Digital Twin–Driven Optimization of Immunotherapy Dosing and Scheduling in Cancer Patients

Abstract

Immunotherapy has transformed cancer treatment, providing sustainability responses in subgroups of patients. But, clinical effectiveness of these treatments is usually limited by patient response heterogeneity, adverse immune-related reactions and challenges in identifying the best dosage and timing regimens. A new direction that can help solve these challenges is the concept of a digital twin technology, which represents the virtual patient-specific copies with multi-omics, imaging, clinical, and real-time physiological features. Digital twins can be used to optimize immunotherapy regimens on the basis of patient-specific data and adaptive optimization approaches by simulating tumor-immune global dynamics and treatment pathways. Such a strategy has potentials of improving therapeutic efficacy, reducing toxicity and streamlining clinical decision making. Combining artificial intelligence and computational modelling increases the predictive accuracy further and provides opportunities of real-time intervention and better outcomes. Even though there are difficulties connected with data standardization, validation, and regulatory frameworks, digital twin-based approaches are a radical transition to precision oncology.