Optimal control theory as a method for designing multidrug adaptive therapy regimens

Evolutionarily informed regimens offer new approaches that hope to combat the development of resistance during cancer treatment. These regimens are often complicated to design. In this work, we use optimal control theory (OCT) to guide the design of a two-drug adaptive therapy regimen. We begin with a logistic differential equation model of a tumor composed of four populations with different resistance to two drugs. OCT analysis identified rules regarding regimen creation. We simulated various regimen designs, and we found that regimens prioritizing maintenance of competition between resistant and sensitive populations performed the best. Results were robust across parameters, indicating that the ability to maintain competition was more important than the technique used. With regimens identified, we compared them to practical and standard-of-care regimens to predict future in vitro performance. Practical regimens still outperform standard-of-care for nearly all parameters. As such, OCT offers one avenue for exploring multidrug adaptive regimen design.