When does high-dose antimicrobial chemotherapy prevent the evolution of resistance?

Abstract

High-dose chemotherapy has long been advocated as a means of controlling drug resistance in infectious diseases but recent empirical and theoretical studies have begun to challenge this view. We show how high-dose chemotherapy engenders opposing evolutionary processes involving the mutational input of resistant strains and their release from ecological competition. Whether such therapy provides the best approach for controlling resistance therefore depends on the relative strengths of these processes. These opposing processes lead to a unimodal relationship between drug pressure and resistance emergence. As a result, the optimal drug dose always lies at either end of the therapeutic window of clinically acceptable concentrations. We illustrate our findings with a simple model that shows how a seemingly minor change in parameter values can alter the outcome from one where high-dose chemotherapy is optimal to one where using the smallest clinically effective dose is best. A review of the available empirical evidence provides broad support for these general conclusions. Our analysis opens up treatment options not currently considered as resistance management strategies, and greatly simplifies the experiments required to determine the drug doses which best retard resistance emergence in patients.\n\nSignificance StatementThe evolution of antimicrobial resistant pathogens threatens much of modern medicine. For over one hundred years, the advice has been to hit hard, in the belief that high doses of antimicrobials best contain resistance evolution. We argue that nothing in evolutionary theory supports this as a good rule of thumb in the situations that challenge medicine. We show instead that the only generality is to either use the highest tolerable drug dose or the lowest clinically effective dose; that is, one of the two edges of the therapeutic window. This approach suggests treatment options not currently considered, and greatly simplifies the experiments required to identify the dose that best retards resistance evolution.