AI-Powered Precision Learning: Advances in RRMM: Clinical Practice Considerations for Optimizing BCMA Bispecific Antibody Combination Therapy

Relapsed/refractory multiple myeloma (RRMM) remains clinically heterogeneous and increasingly difficult to control across successive lines of therapy, particularly among patients with high-risk disease, extramedullary involvement, or cumulative immune compromise. BCMA-targeted bispecific antibodies have expanded treatment options by providing off-the-shelf T-cell redirection with meaningful activity in heavily pretreated patients, but response depth, durability, tolerability, and feasibility vary by disease biology, prior therapy exposure, tumor burden, and patient-specific factors. Emerging combinations of BCMA bispecific antibodies with CD38-directed antibodies offer a strong mechanistic rationale and encouraging early efficacy, yet clinicians must interpret evolving trial data carefully relative to bispecific monotherapy, established second-line regimens, and cellular therapies. As these approaches move into earlier lines of therapy and broader practice settings, clinicians need practical strategies for patient selection, sequencing, step-up dosing, toxicity monitoring, and patient-centered care delivery to support safe, individualized integration of bispecific antibody based regimens in RRMM. In this CE Concepts AI-powered precision learning activity, expert faculty will examine the biologic rationale, emerging clinical evidence, and practical implementation strategies for BCMA bispecific antibody CD38 antibody combinations in RRMM, with emphasis on patient selection, sequencing, toxicity management, and integration into evolving earlier-line treatment pathways.