Drug-induced resistance evolution necessitates less aggressive treatment

Area of Science:

• Evolutionary biology
• Pharmacology
• Cancer research

Background:

• Anticancer and antimicrobial therapies can inadvertently increase drug resistance by elevating mutation rates.
• Understanding the biological costs of treatment, such as induced mutability, is crucial for effective population control.

Purpose of the Study:

• To identify and characterize the trade-off between reducing target population size and generating treatment-induced mutations.
• To develop an evolutionarily informed treatment protocol that optimizes cure probability.

Main Methods:

• Mathematical modeling to link growth rate, pharmacodynamics, and dose-dependent mutation rates.
• Analysis of the relationship between drug dosage and treatment outcomes, considering both population decay and resistance evolution.

Main Results:

• The probability of cure is maximized at an intermediate drug dosage, not necessarily the one causing maximal population decay.
• A general analytical relationship was derived for optimal control laws considering eco-evolutionary costs.
• Less aggressive treatment strategies may improve outcomes by mitigating the generation of resistance mutations.

Conclusions:

• Standard high-dose therapy practices may be suboptimal due to the evolutionary costs of treatment.
• Identifying and quantifying the mutagenic effects of therapies is essential for improving treatment efficacy.
• Further experimental and clinical research is needed to investigate the hidden collateral costs of therapies, including mutagenicity.