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Use In Silico and In Vitro Methods to Screen Hepatotoxic Chemicals and CYP450 Enzyme Inhibitors

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High-Throughput Screening Assays in Toxicology

Part of the book series: Methods in Molecular Biology ((MIMB,volume 2474))

Abstract

In silico and in vitro methods have emerged as valuable tools to rapidly screen and prioritize large numbers of chemicals including new drug entities, food ingredients, and environmental compounds for further in vivo analysis. These methods have been frequently used to conduct screening for a wide range of endpoints including physicochemical properties (e.g., logD), human biokinetic parameters (e.g., metabolism), and human organ toxicities (e.g., hepatotoxicity). This chapter describes a tiered approach of incorporating multiple in silico (quantitative structure–activity relationship, QSAR) and in vitro (e.g., human liver cell models, human liver microsomes) methods into the screening of hepatotoxic chemicals and cytochromes P450 enzyme (CYP) inhibitors. Chemicals are prioritized for further studies (e.g., in vivo animal study) based on the in silico and in vitro results, as well as a literature search for their in vivo exposures (e.g., plasma concentration).

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References

  1. Fisher K, Vuppalanchi R, Saxena R (2015) Drug-induced liver injury. Arch Pathol Lab Med 139(7):876–887. https://doi.org/10.5858/arpa.2014-0214-RA

    Article  CAS  PubMed  Google Scholar 

  2. Katarey D, Verma S (2016) Drug-induced liver injury. Clin Med (Lond) 16(Suppl 6):s104–s109. https://doi.org/10.7861/clinmedicine.16-6-s104

    Article  Google Scholar 

  3. Docea AO, Vassilopoulou L, Fragou D, Arsene AL, Fenga C, Kovatsi L, Petrakis D, Rakitskii VN, Nosyrev AE, Izotov BN, Golokhvast KS, Zakharenko AM, Vakis A, Tsitsimpikou C, Drakoulis N (2017) CYP polymorphisms and pathological conditions related to chronic exposure to organochlorine pesticides. Toxicol Rep 4:335–341. https://doi.org/10.1016/j.toxrep.2017.05.007

    Article  CAS  PubMed  PubMed Central  Google Scholar 

  4. Manikandan P, Nagini S (2018) Cytochrome P450 structure, function and clinical significance: a review. Curr Drug Targets 19(1):38–54. https://doi.org/10.2174/1389450118666170125144557

    Article  CAS  PubMed  Google Scholar 

  5. Gurley BJ, Swain A, Hubbard MA, Hartsfield F, Thaden J, Williams DK, Gentry WB, Tong Y (2008) Supplementation with goldenseal (Hydrastis canadensis), but not kava kava (Piper methysticum), inhibits human CYP3A activity in vivo. Clin Pharmacol Ther 83(1):61–69. https://doi.org/10.1038/sj.clpt.6100222

    Article  CAS  Google Scholar 

  6. Fraczkiewicz R, Lobell M, Goller AH, Krenz U, Schoenneis R, Clark RD, Hillisch A (2015) Best of both worlds: combining pharma data and state of the art modeling technology to improve in silico pKa prediction. J Chem Inf Model 55(2):389–397. https://doi.org/10.1021/ci500585w

    Article  CAS  PubMed  Google Scholar 

  7. Liu Y (2018) Incorporation of absorption and metabolism into liver toxicity prediction for phytochemicals: a tiered in silico QSAR approach. Food Chem Toxicol 118:409–415. https://doi.org/10.1016/j.fct.2018.05.039

    Article  CAS  PubMed  Google Scholar 

  8. Wetmore BA (2015) Quantitative in vitro-to-in vivo extrapolation in a high-throughput environment. Toxicology 332:94–101. https://doi.org/10.1016/j.tox.2014.05.012

    Article  CAS  PubMed  Google Scholar 

  9. Blaauboer BJ (2010) Biokinetic modeling and in vitro-in vivo extrapolations. J Toxicol Environ Health B Crit Rev 13(2–4):242–252. https://doi.org/10.1080/10937404.2010.483940

    Article  CAS  PubMed  Google Scholar 

  10. Liu Y, Mapa MST, Sprando RL (2020) Liver toxicity of anthraquinones: a combined in vitro cytotoxicity and in silico reverse dosimetry evaluation. Food Chem Toxicol 140:111313. https://doi.org/10.1016/j.fct.2020.111313

    Article  CAS  PubMed  Google Scholar 

  11. Saeheng T, Na-Bangchang K, Karbwang J (2018) Utility of physiologically based pharmacokinetic (PBPK) modeling in oncology drug development and its accuracy: a systematic review. Eur J Clin Pharmacol 74(11):1365–1376. https://doi.org/10.1007/s00228-018-2513-6

    Article  CAS  PubMed  Google Scholar 

  12. Liu Y, Mapa MST, Sprando RL (2021) Anthraquinones inhibit cytochromes P450 enzyme activity in silico and in vitro. J Appl Toxicol 41(9):1438–1445. https://doi.org/10.1002/jat.4134

    Article  CAS  PubMed  Google Scholar 

  13. Ghosh J, Lawless MS, Waldman M, Gombar V, Fraczkiewicz R (2016) Modeling ADMET. Methods Mol Biol 1425:63–83. https://doi.org/10.1007/978-1-4939-3609-0_4

    Article  CAS  PubMed  Google Scholar 

  14. Agrawal S, Dhiman RK, Limdi JK (2016) Evaluation of abnormal liver function tests. Postgrad Med J 92(1086):223–234. https://doi.org/10.1136/postgradmedj-2015-133715

    Article  CAS  PubMed  Google Scholar 

  15. Zanger UM, Schwab M (2013) Cytochrome P450 enzymes in drug metabolism: regulation of gene expression, enzyme activities, and impact of genetic variation. Pharmacol Ther 138(1):103–141. https://doi.org/10.1016/j.pharmthera.2012.12.007

    Article  CAS  PubMed  Google Scholar 

  16. Jana S, Rastogi H (2017) Effects of Caffeic acid and quercetin on in vitro permeability, metabolism and in vivo pharmacokinetics of melatonin in rats: potential for herb-drug interaction. Eur J Drug Metab Pharmacokinet 42(5):781–791. https://doi.org/10.1007/s13318-016-0393-7

    Article  CAS  PubMed  Google Scholar 

  17. Nehlig A (2018) Interindividual differences in caffeine metabolism and factors driving caffeine consumption. Pharmacol Rev 70(2):384–411. https://doi.org/10.1124/pr.117.014407

    Article  CAS  PubMed  Google Scholar 

  18. Tornio A, Backman JT (2018) Cytochrome P450 in pharmacogenetics: an update. Adv Pharmacol 83:3–32. https://doi.org/10.1016/bs.apha.2018.04.007

    Article  CAS  PubMed  Google Scholar 

  19. U.S. Food and Drug Administration (2016) Drug development and drug interactions: table of substrates, inhibitors and inducers. https://www.fda.gov/drugs/drug-interactions-labeling/drug-development-and-drug-interactions-table-substrates-inhibitors-and-inducers#table1

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Correspondence to Yitong Liu .

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Liu, Y. (2022). Use In Silico and In Vitro Methods to Screen Hepatotoxic Chemicals and CYP450 Enzyme Inhibitors. In: Zhu, H., Xia, M. (eds) High-Throughput Screening Assays in Toxicology. Methods in Molecular Biology, vol 2474. Humana, New York, NY. https://doi.org/10.1007/978-1-0716-2213-1_17

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  • DOI: https://doi.org/10.1007/978-1-0716-2213-1_17

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  • Publisher Name: Humana, New York, NY

  • Print ISBN: 978-1-0716-2212-4

  • Online ISBN: 978-1-0716-2213-1

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