Senior team members, Dr Jingjing Yu and Dr Cathy Yeung, are instructing a CE course on Principles & Mechanisms of Pharmacokinetic Drug-Drug Interactions for Recently Approved Drugs which provides pharmaceutical scientists and clinical pharmacists with an in-depth understanding of the mechanisms of drug interactions.
This 5-session course was prepared in collaboration with University of Wisconsin-Madison.
To learn more and register, visit here.
Evaluating the potential of new drugs and their metabolites to cause drug‐drug interactions (DDIs) is critical for understanding drug safety and efficacy. Although multiple analyses of proprietary metabolite testing data have been published, no systematic analyses of metabolite data collected according to current testing criteria have been conducted. To address this knowledge gap, 120 new molecular entities approved between 2013 and 2018 were reviewed. Comprehensive data on metabolite‐to‐parent area‐under‐the‐curve ratios (AUCM/AUCP), inhibitory potency of parent and metabolites, and clinical drug‐drug interactions (DDI) were collected. 64% of the metabolites quantified in vivo had AUCM/AUCP≥25% and 75% of these metabolites were tested for cytochrome P450 (CYP) inhibition in vitro, resulting in 15 metabolites with potential DDI risk identification. While 50% of the metabolites with AUCM/AUCP<25% were also tested in vitro, none of them showed meaningful CYP inhibition potential. The metabolite % plasma total radioactivity cutoff of ≥10% did not appear to add value to metabolite testing strategies. No relationship between metabolite versus parent drug polarity and inhibition potency was observed. Comparison of metabolite and parent maximum concentration (Cmax) divided by inhibition constant Ki values suggested that metabolites can contribute to in vivo DDIs and hence, quantitative prediction of clinical DDI magnitude may require both parent and metabolite data. This systematic analysis of metabolite data for newly approved drugs supports an AUCM/AUCP cutoff of ≥25% to warrant metabolite in vitro CYP screening to adequately characterize metabolite inhibitory DDI potential and support quantitative DDI predictions.
Presented virtually at ASCPT Annual Meeting, March 2021
Katie Owens, Sophie Argon, Jingjing Yu, Isabelle Ragueneau-Majlessi, and colleagues at FDA
Food-effect (FE) and gastric pH-mediated drug-drug interactions (DDIs) are absorption-related. Here. we evaluated of the Biopharmaceutical Classification System (BCS) may be correlated with FE or pH-mediated DDI observed.
Presented virtually at ASCPT Annual Meeting, March 2021
Lindsay M. Henderson, Claire Steinbronn, Jingjing Yu, Cathy Yeung, and Isabelle Ragueneau-Majlessi
This study’s objective was to evaluate the consistency in DDI labeling language of recently marketed drugs (2012-2020) when found to alter the exposure of coadministered digoxin, a clinical P-glycoprotein (P-gp) substrate and narrow therapeutic index (NTI) medication.
We have recorded a one-hour long video tutorial suitable as a refresher training or to kick-start the use of DIDB. It starts by introducing the different lists available in the Resource Center of DIDB and then, it shows how to retrieve the human in vitro metabolism and transport information using preformulated queries. Finally it presents the in vivo datasets and how to retrieve clinical PK-based information from drug-drug interaction, food-effect, organ impairment and pharmacogenetics studies.
The video is available in DIDB Resource Center. Please note that you must be signed in to access.
We are pleased to provide access to an innovative online tool: DDPred, developed by the University of Lyon, France, that uses an in vivo static mechanistic model to predict PK-based DDIs with hundreds of substrates and inhibitors/inducers and evaluates the impact of polymorphism.
The link to this application will be available to all DIDB users during a 6-month evaluation. Please take the time to test it and to provide your feedback in the online survey. We value your feedback! Thank you.
DIDB has been updated with the following new features:
Feel free to contact us if you experience any issues or if you have any questions or suggestions. Your feedback is always greatly valued!
The draft guidance newly released by FDA on “Evaluation go Gastric pH-Dependent Drug Interactions with Acid-Reducing Agents: Study Design, Data Analysis, and Clinical Implications” is now available in DIDB Resource Center. Please note that you must be signed in to access.
This draft guidance describes FDA’s recommendations regarding: (1) when clinical DDI studies with acid-reducing agents are needed; (2) the design of clinical DDI studies; (3) how to interpret study results; and (4) communicating findings in product labeling.
DIDB contains absorption-based drug interaction data. The study results are presented based on the underlying mechanism. For example, there are about 450 entries on pH-dependent drugs interactions in DIDB curated from literature and NDA reviews with detailed information about PK assessment results, study design, population, formulation, dosing, and safety results
DIDB has been updated with the following new features:
Feel free to contact us if you experience any issues or if you have any questions or suggestions. Your feedback is always greatly valued!
The draft guidance newly released by FDA on “Clinical Drug Interaction Studies Combined with Oral Contraceptives” is now available in DIDB Resource Center. Please note that you must be signed in to access.
This draft guidance focuses on evaluating the DDI potential of an investigational new drug (i.e., perpetrator) on combined oral contraceptives (COCs; i.e., victim) during drug development and determining how to communicate DDI study results and mitigation strategies to address potential risks associated with increased or decreased exposure of COCs in labeling.
In DIDB (as of Nov 2020), there are over 600 entries evaluating COCs as an object in dedicated clinical PK studies (including PGx data). On the other hand, nearly 200 entries were curated from clinical DDI studies where COCs serve as a precipitant.