Antibiotic Resistance Crisis: From Bacterial Bioprospecting to Artificial Intelligence

Antibiotics revolutionized medicine in the 20th century by drastically reducing mortality from bacterial infections. However, their effectiveness is threatened by the global rise of antimicrobial resistance (AMR), driven by misuse, overuse, and environmental dissemination. This review explores the historical trajectory of antibiotics, the mechanisms of bacterial resistance, and the urgent need for innovation amid a declining antibiotic development pipeline. Herein, we highlight the scientific and economic barriers that have discouraged investment by major pharmaceutical companies and examine emerging strategies to address this crisis. Key advances in microbial bioprospecting, including cultivation improvement techniques and genome mining, are discussed alongside the role of high-throughput sequencing and bioinformatics in unlocking the metabolic potential of uncultivated microorganisms. Particular emphasis is placed on the integration of artificial intelligence and machine learning to accelerate drug discovery, predict antimicrobial activity, and identify resistance genes. Additionally, we present alternative therapeutic strategies beyond traditional antibiotics, such as phage therapy, antimicrobial peptides, quorum sensing inhibitors, synthetic conjugates, and vaccine development. Together, these interdisciplinary approaches offer promising pathways to revitalize the antimicrobial pipeline and address the growing threat of antibiotic resistance.

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Publication Details

Journal
Environmental Microbiology Reports
Published
2025-12-01
DOI
https://doi.org/10.1111/1758-2229.70267
Primary Topic
Antimicrobial Peptides and Activities
Type
article
Field-Weighted Citation Impact
0.00

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article

Antibiotic Resistance Crisis: From Bacterial Bioprospecting to Artificial Intelligence

R. H. Krüger, J. Peixoto, C. S. Vizzotto, I. C. Cunha‐Ferreira
Environmental Microbiology Reports
Antimicrobial Peptides and Activities
article

Antibiotic Resistance Crisis: From Bacterial Bioprospecting to Artificial Intelligence

R. H. Krüger, J. Peixoto, C. S. Vizzotto, I. C. Cunha‐Ferreira
article en

Abstract

Antibiotics revolutionized medicine in the 20th century by drastically reducing mortality from bacterial infections. However, their effectiveness is threatened by the global rise of antimicrobial resistance (AMR), driven by misuse, overuse, and environmental dissemination. This review explores the historical trajectory of antibiotics, the mechanisms of bacterial resistance, and the urgent need for innovation amid a declining antibiotic development pipeline. Herein, we highlight the scientific and economic barriers that have discouraged investment by major pharmaceutical companies and examine emerging strategies to address this crisis. Key advances in microbial bioprospecting, including cultivation improvement techniques and genome mining, are discussed alongside the role of high-throughput sequencing and bioinformatics in unlocking the metabolic potential of uncultivated microorganisms. Particular emphasis is placed on the integration of artificial intelligence and machine learning to accelerate drug discovery, predict antimicrobial activity, and identify resistance genes. Additionally, we present alternative therapeutic strategies beyond traditional antibiotics, such as phage therapy, antimicrobial peptides, quorum sensing inhibitors, synthetic conjugates, and vaccine development. Together, these interdisciplinary approaches offer promising pathways to revitalize the antimicrobial pipeline and address the growing threat of antibiotic resistance.

Environmental Microbiology ReportsVol. 17(6)
Universidade de Brasília (BR), University Center of Brasília (BR)
Coordenação de Aperfeiçoamento de Pessoal de Nível Superior, Conselho Nacional de Desenvolvimento Científico e Tecnológico, Fundação de Apoio à Pesquisa do Distrito Federal
Industry, innovation and infrastructure
Openalex Percentile: Top 33%
Antimicrobial Peptides and Activities
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Antibiotic Resistance Crisis: From Bacterial Bioprospecting to Artificial Intelligence — R. H. Krüger, J. Peixoto, et al. · Environmental Microbiology Reports (2025) | TGRS Research Map | TGRS