Exploratory and Non-Compartmental Analyses of PK PD Data

The first step of any PK/PD data analysis is to look at the data on hand and generate insights.
The next step in early phases is to compute Noncompartmental Analysis (NCA) PK/PD parameters and then to generate initial understanding of the PK/PD properties and of the clinical pharmacology implications. The backbone of most regulatory submissions are clinical pharmacology summaries modules based on NCA results (with support from population PK/PD analyses). Yet these steps are often overlooked as many analysts tend to jump into more complex analysis routines right away.

Hence, the goal of this webinar is to introduce the importance of exploratory data analysis and NCA using several real-life data from African studies. The theoretical and practical aspects will be presented as well as what analyses, tables and figures can be used to communicate the results. All scripts and codes will be provided.

Dr. Samer Mouksassi, the lead faculty member is Senior Director, Integrated Drug Development at Certara Strategic Consulting. Since 2007, he has been responsible for implementing model-based drug development, PK/PD modeling and simulation, advanced statistics, realistic clinical trial simulations to help sponsors with regulatory submissions and decisions where his team got numerous approvals from FDA, MEA and Japan. Since 2014, he has been dedicated to the Knowledge Integration Quantitative Sciences team at the Bill and Melinda Gates Foundation to support various programs for the Maternal, Neonatal, and Child Health Initiative. Samer has a passion for teaching and is currently Associate Professor of Pharmacometrics and Clinical Trial Design at Université de Montréal and Lebanese American University. Samer received his Pharm.D from Lebanese University, and Ph.D. in PK/PD modeling from Université de Montréal. He has co-authored over 100 scientific communications (manuscripts, posters, invited presentations) and supervised several MSc and PhD students. His open source R packages focus on insightful exploratory data analysis.