How molecular mechanisms of resistance affect resistance evolution

Antibiotic treatment may be compromised by the sporadic appearance and selection of drug resistant mutants during therapy. Identifying optimal dosing strategies for treating bacterial infections that minimize the risk of resistance is difficult, and improving dosing guidelines usually requires long and costly in vitro and in vivo experimentation. Previously, we proposed a mathematical model that links bacterial population biology with chemical reaction kinetics and demonstrated that this model had high predictive and explanatory power for antibiotic pharmacodynamics. Here, we extend our model to incorporate several distinct molecular mechanisms of resistance, e.g. altered drug-target affinity or target molecule content, to explore the how these mechanisms affect the risk of acquired resistance during treatment. Our model is based on a system of partial and ordinary differential equations. The central assumption encoded in the model is that bacterial growth declines (and/or bacterial kill rate increases) as the fraction of bound antibiotic target molecules within the bacterium increases. We use experimental data from E. coli exposed to ciprofloxacin and ampicillin to estimate the dependence of bacterial growth and death on the fraction of bound target molecules and to calibrate our model. We then predict how several alternative molecular mechanisms of resistance should affect bacterial growth and compare these predictions with data from experimentally-manipulated bacteria with varying target molecule content. Finally, we use our model to estimate antibiotic susceptibility for different antibiotics that share similar mechanisms of action but differ in their drug-target affinity. Our extended model offers a novel approach for predicting how resistance will evolve during therapy and provides a framework for generating new testable hypotheses about how the risk of resistance depends on the molecular mechanism of resistance.