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

SUMMARY: Background: Antibiotics are considered one of the most important contributions to clinical medicine in the last 100 years. As a consequence of 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 commonly used susceptibility tests. However, upon exposure to the antibiotic, resistance rapidly ascends, and treatment fails. Methods: We generate two distinct mathematical and computer-simulations models of heteroresistance to quantitatively explore the factors contributing to the emergence and ascent of resistance during treatment and the waning of resistance following cessation of treatment. 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. Findings: The conditions where resistance will ascend in the progressive model are narrower than those of the non-progressive model. The rate 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. Interpretations: 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. Funding: Funds for this study were provided by the U.S. National Institute of General Medical Sciences, the U.S. National Institute of Allergy and Infectious Diseases, and the Carlos III Institute of Health of Spain.

RESEARCH IN CONTEXT: Evidence before this study: We searched Google Scholar for studies published before January 2023 for reviews, perspectives, and original articles (excluding case reports) of antibiotic heteroresistance to a variety of classes of antibiotics and numerous species of bacteria. We used a combination of search terms including "antibiotic heteroresistance", "Population Analysis Profile test", "molecular mechanisms", "treatment failure" plus "modeling" and "theory", with no language restrictions. Our particular focus was on finding theoretical and experimental papers that addressed the pharmaco-, population, and evolutionary dynamics of heteroresistance. We found many studies pertaining to the genetic mechanisms of heteroresistance but found only two studies of the pharmacodynamics of heteroresistance, and no studies on the population and evolutionary dynamics. Added value of this study: Our mathematical modeling and computer-simulation study quantitatively explores two broadly different, previously undescribed, classes of heteroresistance. In our analysis, we give particular consideration to 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. Implications of all the available evidence: Our study identifies the broad conditions where heteroresistance may pose a risk for clinical treatment failure. Furthermore, our analysis demonstrates the need to consider the reversion rate from resistant to sensitive in the definition of heteroresistance and questions the sufficiency of the method currently used to identify heteroresistance.