PII-034 - OPTIMIZING DOSE SELECTION FOR CHIMERIC ANTIGEN RECEPTOR T-CELL (CAR-T) THERAPY IN THE TREATMENT OF PATIENTS WITH CANCER : A SYSTEMATIC REVIEW OF MECHANISTIC IN SILICO MODELS.

D. Odujinrin¹, B. Patterson², Y. Mostafa Kamel², G. McClelland²; ¹Author, London, United Kingdom, ²PhD Supervisor, London, United Kingdom.

Background: The treatment of different malignancies using adoptive cell therapy began in the 1980s. Very promising results have been achieved in relapsed/refractory hematologic malignancies with the use of CAR T therapy. Currently there are 6 US FDA approved CAR-T therapies, yet there remains a paucity of evidence to comprehensively characterize the pharmacological variables that are crucial for optimizing efficacy while minimizing adverse effects. This gap hinders the development of individualized dosing strategies, that is required given the significant variability among patients diagnosed with cancer.

Methods: This review aims to evaluate the mechanistic (inclusive of hybrid semi mechanistic) in silico modeling approaches in scientific and grey literature that quantitatively describe the CAR T dose exposure-response correlation, following clinical pharmacology principles.

Results: A systematic review of CAR-T therapy models for cancer using databases such as Medline and Embase up to May 2024, identified 58 English-language articles. Excluded were models of empirical, non CAR T engineered immune cells, and those lacking mathematical simulations. The selected models comprise patient specific factors (e.g., lymphodepletion pre conditioning regimen), product characteristics (e.g., CAR construct), quantitative systems pharmacology, and multi scale cell kinetics and pharmacodynamics. They focus on the mechanistic aspects derived from the biological processes associated with CAR-T, including distribution, expansion, persistence, trafficking, and clearance, and their effects on tumor burden and cytokine release across in vitro, in vivo, and clinical settings.

Conclusion: The findings highlight CAR-T's unique kinetic and dynamic profile, demonstrating the complex interplay between cell dose, target concentration, and efficacy, in eliminating cancer cells while minimizing toxicity. These models explore key factors affecting clinical outcomes, and a framework for optimization. While fully mechanistic models offer detailed theoretical insights into the tumor microenvironment, hybrid models provide a pragmatic dose selection approach by linking sparse empirical data with mechanistic understanding. This approach supports the development of rational and patient centric dosing for this treatment modality.