Optimizing the future: how mathematical models inform treatment schedules for cancer

For decades, mathematical models have influenced how we schedule chemotherapeutics: the most notable example stems from the Norton–Simon hypothesis which led to the advent of dose-dense scheduling to improve disease-free and overall survival. Newer mathematical models have leveraged game theory and ecological principles to propose adaptive therapy scheduling, with the aim of stabilizing a patient's disease and preventing the growth of surviving resistant cell populations. Considering more than one therapy dramatically increases the complexity of predicting the optimal drug order and schedule for individual patients, and competing evidence supports simultaneous, sequential, or alternating treatment plans. Designing optimal therapeutic schedules most likely to benefit the individual will likely require personalized medicine treatment strategies utilizing both mathematical and clinical triage to assess what is both theoretically optimal and most practical. For decades, mathematical models have influenced how we schedule chemotherapeutics. More recently, mathematical models have leveraged lessons from ecology, evolution, and game theory to advance predictions of optimal treatment schedules, often in a personalized medicine manner. We discuss both established and emerging therapeutic strategies that deviate from canonical standard-of-care regimens, and how mathematical models have contributed to the design of such schedules. We first examine scheduling options for single therapies and review the advantages and disadvantages of various treatment plans. We then consider the challenge of scheduling multiple therapies, and review the mathematical and clinical support for various conflicting treatment schedules. Finally, we propose how a consilience of mathematical and clinical knowledge can best determine the optimal treatment schedules for patients. For decades, mathematical models have influenced how we schedule chemotherapeutics. More recently, mathematical models have leveraged lessons from ecology, evolution, and game theory to advance predictions of optimal treatment schedules, often in a personalized medicine manner. We discuss both established and emerging therapeutic strategies that deviate from canonical standard-of-care regimens, and how mathematical models have contributed to the design of such schedules. We first examine scheduling options for single therapies and review the advantages and disadvantages of various treatment plans. We then consider the challenge of scheduling multiple therapies, and review the mathematical and clinical support for various conflicting treatment schedules. Finally, we propose how a consilience of mathematical and clinical knowledge can best determine the optimal treatment schedules for patients. a treatment strategy that dynamically alters dosing in response to tumor progression/regression so as to maintain a stable tumor burden. a treatment plan for chemotherapy in which drugs are administered more frequently than in standard regimens. in this context, the cumulative dose administered over a period of time that affects the biological response. a lower ability of therapy-resistant clones to replicate in the absence of treatment relative to other threapy-sensitive clones in the same tumor. cell proliferation that follows a sigmoidal function, such that smaller tumors grow more rapidly than larger tumors. a treatment strategy in which low doses of chemotherapy are administered continuously. an emerging treatment strategy in which higher doses are administered followed by breaks in treatment.