Competition delays multi-drug resistance evolution during combination therapy

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.