CAR-T Cell Therapy Design and Efficacy Prediction
A pure-Python pipeline for CAR-T cell therapy analysis including antigen scoring, exhaustion analysis, and killing kinetics ODE.
- Antigen target scoring (expression × tumor-specificity × accessibility)
- T-cell exhaustion scoring (TOX/PD-1/LAG-3/TIM-3 axis)
- Tumor killing kinetics ODE (effector-target ratio dynamics)
- CAR construct affinity optimization (optimal KD ~1-10 nM)
- Patient response prediction (logistic model)
- 80 patients, 200 tumor antigens
- Max antigen score: 0.556 (AG167)
- Tumor burden @72h (E:T=1:10): 0.00%
- Optimal CAR KD: 5.34 nM
- Responders: 68/80 (85%)
- Exhaustion score p=0.0001
pip install numpy scipy matplotlib
python cart_cell_engine.pycar-t cell-therapy antigen-presentation t-cell-exhaustion adoptive-transfer immunotherapy