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Biogen, Cambridge, Massachusetts (T.B.); GlaxoSmithKline R&D, Hertfordshire, United Kingdom (A.P.); Janssen R&D, Spring House, Pennsylvania (I.T.); Genentech, South San Francisco, California (Y.C.); Takeda, Cambridge, Massachusetts (C.L.); Eisai Inc., Andover, Massachusetts (G.L.); Pfizer Inc., Groton, Connecticut (L.L., S.T.); Novartis, East Hanover, New Jersey (H.J.E.); Merck & Co., Inc., Kenilworth, New Jersey (Y.-H.W.); Bristol Myers Squibb, Wallingford, Connecticut (M.S.); Vertex Pharmaceuticals Inc., Boston, Massachusetts (R.S.); EMD Serono R&D Institute, Inc., Billerica, Massachusetts (R.W., W.G.); Sanofi, Waltham, Massachusetts (S.S.); Roche Innovation Center, New York, New York (D.M.); Daiichi Sankyo, Edison, New Jersey (L.H.); Amgen Inc., Thousand Oaks, California (J.W.); Astellas, Northbrook, Illinois (J.K.); Celgene Corporation, Summit, New Jersey (R.N.); Bayer Pharma AG, Wuppertal, Germany (D.L.); and Incyte Corporation, Wilmington, Delaware (X.Y.).
Published: August 2016
AI Article Synopsis
Article Abstract
Under the guidance of the International Consortium for Innovation and Quality in Pharmaceutical Development (IQ), scientists from 20 pharmaceutical companies formed a Victim Drug-Drug Interactions Working Group. This working group has conducted a review of the literature and the practices of each company on the approaches to clearance pathway identification (fCL), estimation of fractional contribution of metabolizing enzyme toward metabolism (fm), along with modeling and simulation-aided strategy in predicting the victim drug-drug interaction (DDI) liability due to modulation of drug metabolizing enzymes. Presented in this perspective are the recommendations from this working group on: 1) strategic and experimental approaches to identify fCL and fm, 2) whether those assessments may be quantitative for certain enzymes (e.g., cytochrome P450, P450, and limited uridine diphosphoglucuronosyltransferase, UGT enzymes) or qualitative (for most of other drug metabolism enzymes), and the impact due to the lack of quantitative information on the latter. Multiple decision trees are presented with stepwise approaches to identify specific enzymes that are involved in the metabolism of a given drug and to aid the prediction and risk assessment of drug as a victim in DDI. Modeling and simulation approaches are also discussed to better predict DDI risk in humans. Variability and parameter sensitivity analysis were emphasized when applying modeling and simulation to capture the differences within the population used and to characterize the parameters that have the most influence on the prediction outcome.
