Explaining the stable coexistence of drug-resistant and-susceptible pathogens: a simple model reveals the chaos beneath the calm

Abstract

AbstractDrug resistance is a problem in many pathogens, including viruses, bacteria, fungi and parasites [1]. While overall, levels of resistance have risen in recent decades, there are many examples where after an initial rise, levels of resistance have stabilized [2–6]. The stable coexistence of resistance and susceptibility has proven hard to explain – in most evolutionary models, either resistance or susceptibility ultimately “wins” and takes over the population [2,3,7–9]. Here, we show that a simple stochastic model, mathematically akin to mutation-selection balance theory, can explain several key observations about drug resistance: (1) the stable coexistence of resistant and susceptible strains (2) at levels that depend on population-level drug usage and (3) with resistance often due to many different strains (resistance is present on many different genetic backgrounds). The model works for resistance due to both mutations or horizontal gene transfer (HGT). It predicts that new resistant strains should continuously appear (through mutation or HGT and positive selection within treated hosts) and disappear (due to the cost of resistance). The result is that while resistance is stable, which strains carry resistance is constantly changing. We used data on 37,000E. coliisolates to test this prediction for a known resistance mutation and a resistance gene in the UK and found that the data are consistent with the prediction. Having a model that explains the dynamics of drug resistance will allow us to plan science-backed interventions to reduce the burden of drug resistance. Next, it will be of interest to test our model on data from different drug-pathogen combinations and to estimate model parameters for these drug–pathogen combinations in different geographic regions with varying levels of drug use. It will then be possible to predict the results of interventions, especially drug restriction policies and increase our understanding of whether and how fast such restrictions should result in reduced resistance [10,11].