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    Cellular and T cell engager Immunotherapy

    Digital Remote Patient Monitoring for Cytokine Release Syndrome in ABBV-383 Treatment: Methodology and Setup

    P-009: Digital Remote Patient Monitoring for Cytokine Release Syndrome in ABBV-383 Treatment: Methodology and Setup

    Thursday, September 26, 2024
    Introduction: Trial in progress. Patients with triple-class relapsed or refractory multiple myeloma (RRMM) have limited treatment options after progression. B-cell maturation antigen (BCMA) is a validated target for RRMM, and several BCMA-directed therapies (BDTs) are approved. With the use of some approved BDTs (ie, chimeric antigen receptor T-cell and T-cell engagers), cytokine release syndrome (CRS) is a common adverse event, occurring in 45–95% of treated patients. Novel T-cell based immunotherapies with improved safety and tolerability are needed. The BCMA×CD3 bispecific antibody ABBV-383 comprises 2 high-affinity BCMA-binding domains and a low-affinity CD3-binding domain designed to reduce cytokine release with potential for minimal T-cell exhaustion. In an ongoing phase 1 study, mainly low-grade CRS events were observed; all events resolved with a median resolution time of 2 days.. To better understand CRS and guide interventions, we aim to characterize CRS-related vital sign changes before, during, and after CRS and assess their association with baseline clinical, demographic, and bio-sampling data in clinical trial patients receiving ABBV-383.

    Methods: In selected countries, patients receiving ABBV-383 treatment in two clinical trials (NCT06158841 and NCT05650632) will be equipped with wearable digital health devices (Current Health Wearable and Vivalnk Fever Scout™), allowing continuous monitoring of vital signs. Vital signs that will be assessed include axillary temperature, cardiac and respiratory vital signs, and physical activity. The monitoring will commence before cycle 1 infusion and continue for a predefined period.

    The collected data will be used for exploratory characterization of the CRS profile. Granular characterization of CRS onset and resolution will be assessed retrospectively. Statistical and machine learning techniques will be used to identify patterns and establish predictive models for CRS and characterize vital sign dynamics upon CRS-mitigative intervention.

    Results: The remote patient monitoring data will be analyzed to identify any significant changes in vital signs preceding the onset of CRS. We will employ statistical and machine learning techniques to identify patterns and establish predictive models for CRS. We will also evaluate the efficacy of interventions (eg, reduced hospitalizations and CRS severity) by tracking the return of CRS-related vital sign changes to normal levels after appropriate interventions.

    Conclusions: Selected trials within the ABBV-383 clinical trial program will utilize digital remote patient monitoring to comprehensively evaluate CRS, with the aim to identify early warning signs of CRS and assess the effectiveness of interventions. The findings from this study will contribute to a better understanding of CRS, inform the needed resource utilization with respect to routine monitoring and required hospitalization, and guide personalized interventions in patients receiving ABBV-383.