OPTIMIS: Optimizing Personalized Therapies through Integrated Multiscale Intelligent Simulation

Controlling complex biological systems across multiple scales remains a major challenge in computational medicine, because whole-body disease behavior is closely shaped by noisy cellular events at much smaller scales. Standard deterministic models often miss this molecular variability, while fully stochastic simulations are too slow for the repeated, high-throughput interactions needed to train artificial intelligence. To address this problem, we developed a new AI-based framework that combines a discrete stochastic Gillespie algorithm for microscale receptor dynamics with continuous, nonlinear ordinary differential equations for systemic macroscale behavior. To reach the speed needed for deep reinforcement learning (RL), we compress this hybrid system into a differentiable Neural ODE surrogate that acts as a fast digital twin. As a proof of concept, we applied this framework to engineered cellular therapy and used RL agents to learn dynamic, closed-loop treatment policies inside the surrogate environment. By tracking microscopic, unpredictable cellular activity as an early-warning signal, the AI learned to continuously adjust the drug dose--anticipating and stopping dangerous immune reactions before they could spiral out of control. This computational advance improved successful control rates to more than 70% in highly unstable simulated phenotypes and provides a practical, general framework for adaptive intervention in multiscale biological systems.