TwinRL-Onco: A World Model-Empowered Digital Twin Framework with Hierarchical Reinforcement Learning for Venetoclax...

Chronic lymphocytic leukaemia (CLL) presents considerable therapeutic obstacles due to the development of treatment-resistant disease, especially with BCL-2 inhibitors like venetoclax, and is among the most challenging haematological malignancies to treat. A critical unmet computational need is the ability to predict patient-specific treatment resistance trajectories and to develop optimal adaptive therapy schedules based on a patient's history and experience with treatments. To address these issues, we have developed TwinRL-Onco, a novel computational framework that fuses digital twin technology, driven by world model-based simulations, with hierarchical reinforcement learning (HRL), to predict in silico treatment response and optimize the adaptive therapy process. TwinRL-Onco consists of three synergistic components: (1) a Variational Recurrent State-Space Model (VRSSM); (2) a hierarchical policy structure with two levels of control; and (3) a resistance trajectory predictor that uses Monte Carlo rollouts generated from the world model to simulate treatment response. TwinRL-Onco achieved an AUROC of 0.917 for 12-month simulated prediction of progression, an improvement of 4.6 percentage points over the DreamerV3 algorithm and 7.0 percentage points over the LSTM-Traj algorithm. TwinRL-Onco is an emphasis on an artificial intelligence-based online environment for hypothesis generation; all on-site metrics indicate inventory-only consistency and need actual clinical validation before any conclusions regarding clinical prediction.