Chia-Hsiang Hsueh1,2, Vicky Hsu3, Yuzhuo Pan3,4, Ping Zhao3,5. 1. Office of Clinical Pharmacology, Office of Translational Sciences, Center for Drug Evaluation and Research, US Food and Drug Administration, Silver Spring, MD, 20993, USA. sean.hsueh@gilead.com. 2. Currently Affiliated with the Department of Clinical Pharmacology, Gilead Sciences, Inc, Foster City, CA, 94404, USA. sean.hsueh@gilead.com. 3. Office of Clinical Pharmacology, Office of Translational Sciences, Center for Drug Evaluation and Research, US Food and Drug Administration, Silver Spring, MD, 20993, USA. 4. Currently Affiliated with the Office of Generic Drugs, Center for Drug Evaluation and Research, US Food and Drug Administration, Silver Spring, MD, 20993, USA. 5. Currently Affiliated with Bill and Melinda Gates Foundation, Seattle, WA, 98102, USA.
Abstract
BACKGROUND: Physiologically-based pharmacokinetic (PBPK) modeling in predicting metabolic drug-drug interactions (mDDIs) is routinely used in drug development. Currently, the US FDA endorses the use of PBPK to potentially support dosing recommendations for investigational drugs as enzyme substrates of mDDIs, and to inform a lack of mDDIs for investigational drugs as enzyme modulators. METHODS: We systematically evaluated the performance of PBPK modeling in predicting mDDIs published in the literature. Models developed to assess both investigational drugs as enzyme substrates (Groups 1 and 2, as being inhibited and induced, respectively) or enzyme modulators (Groups 3 and 4, as inhibitors and inducers, respectively) were evaluated. Predicted ratios of the area under the curve (AUCRs) and/or maximum plasma concentration (CmaxRs) with and without comedication were compared with the observed ratios. RESULTS: For Groups 1, 2, 3, and 4, 62, 50, 44, and 43% of model-predicted AUCRs, respectively, were within a predefined threshold of 1.25-fold of observed values (0.8-1.25x). When the threshold was widened to twofold, the values increased to 100, 80, 81, and 86% (0.5-2.0x). For Groups 3 and 4, prediction for mDDI liability (the existence or lack of mDDIs) using PBPK appears to be satisfactory. CONCLUSION: Our analysis supports the FDA's current recommendations on the use of PBPK to predict mDDIs.
BACKGROUND: Physiologically-based pharmacokinetic (PBPK) modeling in predicting metabolic drug-drug interactions (mDDIs) is routinely used in drug development. Currently, the US FDA endorses the use of PBPK to potentially support dosing recommendations for investigational drugs as enzyme substrates of mDDIs, and to inform a lack of mDDIs for investigational drugs as enzyme modulators. METHODS: We systematically evaluated the performance of PBPK modeling in predicting mDDIs published in the literature. Models developed to assess both investigational drugs as enzyme substrates (Groups 1 and 2, as being inhibited and induced, respectively) or enzyme modulators (Groups 3 and 4, as inhibitors and inducers, respectively) were evaluated. Predicted ratios of the area under the curve (AUCRs) and/or maximum plasma concentration (CmaxRs) with and without comedication were compared with the observed ratios. RESULTS: For Groups 1, 2, 3, and 4, 62, 50, 44, and 43% of model-predicted AUCRs, respectively, were within a predefined threshold of 1.25-fold of observed values (0.8-1.25x). When the threshold was widened to twofold, the values increased to 100, 80, 81, and 86% (0.5-2.0x). For Groups 3 and 4, prediction for mDDI liability (the existence or lack of mDDIs) using PBPK appears to be satisfactory. CONCLUSION: Our analysis supports the FDA's current recommendations on the use of PBPK to predict mDDIs.
Authors: Md L T Vieira; B Kirby; I Ragueneau-Majlessi; A Galetin; J Y L Chien; H J Einolf; O A Fahmi; V Fischer; A Fretland; K Grime; S D Hall; R Higgs; D Plowchalk; R Riley; E Seibert; K Skordos; J Snoeys; K Venkatakrishnan; T Waterhouse; R S Obach; E G Berglund; L Zhang; P Zhao; K S Reynolds; S-M Huang Journal: Clin Pharmacol Ther Date: 2013-09-18 Impact factor: 6.875
Authors: Odette A Fahmi; Susan Hurst; David Plowchalk; Jack Cook; Feng Guo; Kuresh Youdim; Maurice Dickins; Alex Phipps; Amanda Darekar; Ruth Hyland; R Scott Obach Journal: Drug Metab Dispos Date: 2009-04-30 Impact factor: 3.922