BACKGROUND AND PURPOSE: Drugs metabolically eliminated by several enzymes are less vulnerable to variable compound exposure in patients due to drug-drug interactions (DDI) or if a polymorphic enzyme is involved in their elimination. Therefore, it is vital in drug discovery to accurately and efficiently estimate and optimize the metabolic elimination profile. EXPERIMENTAL APPROACH: CYP3A and/or CYP2D6 substrates with well described variability in vivo in humans due to CYP3A DDI and CYP2D6 polymorphism were selected for assessment of fraction metabolized by each enzyme (fmCYP ) in two in vitro systems: (i) human recombinant P450s (hrP450s) and (ii) human hepatocytes combined with selective P450 inhibitors. Increases in compound exposure in poor versus extensive CYP2D6 metabolizers and by the strong CYP3A inhibitor ketoconazole were mathematically modelled and predicted changes in exposure were compared with in vivo data. KEY RESULTS: Predicted changes in exposure were within twofold of reported in vivo values using fmCYP estimated in human hepatocytes and there was a strong linear correlation between predicted and observed changes in exposure (r2 = 0.83 for CYP3A, r2 = 0.82 for CYP2D6). Predictions using fmCYP in hrP450s were not as accurate (r2 = 0.55 for CYP3A, r2 = 0.20 for CYP2D6). CONCLUSIONS AND IMPLICATIONS: The results suggest that variability in human drug exposure due to DDI and enzyme polymorphism can be accurately predicted using fmCYP from human hepatocytes and CYP-selective inhibitors. This approach can be efficiently applied in drug discovery to aid optimization of candidate drugs with a favourable metabolic elimination profile and limited variability in patients.
BACKGROUND AND PURPOSE: Drugs metabolically eliminated by several enzymes are less vulnerable to variable compound exposure in patients due to drug-drug interactions (DDI) or if a polymorphic enzyme is involved in their elimination. Therefore, it is vital in drug discovery to accurately and efficiently estimate and optimize the metabolic elimination profile. EXPERIMENTAL APPROACH: CYP3A and/or CYP2D6 substrates with well described variability in vivo in humans due to CYP3A DDI and CYP2D6 polymorphism were selected for assessment of fraction metabolized by each enzyme (fmCYP ) in two in vitro systems: (i) human recombinant P450s (hrP450s) and (ii) human hepatocytes combined with selective P450 inhibitors. Increases in compound exposure in poor versus extensive CYP2D6 metabolizers and by the strong CYP3A inhibitor ketoconazole were mathematically modelled and predicted changes in exposure were compared with in vivo data. KEY RESULTS: Predicted changes in exposure were within twofold of reported in vivo values using fmCYP estimated in human hepatocytes and there was a strong linear correlation between predicted and observed changes in exposure (r2 = 0.83 for CYP3A, r2 = 0.82 for CYP2D6). Predictions using fmCYP in hrP450s were not as accurate (r2 = 0.55 for CYP3A, r2 = 0.20 for CYP2D6). CONCLUSIONS AND IMPLICATIONS: The results suggest that variability in human drug exposure due to DDI and enzyme polymorphism can be accurately predicted using fmCYP from human hepatocytes and CYP-selective inhibitors. This approach can be efficiently applied in drug discovery to aid optimization of candidate drugs with a favourable metabolic elimination profile and limited variability in patients.
Authors: Michael J Curtis; Richard A Bond; Domenico Spina; Amrita Ahluwalia; Stephen P A Alexander; Mark A Giembycz; Annette Gilchrist; Daniel Hoyer; Paul A Insel; Angelo A Izzo; Andrew J Lawrence; David J MacEwan; Lawrence D F Moon; Sue Wonnacott; Arthur H Weston; John C McGrath Journal: Br J Pharmacol Date: 2015-07 Impact factor: 8.739
Authors: Tonika Bohnert; Aarti Patel; Ian Templeton; Yuan Chen; Chuang Lu; George Lai; Louis Leung; Susanna Tse; Heidi J Einolf; Ying-Hong Wang; Michael Sinz; Ralph Stearns; Robert Walsky; Wanping Geng; Sirimas Sudsakorn; David Moore; Ling He; Jan Wahlstrom; Jim Keirns; Rangaraj Narayanan; Dieter Lang; Xiaoqing Yang Journal: Drug Metab Dispos Date: 2016-04-06 Impact factor: 3.922
Authors: K Yoshida; B Sun; L Zhang; P Zhao; D R Abernethy; T D Nolin; A Rostami-Hodjegan; I Zineh; S-M Huang Journal: Clin Pharmacol Ther Date: 2016-03-07 Impact factor: 6.875