研究目的
药物代谢是药物清除的主要机制，据报道约 75% 的上市药物主要通过代谢途径从体内清除 [15]，其中约 48% 的药物经细胞色素 P450（Cytochrome P450, CYP）酶代谢（图 1a），各 CYP 贡献占比约 1%-40%，CYP3A4 占比最大（图 1b）；约24% 的药物经非 CYP 代谢，各非 CYP 贡献占比约 2%-45%，尿苷二磷酸葡萄糖醛酸转移酶（UGT）占比最大（图 1c） [16]。药物代谢主要发生在肝脏和肠道，其中肝脏代谢主要由 CYP 酶系催化，也可通过非 CYP 酶催化（如 UGT）。尽管 CYP 酶在物种间的水平来看有一定的相同，但总体来说表达差异很大。在人类不同群体间，CYP 的亚型种类和表达量有显著不同，因此对药物的代谢存在很大差异。在不同的组织中，CYP 的亚型种类和表达比率也存在差异，其中肝脏和小肠中表达量最高。药物清除在人类不同群体间难以预测，由动物数据对人体进行的预测更为困难。目前，使用人源的 CYP 酶，包括重组人细胞色素 P450 酶或混合人的肝微粒体细胞色素 P450 酶，预测药物清除是最有效的方法。
药物相互作用（DDI）评估通常从体外实验开始，确定可能影响药物处置的因素以阐明潜在的 DDI 机制，并获得用于进一步研究的动力学参数，由代谢酶介导的药物相互作用（DDI）包括：确定药物的主要消除途径以及评估相关代谢酶对药物处置的贡献（酶代谢反应表型实验）；考察药物对代谢酶的影响（酶抑制或诱导实验）。能够抑制或诱导酶的药物被称为“促变药”，而由于代谢酶被抑制或诱导而使其代谢发生变化的药物则被称为“受变药”。如果“促变药”抑制某细胞色素 P450 酶的活性，就会降低被该细胞色素 P450 酶代谢的“受变药”的清除。同样，如果“促变药”诱导某细胞色素 P450 酶的表达，就会加快“受变药”的代谢清除。同样如果“受变药”在某细胞色素 P450 酶作用下代谢成为活性药物成分，“促变药”的抑制或诱导作用将使血浆中的活性药物成分浓度减少或增加，从而可能会导致疗效不佳或毒性增加。细胞色素 P450 酶的抑制引起药物毒副作用虽然可能通过改变治疗方案会有改善，但是药物相互作用可能导致的严重不良反应，使得一些药物在研发的早期阶段就被终止，即使已经上市的也可能被撤出或被限制使用。
Figure 1. Metabolic pathways of drugs, data derived from Anitha Saravanakumar et al
注：图 1a：参与药物代谢的酶、图 1b：参与药物代谢的细胞色素 P450 酶（CYPs）、图 1c：参与药物代谢的非细胞色素P450 酶（CYPs）。ADH：醇脱氢酶、ALDH：醛脱氢酶、CES：羧酸酯酶、FMO：黄素单加氧酶、MAO：单胺氧化酶、UGT：尿苷二磷酸葡萄糖醛酸转移酶、XO：黄嘌呤氧化酶、AO：醛氧化酶平台介绍
药明康德体外药物相互作用测试平台提供针对不同层次的药物相互作用的检测，满足在先导化合物的发现阶段（Hit to lead）、先导化合物优化阶段（Lead optimization, LO）、临床前候选化合物阶段（Pre-clinical candidate, PCC）和新药临床研究申请阶段（Investigational new drug, IND）等药物发现和开发不同阶段的需要。
验证数据示例
针对化合物对 CYP3A4 的诱导作用，FDA 和 NMPA 指导原则建议进一步测试化合物对 CYP2C（CYP2C8、CYP2C9和 CYP2C19）的诱导潜能，我们通过长时间的探索及验证，建立了独有的 CYP2C 诱导测试平台（图 2 和图 3）。我们的测试体系中已知诱导剂的最大诱导倍数明显大于文献中报道的数据，为评估 CYP2C 诱导提供了更为灵敏的方法，从而有效预测药物相互作用的风险，并为临床药物相互作用研究方案提供参考依据。
CYP Induciton Assay in Human Hepatocytes
Emax Comparison-CYP2C8 and CYP2C9
CYP Induciton Assay in Human Hepatocytes
Induction fold Comparison of CYP2C19
参考文献
1. Frank R, Hargreaves R. 2003. Clinical biomarkers in drug discovery and development. Nature Reviews Drug Discovery. 2(7):566-80
2. Di L, Feng B, Goosen TC, Lai Y, Steyn SJ, Varma MV, Obach RS. 2013. A perspective on the prediction of drug pharmacokinetics and disposition in drug research and development. Drug Metabolism and Disposition. 41(12):1975-93
3. Nomeir, Amin A. 2010. ADME Strategies in Lead Optimization (book chapter) 25-88 in Early Drug Development: Strategies and Routes to First‐in‐Human Trials (book) Editor(s): Mitchell N. Cayen DOI:10.1002/9780470613191
4. Press, B. & Di, G. D. Permeability for intestinal absorption: Caco-2 assay and related issues. Curr. Drug Metab. 9, 893-900 (2008)
5. Giacomini, K.M. et al. Membrane transporters in drug development. Nature reviews drug discovery 9, 215-236 (2010)
6. Lipid-PAMPA with the MultiScreen Filer Plates, Millipore #PC040EN00, June 2004
7. Kansy, M., Senner, F. & Gubernator, K. Physicochemical high throughput screening: parallel artificial membrane permeability assay in the description of passive absorption processes. J. Med. Chem. 41, 1007‒1010 (1998)
8. Food and Drug Administration Center for Drug Evaluation and Research (2020). Clinical Drug Interaction Studies —Cytochrome P450 Enzyme- and Transporter-Mediated Drug Interactions Guidance for Industry (FDA Maryland)
9. Suzanne, S. et al. Towards Prediction of in vivo intestinal absorption using a 96-well Caco-2 Assay. J. Pharm. Sci. 99, 32463265 (2010)
10. Smith, D.A. et al. 2010. The effect of plasma protein binding on in vivo efficacy: misconceptions in drug discovery. Nature Reviews Drug Discovery 9, 929‒939
11. Di L, Breen C, Chambers R, Eckley ST, Fricke R, Ghosh A, Harradine P, Kalvass JC, Ho S, Lee CA, Marathe P, Perkins EJ, Qian M, Tse S, Yan Z, Zamek-Gliszczynski MJ. 2017. Industry Perspective on Contemporary Protein-Binding Methodologies: Considerations for Regulatory Drug-Drug Interaction and Related Guidelines on Highly Bound Drugs, Journal of Pharmaceutical Sciences, 106(12), 3442-3452
12. Obach RS, Lombardo F, Waters NJ. 2008. Trend analysis of a database of intravenous pharmacokinetic parameters in humans for 670 drug compounds. Drug Metabolism and Disposition, 36(7):1385-1405
13. Di L. 2014. The role of drug-metabolizing enzymes in clearance. Expert Opinion on Drug Metabolism and Toxicology. 10(3):379-93
14. Poulin P, Haddad S. 2013. Toward a new paradigm for the efficient in vitro-in vivo extrapolation of metabolic clearance in humans from hepatocyte data. Journal of Pharmaceutical Sciences. 102(9):3239-51
15. Di L. 2014. The role of drug-metabolizing enzymes in clearance. Expert Opinion on Drug Metabolism and Toxicology. 10(3):379-93
16. Anitha Saravanakumar et al. 2019. Physicochemical Properties, Biotransformation, and Transport Pathways of Established and Newly Approved Medications: A Systematic Review of the Top 200 Most Prescribed Drugs vs. the FDA-Approved Drugs Between 2005 and 2016
2. Di L, Feng B, Goosen TC, Lai Y, Steyn SJ, Varma MV, Obach RS. 2013. A perspective on the prediction of drug pharmacokinetics and disposition in drug research and development. Drug Metabolism and Disposition. 41(12):1975-93
3. Nomeir, Amin A. 2010. ADME Strategies in Lead Optimization (book chapter) 25-88 in Early Drug Development: Strategies and Routes to First‐in‐Human Trials (book) Editor(s): Mitchell N. Cayen DOI:10.1002/9780470613191
4. Press, B. & Di, G. D. Permeability for intestinal absorption: Caco-2 assay and related issues. Curr. Drug Metab. 9, 893-900 (2008)
5. Giacomini, K.M. et al. Membrane transporters in drug development. Nature reviews drug discovery 9, 215-236 (2010)
6. Lipid-PAMPA with the MultiScreen Filer Plates, Millipore #PC040EN00, June 2004
7. Kansy, M., Senner, F. & Gubernator, K. Physicochemical high throughput screening: parallel artificial membrane permeability assay in the description of passive absorption processes. J. Med. Chem. 41, 1007‒1010 (1998)
8. Food and Drug Administration Center for Drug Evaluation and Research (2020). Clinical Drug Interaction Studies —Cytochrome P450 Enzyme- and Transporter-Mediated Drug Interactions Guidance for Industry (FDA Maryland)
9. Suzanne, S. et al. Towards Prediction of in vivo intestinal absorption using a 96-well Caco-2 Assay. J. Pharm. Sci. 99, 32463265 (2010)
10. Smith, D.A. et al. 2010. The effect of plasma protein binding on in vivo efficacy: misconceptions in drug discovery. Nature Reviews Drug Discovery 9, 929‒939
11. Di L, Breen C, Chambers R, Eckley ST, Fricke R, Ghosh A, Harradine P, Kalvass JC, Ho S, Lee CA, Marathe P, Perkins EJ, Qian M, Tse S, Yan Z, Zamek-Gliszczynski MJ. 2017. Industry Perspective on Contemporary Protein-Binding Methodologies: Considerations for Regulatory Drug-Drug Interaction and Related Guidelines on Highly Bound Drugs, Journal of Pharmaceutical Sciences, 106(12), 3442-3452
12. Obach RS, Lombardo F, Waters NJ. 2008. Trend analysis of a database of intravenous pharmacokinetic parameters in humans for 670 drug compounds. Drug Metabolism and Disposition, 36(7):1385-1405
13. Di L. 2014. The role of drug-metabolizing enzymes in clearance. Expert Opinion on Drug Metabolism and Toxicology. 10(3):379-93
14. Poulin P, Haddad S. 2013. Toward a new paradigm for the efficient in vitro-in vivo extrapolation of metabolic clearance in humans from hepatocyte data. Journal of Pharmaceutical Sciences. 102(9):3239-51
15. Di L. 2014. The role of drug-metabolizing enzymes in clearance. Expert Opinion on Drug Metabolism and Toxicology. 10(3):379-93
16. Anitha Saravanakumar et al. 2019. Physicochemical Properties, Biotransformation, and Transport Pathways of Established and Newly Approved Medications: A Systematic Review of the Top 200 Most Prescribed Drugs vs. the FDA-Approved Drugs Between 2005 and 2016
