Optimizing immune cell therapies with artificial intelligence

Purpose: We determine an optimal injection pattern for anti-vascular endothelial growth factor (VEGF) and for the combination of anti-VEGF and unlicensed dendritic cells. Methods: We rely on the mathematical model of Soto-Ortiz and Finley (2016) for the interactions between the tumor growth, angiogenesis and immune system reactions. Our optimization algorithm belongs to the class of Monte-Carlo tree search algorithms. The objective consists in finding the minimal total drug doses for which an injection pattern yields tumor eradication. Results: Our results are twofold. First, optimized injection protocols enable to significantly reduce the total drug dose for tumor elimination. For instance, for an early diagnosis date, a total dose equal to 58% of the standard anti-VEGF dose enables to eliminate the tumor. In the case of drug combination, associating 25% of the total standard anti-VEGF dose to 10% of the dendritic cell total standard dose eradicates tumor. Our second result is that administering a dose equal to the maximal standard dose allows for later diagnosis date compared to standard protocol. For instance, in the case of anti-VEGF injection, the optimal protocol postpones the maximal diagnosis date by more than one month. Conclusions. Overall, our optimization based on artificial intelligence delivers significant gains in total drug administration or in the length of the therapeutic window. Our method is flexible and could be adapted to other drug combinations.