Optimization of chemotherapy and immunotherapy: In silico analysis using pharmacokinetic–pharmacodynamic and tumor growth models

Chemotherapy is one of the most prominent cancer treatment modalities. However, it is not always a comprehensive solution for tumor regression. This led to the advent of novel strategies to combine chemotherapy with other emerging therapies to treat cancer patients keeping side effects to a minimum. In this work, the focus is on the optimization of chemotherapy using doxorubicin and its combination with adoptive-cell-transfer therapy which is one of the schemes of immunotherapy. The key challenge in the combination therapy is to find the sequence, timing and the dosage of therapies for a given patient. In this regard, an in silico pharmacokinetic/pharmacodynamic model describing the interaction between tumor cells, immune cells and doxorubicin is used to formulate a multi-objective optimization problem by considering clinically relevant objectives and constraints. Then, the multi-objective optimization problem is solved using genetic algorithm and the results obtained for the different cases are compared to discover a therapeutically efficacious treatment regimen. And the metrics used to compare different cases are final tumor size and tumor relapse time. The comparison between chemotherapy alone and its combination with immunotherapy shows that combination therapy is effective in controlling the tumor growth.

► A multi-objective optimization problem is formulated and solved for different therapies.
► Proposed an alternative protocol of doxorubicin which is better than the standard protocol.
► Simulation results suggest that immunogenicity factor should be accounted during therapeutic interventions.
► Proposed combination therapy is effective in controlling the tumor growth than chemotherapy.