ArticlePloS one2015
Rational design of antibiotic treatment plans: a treatment strategy for managing evolution and reversing resistance.
Article in PloS one, 2015. The graph could read no effect estimate from its abstract, so it casts no vote on the map. An erratum has been issued. Cited by 33 papers.
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Who cites it
33 citing papers in PubMed.
- Cryptic phenotypic variation emerges rapidly during the adaptive evolution of a carbapenemase.Nature ecology & evolution · 2026Article
- Saturation mutagenesis map of generalist versus specialist adaptations of β-lactamase to novel antibiotics.bioRxiv : the preprint server for biology · 2026Article
- Elucidation of the growth phenotypes of 82 strains from theFrontiers in bioengineering and biotechnology · 2026Article
- Computational framework for streamlining the success of sequential antibiotic therapy.npj antimicrobials and resistance · 2025Article
- Automated Platform for the Analysis of Multi-Plate Growth and Reporter Data.Microorganisms · 2025Article
- Fitness seascapes are necessary for realistic modeling of the evolutionary response to drug therapy.Science advances · 2025Article
- Saturated Transposon Analysis in Yeast as a one-step method to quantify the fitness effects of gene disruptions on a genome-wide scale.PloS one · 2025Article
- Distinguishing mutants that resist drugs via different mechanisms by examining fitness tradeoffs.eLife · 2024Article
- Distinguishing mutants that resist drugs via different mechanisms by examining fitness tradeoffs.bioRxiv : the preprint server for biology · 2024Article
- Reinforcement learning informs optimal treatment strategies to limit antibiotic resistance.Proceedings of the National Academy of Sciences of the United States of America · 2024Article
- Diverse mutant selection windows shape spatial heterogeneity in evolving populations.PLoS computational biology · 2024Article
- Pervasive epistasis exposes intramolecular networks in adaptive enzyme evolution.Nature communications · 2023Article
- Reinforcement Learning informs optimal treatment strategies to limit antibiotic resistance.bioRxiv : the preprint server for biology · 2023Article
- Diverse mutant selection windows shape spatial heterogeneity in evolving populations.bioRxiv : the preprint server for biology · 2023Article
- Exploiting evolutionary trade-offs for posttreatment management of drug-resistant populations.Proceedings of the National Academy of Sciences of the United States of America · 2020Article
- Microbiological Air Quality and Drug Resistance in Airborne Bacteria Isolated from a Waste Sorting Plant Located in Poland-A Case Study.Microorganisms · 2020Article
- Article
- Model genotype-phenotype mappings and the algorithmic structure of evolution.Journal of the Royal Society, Interface · 2019Article
- Evolving generalists in switching rugged landscapes.PLoS computational biology · 2019Article
- Adaptive Landscapes in the Age of Synthetic Biology.Molecular biology and evolution · 2019Review
Corrections and comments
- Erratum issued
Authors and funding
6 authors.
Funding
No grant is acknowledged in the PubMed record.
Abstract
The development of reliable methods for restoring susceptibility after antibiotic resistance arises has proven elusive. A greater understanding of the relationship between antibiotic administration and the evolution of resistance is key to overcoming this challenge. Here we present a data-driven mathematical approach for developing antibiotic treatment plans that can reverse the evolution of antibiotic resistance determinants. We have generated adaptive landscapes for 16 genotypes of the TEM β-lactamase that vary from the wild type genotype "TEM-1" through all combinations of four amino acid substitutions. We determined the growth rate of each genotype when treated with each of 15 β-lactam antibiotics. By using growth rates as a measure of fitness, we computed the probability of each amino acid substitution in each β-lactam treatment using two different models named the Correlated Probability Model (CPM) and the Equal Probability Model (EPM). We then performed an exhaustive search through the 15 treatments for substitution paths leading from each of the 16 genotypes back to the wild type TEM-1. We identified optimized treatment paths that returned the highest probabilities of selecting for reversions of amino acid substitutions and returning TEM to the wild type state. For the CPM model, the optimized probabilities ranged between 0.6 and 1.0. For the EPM model, the optimized probabilities ranged between 0.38 and 1.0. For cyclical CPM treatment plans in which the starting and ending genotype was the wild type, the probabilities were between 0.62 and 0.7. Overall this study shows that there is promise for reversing the evolution of resistance through antibiotic treatment plans.
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Read under generation 80e0d062 · epoch 390. Bibliography from PubMed, PubMed Central and OpenAlex; grants from NIH RePORTER; trial links from ClinicalTrials.gov; estimates, votes and beliefs from the OpenQuestion graph.