Clinical Pharmacology and Safety Sciences, BioPharmaceuticals R&D, AstraZeneca, Cambridge, United Kingdom (H.E.R., K.S.F.); Department of Drug Metabolism and Pharmacokinetics, Boehringer Ingelheim Pharmaceuticals Inc., Ridgefield, Connecticut (P.M., M.T.); Quantitative Clinical Pharmacology, Development Sciences, UCB Biopharma SRL, Braine-L'Alleud, Belgium (H.C.); NCE Drug Metabolism and Pharmacokinetics, the healthcare business of Merck KGaA, Darmstadt, Germany (Z.F.); Drug Metabolism, Gilead Sciences, Inc. Foster City, California (X.L., Y.L.); Preclinical Sciences and Translational Safety, Janssen R&D LLC, Spring House, Pennsylvania (S.H.P.); Pharmacokinetics, Dynamics and Metabolism, Medicine Design, Worldwide R&D, Pfizer Inc, Groton, Connecticut (C.C.); Pharmacokinetic Sciences, Novartis Institutes for Biomedical Research, East Hanover, New Jersey (I.H.); Clinical Pharmacology Modelling and Simulations, GlaxoSmithKline Research and Development, Collegeville, Pennsylvania (N.T., K.M.M.); IQ Secretariat, Faegre Drinker Biddle & Reath, LLP., Washington DC (J.M.V.); Quantitative, Translational and ADME Sciences, AbbVie Inc., North Chicago, Illinois (D.A.J.B.); Investigative Drug Disposition, Lilly Research Laboratories, Eli Lilly Inc, Indianapolis, Indiana (K.M.H.); Nonclinical Biostatistics, Genentech, Inc., South San Francisco, California (J.B.); ADME and Discovery Toxicity, Merck & Co., Inc., Rahway, New Jersey (X.C.); Departments of Drug Metabolism and Pharmacokinetics (C.E.C.A.H., L.S.) and Clinical Pharmacology (R.S.), Genentech, Inc., South San Francisco, California; Department of Pharmacokinetics and Drug Metabolism, Amgen Inc. South San Francisco, California (C.Y.L.); Department of Drug Metabolism and Pharmacokinetics, Bristol Myers Squibb Research and Development, Princeton, New Jersey (H.S.); DMPK Modeling, IVIVT, Research, GSK, Stevenage, United Kingdom (Ku.T.); Takeda Pharmaceutical Company Limited, Fujisawa, Japan (Ki.T.); and Pharmacokinetics, Dynamics, and Metabolism, Translational Medicine and Early Development, Sanofi US, Bridgewater, NJ (C.X.).
Clinical Pharmacology and Safety Sciences, BioPharmaceuticals R&D, AstraZeneca, Cambridge, United Kingdom (H.E.R., K.S.F.); Department of Drug Metabolism and Pharmacokinetics, Boehringer Ingelheim Pharmaceuticals Inc., Ridgefield, Connecticut (P.M., M.T.); Quantitative Clinical Pharmacology, Development Sciences, UCB Biopharma SRL, Braine-L'Alleud, Belgium (H.C.); NCE Drug Metabolism and Pharmacokinetics, the healthcare business of Merck KGaA, Darmstadt, Germany (Z.F.); Drug Metabolism, Gilead Sciences, Inc. Foster City, California (X.L., Y.L.); Preclinical Sciences and Translational Safety, Janssen R&D LLC, Spring House, Pennsylvania (S.H.P.); Pharmacokinetics, Dynamics and Metabolism, Medicine Design, Worldwide R&D, Pfizer Inc, Groton, Connecticut (C.C.); Pharmacokinetic Sciences, Novartis Institutes for Biomedical Research, East Hanover, New Jersey (I.H.); Clinical Pharmacology Modelling and Simulations, GlaxoSmithKline Research and Development, Collegeville, Pennsylvania (N.T., K.M.M.); IQ Secretariat, Faegre Drinker Biddle & Reath, LLP., Washington DC (J.M.V.); Quantitative, Translational and ADME Sciences, AbbVie Inc., North Chicago, Illinois (D.A.J.B.); Investigative Drug Disposition, Lilly Research Laboratories, Eli Lilly Inc, Indianapolis, Indiana (K.M.H.); Nonclinical Biostatistics, Genentech, Inc., South San Francisco, California (J.B.); ADME and Discovery Toxicity, Merck & Co., Inc., Rahway, New Jersey (X.C.); Departments of Drug Metabolism and Pharmacokinetics (C.E.C.A.H., L.S.) and Clinical Pharmacology (R.S.), Genentech, Inc., South San Francisco, California; Department of Pharmacokinetics and Drug Metabolism, Amgen Inc. South San Francisco, California (C.Y.L.); Department of Drug Metabolism and Pharmacokinetics, Bristol Myers Squibb Research and Development, Princeton, New Jersey (H.S.); DMPK Modeling, IVIVT, Research, GSK, Stevenage, United Kingdom (Ku.T.); Takeda Pharmaceutical Company Limited, Fujisawa, Japan (Ki.T.); and Pharmacokinetics, Dynamics, and Metabolism, Translational Medicine and Early Development, Sanofi US, Bridgewater, NJ (C.X.)
Drug Metab Dispos. 2024 Jun 17;52(7):582-596. doi: 10.1124/dmd.123.001587.
The International Consortium for Innovation and Quality in Pharmaceutical Development Transporter Working Group had a rare opportunity to analyze a crosspharma collation of in vitro data and assay methods for the evaluation of drug transporter substrate and inhibitor potential. Experiments were generally performed in accordance with regulatory guidelines. Discrepancies, such as not considering the impact of preincubation for inhibition and free or measured in vitro drug concentrations, may be due to the retrospective nature of the dataset and analysis. Lipophilicity was a frequent indicator of crosstransport inhibition (P-gp, BCRP, OATP1B, and OCT1), with high molecular weight (MW ≥500 Da) also common for OATP1B and BCRP inhibitors. A high level of overlap in in vitro inhibition across transporters was identified for BCRP, OATP1B1, and MATE1, suggesting that prediction of DDIs for these transporters will be common. In contrast, inhibition of OAT1 did not coincide with inhibition of any other transporter. Neutrals, bases, and compounds with intermediate-high lipophilicity tended to be P-gp and/or BCRP substrates, whereas compounds with MW <500 Da tended to be OAT3 substrates. Interestingly, the majority of in vitro inhibitors were not reported to be followed up with a clinical study by the submitting company, whereas those compounds identified as substrates generally were. Approaches to metabolite testing were generally found to be similar to parent testing, with metabolites generally being equally or less potent than parent compounds. However, examples where metabolites inhibited transporters in vitro were identified, supporting the regulatory requirement for in vitro testing of metabolites to enable integrated clinical DDI risk assessment. SIGNIFICANCE STATEMENT: A diverse dataset showed that transporter inhibition often correlated with lipophilicity and molecular weight (>500 Da). Overlapping transporter inhibition was identified, particularly that inhibition of BCRP, OATP1B1, and MATE1 was frequent if the compound inhibited other transporters. In contrast, inhibition of OAT1 did not correlate with the other drug transporters tested.
国际药品创新和质量联盟转运体工作组难得有机会分析药物转运体底物和抑制剂潜力评估的体外数据和检测方法的交叉药物研发荟萃分析。实验通常是按照监管指南进行的。不一致的地方,如不考虑抑制的预孵育的影响以及游离或体外测量的药物浓度,可能是由于数据集和分析的回顾性性质。亲脂性是跨转运体抑制(P-糖蛋白、BCRP、OATP1B 和 OCT1)的常见指标,高相对分子质量(MW≥500 Da)也是 OATP1B 和 BCRP 抑制剂的常见特征。鉴定出 BCRP、OATP1B1 和 MATE1 的体外抑制具有高度的重叠性,这表明这些转运体的药物相互作用的预测将很常见。相反,OAT1 的抑制与任何其他转运体的抑制并不一致。中性、碱性化合物和具有中等至高亲脂性的化合物往往是 P-糖蛋白和/或 BCRP 的底物,而 MW<500 Da 的化合物往往是 OAT3 的底物。有趣的是,大多数体外抑制剂并没有被提交公司报告进行临床研究,而那些被鉴定为底物的化合物通常是这样的。代谢产物检测方法通常与母药检测方法相似,代谢产物通常与母药的活性相当或较弱。然而,鉴定出一些代谢产物在体外抑制转运体的例子,支持了代谢产物的体外检测的监管要求,以便进行综合的临床药物相互作用风险评估。意义陈述:一个多样化的数据集表明,转运体抑制通常与亲脂性和分子量(>500 Da)相关。鉴定出转运体抑制的重叠性,特别是如果化合物抑制其他转运体,则 BCRP、OATP1B1 和 MATE1 的抑制经常发生。相反,OAT1 的抑制与其他测试的药物转运体没有相关性。