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Antibiotics
Special Issues
Multidrug-Resistant Pathogens: Genomic Analysis, Resistance Mechanisms, and Clinical Challenges
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Multidrug-Resistant Pathogens: Genomic Analysis, Resistance Mechanisms, and Clinical Challenges

A special issue of Antibiotics (ISSN 2079-6382). This special issue belongs to the section "Mechanism and Evolution of Antibiotic Resistance".

Deadline for manuscript submissions: 31 July 2025 | Viewed by 922

Special Issue Editors

Dr. Dusan Kekic

E-Mail Website
Guest Editor
Institute for Microbiology and Immunology, Medical Faculty University of Belgrade, Belgrade, Serbia
Interests: microbiology; bacteriology; antimicrobial resistance; pathogenesis of infectious diseases
Dr. Nemanja Stanisavljevic

E-Mail Website
Guest Editor
Institute of Molecular Genetics and Genetic Engineering, Belgrade, Serbia
Interests: quorum sensing inhibitors; bacteriophages; antimicrobial peptides; metagenomic analysis of bacterial communities

Special Issue Information

Dear Colleagues,

The alarming impact of multidrug-resistant pathogens worldwide requires urgent attention. The latest WHO reports reveal serious warnings of rising resistance rates, increased morbidity and mortality in patients, especially in children, and economic toll on public health systems. The prognosis and calculated trends are even worse, demanding innovative therapeutic strategies, enhanced preventive and surveillance systems, and interdisciplinary approaches that combat the escalating threat of multidrug resistance to public health.

In this Special Issue, entitled “Multidrug-Resistant Pathogens: Genomic Analysis, Resistance Mechanisms and Clinical Challenges, we welcome submissions that highlight recent research on multidrug-resistant pathogens, their mechanisms and spread, and the effects of new antimicrobials, especially those employed in clinical studies and other innovative strategies. In addition, we welcome papers that propose clinical strategies, dosage regiments and applications, and preventive measures. We also invite authors to submit original research or review articles that address the use of AI in dealing with multidrug-resistant pathogens.

Dr. Dusan Kekic
Dr. Nemanja Stanisavljevic
Guest Editors

Manuscript Submission Information

Manuscripts should be submitted online at www.mdpi.com by registering and logging in to this website. Once you are registered, click here to go to the submission form. Manuscripts can be submitted until the deadline. All submissions that pass pre-check are peer-reviewed. Accepted papers will be published continuously in the journal (as soon as accepted) and will be listed together on the special issue website. Research articles, review articles as well as short communications are invited. For planned papers, a title and short abstract (about 100 words) can be sent to the Editorial Office for announcement on this website.

Submitted manuscripts should not have been published previously, nor be under consideration for publication elsewhere (except conference proceedings papers). All manuscripts are thoroughly refereed through a single-blind peer-review process. A guide for authors and other relevant information for submission of manuscripts is available on the Instructions for Authors page. Antibiotics is an international peer-reviewed open access monthly journal published by MDPI.

Please visit the Instructions for Authors page before submitting a manuscript. The Article Processing Charge (APC) for publication in this open access journal is 2900 CHF (Swiss Francs). Submitted papers should be well formatted and use good English. Authors may use MDPI's English editing service prior to publication or during author revisions.

Keywords

  • multidrug-resistant pathogens
  • genomic analysis
  • antimicrobial resistance
  • resistance mechanisms
  • antimicrobial therapy challenges
  • new antimicrobial drugs and therapies
  • clinical antimicrobial guidelines

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Further information on MDPI's Special Issue policies can be found here.

Published Papers (1 paper)

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Research

14 pages, 3373 KiB  
Article
Predicting the Effect of Meropenem Against Klebsiella pneumoniae Using Minimum Inhibitory Concentrations Determined at High Inocula
by Maria V. Golikova, Kamilla N. Alieva, Elena N. Strukova, Julia R. Savelieva, Daria A. Kondratieva, Svetlana A. Dovzhenko, Mikhail B. Kobrin, Vladimir A. Ageevets, Alisa A. Avdeeva and Stephen H. Zinner
Antibiotics 2025, 14(3), 258; https://doi.org/10.3390/antibiotics14030258 - 3 Mar 2025
Viewed by 639
Abstract
Background/Objectives: Assessing antibiotic MICs at high bacterial counts is likely to disclose hidden bacterial resistance and the inoculum effect if present and therefore also reveal potential decreased antibiotic effectiveness. In the current study, we evaluated the predictive potential of MICs determined at high [...] Read more.
Background/Objectives: Assessing antibiotic MICs at high bacterial counts is likely to disclose hidden bacterial resistance and the inoculum effect if present and therefore also reveal potential decreased antibiotic effectiveness. In the current study, we evaluated the predictive potential of MICs determined at high bacterial inocula to evaluate meropenem effectiveness and emergence of resistance in Klebsiella pneumoniae. Methods: Nine carbapenemase-free or carbapenemase-producing K. pneumoniae strains were exposed to meropenem in an in vitro hollow-fiber infection model (HFIM). The treatment effects were correlated with simulated antibiotic ratios of the area under the concentration–time curve (AUC) to the MIC (AUC/MIC) and to MICs determined at high inocula (AUC/MICHI). Results: Based on MICs determined at standard inocula, meropenem effects at different AUC/MIC ratios for both carbapenemase-free and carbapenemase-producing K. pneumoniae strains were stratified and could not be described by a single relationship. In contrast, when AUC/MICHI ratios were used, a single relationship with the antibiotic effect was obtained for all tested strains. Similarly, the emergence of meropenem resistance in HFIM was concordant with AUC/MICHI, but not with AUC/MIC ratios. Conclusions: MICs determined at high bacterial inocula enable the prediction of meropenem effects both for carbapenemase-free and for carbapenemase-producing K. pneumoniae strains. Also, MICs at standard and high inocula can identify carbapenemase-producing strains by revealing the inoculum effect. Full article
(This article belongs to the Special Issue Multidrug-Resistant Pathogens: Genomic Analysis, Resistance Mechanisms, and Clinical Challenges)
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