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Drug-induced resistance evolution necessitates less aggressive treatment

Kuosmanen, T., Cairns, J. G., Noble, R. ORCID: 0000-0002-8057-4252, Beerenwinkel, N., Mononen, T. and Mustonen, V. (2021). Drug-induced resistance evolution necessitates less aggressive treatment. PLoS Computational Biology, 17(9), e1009418. doi: 10.1371/journal.pcbi.1009418

Abstract

Increasing body of experimental evidence suggests that anticancer and antimicrobial therapies may themselves promote the acquisition of drug resistance by increasing mutability. The successful control of evolving populations requires that such biological costs of control are identified, quantified and included to the evolutionarily informed treatment protocol. Here we identify, characterise and exploit a trade-off between decreasing the target population size and generating a surplus of treatment-induced rescue mutations. We show that the probability of cure is maximized at an intermediate dosage, below the drug concentration yielding maximal population decay, suggesting that treatment outcomes may in some cases be substantially improved by less aggressive treatment strategies. We also provide a general analytical relationship that implicitly links growth rate, pharmacodynamics and dose-dependent mutation rate to an optimal control law. Our results highlight the important, but often neglected, role of fundamental eco-evolutionary costs of control. These costs can often lead to situations, where decreasing the cumulative drug dosage may be preferable even when the objective of the treatment is elimination, and not containment. Taken together, our results thus add to the ongoing criticism of the standard practice of administering aggressive, high-dose therapies and motivate further experimental and clinical investigation of the mutagenicity and other hidden collateral costs of therapies.

Publication Type: Article
Additional Information: © 2021 Kuosmanen et al. This is an open access article distributed under the terms of the Creative Commons Attribution License, which permits unrestricted use, distribution, and reproduction in any medium, provided the original author and source are credited.
Subjects: Q Science > QA Mathematics
R Medicine > RC Internal medicine
R Medicine > RC Internal medicine > RC0254 Neoplasms. Tumors. Oncology (including Cancer)
Departments: School of Mathematics, Computer Science & Engineering > Mathematics
Date available in CRO: 03 Nov 2021 09:25
Date deposited: 3 November 2021
Date of acceptance: 3 September 2021
Date of first online publication: 23 September 2021
URI: https://openaccess.city.ac.uk/id/eprint/27002
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