# Predicting the dynamics of bacterial growth inhibition by ribosome-targeting antibiotics

## TL;DR

This paper models bacterial growth inhibition by antibiotics with different binding affinities, predicting distinct dynamical responses to time-dependent dosing, which could inform clinical treatment strategies.

## Contribution

It introduces a theoretical model for bacterial response to time-dependent antibiotic treatment, highlighting differences based on antibiotic affinity and transport reversibility.

## Key findings

- Low-affinity antibiotics cause growth inhibition dependent on pulse duration.
- High-affinity antibiotics lead to growth suppression based on peak dose.
- Post-antibiotic effects are predicted due to hysteresis in high-affinity antibiotics.

## Abstract

Understanding how antibiotics inhibit bacteria can help to reduce antibiotic use and hence avoid antimicrobial resistance - yet few theoretical models exist for bacterial growth inhibition by a clinically relevant antibiotic treatment regimen. In particular, in the clinic, antibiotic treatment is time dependent. Here, we use a recently-developed model to obtain predictions for the dynamical response of a bacterial cell to a time-dependent dose of ribosome-targeting antibiotic. Our results depend strongly on whether the antibiotic shows reversible transport and/or low-affinity ribosome binding ("low-affinity antibiotic") or, in contrast, irreversible transport and/or high affinity ribosome binding ("high-affinity antibiotic"). For low-affinity antibiotics, our model predicts that growth inhibition depends on the duration of the antibiotic pulse, with a transient period of very fast growth following removal of the antibiotic. For high-affinity antibiotics, growth inhibition depends on peak dosage rather than dose duration, and the model predicts a pronounced post-antibiotic effect, due to hysteresis, in which growth can be suppressed for long times after the antibiotic dose has ended. These predictions are experimentally testable and may be of clinical significance.