1. J.L. Gevertz, H.V. Jain, I. Kareva, K.P. Wilkie, J. Brown, Y.P. Huang, E.D. Sontag, V. Vinogradov, and M. Davies. Delaying cancer progression by integrating toxicity constraints in a model of adaptive therapy. 2025. Note: Submitted.Keyword(s): toxicity, adaptive anti-cancer therapy, virtual populations, cancer treatment resistance, mathematical model, mathematical oncology. Abstract:

   Cancer therapies often fail when intolerable toxicity or drug-resistant cancer cells undermine otherwise effective treatment strategies. Over the past decade, adaptive therapy has emerged as a promising approach to postpone emergence of resistance by altering dose timing based on tumor burden thresholds. Despite encouraging results, these protocols often overlook the crucial role of toxicity-induced treatment breaks, which may permit tumor regrowth. Herein, we explore the following question: would toxicity feedback improve or hinder the efficacy of adaptive therapy? To address this question, we propose a mathematical framework for incorporating toxic feedback into treatment design. We find that the degree of competition between sensitive and resistant populations, along with the growth rate of resistant cells, critically modulates the impact of toxicity feedback on time to progression. Further, our model identifies circumstances where strategic treatment breaks, which may be based on either tumor size or toxicity, can mitigate overtreatment and extend time to progression, both at the baseline parameterization and across a heterogeneous virtual population. Taken together, these findings highlight the importance of integrating toxicity considerations into the design of adaptive therapy.

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