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Bacterial Antibiotic Resistance: From Mechanisms to Strategies for Restoring Antibiotic...
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Bacterial Antibiotic Resistance: From Mechanisms to Strategies for Restoring Antibiotic Efficacy

A special issue of Life (ISSN 2075-1729). This special issue belongs to the section "Medical Research".

Deadline for manuscript submissions: 20 April 2026 | Viewed by 1468

Special Issue Editor

Dr. Ishan Ghai

E-Mail Website
Guest Editor
Department of Physiology, University of Freiburg, 79098 Freiburg, Germany
Interests: membrane biophysics; antibiotic translocation; bacterial resistance mechanisms
Special Issues, Collections and Topics in MDPI journals

Special Issue Information

Dear Colleagues,

This Special Issue focuses on the urgent global challenge of bacterial antibiotic resistance, bringing together cutting-edge research on resistance mechanisms and innovative strategies to restore antibiotic efficacy. Contributions are invited that explore molecular, genetic, and structural determinants of resistance, as well as novel approaches to enhance drug uptake, inhibit efflux, or disrupt protective barriers. Studies on synergistic therapies, membrane-targeting agents, and diagnostics that guide effective treatment are also welcome. By highlighting interdisciplinary advances, this collection aims to deepen our understanding of bacterial defenses and inspire new interventions to combat resistant infections. We welcome original research articles, reviews, and perspectives that address these critical issues.

Dr. Ishan Ghai
Guest Editor

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 250 words) can be sent to the Editorial Office for assessment.

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. Life 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 2600 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

  • membrane transport
  • antibiotic resistance
  • pathogenic bacteria
  • bacterial defense
  • therapeutic strategies

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Published Papers (2 papers)

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Review

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21 pages, 658 KB  
Review
Understanding Drug Permeability in Pseudomonas aeruginosa
by Ishan Ghai
Life 2025, 15(11), 1705; https://doi.org/10.3390/life15111705 - 4 Nov 2025
Viewed by 787
Abstract
Pseudomonas aeruginosa is a Gram-negative bacterium that poses a serious threat to patients with weakened immunity, cystic fibrosis, severe burns, or those in hospitals. Its ability to resist antibiotics comes largely from its outer membrane, which blocks drug entry. This means higher doses [...] Read more.
Pseudomonas aeruginosa is a Gram-negative bacterium that poses a serious threat to patients with weakened immunity, cystic fibrosis, severe burns, or those in hospitals. Its ability to resist antibiotics comes largely from its outer membrane, which blocks drug entry. This means higher doses are often needed, raising the risk of side effects. To design new treatments, researchers need drugs that not only bind strongly to bacterial targets but also cross this tough membrane. Unfortunately, there are few reliable methods to directly measure how easily drugs pass through the Pseudomonas aeruginosa cell envelope. Recent advances, such as electrophysiology-based flux studies, have started to reveal how different antibiotics particularly β-lactams move through porin channels. These studies show large differences in permeability, but the findings remain scattered. What is missing is a unified dataset that captures permeability under varied conditions. Such a resource would clarify how porin structures influence drug entry and help chemists design better compounds. This review brings together current knowledge on drug permeability in Pseudomonas aeruginosa, with a focus on electrophysiological and related methods. This review highlights the need for standardized approaches that generate consistent and comparable data. A comprehensive “permeability atlas” could guide the development of new antibiotics by fine-tuning molecular properties like size, charge, and lipophilicity, ultimately improving porin passage and restoring treatment effectiveness against this challenging pathogen. Full article
(This article belongs to the Special Issue Bacterial Antibiotic Resistance: From Mechanisms to Strategies for Restoring Antibiotic Efficacy)
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14 pages, 726 KB  
Brief Report
Guiding Antibiotic Therapy with Machine Learning: Real-World Applications of a CDSS in Bacteremia Management
by Juan Carlos Gómez de la Torre, Ari Frenkel, Carlos Chavez-Lencinas, Alicia Rendon, Yoshie Higuchi, Jose M. Vela-Ruiz, Jacob Calpey, Ryan Beaton, Isaac Elijah, Inbal Shachar, Everett Kim, Sofia Valencia Osorio, Jason James Lee, Gabrielle Grogan, Jessica Siegel, Stephanie Allman and Miguel Hueda-Zavaleta
Life 2025, 15(11), 1756; https://doi.org/10.3390/life15111756 - 15 Nov 2025
Viewed by 498
Abstract
Bacteremia is a life-threatening condition contributing significantly to sepsis-related mortality worldwide. With delayed appropriate antibiotic therapy, mortality increases by 20% regardless of antimicrobial resistance. This study evaluated the perceived clinical utility of Artificial Intelligence (AI)-powered Clinical Decision Support Systems (CDSSs) (OneChoice and OneChoice [...] Read more.
Bacteremia is a life-threatening condition contributing significantly to sepsis-related mortality worldwide. With delayed appropriate antibiotic therapy, mortality increases by 20% regardless of antimicrobial resistance. This study evaluated the perceived clinical utility of Artificial Intelligence (AI)-powered Clinical Decision Support Systems (CDSSs) (OneChoice and OneChoice Fusion) among specialist physicians managing bacteremia cases. A cross-sectional survey was conducted with 65 unique specialist physicians from multiple medical specialties who were presented with clinical vignettes describing patients with bacteremia and 90 corresponding AI-CDSS recommendations. Participants assessed the perceived helpfulness of AI decision-making, the impact of AI recommendations on their own clinical judgment, and the concordance between AI recommendations and their own clinical judgment, as well as the validity of changing therapy based on CDSS recommendations. The study encompassed a diverse range of bacterial pathogens, with Escherichia coli representing 38.7% of the isolates and 30% being extended-spectrum β-lactamase (ESBL) producers. Findings show that 97.8% [(95% CI: 92.2–99.7%)] of physicians reported that AI facilitated decision-making and substantial concordance (87.8% [95% CI: 79.2–93.7%; Cohen’s κ = 0.76]) between AI recommendations and physicians’ therapeutic recommendations. Stratification by pathogen revealed the highest concordance for Escherichia coli bacteremia (96.6%, 28/29 cases). Implementation analysis revealed a meaningful clinical impact, with 68.9% [(95% CI: 58.3–78.2%)] of cases resulting in AI-guided treatment modifications. These findings indicate that AI-powered CDSSs effectively bridge critical gaps in infectious disease expertise and antimicrobial stewardship, providing clinicians with evidence-based therapeutic recommendations that can be integrated into routine practice to optimize antibiotic selection, particularly in settings with limited access to infectious disease specialists. For optimal clinical integration, we recommend that clinicians utilize AI-CDSS recommendations as an adjunct to clinical judgment rather than a replacement, particularly in complex cases involving immunocompromised hosts or polymicrobial infections. Future research should prioritize prospective clinical trials that evaluate direct patient outcomes to establish evidence of broader clinical effectiveness and applicability across diverse healthcare settings. Full article
(This article belongs to the Special Issue Bacterial Antibiotic Resistance: From Mechanisms to Strategies for Restoring Antibiotic Efficacy)
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