Global sensitivity analysis and model-based reactive scheduling of targeted cancer immunotherapy

Intra-patient variability is a key challenge in cancer treatment. This makes it necessary to find the factors affecting tumor growth and accordingly schedule therapies over the treatment horizon for the patient. In this work, model-based studies are performed to investigate these issues for optimal immunotherapeutic intervention. Dendritic cell therapy is a targeted immunotherapy where the dendritic cells and its activating agents such as interleukin are engineered, stimulated to recognize and specifically eradicate tumors. A mathematical model that integrates tumor dynamics and dendritic cell therapy is used to perform the analysis. Global sensitivity analysis of the model is done using high dimensional model reduction (HDMR) technique and the key parameters altering the tumor growth are identified. The variations in these key parameters are deemed to result in intra-patient variability during the treatment phase. Then, reactive scheduling is used to schedule dendritic cell interventions with and without interleukin interventions under the varying conditions of the patient. Moreover, the key parameters obtained from HDMR are verified using the reactive scheduling and nominal scheduling approaches. Besides saving costs, the in silico analysis done in this paper may be useful to the oncology community in designing experiments to clinically measure the influential parameters. It can also be used as a decision making tool to determine the required intervention dosage during the treatment.