Integrating <scp>MBMA</scp> and <scp>QSP</scp> to Identify Key Covariates and Predict Treatment Outcomes in Relapsed/Refractory Multiple Myeloma

This study demonstrates the application of a model based meta analysis (MBMA) framework to characterize the safety and efficacy profiles of therapies in relapsed and refractory multiple myeloma (RRMM). Published clinical trial data were analyzed to evaluate the incidence of Grade ≥ 3 neutropenia and overall response rate (ORR), providing a quantitative foundation for model-informed drug development. The final model incorporated trial- and treatment-level covariates and was evaluated using visual predictive checks and predictive simulations. Results revealed increased neutropenia risk associated with alkylating agents and higher ORR in regimens with background corticosteroids and in patients with only one prior line of therapy. MBMA-derived estimates facilitated systematic comparisons across regimens, accounting for heterogeneity in trial design and populations. The MBMA estimates can also support benchmarking of internal regimens against current standards. A quantitative systems pharmacology (QSP) model, developed in parallel, was also used to simulate patient responses across a broad array of RRMM treatments, including novel combinations involving T-cell engagers (TCEs) and CELMoD agents. Trial-calibrated virtual patients and a classifier for prior therapy exposure enabled the prediction of regimen-specific ORR across different treatment histories. Together, the MBMA-informed and QSP-supported modeling strategy enabled a comprehensive benefit-risk assessment by combining statistical estimation with mechanistic simulation. This coordinated approach enhances clinical decision-making by enabling comparison of novel or investigational therapies to the evolving treatment landscape, particularly in the absence of head-to-head trials.