Abstract

Drug resistance is a common problem in the fight against infectious diseases. Recent studies have shown conditions (which we call antiR) that select against resistant strains. However, no specific drug administration strategies based on this property exist yet. Here, we mathematically compare growth of resistant versus sensitive strains under different treatments (no drugs, antibiotic, and antiR), and show how a precisely timed combination of treatments may help defeat resistant strains. Our analysis is based on a previously developed model of infection and immunity in which a costly plasmid confers antibiotic resistance. As expected, antibiotic treatment increases the frequency of the resistant strain, while the plasmid cost causes a reduction of resistance in the absence of antibiotic selection. Our analysis suggests that this reduction occurs under competition for limited resources. Based on this model, we estimate treatment schedules that would lead to a complete elimination of both sensitive and resistant strains. In particular, we derive an analytical expression for the rate of resistance loss, and hence for the time necessary to turn a resistant infection into sensitive (tclear). This time depends on the experimentally measurable rates of pathogen division, growth and plasmid loss. Finally, we estimated tclear for a specific case, using available empirical data, and found that resistance may be lost up to 15 times faster under antiR treatment when compared to a no treatment regime. This strategy may be particularly suitable to treat chronic infection. Finally, our analysis suggests that accounting explicitly for a resistance-decaying rate may drastically change predicted outcomes in host-population models.

Highlights

  • Drug resistance is an important problem during infection treatment, in intensive care units [1]

  • We show that the incorporation of a similar resistance attenuation term into host-population models may change the current perspective on optimal strategies to reduce incidence of antibiotic resistance

  • Treating against resistance We used the model of Equation 1 to predict optimal strategies for healing infections that involve strains resistant to a single antibiotic

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Summary

Introduction

Drug resistance is an important problem during infection treatment, in intensive care units [1]. Cases of resistance have been described in infections caused by different types of pathogens, such as viruses, bacteria, fungi and protozoa [2,3,4,5] and the increasing incidence has made resistance a major public health issue [6] This fact can be exemplified by, but it is not exclusive to, infections caused by the methicillin-resistant Staphylococcus aureus (MRSA), whose incidence rate has almost doubled (city of Atlanta) or tripled (city of Baltimore) in a period of three years, from 2002 to 2005 [6]. More work is required to determine the potential effectiveness of these strategies in reducing or fighting drug resistance and to gain a quantitative understanding of their mechanisms, both at the single-host and the hostpopulation level

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