Evaluation of a New Molecular Entity as a Victim of Metabolic Drug-Drug Interactions—an Industry Perspective
Drug Metabolism and Disposition, ISSN: 0090-9556, Vol: 44, Issue: 8, Page: 1399-1423
2016
- 85Citations
- 73Captures
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Example: if you select the 1-year option for an article published in 2019 and a metric category shows 90%, that means that the article or review is performing better than 90% of the other articles/reviews published in that journal in 2019. If you select the 3-year option for the same article published in 2019 and the metric category shows 90%, that means that the article or review is performing better than 90% of the other articles/reviews published in that journal in 2019, 2018 and 2017.
Citation Benchmarking is provided by Scopus and SciVal and is different from the metrics context provided by PlumX Metrics.
Metrics Details
- Citations85
- Citation Indexes85
- CrossRef85
- 77
- Captures73
- Readers73
- 73
Article Description
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 (f CL ), estimation of fractional contribution of metabolizing enzyme toward metabolism (f m ), 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 f CL and f m, 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.
Bibliographic Details
http://www.sciencedirect.com/science/article/pii/S0090955624113499; http://dx.doi.org/10.1124/dmd.115.069096; http://www.scopus.com/inward/record.url?partnerID=HzOxMe3b&scp=85011332601&origin=inward; http://www.ncbi.nlm.nih.gov/pubmed/27052879; https://linkinghub.elsevier.com/retrieve/pii/S0090955624113499; https://dx.doi.org/10.1124/dmd.115.069096; https://dmd.aspetjournals.org/content/44/8/1399
Elsevier BV
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