# Evolutionary dynamics of combinational antimicrobial treatments

> **NIH NIH R01** · DUKE UNIVERSITY · 2022 · $303,574

## Abstract

Evolutionary dynamics of combinational antimicrobial treatments
Abstract
Due to over-prescription and misuse, antibiotics are losing their efficacy due to emergence
and rapid rise of antibiotic-resistant bacteria. Of different types of antibiotics, β-lactams
have been prescribed to treat the majority of infections since the discovery of penicillin.
Since then, bacterial resistance to β-lactams, mediated by the production of extended
spectrum β-lactamase (ESBL) enzymes, has become widespread. Using β-lactamase
(Bla) inhibitors can restore the efficacy of β-lactams against resistant bacteria, a strategy
which is necessary to preserve existing antibiotics in the face of declining investment in
new antibiotics. However, the effect of combination treatment on selection for β-lactam
resistance is not well understood. Since Bla production benefits both resistant cells and
growth-advantaged sensitive cells, and these benefits may be differentially impacted by
the introduction of Bla inhibitor, leading to non-intuitive evolutionary dynamics. Our
preliminary work suggests that the evolutionary impact of combination treatment depends
on three strain-specific factors: the extent to which producing cells are resistant to the
antibiotic at the individual cell level, the extent to which the inhibitor can suppress this
resistance, and the burden of Bla production. In particular, for Bla variants that offer a
greater degree of private benefit (for the producing cells), the combination treatment can
substantially select for the resistant fraction. However, for variants that primarily serve as
a public good, the combination treatment will be highly effective in selecting against the
resistant cells. Our proposed research will examine these evolutionary dynamics in depth
using a combination of mathematical modeling and quantitative experiments. In particular,
we will use engineered bacteria as well-controlled model systems to test the predicted
evolutionary dynamics. Then, we will test the predicted evolutionary dynamics by using
microbial communities consisting of both resistant pathogens and sensitive bacteria.
Insights learned from the proposed work have implications for guiding effective design of
combination treatments against β-lactam-resistant bacterial pathogens.

## Key facts

- **NIH application ID:** 10445962
- **Project number:** 2R01GM098642-09
- **Recipient organization:** DUKE UNIVERSITY
- **Principal Investigator:** LINGCHONG YOU
- **Activity code:** R01 (R01, R21, SBIR, etc.)
- **Funding institute:** NIH
- **Fiscal year:** 2022
- **Award amount:** $303,574
- **Award type:** 2
- **Project period:** 2011-09-15 → 2026-07-31

## Primary source

NIH RePORTER: https://reporter.nih.gov/project-details/10445962

## Citation

> US National Institutes of Health, RePORTER application 10445962, Evolutionary dynamics of combinational antimicrobial treatments (2R01GM098642-09). Retrieved via AI Analytics 2026-05-23 from https://api.ai-analytics.org/grant/nih/10445962. Licensed CC0.

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