Sponsored by Certara
Model-Based Meta-Analysis (MBMA) is a powerful quantitative tool that integrates data from publicly available clinical trial data to enable deeper insights, smarter decisions, and more efficient drug development. By combining evidence across studies, MBMA can inform dose selection, benchmark efficacy, support trial design, and predict long-term outcomes—bridging gaps where head-to-head trials or data are lacking.
In this webinar, experts from Certara and GSK will explore the transformative impact of MBMA across multiple therapeutic areas, including inflammatory diseases, hepatitis B, and oncology. Through real-world case studies, you’ll see how MBMA has been applied to:
• Bridge dose-response findings across indications
• Guide treatment strategies by benchmarking to existing treatment options
• Predict long-term survival outcomes from short-term response data
Join us to discover how MBMA can unlock new possibilities in your clinical development strategy—and gain insights into practical applications that have already informed global R&D decisions.
Key Takeaways
By participating in this webinar, you will learn how to:
• Apply MBMA to drive data-informed decisions across all stages of drug development
• Bridge critical evidence gaps across indications, populations, and timepoints
• Use MBMA to predict long-term clinical outcomes and support regulatory strategies
• Optimize dose selection and trial design through aggregated clinical data
• Strengthen regulatory submissions by integrating MBMA into your MIDD framework
• Scale MBMA applications across diverse therapeutic areas—from inflammation and virology to oncology
Privacy Policy
Previously, Matt was at Janssen (J&J) where he led the development of their global MBMA capability and worked together with many clinical development teams to ensure optimal impact of MBMA on critical program decisions. Matt also held positions at Pfizer and Amylin as a pharmacometrician, implementing model-informed drug development strategies across a variety of programs and therapeutic areas. He started his professional career as a business consultant at Applied Predictive Technologies, where he designed and analyzed experimental trials to provide business recommendations for multiple Fortune 500 companies.
Matt received his PhD in Bioengineering from UC Berkeley and UCSF and holds an MBA from UC Berkeley, Haas School of Business.
Richard studied at the University of Oxford; he studied Physics before completing his doctorate in Engineering Science. At Oxford, Richard also conducted research in Computational Biochemistry and in Materials Science.
Richard enjoys running, cooking, and tinkering with cars in his free time; he was a competitive track and cross-country runner for 10 years and he spent several years coaching track and field.
After obtaining a Ph.D. in Biomedical Engineering from the Polytechnic of Milan, Italy, she has been an employee of the Department of Computer Science and Systems Engineering, University of Pavia, Italy, assigned to collaborative projects with pharma industry.
She has more than 20 years of experience in the pharmaceutical field across different therapeutic areas such as immuno-inflammation, neurosciences, oncology and glucose-insulin metabolism. Beyond model based meta-analysis, her areas of expertise include non-linear mixed-effect population modelling applied to pharmacokinetics and pharmacodynamics at clinical and preclinical level, efficacy and toxicity evaluation and target mediated drug disposition modelling. Further areas of interest are disease progression modeling, joint modelling, estimation methods, covariate analysis.
She is a member of the Scientific Organizing Committee of the PAGE conference.
She is the current pharmacometrics co-chair in the Stats and Pharmacometrics Special Interest Group (SxP SIG) sponsored by the American Statistical Association and International Society of Pharmacometrics. She has been co-chair of the MBMA Special Interest sub-Group (subSIG) and she recently joined the AI/ML subSIG.
