Evaluation of a New Molecular Entity as a Victim of Metabolic Drug-Drug Interactions-an Industry Perspective

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 co...

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Published in:Drug metabolism and disposition 2016-08, Vol.44 (8), p.1399-1423
Main Authors: Bohnert, Tonika, Patel, Aarti, Templeton, Ian, Chen, Yuan, Lu, Chuang, Lai, George, Leung, Louis, Tse, Susanna, Einolf, Heidi J, Wang, Ying-Hong, Sinz, Michael, Stearns, Ralph, Walsky, Robert, Geng, Wanping, Sudsakorn, Sirimas, Moore, David, He, Ling, Wahlstrom, Jan, Keirns, Jim, Narayanan, Rangaraj, Lang, Dieter, Yang, Xiaoqing
Format: Article
Language:English
Subjects:
Animals
Biotransformation
Computer Simulation
Decision Trees
Drug Discovery - methods
Drug Discovery - standards
Drug Industry - standards
Drug Interactions
Enzymes - metabolism
Humans
Kinetics
Models, Theoretical
Pharmaceutical Preparations - chemistry
Pharmaceutical Preparations - metabolism
Risk Assessment
Species Specificity
Substrate Specificity
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Summary: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.
ISSN:1521-009X
0090-9556
1521-009X
DOI:10.1124/dmd.115.069096