Microbial Bioactives

Microbial Bioactives | Online ISSN 2209-2161
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Microbial Bioactives

Volume 9 Number 1 2026

Article Contents

Figures and Tables
REVIEWS   (Open Access)
Contents Vol 9 (1)

Last-Resort Antibiotic Resistance: Mechanisms, Environmental Spread, and Detection Tools — A Narrative Review

Md Javed Alam 1*, Afia Ibnath 2

+ Author Affiliations

Microbial Bioactives 9 (1) 1-8 https://doi.org/10.25163/microbbioacts.9110891

Submitted: 08 June 2026 Revised: 13 August 2026  Published: 25 August 2026 

Abstract

Background: Antimicrobial resistance (AMR) has narrowed the antibiotic arsenal for critically ill patients to a shrinking set of last-resort agents — polymyxins, carbapenems, glycopeptides, oxazolidinones, and glycylcyclines — and even these are now eroding under sustained selective pressure (Wahnou et al., 2026). Whether artificial intelligence (AI) can meaningfully close the gap between the emergence of resistance and its clinical recognition remains, we think, an open and worthwhile question.Methods: We conducted a narrative literature review of peer-reviewed articles, indexed preprints, and surveillance reports published between 2007 and 2026, retrieved from PubMed, Scopus, and Google Scholar using structured keyword combinations and supplemented by hand-searching of reference lists.Results: Resistance to last-resort agents arises through convergent mechanisms — mcr-mediated colistin resistance, carbapenemase production, van-gene peptidoglycan remodeling, and efflux-driven tigecycline resistance — disseminated via horizontal gene transfer across clinical, agricultural, and environmental reservoirs (Solanki & Kumar Das, 2024). AI-based annotation tools such as DeepARG and PLM-ARG now identify divergent resistance genes with precision exceeding 97% (Arango-Argoty et al., 2018; Wu et al., 2023), while optical and microfluidic platforms compress susceptibility testing from 24–72 hours to under 30 minutes in several validated systems (Liao et al., 2025). Generative models have also identified candidate antimicrobials, including halicin and abaucin (Bagdad & Miteva, 2024).Conclusion: AI is accelerating both detection and discovery, but database bias, limited interpretability, and pharmacokinetic translation failure remain substantial barriers. Integrating environmental metagenomic surveillance with clinical decision support, under a coordinated One Health framework, appears to be the most promising path toward preserving last-resort antibiotic efficacy.

Keywords: antimicrobial resistance; last-resort antibiotics; horizontal gene transfer; artificial intelligence; One Health surveillance

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