Theoretical Considerations and Empirical Predictions of the Pharmaco- and Population Dynamics of Heteroresistance

Abstract Antibiotics are considered one of the most important contributions to clinical medicine in the last 100 years. Due to the use and overuse of these drugs, there have been increasing frequencies of infections with resistant pathogens. One form of resistance, heteroresistance, is particularly problematic; pathogens appear sensitive to a drug by common susceptibility tests. However, upon exposure to the antibiotic, resistance rapidly ascends, and treatment fails. To quantitatively explore the processes contributing to the emergence and ascent of resistance during treatment and the waning of resistance following cessation of treatment, we develop two distinct mathematical and computer-simulations models of heteroresistance. In our analysis of the properties of these models, we consider the factors that determine the response to antibiotic-mediated selection. In one model, heteroresistance is progressive, with each resistant state sequentially generating a higher resistance level. In the other model, heteroresistance is non-progressive, with a susceptible population directly generating populations with different resistance levels. The conditions where resistance will ascend in the progressive model are narrower than those of the non-progressive model. The rates of reversion from the resistant to the sensitive states are critically dependent on the transition rates and the fitness cost of resistance. Our results demonstrate that the standard test used to identify heteroresistance is insufficient. The predictions of our models are consistent with empirical results. Our results demand a reevaluation of the definition and criteria employed to identify heteroresistance. We recommend the definition of heteroresistance should include a consideration of the rate of return to susceptibility. Significance Statement This mathematical modeling and computer-simulation study quantitatively explores two broadly different, previously undescribed, classes of heteroresistance. In our analysis of the properties of these models, we consider the response of heteroresistant populations to antibiotic exposure, focusing on the conditions where heteroresistance could lead to clinical treatment failure. We also provide novel consideration to the rate of reversion from a resistant to sensitive state. Our analysis illustrates the need to include the reversion rate from resistant to sensitive in the definition of heteroresistance and questions the sufficiency of the method currently used to identify heteroresistance.