Quantifying the contribution of four resistance mechanisms to ciprofloxacin minimum inhibitory concentration in<i>Escherichia coli</i>: a systematic review

Bc, van der Putten; Remondini, Daniel; Pasquini, Giovanni; Janes, Victoria A.; Matamoros, Sébastien; Schultsz, Constance

doi:10.1101/372086

Cited by 2 publications

(1 citation statement)

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“…COMBAT offers insight into determinants of the resistance selection window and builds transferrable knowledge that allows estimating useful dose ranges. In concordance with a recent meta-analysis of experimental data [49], our sensitivity analyses predict that changes in drug target binding and unbinding have a greater impact on susceptibility than changes in target molecule content or down-stream processes. Thus, a more comprehensive characterization of the binding parameters of spontaneous resistant mutants would allow an overview of the maximal biologically plausible levels of resistance that can arise with one mutation.…”

Section: Plos Computational Biologysupporting

confidence: 83%

Drug-target binding quantitatively predicts optimal antibiotic dose levels in quinolones

et al. 2020

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Antibiotic resistance is rising and we urgently need to gain a better quantitative understanding of how antibiotics act, which in turn would also speed up the development of new antibiotics. Here, we describe a computational model (COMBAT-COmputational Model of Bacterial Antibiotic Target-binding) that can quantitatively predict antibiotic dose-response relationships. Our goal is dual: We address a fundamental biological question and investigate how drug-target binding shapes antibiotic action. We also create a tool that can predict antibiotic efficacy a priori. COMBAT requires measurable biochemical parameters of drugtarget interaction and can be directly fitted to time-kill curves. As a proof-of-concept, we first investigate the utility of COMBAT with antibiotics belonging to the widely used quinolone class. COMBAT can predict antibiotic efficacy in clinical isolates for quinolones from drug affinity (R 2 >0.9). To further challenge our approach, we also do the reverse: estimate the magnitude of changes in drug-target binding based on antibiotic dose-response curves. We overexpress target molecules to infer changes in antibiotic-target binding from changes in antimicrobial efficacy of ciprofloxacin with 92-94% accuracy. To test the generality of our approach, we use the beta-lactam ampicillin to predict target molecule occupancy at MIC from antimicrobial action with 90% accuracy. Finally, we apply COMBAT to predict antibiotic concentrations that can select for resistance due to novel resistance mutations. Using ciprofloxacin and ampicillin as well defined test cases, our work demonstrates that drug-target binding is a major predictor of bacterial responses to antibiotics. This is surprising because antibiotic action involves many additional effects downstream of drug-target binding. In addition, COMBAT provides a framework to inform optimal antibiotic dose levels that maximize efficacy and minimize the rise of resistant mutants.

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Section: Plos Computational Biologysupporting

confidence: 83%

Drug-target binding quantitatively predicts optimal antibiotic dose levels in quinolones

et al. 2020

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Drug-target binding quantitatively predicts optimal antibiotic dose levels

Clarelli

Palmer

Singh

et al. 2018

Preprint

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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 experimentallymanipulated 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.All rights reserved. No reuse allowed without permission.

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Quantifying the contribution of four resistance mechanisms to ciprofloxacin minimum inhibitory concentration inEscherichia coli: a systematic review

Cited by 2 publications

References 106 publications

Drug-target binding quantitatively predicts optimal antibiotic dose levels in quinolones

Drug-target binding quantitatively predicts optimal antibiotic dose levels in quinolones

Drug-target binding quantitatively predicts optimal antibiotic dose levels

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