MBMA SubSIG: a collaborative example for promoting model-based meta-analysis and its application in drug development
Monica Simeoni (1), Phyllis Chan (2), Yaming Hang (3), Rana Jreich (4), Junshan Qiu (5), Clemence Rigaux (4), Chandni R. Valiathan (6), Jian Zhou (7), Hao Zhu (5), Matthew L. Zierhut (8)a, Marion Bouillon-Pichault (7)a; aco-senior
(1) Clinical Pharmacology Modelling & Simulation, Glaxo SmithKline, UK, (2) Clinical Pharmacology, Genentech, (3) Quantitative Solutions, Takeda, (4) Data & Data Sciences, Sanofi, (5) Division of Pharmacometrics, Office of Clinical Pharmacology, Office of Translational Sciences, CDER, FDA, (6) Clinical Pharmacology and Pharmacometrics, Janssen R&D, (7) Clinical Pharmacology and Pharmacometrics, Bristol-Myers Squibb, (8) Integrated Drug Development, Certara USA, Inc.
Model-based meta-analysis (MBMA) is a method used 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. Sheiner and Steimer [1] first used the term model-based meta-analysis to label work from Holford and Peace (from 1994) that described a population analysis on individual data from multiple trials [2]. In 2005, Mandema et al [3] published an MBMA developed on aggregate level data and applied it to predict comparative efficacy between two drugs at doses that had not been directly compared in a head-to-head study, including multiple response time points. From 2010 onwards there has been an exponential number of articles published on this methodology with a total of 134 publications – 32 last year alone. This increased interest relies on the fact that MBMA is a powerful tool for integrating prior knowledge to inform and de-risk drug development decisions. By leveraging all available clinical trial results, both summary level data in the public domain and proprietary data, MBMA can help compare efficacy or safety of drugs and therapeutic classes, optimize clinical trial designs, predict long-term outcomes based on short-term responses and/or biomarkers [4], interpret new trial results in the context of historical data and guide dosing recommendations for unstudied indications or populations.
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) [5] sponsored the creation of the MBMA Special Interest Sub-Group (MBMA SubSIG) at the end of 2019. The mission of the MBMA SubSIG is to promote model-based meta-analysis and its application in drug development, to raise awareness and interest, and to support the education of others in MBMA. The SubSIG is composed of two co-chairs (Marion Bouillon-Pichault [BMS] & Matt Zierhut [Certara]), a core team, and an advisor team. The thirteen members of the core team come from pharmaceutical companies, consulting companies, and regulatory agencies, and are acting as volunteers for the achievement of the SubSIG workstream goals, which are:
- Raising awareness and building a community of practice
- Identifying training opportunities for MBMA
- Establishing best practices in MBMA
- Broadening the scope of MBMA beyond PMx/Statistics
The advisor team is composed of senior members from pharmaceutical and consulting companies and provides strategic support and leadership.
As part of the workstream deliverables, a first webinar – “Introduction to MBMA” was delivered on January 26, 2021 [6]. Additionally, a LinkedIn group has been created to provide a venue to facilitate learning about this methodology and to share ideas [7]. Relevant literature and MBMA impact cases have been selected and will be organized and made available.
In conclusion, the MBMA SubSIG is a collaborative effort to promote MBMA methodology and interdisciplinary communication in drug development, leveraging the experiences of a broader community. The MBMA SubSIG is keen to learn from individual users of this methodology, and participation is open, with no restrictions, to all who are interested. Please send any questions to: MBMAsig@gmail.com.
References:
[1] Sheiner LB and Steimer J-L. Pharmacokinetic/Pharmacodynamic Modeling in Drug Development. Annual Review of Pharmacology and Toxicology 2000 40:1, 67-95. (https://doi.org/10.1146/annurev.pharmtox.40.1.67)
[2] Holford NHG and Peace K. The effect of tacrine and lecithin in Alzheimer's disease. A population pharmacodynamic analysis of five clinical trials. Eur J Clin Pharmacol (1994) 47:17-23.
[3] Mandema JW, Cox E, Alderman J. Therapeutic benefit of eletriptan compared to sumatriptan for the acute relief of migraine pain—results of a model-based meta-analysis that accounts for encapsulation. Cephalalgia 25, 715– 725 (2005). (https://doi.org/10.1111/j.1468-2982.2004.00939.x)
[4] Leil TA, Lu Y, Bouillon-Pichault M, Wong R, Nowak M. Model-based meta-analysis compares DAS28 rheumatoid arthritis treatment effects and suggests an expedited trial design for early clinical development. Clin Pharmacol Ther. 2021; 109: 517- 527. (https://doi.org/10.1002/cpt.2023)
[5] Statistics and Pharmacometrics (SxP) SIG (go-isop.org)
[6] https://youtu.be/bf7_-cNjydQ
[7] https://www.linkedin.com/groups/12564166/
