Preclinical Investigation of Resveratrol and CAR-Macrophage Synergy for IDH1-Mutant Glioblastoma: An AI-Driven, Multi-Scale Modelling Rationale and Protocol

Glioblastoma (GBM) remains a clinical death trap with limited progress in decades and dismal survival rates. In this study, we present a computationally optimized, first-in-class therapeutic strategy combining Resveratrol and CAR-macrophages for IDH1-mutant GBM. Using an AI-driven, multi-scale modeling framework integrating ODEs, agent-based modeling, and reaction-diffusion dynamics, we simulated over 740 compute hours and 250 experimental conditions. The optimized combination achieved a predicted 93% tumor volume reduction in silico (95% CI: 90–96%, p < 0.001), far exceeding benchmarks for monotherapies or temozolomide (~30%). Synergy was driven by Resveratrol-induced metabolic reprogramming, antigen upregulation (+33% IL13Rα2/EGFR), and enhanced CAR-macrophage persistence and cytotoxicity (M1/M2 ratio 5.8:1). Monte Carlo sensitivity analysis (n=250) confirmed robustness across parameter ranges, with a peak synergy index of 2.35. This study provides a mechanistically grounded, computationally validated hypothesis for a novel, testable GBM therapy, and lays the foundation for in vitro and in vivo validation. Key model logic, simulation parameters, and setup conditions will be released upon publication to support community replication and refinement.