically based pharmacokinetic modelling: impact of inactivation parameters and enzyme turnover. Eur J Pharm Sci. 43:160–173.
  • Teng JF, Mabasa VH, Ensom MH. 2012. The role of therapeutic drug monitoring of imatinib in patients with chronic myeloid leukemia and metastatic or unresectable gastrointestinal stromal tumors. Ther Drug Monit. 34:85–97.
  • van Erp NP, Gelderblom H, Karlsson MO, Li J, Zhao M, Ouwerkerk J, Nortier JW, Guchelaar HJ, Baker SD, Sparreboom A. 2007. Influence of CYP3A4 inhibition on the steady-state pharmacokinetics of imatinib. Clin Cancer Res. 13:7394–7400.

References

  • Ingelman-Sundberg M, Sim SC, Gomez A, Rodriguez-Antona C. 2007. Influence of cytochrome P450 polymorphisms on drug therapies: pharmacogenetic, pharmacoepigenetic and clinical aspects. Pharmacol Ther. 116:496–526.
  • Jacqz E, Billante C, Moysan F, Mathieu H. 1988. The non-human primate: a possible model for human genetically determined polymorphisms in oxidative drug metabolism. Mol Pharmacol. 34:215–217.
  • Liu J, Ericksen SS, Besspiata D, Fisher CW, Szklarz GD. 2003. Characterization of substrate binding to cytochrome P450 1A1 using molecular modeling and kinetic analyses: case of residue 382. Drug Metab Dispos. 31:412–420.
  • Uno Y, Uehara S, Murayama N, Yamazaki H. 2011. CYP1D1, pseudogenized in human, is expressed and encodes a functional drug-metabolizing enzyme in cynomolgus monkey . Biochem Pharmacol. 81:442–450.
  • Uno Y, Matsushita A, Osada N, Uehara S, Kohara S, Nagata R, Fukuzaki K, Utoh M, Murayama N, Yamazaki H. 2010. Genetic variants of CYP3A4 and CYP3A5 in cynomolgus and rhesus macaques. Drug Metab Dispos. 38:209–214.
  • Uno Y, Matsushita A, Shukuya M, Matsumoto Y, Murayama N, and, Yamazaki H. 2014. CYP2C19 polymorphisms account for inter-individual variability of drug metabolism in cynomolgus macaques. Biochem Pharmacol. 91:242–248.

References

  • Clarke SE, Austin NE, Bloomer JC, Haddock RE, Higham FC, Hollis FJ, Nash M, Shardlow PC, Tasker TC, Woods FR. 1994. Metabolism and disposition of 14C-granisetron in rat, dog and man after intravenous and oral dosing. Xenobiotica. 24:1119–1131.
  • Ciolino HP, Daschner PJ, Yeh GC. 1998. Resveratrol inhibits transcription of CYP1A1 in vitro by preventing activation of the aryl hydrocarbon receptor. Cancer Res. 58:5707–5712.
  • Frey R, Becker C, Unger S, Schmidt A, Wensing G, Muck W. 2016. Assessment of the effects of hepatic impairment and smoking on the pharmacokinetics of a single oral dose of the soluble guanylate cyclase stimulator riociguat (BAY 63-2521)). Pulm Circ. 6:S5–S14.
  • Kim JH, Sherman ME, Curriero FC, Guengerich FP, Strickland PT, Sutter TR. 2004. Expression of cytochromes P450 1A1 and 1B1 in human lung from smokers, non-smokers, and ex-smokers. Toxicol Appl Pharmacol. 199:210–219.
  • Nakamura H, Ariyoshi N, Okada K, Nakasa H, Nakazawa K, Kitada M. 2005. CYP1A1 is a major enzyme responsible for the metabolism of granisetron in human liver microsomes. CDM. 6:469–480.
  • Paine MF, Schmiedlin-Ren P, Watkins PB. 1999. Cytochrome P-450 1A1 expression in human small bowel: interindividual variation and inhibition by ketoconazole. Drug Metab Dispos. 27:360–364.
  • Parkinson A, Mudra DR, Johnson C, Dwyer A, Carroll KM. 2004. The effects of gender, age, ethnicity, and liver cirrhosis on cytochrome P450 enzyme activity in human liver microsomes and inducibility in cultured human hepatocytes. Toxicol Appl Pharmacol. 199:193–209.
  • Shimada T, Yamazaki H, Mimura M, Inui Y, Guengerich FP. 1994. Interindividual variations in human liver cytochrome P-450 enzymes involved in the oxidation of drugs, carcinogens and toxic chemicals: studies with liver microsomes of 30 Japanese and 30 Caucasians. J Pharmacol Exp Ther. 270:414–423.

References

  • Aithal GP, Day CP, Kesteven PJL, Daly AK. 1999. Association of polymorphisms in the cytochrome P450 CYP2C9 with warfarin dose requirement and risk of bleeding complications. Lancet. 353:717–719.
  • Brents LK, Reichard EE, Zimmerman SM, Moran JH, Fantegrossi WE, Prather PL. 2011. Phase I hydroxylated metabolites of the K2 synthetic cannabinoid JWH-018 retain in vitro and in vivo cannabinoid 1 receptor affinity and activity. PLoS One. 6:e21917.
  • Caruso MG, Gazzerro P, Notarnicola M, Cisternino AM, Guerra V, Misciagna G, Laezza C, Bifulco M. 2012. Cannabinoid type 1 receptor gene polymorphism and macronutrient intake. J Nutrigenet Nutrigenomics. 5:305–313.
  • Chimalakonda KC, Seely KA, Bratton SM, Brents LK, Moran CL, Endres GW, James LP, Hollenberg PF, Prather PL, Radominska-Pandya A, et al. 2012. Cytochrome P450-mediated oxidative metabolism of abused synthetic cannabinoids found in K2/Spice: identification of novel cannabinoid receptor ligands. Drug Metab. Dispos. 40:2174–2184.
  • Lapoint J, James LP, Moran CL, Nelson LS, Hoffman RS, Moran JH. 2011. Severe toxicity following synthetic cannabinoid ingestion. Clin Toxicol. 49:760–764.
  • Patton AL, Seely KA, Chimalakonda KC, Tran JP, Trass M, Miranda A, Fantegrossi WE, Kennedy PD, Dobrowolski P, Radominska-Pandya A, et al. 2013. Targeted metabolomic approach for assessing human synthetic cannabinoid exposure and pharmacology. Anal Chem. 85:9390–9399.
  • Rosemary J, Adithan C. 2007. The pharmacogenetics of CYP2C9 and CYP2C19: ethnic variation and clinical significance. Curr Clin Pharmacol. 2:93–109.
  • Van Booven D, Marsh S, McLeod H, Carrillo MW, Sangkuhl K, Klein TE, Altman RB. 2010. Cytochrome P450 2C9-CYP2C9. Pharmacogenet Genomics. 20:277–281.

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