Use of Physiologically Based Pharmacokinetic (PBPK) Modeling for Predicting Drug-Food Interactions: an Industry Perspect
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Research Article Theme: Use of PBPK Modeling to Inform Clinical Decisions: Current Status of Prediction of Drug-Food Interactions Guest Editor: Filippos Kesisoglou
Use of Physiologically Based Pharmacokinetic (PBPK) Modeling for Predicting Drug-Food Interactions: an Industry Perspective Arian Emami Riedmaier,1,24 Kevin DeMent,2 James Huckle,3 Phil Bransford,4 Cordula Stillhart,5 Richard Lloyd,6 Ravindra Alluri,7 Sumit Basu,8 Yuan Chen,9 Varsha Dhamankar,10,11 Stephanie Dodd,12 Priyanka Kulkarni,13 Andrés Olivares-Morales,14 Chi-Chi Peng,13,15 Xavier Pepin,16 Xiaojun Ren,17 Thuy Tran,18 Christophe Tistaert,19 Tycho Heimbach,20 Filippos Kesisoglou,21 Christian Wagner,22 and Neil Parrott23
Received 29 June 2020; accepted 1 September 2020 Abstract. The effect of food on pharmacokinetic properties of drugs is a commonly observed occurrence affecting about 40% of orally administered drugs. Within the pharmaceutical industry, significant resources are invested to predict and characterize a clinically relevant food effect. Here, the predictive performance of physiologically based pharmacokinetic (PBPK) food effect models was assessed via de novo mechanistic absorption models for 30 compounds using controlled, pre-defined in vitro, and modeling methodology. Compounds for which absorption was known to be limited by intestinal transporters were excluded in this analysis. A decision tree for model verification and optimization was followed, leading to high, moderate, or low food effect prediction confidence. High (within 0.8- to 1.25-fold) to moderate confidence (within 0.5- to 2-fold) was achieved for most of the compounds (15 and 8, respectively). While for 7 compounds, prediction confidence was found to be low (> 2-fold). There was no clear difference in prediction success for positive or negative food effects and no clear relationship to the BCS category of tested drug molecules. However, an association could be demonstrated when the food effect was mainly related to Guest Editor: Filippos Kesisoglou Electronic supplementary material The online version of this article (https://doi.org/10.1208/s12248-020-00508-2) contains supplementary material, which is available to authorized users. 1
DMPK and Translational Modeling, AbbVie Inc., North Chicago, Illinois, USA. 2 Global DMPK, Takeda Pharmaceutical Co., Ltd., San Diego, California, USA. 3 Drug Product Technology, Amgen, Thousand Oaks, California, USA. 4 Modeling & Informatics, Vertex Pharmaceuticals, Boston, Massachusetts, USA. 5 Pharmaceutical R&D, Formulation & Process Sciences, F. Hoffmann-La Roche Ltd., Basel, Switzerland. 6 Computational & Modelling Sciences, Platform Technology Sciences, GlaxoSmithKline R&D, Ware, Hertfordshire, UK. 7 Clinical Pharmacology and Safety Sciences, R&D, AstraZeneca, Cambridge, UK. 8 Pharmacokinetic, Pharmacodynamic and Drug MetabolismQuantitative Pharmacology and Pharmacometrics (PPDM-QP2), Merck & Co, Inc., West Point, Pennsylvania, USA. 9 Department of Drug Metabolism and Pharmacokinetics, Genentech, South San Francisco, California, USA. 10
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