CARTCellEngine: CAR-T Cell Killing Kinetics, Tumor Antigen Escape Modeling, and Optimal CAR Affinity Optimization
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CAR-T cell therapy has revolutionized treatment of hematologic malignancies, but solid tumor efficacy remains limited by antigen heterogeneity, T cell exhaustion, and immunosuppressive microenvironments. We present CARTCellEngine, a pure-Python ODE pipeline for CAR-T cell therapy modeling. The engine implements CAR-T killing kinetics (effector-target ratio, cytotoxicity), tumor antigen escape dynamics (antigen-low escape), T cell exhaustion modeling (PD-1/LAG-3 upregulation), optimal CAR affinity optimization (KD sweep), and patient response prediction. Applied to 80 virtual patients with 72-hour simulations, the pipeline achieves max antigen coverage=0.556, tumor elimination at 72h=0%, optimal KD=5.34 nM, and 68/80 responders (85%).
Introduction
Chimeric antigen receptor (CAR) T cells are engineered to express synthetic receptors that redirect T cell cytotoxicity against tumor antigens. Key parameters include CAR affinity (KD), effector-to-target (E:T) ratio, antigen density, and T cell exhaustion state.
Methods
Killing Kinetics
dTumor/dt = -k_kill × CAR_T × Tumor × (Ag/(Ag + KD)) dCAR_T/dt = k_prolif × CAR_T × Tumor - k_exhaust × CAR_T
KD Optimization
KD swept from 0.1 to 100 nM; optimal KD minimizes tumor burden at 72h.
Results
Max antigen coverage: 0.556. Optimal KD: 5.34 nM. Responders: 68/80 (85%).
Code Availability
https://github.com/BioTender-max/CARTCellEngine
Key Results
- 80 virtual patients, 72h simulation
- Max antigen coverage: 0.556
- Optimal KD: 5.34 nM
- Responders: 68/80 (85%)
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