Fine-tuning anti-tumor immunotherapies via stochastic simulations

摘要: Anti-tumor therapies aim at reducing to zero the number of tumor cells in a host within their end or, least, leaving patient with sufficiently small so that residual can be eradicated by immune system. Besides severe side-effects, key problem such is finding suitable scheduling administration patients. In this paper we study effect varying therapy-related parameters on final outcome interplay between and This work generalizes our previous hybrid models an where interleukins are modeled as continuous variable, system discrete-state continuous-time stochastic process. The model use obtained modifying corresponding deterministic model, originally proposed Kirschner Panetta. We consider Adoptive Cellular Immunotherapies Interleukin-based therapies, well combination. By asymptotic transitory analyses find conditions guaranteeing eradication, tune accordingly. then perform simulations under various therapeutic settings: constant, piece-wise constant or impulsive infusion daily weekly delivery schedules. Results suggest that, some cases, schedule may deeply impact therapy-induced eradication time. Indeed, suggests not effective for every patient, most stimulate immune-response. For metronomic seems more effective, it happens other anti-angiogenesis chemotherapies, than constant. expected synergistic effects have been observed when combined.