E-004 - DEVELOPMENT OF QSP PLATFORM MODEL FOR PREDICTING CLINICAL EFFICACY AND CRS INCIDENCE OF CD3 BISPECIFICS IN STEAP1 PROSTATE CANCER

G. NAIR¹, D. BEDATHARU², B. PALEJA³, D. MITRA¹, U. NAKUL¹, S. PALLIKONDA CHAKRAVARTHY¹; ¹VANTAGE RESEARCH, TAMIL NADU, INDIA, ²VANTAGE RESEARCH, LEWES, DE, USA, ³VANTAGE RESEARCH, TAMILNADU, INDIA.

Background: CD3 bispecifics are engineered antibodies that bind CD3 on T cells and target antigens on cancer cells (TAAs), activating T cells to kill cancerous cells. However, they may induce adverse effects such as CRS (Cytokine Release Syndrome) from on-target off-tumor toxicity, prompting ongoing research to define the optimal therapeutic balance. Research aimed at optimizing CD3 bispecifics for cancer therapy involves integrating mechanistic modeling with experimental data to comprehensively elucidate the drug's mechanism of action in tumor and normal tissues, emphasizing tumor-associated antigen (TAA) receptor occupancy as pivotal in determining both efficacy and potential adverse events like cytokine release syndrome (CRS). The primary goal of developing the CD3 bispecifics QSP platform model is to understand the physiological drivers of efficacy and adverse events and apply that to recommend an efficacious clinical dose, while proposing dosing strategies to mitigate CRS. For the current effort, we focus on recommending a clinical efficacious dose via clinical translation from mouse Tumor Growth Inhibition (TGI) studies.

Methods: In this study, a QSP platform model was developed for a 2+1 STEAP1 x CD3 bispecific antibody targeting prostate cancer. This platform model was built upon earlier work by Hosseini et al. (2020) [1]. In the current work, we have further extended this model to a 1+2 bivalent bispecific antibody for Prostate cancer indication to determine the optimal efficacy and dose priming strategy to mitigate CRS. We have used the clinical data for Xaluritamig available from the public source [2] to calibrate and validate this expanded platform model.

Results: The QSP model effectively represented complex PBPK characteristics of drug and T Cells, clinical efficacy and cytokine biomarker data post-Xaluritamig dosing in prostate cancer. It reasonably predicted the efficacious dose observed in clinical settings based on Mouse TGI studies.

Conclusion: The model also has capabilities to study the impact of antigen expression levels, binding affinities and baseline T Cell concentration on anti-tumor efficacy and CRS incidence. This platform approach can be used in early clinical drug development stages to inform the therapeutic index.