A Minimal Model Framework for Robust CAR-T Cell and Oncolytic Virus Combination Therapy

Glioblastoma remains one of the most lethal brain cancers. Combination therapy using CAR-T cells and oncolytic viruses shows promise, yet the mechanisms underlying synergy remain poorly understood. We develop mathematical models to analyze IL-13R$\alpha$2-targeting CAR-T cells and the oncolytic virus C134 using patient-derived glioblastoma data. We present a minimal model framework for predicting combination immunotherapy outcomes. Applying timescale separation between rapid viral and slower cellular dynamics, we derive quasi-steady-state (QSS) approximations that reduce complexity while maintaining accuracy. The QSS model uses 9 parameters compared with 11 in the full model and achieves comparable fits. Model comparisons using the Akaike Information Criterion indicate that the QSS model is generally favored; it consistently yields lower AIC values for oncolytic virus monotherapy and produces lower AIC values in three of four combination therapy conditions. Models with and without CAR-T exhaustion produce identical fits, indicating that exhaustion dynamics do not improve predictions within the 72-hour observation window. Overall, our results demonstrate that simplified QSS formulations effectively capture viral dynamics and provide a practical framework for optimizing combination immunotherapies.