Literature DB >> 27052879

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

Tonika Bohnert1, Aarti Patel2, Ian Templeton2, Yuan Chen2, Chuang Lu2, George Lai2, Louis Leung2, Susanna Tse2, Heidi J Einolf2, Ying-Hong Wang2, Michael Sinz2, Ralph Stearns2, Robert Walsky2, Wanping Geng2, Sirimas Sudsakorn2, David Moore2, Ling He2, Jan Wahlstrom2, Jim Keirns2, Rangaraj Narayanan2, Dieter Lang2, Xiaoqing Yang2.   

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.
Copyright © 2016 by The American Society for Pharmacology and Experimental Therapeutics.

Entities:  

Mesh:

Substances:

Year:  2016        PMID: 27052879     DOI: 10.1124/dmd.115.069096

Source DB:  PubMed          Journal:  Drug Metab Dispos        ISSN: 0090-9556            Impact factor:   3.922


  18 in total

1.  Human hepatocytes and cytochrome P450-selective inhibitors predict variability in human drug exposure more accurately than human recombinant P450s.

Authors:  Bo Lindmark; Anna Lundahl; Kajsa P Kanebratt; Tommy B Andersson; Emre M Isin
Journal:  Br J Pharmacol       Date:  2018-04-19       Impact factor: 8.739

2.  In vitro to in vivo extrapolation of the complex drug-drug interaction of bupropion and its metabolites with CYP2D6; simultaneous reversible inhibition and CYP2D6 downregulation.

Authors:  Jennifer E Sager; Sasmita Tripathy; Lauren S L Price; Abhinav Nath; Justine Chang; Alyssa Stephenson-Famy; Nina Isoherranen
Journal:  Biochem Pharmacol       Date:  2016-11-09       Impact factor: 5.858

3.  Centella asiatica Water Extract Shows Low Potential for Cytochrome P450-Mediated Drug Interactions.

Authors:  Kirsten M Wright; Armando Alcazar Magana; Ronald M Laethem; Caroline L Moseley; Troy T Banks; Claudia S Maier; Jan F Stevens; Joseph F Quinn; Amala Soumyanath
Journal:  Drug Metab Dispos       Date:  2020-06-24       Impact factor: 3.922

4.  How Science Is Driving Regulatory Guidances.

Authors:  Xinning Yang; Jianghong Fan; Lei Zhang
Journal:  Methods Mol Biol       Date:  2021

Review 5.  Time-dependent enzyme inactivation: Numerical analyses of in vitro data and prediction of drug-drug interactions.

Authors:  Jaydeep Yadav; Erickson Paragas; Ken Korzekwa; Swati Nagar
Journal:  Pharmacol Ther       Date:  2019-12-11       Impact factor: 12.310

6.  Quantitative Prediction of Drug-Drug Interactions Involving Inhibitory Metabolites in Drug Development: How Can Physiologically Based Pharmacokinetic Modeling Help?

Authors:  I E Templeton; Y Chen; J Mao; J Lin; H Yu; S Peters; M Shebley; M V Varma
Journal:  CPT Pharmacometrics Syst Pharmacol       Date:  2016-09-19

Review 7.  Recent advances in understanding hepatic drug transport.

Authors:  Bruno Stieger; Bruno Hagenbuch
Journal:  F1000Res       Date:  2016-10-06

Review 8.  Progress in Prediction and Interpretation of Clinically Relevant Metabolic Drug-Drug Interactions: a Minireview Illustrating Recent Developments and Current Opportunities.

Authors:  Stephen Fowler; Peter N Morcos; Yumi Cleary; Meret Martin-Facklam; Neil Parrott; Michael Gertz; Li Yu
Journal:  Curr Pharmacol Rep       Date:  2017-02-01

9.  Metabolism of the Selective Matrix Metalloproteinase-9 Inhibitor (R)-ND-336.

Authors:  Charles Edwin Raja Gabriel; Trung T Nguyen; Emanuele Marco Gargano; Jed F Fisher; Mayland Chang; Shahriar Mobashery
Journal:  ACS Pharmacol Transl Sci       Date:  2021-04-06

Review 10.  Translational Biomedical Informatics and Pharmacometrics Approaches in the Drug Interactions Research.

Authors:  Pengyue Zhang; Heng-Yi Wu; Chien-Wei Chiang; Lei Wang; Samar Binkheder; Xueying Wang; Donglin Zeng; Sara K Quinney; Lang Li
Journal:  CPT Pharmacometrics Syst Pharmacol       Date:  2018-01-09
View more

北京卡尤迪生物科技股份有限公司 © 2022-2023.