# Post-antibiotic effect and design of optimal antibiotic dosing protocols

> **NIH NIH R01** · DUKE UNIVERSITY · 2020 · $291,821

## Abstract

Abstract
Antibiotics have been considered the single most significant medical discovery of the
20th century. Due to decades of over-prescription and misuse, however, antibiotics are
losing their efficacy due to the emergence and rapid rise of antibiotic-resistant bacteria.
To combat this imminent threat, in addition to developing new drugs, it is equally critical
to develop strategies that enable more effective use of existing antibiotics. Doing so
requires a mechanistic understanding of both short-term and long-term bacterial
population dynamics in response to antibiotic treatment. An intriguing phenomenon that
arises from antibiotic treatment is the post-antibiotic effect (PAE) – after transient
treatment, the growth of a bacterial population is often temporarily suppressed even after
the antibiotic is removed. This phenomenon was first described in the 1940s and has
since been reported in the majority of, but not all, antibiotics. Despite the prevalence of
PAE, however, the underlying mechanisms are poorly understood. The objective of our
proposed research is to elucidate the molecular mechanism underlying
generation of PAE and, based on this understanding, to design effective antibiotic
treatment protocols. In our preliminary work, we measured PAE arising from treatment
of E. coli with several well-characterized antibiotics. Based on these measurements and
the literature data, we hypothesized that PAE can be explained by the uptake and
export kinetics of antibiotics by cells. Our proposed research will examine this
hypothesis and its alternatives in depth and breadth. Moreover, we will use computation
modeling to design and experimentally test antibiotic protocols to exploit PAE. In
particular, we aim to design treatment protocols that achieve similar treatment efficacy
while using minimal amounts of drugs. Such protocols will reduce perturbation to the
native microbiota and exert less selection pressure that can drive emergence and
spread of antibiotic resistance.

## Key facts

- **NIH application ID:** 9858172
- **Project number:** 5R01GM098642-08
- **Recipient organization:** DUKE UNIVERSITY
- **Principal Investigator:** LINGCHONG YOU
- **Activity code:** R01 (R01, R21, SBIR, etc.)
- **Funding institute:** NIH
- **Fiscal year:** 2020
- **Award amount:** $291,821
- **Award type:** 5
- **Project period:** 2011-09-15 → 2021-01-31

## Primary source

NIH RePORTER: https://reporter.nih.gov/project-details/9858172

## Citation

> US National Institutes of Health, RePORTER application 9858172, Post-antibiotic effect and design of optimal antibiotic dosing protocols (5R01GM098642-08). Retrieved via AI Analytics 2026-05-24 from https://api.ai-analytics.org/grant/nih/9858172. Licensed CC0.

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