Evidence map›Paper›PMID 41922382›Full record

ArticleScientific reports2026

Modeling rotational fluoroquinolone therapy as a novel treatment for ophthalmic MRSA infections.

Alejandro Storper, Darlene Miller, Xi Huo

Abstract read
In one paragraph

Article in Scientific reports, 2026. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 1 paper.

0numbers the graph read from it
0cells of the map it votes in
1citing papers in PubMed
–field-weighted citation impact
1 · What the graph read from it

What it found

Each row is one number read from the abstract, on the scale the paper reported it, with its interval. Left of the dashed line favours the treatment, right favours the comparator. Under each row is the sentence it came from. New to these charts? A ten-minute tutorial.

The abstract states no effect estimate the extractor could read, or names no intervention and outcome on the map, so this paper lights no cell and moves no belief. It is still indexed, cited and linked below.

2 · The registry

The trial behind it

Trials whose registry record cites this paper, or whose number appears in the abstract. A trial that started after this paper was published is citing it as background, not reporting it.

Neither the registry nor the abstract names a trial number. If this is a trial report, that itself is worth knowing.

3 · Its place in the literature

Who cites it

1 citing paper in PubMed.

  1. Article
4 · The record

Corrections and comments

PubMed lists nothing against this paper. Absence here is not a guarantee, only a check that was made.

5 · Who and what money

Authors and funding

3 authors.

Alejandro StorperDepartment of Microbiology and Immunology, University of Miami Miller School of Medicine, Miami, FL, USA.
Darlene MillerOcular Microbiology Laboratory, Bascom Palmer Eye Institute, Miami, FL, USA.
Xi HuoDepartment of Mathematics, University of Miami, Coral Gables, FL, USA. x.huo@math.miami.edu.

Funding

National Science Foundation NSF-DMS-2052648
6 · The paper itself

Abstract

Methicillin-resistant Staphylococcus aureus (MRSA) increasingly undermines the effectiveness of topical fluoroquinolone monotherapy in ophthalmic infections. We present a theoretical evaluation of rotational fluoroquinolone therapy, in which moxifloxacin and trovafloxacin are alternated to enhance bacterial suppression and mitigate resistance. Experimental time-kill data were fit using Bayesian parameter estimation via Markov Chain Monte Carlo (MCMC) to derive drug-specific growth and kill parameters, which were integrated into spatiotemporal pharmacodynamic models. These models incorporate radial anterior chamber geometry, intraocular diffusion, and aqueous humor pharmacokinetics governed by a circadian clearance function. Structural docking simulations of topoisomerase IV mutants reveal that common resistance mutations disrupt moxifloxacin binding while preserving trovafloxacin affinity, supporting a collateral-sensitivity mechanism. Under high-resistance conditions, rotational therapy improves bacterial clearance compared to monotherapy by increasing heterogeneity in antimicrobial exposure fronts and reducing overall bacterial burden. By alternating fluoroquinolones with complementary resistance profiles, rotational therapy leverages physiological clearance rhythms and spatial drug gradients to enhance anterior-segment efficacy, providing a quantitative foundation for its use as a rational alternative to monotherapy in ocular MRSA management.

Indexed as

Anti-Bacterial AgentsFluoroquinolonesMethicillin-Resistant Staphylococcus aureusStaphylococcal InfectionsBayes TheoremDNA Topoisomerase IVDrug Resistance, BacterialHumansMicrobial Sensitivity TestsAnti-Bacterial AgentsDNA Topoisomerase IVFluoroquinolones

Identifiers

PMID41922382
PMCPMC13184320

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LicenceCC BY
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Registered trials

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Read under generation 80e0d062 · epoch 390. Bibliography from PubMed, PubMed Central and OpenAlex; grants from NIH RePORTER; trial links from ClinicalTrials.gov; estimates, votes and beliefs from the OpenQuestion graph.