CPT Pharmacometrics Syst Pharmacol
December 2024
Multiple myeloma (MM) treatment guidelines recommend waiting for formal progression criteria (FPC) to be met before proceeding to the next line of therapy. As predicting progression may allow early switching to next-line therapy while the disease burden is relatively low, we evaluated the predictive accuracy of a mathematical model to anticipate relapse 180 days before the FPC is met. A subset of 470/1143 patients from the IA16 dataset who were initially treated with VRd (Velcade (bortezomib), Revlimid (lenalidomide), and dexamethasone) in the CoMMpass study (NCT01454297) were randomly split 2:1 into training and testing sets.
View Article and Find Full Text PDFMultiple myeloma management requires a balance between maximizing survival, minimizing adverse events to therapy, and monitoring disease progression. While previous work has proposed data-driven models for individual tasks, these approaches fail to provide a holistic view of a patient's disease state, limiting their utility to assist physician decision-making. To address this limitation, we developed a transformer-based machine learning model that jointly (1) predicts progression-free survival (PFS), overall survival (OS), and adverse events (AE), (2) forecasts key disease biomarkers, and (3) assesses the effect of different treatment strategies, e.
View Article and Find Full Text PDFNovel therapies have improved outcomes for multiple myeloma (MM) patients, but most ultimately relapse, making treatment decisions for relapsed/refractory MM (RRMM) patients increasingly challenging. We report the final analysis of a single-arm, phase 2 study evaluating the oral proteasome inhibitor (PI) ixazomib combined with daratumumab and dexamethasone (IDd; NCT03439293). Sixty-one RRMM patients (ixazomib/daratumumab-naïve; 1-3 prior therapies) were enrolled to receive IDd (28-day cycles) until disease progression/unacceptable toxicity.
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