Impact of Resistance Development Mechanisms on Antibiotic Treatment Outcomes

TL;DR

This work develops two periodic ODE models to compare plasmid-mediated versus mutation-driven antibiotic resistance under MRSA treated with moxifloxacin. By deriving basic reproduction numbers and conducting uniform persistence analyses, the authors quantify how each resistance mechanism alters infection clearance and resistance evolution. Numerical simulations show that plasmid-mediated resistance more readily permits infection clearance and presents a lower risk of selecting fully resistant strains upon treatment failure, whereas mutation-driven resistance tends to foster persistence and rapid dominance of resistant strains. Additionally, the study finds that twice-daily dosing outperforms once-daily regimens, and a catch-up dose is preferable to compensatory double-dosing for short-half-life antibiotics, with implications for mechanism-specific PK/PD guidelines albeit without immune-system dynamics.

Abstract

Bacteria develop resistance to antibiotics through various mechanisms, with the specific mechanism depending on the drug-bacteria pair. It remains unclear, however, which resistance mechanism best supports favorable treatment outcomes, specifically in clearing infections and inhibiting further resistance. In this study, we use periodic ordinary differential equation models to simulate different antibiotic treatment protocols for bacterial infections. Using stability analysis and numerical simulations, we investigate how different resistance mechanisms, including plasmid-induced and mutation-induced resistance, affect treatment outcomes. Our findings suggest that antibiotic treatments with fixed dosing schedules are more likely to be effective when resistance arises exclusively through plasmid-mediated transmission. Further, when treatment fails, mutation-driven mechanisms tend to favor the selection of fully resistant bacterial strains. We also investigated the efficacy of different treatment strategies based on these mechanisms, finding that a twice-daily regimen consistently outperforms a once-daily regimen in terms of infection clearance. Additionally, our simulations with short half-life antibiotics indicate that the "catch-up" strategy outperforms the "compensatory double-dose" approach after a missed dose, a finding that aligns with general pharmaceutical advice for short-half-life drugs.

Figures (4)

• Figure 1: Relationship between MSW and $C_{\text{max}}$ under fixed AUC. A higher $C_{\text{max}}$ sharpens the concentration–time profile and shortens exposure within the MSW, minimizing resistance risk; lower $C_{\text{max}}$ prolongs MSW exposure.
• Figure 2: Simulation of Model (M1). $(a)$ The basic reproductive number $\mathcal{R}_0$ is plotted as a function of the MIC for once-daily and twice-daily regimens. In the figure, it is assumed that the maximal antibiotic concentration for the twice-daily strategy achieves 56%, 70%, 80%, and 90% of the maximal concentration for the once-daily strategy. $(b)$ Threshold MIC value for $\mathcal{R}_0=1$ under various possibilities of the maximal concentration for the twice-daily regimen. The horizontal axis shows the ratio of $C{\text{max}}$ for the twice-daily regimen relative to the once-daily regimen.
• Figure 3: Simulations for Models (M2) and (M3). (a) Threshold MIC for Infection Clearance under Different Resistance Development Mechanisms. Simulations are based on models (M2) and (M3), where the threshold MIC values for the resistant bacterial population ($\text{MIC}_p$ and $\text{MIC}_m$, respectively) were calculated such that $\mathcal{R}_0 = 1$ in each model. The horizontal axis shows the ratio of $C{\text{max}}$ for the twice-daily regimen relative to the once-daily regimen. (b) In both models, we set $\text{MIC}_s = 0.5$ and assign identical resistant strain values, $\text{MIC}_p = \text{MIC}_m = 3$.
• Figure 4: The Impact of Make-Up Protocols on Total Bacterial Load Following Missed Doses. The figure illustrates the total bacterial load over time for different missed-dose protocols in a twice-daily dosing regimen, as simulated by Model \ref{['Bsp']}. The missed dose is realized midway between consecutive scheduled doses. Parameters were specifically chosen to demonstrate that a twice-daily schedule effectively clears the bacterial infection, whereas a once-daily regimen with the same dose amount at each intake fails to do so.

Theorems & Definitions (27)

• Proposition 2.1
• Theorem 2.1
• Proposition 2.2
• proof
• Theorem 2.2
• proof
• Theorem 2.3
• proof
• Lemma 2.4
• proof