Abstract
CAR-T cell therapy is a personalized immunotherapy that has shown promising results in treating hematologic cancers. However, its therapeutic efficacy in solid cancers is often limited by tumor evasion mechanisms, resistance pathways, and an immunosuppressive tumor microenvironment. These challenges highlight the need for advanced predictive models to better capture the intricate interactions between CAR-T cells and tumors to enhance their potential. Digital Twins represent a transformative approach for optimizing CAR-T cell therapy by providing a virtual representation of the therapy-tumor trajectory using high-dimensional patient data. In this review, we first define Digital Twins and outline the fundamental steps in their development. We then explore the critical parameters required for designing CAR-T-specific Digital Twins. We examine published case studies demonstrating a few applications of Digital Twins in addressing key challenges in CAR-T cell therapy, including their impact on clinical trials and manufacturing processes. Finally, we discuss the limitations associated with integrating Digital Twins into CAR-T therapy. As Digital Twin technology continues to evolve, the potential to enhance CAR-T therapy through precision modeling and real-time adaptation could redefine the landscape of personalized cancer treatment.