Modeling bacterial resistance to antibiotics: bacterial conjugation and drug effects

Abstract

Abstract Antibiotic resistance is a major burden in many hospital settings as it drastically reduces the successful probability of treating bacterial infections. Generally, resistance is associated with bacterial fitness reduction and selection pressure from antibiotic usage. Here, we investigate the effects of bacterial conjugation, plasmid loss, and drug responses on the population dynamics of sensitive and resistant bacteria by using a mathematical model. Two types of drugs are considered here: antibiotic M that kills only sensitive bacteria and antibiotic N that kills both bacteria. Our results highlight that larger dose and longer dosing interval of antibiotic M may result in the higher prevalence of resistant bacteria while they do the opposite for antibiotic N. When delays in administering initial and second doses are incorporated, the results demonstrate that the delays may lead to the higher prevalence of resistant bacteria when antibiotic M or N is administered with the longer time of bacteria remaining at the lower prevalence of the latter. Our results highlight that switching antibiotic agents during a treatment course and different bacterial strain characteristics result in a significant impact on the prevalence of resistant bacteria.

Keywords

• Antimicrobial resistance
• Bacterial conjugation
• Therapeutic strategies
• Mathematical modeling