[Paper Review] Rational Design of Antibiotic Treatment Plans

Portia Mira, Kristina Crona+4 more|arXiv (Cornell University)|Jun 6, 2014

Evolution and Genetic Dynamics18 references3 citations

TL;DR

This study presents a data-driven mathematical framework to rationally design antibiotic treatment plans that reverse antibiotic resistance in TEM-1 beta-lactamase. By modeling fitness landscapes across 16 genotypes and 15 antibiotics, it identifies optimal treatment sequences that maximize the probability of reversion to the wild-type, drug-susceptible genotype—achieving up to 1.0 probability under the Correlated Probability Model and 0.7 in cyclical plans.

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 beta-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 beta-lactam antibiotics. By using growth rates as a measure of fitness, we computed the probability of each amino acid substitution in each beta-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 those 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.

Motivation & Objective

• To address the lack of reliable methods for restoring antibiotic susceptibility after resistance evolves.
• To understand the evolutionary dynamics between antibiotic administration and resistance development in TEM-1 beta-lactamase.
• To design treatment plans that maximize the probability of reversion to the wild-type, susceptible genotype.
• To evaluate the effectiveness of different probabilistic models in predicting reversion paths.

Proposed method

• Constructed adaptive landscapes for 16 TEM-1 beta-lactamase genotypes with all combinations of four amino acid substitutions.
• Measured growth rates (as fitness proxies) of each genotype under 15 different beta-lactam antibiotics.
• Applied two probabilistic models—Correlated Probability Model (CPM) and Equal Probability Model (EPM)—to estimate substitution probabilities under each antibiotic.
• Performed exhaustive search over all 15 antibiotic treatments to identify optimal reversion paths from resistant genotypes back to wild-type TEM-1.
• Evaluated cyclical treatment plans where the starting and ending genotype is wild-type, optimizing for reversion probability.
• Calculated the probability of selecting reversion mutations for each treatment path using fitness-based models.

Experimental results

Research questions

• RQ1 What antibiotic treatment sequences maximize the probability of reversion from resistant TEM-1 genotypes back to the wild-type, drug-susceptible state?
• RQ2 How do different probabilistic models (CPM and EPM) affect the predicted likelihood of resistance reversal?
• RQ3 Which treatment sequences yield the highest reversion probabilities across diverse resistant genotypes?
• RQ4 Can cyclical treatment regimens enhance the likelihood of returning to the wild-type genotype?
• RQ5 What is the quantitative range of reversion probabilities achievable through optimized treatment plans?

Key findings

• For the Correlated Probability Model (CPM), optimized treatment paths achieved reversion probabilities ranging from 0.6 to 1.0.
• For the Equal Probability Model (EPM), optimized paths yielded reversion probabilities between 0.38 and 1.0.
• In cyclical CPM treatment plans—starting and ending at wild-type—reversion probabilities ranged from 0.62 to 0.7.
• The study demonstrates that resistance reversal is feasible through rational, sequence-specific antibiotic scheduling.
• The highest reversion probabilities were achieved when treatment sequences were systematically selected based on fitness landscapes and probabilistic modeling.
• The results indicate strong potential for clinical translation of such treatment plans to combat antibiotic resistance.