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Drug-target binding quantitatively predicts optimal antibiotic dose levels in quinolones
Fabrizio Clarelli1,2,3, Adam Palmer4, Bhupender Singh1
1Department of Pharmacy, Faculty of Health Sciences, UiT-The Arctic University of Norway, Tromsø, Norway.
Plos Computational Biology
|August 16, 2020
Summary
A new computational model, COMBAT, quantitatively predicts how antibiotics work by analyzing drug-target binding. This tool aids in developing new antibiotics and optimizing current treatments to combat rising antibiotic resistance.
Area of Science:
- Microbiology
- Computational Biology
- Pharmacology
Background:
- Antibiotic resistance is a growing global health threat, necessitating a deeper quantitative understanding of antibiotic mechanisms.
- Developing new antibiotics and optimizing existing ones requires precise knowledge of how drugs interact with their bacterial targets.
Purpose of the Study:
- To introduce COMBAT (COmputational Model of Bacterial Antibiotic Target-binding), a model for quantitatively predicting antibiotic dose-response relationships.
- To investigate the fundamental biological question of how drug-target binding influences antibiotic action.
- To create a predictive tool for antibiotic efficacy and to inform strategies for minimizing resistance development.
Main Methods:
- Developed the COMBAT computational model, which requires biochemical parameters of drug-target interaction.
- Validated COMBAT using quinolone antibiotics, fitting the model to time-kill curves and predicting efficacy in clinical isolates.
- Tested the model's reverse application by inferring drug-target binding changes from dose-response data and by using beta-lactam antibiotics.
Main Results:
- COMBAT accurately predicted quinolone antibiotic efficacy in clinical isolates based on drug affinity (R2>0.9).
- The model successfully inferred changes in antibiotic-target binding from antimicrobial efficacy data for ciprofloxacin (92-94% accuracy) and ampicillin (90% accuracy).
- Drug-target binding was identified as a primary predictor of bacterial response to antibiotics, even considering downstream effects.
Conclusions:
- COMBAT provides a robust framework for quantitatively predicting antibiotic efficacy based on drug-target binding.
- The model can predict antibiotic concentrations that may select for novel resistance mutations.
- COMBAT offers a tool to optimize antibiotic dosing, maximizing efficacy while minimizing the emergence of resistance.
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