Patient‐Specific Determinants of Response to BCMA ‐ and GPRC5D ‐Targeted CAR T‐Cell Therapy in Mu

Despite promising outcomes in CAR T‐cell therapy for relapsed/refractory multiple myeloma (RRMM), nearly all patients eventually relapse. Resistance and relapse may be driven by CAR T‐cell and tumor‐intrinsic factors. Here, we developed a mechanistic quantitative systems pharmacology (QSP) model of multiple myeloma growth and CAR T‐cell therapy using measurable biomarkers to predict and identify factors associated with response and relapse. The model incorporates key components to explore disease dynamics and CAR T‐cell expansion. Our model reproduced published pharmacokinetics and biomarker response data from anti‐BCMA and anti‐GPRC5D CAR T‐cell therapies. We then validated the model using clinical biomarker data from a total of 29 real‐world RRMM patients treated with commercial anti‐BCMA CAR T. Virtual trial simulations, exploring the impact of variable baseline disease and CAR T characteristics on response, predicted that factors associated with worse outcomes are intrinsic to tumor cells (disease burden, low‐antigen expression) and CAR T cells (low CAR T‐induced killing rate). Interestingly, simulations suggested that a lower baseline percentage of normal plasma cells is associated with higher overall response. The developed model was also used to predict the outcome of BCMA‐targeted and GPRC5D‐targeted combination CAR T‐cell treatment. Sequential combination therapy simulations predicted a better response in scenarios starting with anti‐GPRC5D CAR T infusion, followed by anti‐BCMA CAR T infusion. Our model can serve as a framework to investigate response mechanisms as well as multi‐antigen targeting, and to optimize clinical trial design and dosing regimens.