Tutorial: Model-Based Meta-Analysis: towards more precisely predicted clinical scenarios

Thursday, November 14, 2024

8:00 AM – 12:00 PM MST

Location: Sonoran Sky Ballroom 5 - 8

Chair and Speaker(s)

Monica Simeoni, PhD

Director
Clinical Pharmacology Modelling and Simulation, Precision Medicine, GSK, United Kingdom

Disclosure(s): GSK: Employment (Ongoing), Ownership Interest (stocks, stock options, patent or other intellectual property or other ownership interest excluding diversified mutual funds) (Ongoing)

Chair(s)

Phyllis Chan, PhD

Distinguished Scientist
Modeling & Simulation, Clinical Pharmacology, Genentech, United States

Disclosure(s): Genentech: Employment (Ongoing), Ownership Interest (stocks, stock options, patent or other intellectual property or other ownership interest excluding diversified mutual funds) (Ongoing)

Speaker(s)

Matthew L. Zierhut, PhD MBA (he/him/his)

VP
Certara Drug Development Solutions
San Diego, California, United States

Disclosure(s): Certara: Employment (Ongoing), Ownership Interest (stocks, stock options, patent or other intellectual property or other ownership interest excluding diversified mutual funds) (Ongoing); Neurocrine Biosciences: Employment (Ongoing), Ownership Interest (stocks, stock options, patent or other intellectual property or other ownership interest excluding diversified mutual funds) (Ongoing)
Rana JREICH, PhD

Associate Director
Clinical Modeling, Clinical Modeling & Evidence Integration, Sanofi, United States

Disclosure(s): Sanofi: Employment (Ongoing), Ownership Interest (stocks, stock options, patent or other intellectual property or other ownership interest excluding diversified mutual funds) (Ongoing)
ED

Elyes Dahmane, PhD

Pharmacometrics Reviewer
FDA, United States

Disclosure(s): No financial relationships to disclose
Joseph Chen, PharmD

Senior Principal Scientist
Genentech, California, United States

Disclosure(s): Genentech: Employment (Ongoing), Ownership Interest (stocks, stock options, patent or other intellectual property or other ownership interest excluding diversified mutual funds) (Ongoing); Pfizer: Ownership Interest (stocks, stock options, patent or other intellectual property or other ownership interest excluding diversified mutual funds) (Ongoing)

PDF Slides

Disclosure(s):

Monica Simeoni, PhD: GSK: Employment (Ongoing), Ownership Interest (stocks, stock options, patent or other intellectual property or other ownership interest excluding diversified mutual funds) (Ongoing)

Phyllis Chan, PhD: Genentech: Employment (Ongoing), Ownership Interest (stocks, stock options, patent or other intellectual property or other ownership interest excluding diversified mutual funds) (Ongoing)

Matthew L. Zierhut, PhD MBA: Certara: Employment (Ongoing), Ownership Interest (stocks, stock options, patent or other intellectual property or other ownership interest excluding diversified mutual funds) (Ongoing); Neurocrine Biosciences: Employment (Ongoing), Ownership Interest (stocks, stock options, patent or other intellectual property or other ownership interest excluding diversified mutual funds) (Ongoing)

Rana JREICH, PhD: Sanofi: Employment (Ongoing), Ownership Interest (stocks, stock options, patent or other intellectual property or other ownership interest excluding diversified mutual funds) (Ongoing)

Elyes Dahmane, PhD: No financial relationships to disclose

Joseph Chen, PharmD: Genentech: Employment (Ongoing), Ownership Interest (stocks, stock options, patent or other intellectual property or other ownership interest excluding diversified mutual funds) (Ongoing); Pfizer: Ownership Interest (stocks, stock options, patent or other intellectual property or other ownership interest excluding diversified mutual funds) (Ongoing)

Description: In recent times there has been a growing interest in reutilizing clinical data available in the public domain to efficiently design clinical trials with new molecular entities, while benchmarking them against the competitor landscape. Model-based meta-analysis (MBMA) is a method to integrate data from multiple studies using mathematical models to quantitatively describe the effect of treatment, time, and patient population characteristics on the trial outcomes. In response to the increasing attention to this methodology, and because this topic offers a unique opportunity for the collaboration between Pharmacometricians and Biostatisticians, the ISoP-ASA Statistics and Pharmacometrics Special Interest Group (SxP SIG) sponsored the creation of the MBMA Special Interest SubGroup (MBMA SubSIG). Since then, the core team, composed by members from different companies and organizations, has pursued its objective of continuing to raise awareness and interest, and to support the education of others in MBMA, among the subSIG members and across the broader audience.
In the last 15 years while MBMA has found application in different indications such as immunology, oncology and infectious diseases, among the others, the attention to the methodology has also increased in the attempt to improve the interpretation of the external data and the precision in predicting the internal outcomes. In this session in addition to an introduction on the state-of-the-art, a closer look will be given to some methodological aspects, like comparing relative, vs. absolute outcome analyses, and handling covariate and variance components. The tutorial will conclude with the perspective from one regulatory authority.

Learning Objectives:

Upon completion, the audience will learn general concepts about Model-Based Meta-Analysis (MBMA), including how to build disease progression models from longitudinal clinical aggregate-level data as well as how to perform landmark analyses. Then the focus will be concentrated on specific methodological aspect like the implications of using relative vs. absolute outcomes. Ways of handling missing covariates as well as variance components will be illustrated. Applications of these methodologies will be shown across selected disease areas and coding examples will be also presented in some instances.