Abstract

CAR T cell therapy is one of the most exciting advances in modern cancer treatment. In this approach, a patient’s own immune cells are reprogrammed in the laboratory with a synthetic “chimeric antigen receptor” (CAR) so that they can recognise and destroy cancer cells. However, relapse and primary resistance remain common, and we do not fully understand why. Experimental systems (in vitro and in vivo) are invaluable for probing CAR T functionality and persistence, but many mechanisms and “what-if” dosing questions cannot be tested directly in the lab. Agent-based models (ABMs) complement experiments by enabling exploration of dosimetry strategies and revealing emergent behaviours, while aligning with the 3Rs (Replace, Reduce, Refine) to save time, cost and animals. ABMs nevertheless require rigorous calibration and validation; without them, models may fail to capture tumour progression and lose predictive power. Progress is further limited by the scarcity of robust, organ-resolved datasets and by implementations that are computationally intensive and hard to use in clinical workflows. To address these gaps, we are assembling an integrative platform linking dry-lab modelling, wet-lab measurement and translational read-outs. At its core, our published ABM serves as the mechanistic engine; around it, an organ-to-organ atlas provides concise, identifiable, tissue-specific priors for CAR T behaviour (initially in the B-cell acute lymphoblastic leukaemia mouse context), with targeted assays to inform calibration and validation. To make virtual-trial exploration practical and accessible, we are porting the model to a high-performance ABM backend and exposing a simple interface aimed at non-computational users. By calibrating the ABM to patient-specific measurements, we instantiate a digital twin, a mechanistic replica on which to test dose, fractionation, timing and route, turning the platform into a clinically useful decision aid and closing the loop from measurement to decision. In this poster, I will present our ABM ’s current capabilities and insights, outline the platform blueprint (including the atlas and high-performance deployment), and share a clear roadmap for translating tissue-aware computational twins into practical dosing and route decisions.