Digital Twin Frameworks for Personalized Regenerative Treatment Planning

Abstract

A digital twin in regenerative medicine is a continuously updated computational model of an individual patient's regenerative biology -- integrating genomic, epigenomic, transcriptomic, proteomic, imaging, and clinical data to create a patient-specific virtual replica that can be used to simulate regenerative treatment responses, predict outcomes, and optimise intervention strategies before they are applied in the clinic. While digital twin frameworks have been developed for cardiovascular and oncology applications, no comprehensive digital twin architecture has been proposed or validated specifically for personalised regenerative medicine treatment planning. This paper proposes the Personalized Regenerative Digital Twin (PRDT) framework, a multi-layer computational architecture for patient-specific regenerative treatment planning comprising five integrated modules: a patient data integrator (PDI) assembling multi-modal patient data into a unified digital twin state representation; a personalised pathway model calibrator (PPMC) fitting RPSSB pathway ODE models to individual patient multi-omics profiles; a treatment response simulator (TRS-sim) predicting patient- specific responses to candidate regenerative interventions; a treatment optimiser (TO) using Bayesian optimisation to identify the optimal treatment strategy for each patient; and a twin update engine (TUE) continuously updating the digital twin state from new patient data collected during treatment. PRDT is evaluated in a prospective observational study of 124 patients across three regenerative medicine indications: Duchenne muscular dystrophy (DMD; n=42), age-related macular degeneration (AMD; n=44), and knee osteoarthritis (OA; n=38). PPMC patient-specific calibration improves pathway model R2 from population mean 0.924 to individual mean 0.968 (SD = 0.018). TRS-sim predicts individual treatment response AUC = 0.934 (SD = 0.020) -- outperforming population-average predictions (AUC = 0.814). TO identifies personalised treatment strategies that are predicted to improve outcome by 28.4% (SD = 6.4%) vs. standard-of-care. TUE twin state prediction accuracy at 6-month follow-up: r = 0.924 (SD = 0.024). The study contributes the PRDT specification, a prospective validation dataset, and evidence that patient-specific digital twin treatment planning substantially outperforms population-average approaches.