Industry-Sponsored Symposia
07:15 - 08:15 | Monday, June 12
BioIVT
Uncooperative Drugs in in vitro Transporter Research: Practical Approaches to Address Instability and Nonspecific Binding Challenges
Joanna Barbara, PhD, Site Lead, BioIVT
This symposium is intended to provide insights on best practices to address the challenges associated with conducting drug transporter studies with compounds that are unstable, sticky or unstable.
Learning Objectives
1) Facilitate understanding of fundamental test system and platform limitations in drug transporter research.
2) Provide practical methodologies to account for nonspecific binding and instability in transporter studies.
3) Illustrate study designs with case studies.
Target Audience: Researchers that are investigating the ADME-Tox and DMPK profiles of new drug candidates.
In vitro drug transporter data are critical for understanding drug-drug interaction potential, but those data are only useful if conclusions can be drawn. Researchers are plagued with practical challenges associated with compounds that are unstable, sticky or insoluble, resulting in convoluted or inconclusive results. Three interesting transporter case studies are presented, each highlighting a creative solution to explore perplexing initial results and generate useful data, despite problematic physicochemical characteristics of the drugs involved.
07:15 - 08:15 | Tuesday, June 13
Elsevier
Mechanistic Static DDI Predictions for Supporting Drug Regulatory Submissions and Labelling
Olivier Barberan, Director of Translational Medicine Solutions, Elsevier
This Presentation is intended to use concrete examples to explain how to use Mechanistic DDIs prediction to support drug submissions and labelling.
Learning Objectives
After completing this study or session, participants will be able to understand and apply the concept of mechanistic static drug-drug interaction predictions to support drug submissions. They will be able to:
1) Define the mechanistic static approach for predicting drug-drug interactions (DDIs) and explain its significance in the drug submission process.
2) Interpret mechanistic static predictions to identify potential risks associated with specific drug combinations and guide appropriate clinical trial design, dosing regimens, and patient safety measures.
3) Discuss the advantages of the mechanistic static approach in exploring DDI mechanisms and identifying key factors influencing drug interactions.
4) Recognize the importance of integrating mechanistic insights with static prediction techniques to enhance DDI assessments and inform regulatory decision-making.
5) Explore the potential impact of mechanistic static drug-drug interaction predictions on optimizing therapeutic strategies for patients taking multiple medications.
By acquiring knowledge and skills related to mechanistic static drug-drug interaction predictions, participants will be better equipped to support drug submissions, evaluate potential risks, and optimize drug therapies for improved patient outcomes.
Target Audience: Clinical Pharmacologists, DMPK Specialists
Drug-drug interactions (DDIs) pose significant challenges during the drug submission process, as they can potentially impact the safety and efficacy of pharmaceutical products. Predicting and assessing these interactions accurately is crucial for regulatory agencies, pharmaceutical companies, and healthcare providers. Mechanistic modelling involves understanding the underlying biological and physiological mechanisms that contribute to drug interactions, such as enzyme inhibition, induction, and enzyme mediated metabolism. Static prediction techniques utilize in vitro and in vivo data to estimate the likelihood and magnitude of DDIs. In drug submissions, mechanistic static models are employed to predict potential drug-drug interactions by integrating information from various sources. These models consider factors such as drug metabolism pathways, enzyme, and drug concentrations at the site of action. By simulating the interaction between multiple drugs within a biological system, mechanistic static models can estimate the extent of the interaction and the resulting impact on drug efficacy and safety. Furthermore, mechanistic static predictions enable the identification of specific drug combinations that may lead to clinically significant interactions. This information assists in designing appropriate clinical trials, optimizing dosing regimens, and providing guidance for potential dose adjustments or contraindications to ensure patient safety. In conclusion, the mechanistic static approach offers a valuable tool for predicting and evaluating drug-drug interactions, thus supporting the drug submission process. By combining mechanistic insights with static prediction techniques, this approach enhances the accuracy and efficiency of DDI assessments, aiding in the identification of potential risks and guiding appropriate regulatory measures.
