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Competition delays multi-drug resistance evolution during combination therapy

Ernesto Berríos-Caro1, Danna R Gifford2, Tobias Galla3

  • 1Theoretical Physics, Department of Physics and Astronomy, School of Natural Sciences, Faculty of Science and Engineering, The University of Manchester, Manchester M13 9PL, United Kingdom.

Journal of Theoretical Biology
|October 13, 2020
PubMed

Insights

Predicting drug resistance is key to improving treatments. This study shows that competition between resistant strains significantly impacts double drug resistance, unlike simpler models, offering new strategies to suppress resistance.

Area of Science:

  • Mathematical Biology
  • Evolutionary Biology
  • Pharmacology

Background:

  • Combination therapies are vital for treating infectious diseases and cancer, yet drug resistance remains a significant challenge.
  • Accurate prediction of drug resistance emergence is crucial for optimizing treatment strategies.
  • Current theoretical models often use exponential growth, which may not reflect real-world scenarios with limited resources and competition.

Purpose of the Study:

  • To investigate the emergence of single and double drug resistance under different growth models.
  • To compare the impact of exponential vs. logistic growth with and without inter-strain competition on resistance evolution.
  • To determine how resource availability and competition influence the probability of developing resistance to one or multiple drugs.

Main Methods:

  • Development of a mathematical model including sensitive, single-resistant, and double-resistant strains.
  • Comparison of resistance emergence probabilities using three growth laws: exponential, logistic without competition, and logistic with competition.
  • Utilizing mathematical estimations and numerical simulations to analyze model outcomes.

Main Results:

  • Inter-strain competition affects single drug resistance emergence primarily under scarce resource conditions.
  • Competition significantly influences double drug resistance emergence across a broader range of resource availabilities.
  • Logistic growth models with competition provide a more nuanced understanding of resistance evolution compared to exponential models.

Conclusions:

  • Competition between resistant strains is a critical factor in suppressing multi-drug resistance.
  • Exponential growth models may overestimate the likelihood of multiple drug resistance.
  • The study offers an efficient method for evaluating resistance probabilities in models with sequential mutations, applicable to various resistance research areas.
Keywords:
Combination therapyProbability of drug resistanceSimulation of individual-based modelsStochastic processes

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