(M-099) Advancing Drug Development in Relapsed and Refractory Multiple Myeloma (RRMM): Assessing the Safety and Efficacy Landscape Utilizing Model-Based Meta-Analysis

Amol Dhamane, Amol.Dhamane@bms.com – Director, Health Economics and Outcomes Research (HEOR), Bristol Myers Squibb; Chuanpu Hu, chuanpu.hu@bms.com – Senior Director, Clinical Pharmacology, Pharmacometrics & Bioanalysis, Bristol Myers Squibb; Anna Kondic, anna.kondic@bms.com – Executive Director, Clinical Pharmacology, Pharmacometrics & Bioanalysis, Bristol Myers Squibb; Marion Bouillon-Pichault, marion.bouillon-pichault@bms.com – Director, Health Economics and Outcomes Research (HEOR), Bristol Myers Squibb; Jian Zhou, Jian.Zhou1@bms.com – Associate Director, Clinical Pharmacology, Pharmacometrics & Bioanalysis, Bristol Myers Squibb

Objectives: The primary aim of this study was to understand the comparative safety and efficacy profiles of existing drugs in the RRMM market. Published clinical trial data was analyzed using the model-based meta-analysis (MBMA) technique, a quantitative approach to mine information from summary level data and aid decision making, thus fitting in the model-informed drug development (MIDD) framework. Specifically, the focus was on evaluating Grade 3+ neutropenia as a safety endpoint and Overall Response Rate (ORR) as an efficacy endpoint within the context of the current therapeutic landscape. The impact of potential prognostic variables on these endpoints was investigated alongside using simulations. The MBMA approach, aims to overcome data heterogeneity challenges and enable comparisons between existing treatments, incorporating dose-response relationships and covariate effects.

Methods: A literature database comprising of 352 randomized clinical trials in RRMM with 512 treatment arms and 177,692 RRMM patients, was utilized. The database was further refined to only include treatment arms with more than 10 patients and treatments with at least 3 studies to ensure that the data used for modelling is statistically robust. This resulted in a total of 130 trials (154 treatment arms) representing 17,246 patients for Grade 3+ neutropenia. For the ORR endpoint, 192 trials (239 treatment arms) were included, covering 22,227 patients. The probability of Grade 3+ neutropenia and ORR incidence were estimated using Binomial regression. Between-trial and between-treatment-arm variabilities were applied to the model parameters and potential covariates (patient demographics, prior treatments, and clinical trial design) influencing the probability of the endpoints were evaluated. Covariate selection was performed using stepwise covariate modelling approach (∆ BIC).

Results: The preliminary model successfully predicted the probability of both endpoints in alignment with observed trial data which was assessed by visual predictive check plots. Key findings indicated a decrease in Grade 3+ neutropenia probability with the progression of study phases, and that the probability of ORR increased with background steroid therapy and higher percentage of patients in stage 1 of the disease but decrease with therapy line advancements.

Conclusion: The study highlights the utility of MBMA as a powerful tool in ranking different RRMM interventions with respect to Grade 3+ Neutropenia and ORR endpoints sourcing data from a publicly available clinical trial information. This MBMA model could ultimately aid in comprehensive evaluation of safety and efficacy of RRMM treatments strategies.

Citations: NA