Quantitative systems pharmacology models: unleashing their potential in cancer immunotherapy

Abstract

Background Significant breakthroughs in cancer immunotherapy have been made in recent years. However, owing to the heterogeneity of patients' immune status, treatment response varies substantially, necessitating optimized dosing strategies and the use of predictive biomarkers. Quantitative systems pharmacology (QSP) modeling, an emerging computational approach, integrates multiscale data to characterize tumor-immune interactions and drug mechanisms, offering potential for precision immunotherapy. Area covered This review outlines the fundamentals of QSP, including modeling strategies, validation methods, and uncertainty quantification, and summarizes its applications in immuno-oncology. We discuss the role of QSP in characterizing immune-tumor dynamics and its use in optimizing immune checkpoint inhibitors (ICIs), T-cell engagers (TCEs), chimeric antigen receptor T (CAR-T) therapies, and cancer vaccines. The key applications of QSP modeling include predicting first-in-human (FIH) doses, designing virtual clinical trials for regimen optimization, identifying biomarkers, and mitigating cytokine release syndrome (CRS). Finally, we highlight current challenges, such as standardization, validation, and artificial intelligence (AI) integration, and future directions to enhance the utility of QSP in drug development and precision medicine. Expert opinion QSP enables the quantitative prediction of disease progression and drug effects. Its ability to optimize dosing and identify responders positions it as a powerful tool for precision immunotherapy as advances in standardization and cross-disciplinary collaboration develop.