Evidence map›Paper›PMID 41429182›Full record

ReviewEnvironmental microbiology reports2025

Antibiotic Resistance Crisis: From Bacterial Bioprospecting to Artificial Intelligence.

I C Cunha-Ferreira, C S Vizzotto, J Peixoto, R H Krüger

Abstract readReview
In one paragraph

Review in Environmental microbiology reports, 2025. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 4 papers.

0numbers the graph read from it
0cells of the map it votes in
4citing 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

4 citing papers in PubMed.

  1. Review
  2. Review
  3. Frontiers in bioinformatics · 2026
    Review
  4. Review
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

4 authors.

I C Cunha-FerreiraLaboratory of Enzymology, Department of Cellular Biology, University of Brasília (UnB), Brasília, Brazil.
C S VizzottoMolecular Biotechnology Centre, Universidade de Brasília (UnB), Brasília, Brazil.
J PeixotoLaboratory of Enzymology, Department of Cellular Biology, University of Brasília (UnB), Brasília, Brazil.
R H KrügerLaboratory of Enzymology, Department of Cellular Biology, University of Brasília (UnB), Brasília, Brazil.ORCID https://orcid.org/0000-0002-8443-9402

Funding

Conselho Nacional de Desenvolvimento Científico e Tecnológico 310565/2021-9Coordenação de Aperfeiçoamento de Pessoal de Nível Superior PROEX-CAPESFundação de Apoio à Pesquisa do Distrito Federal 00193-00001746/2022The Faculty of Technology - FT/UnB SEI23106.121523/2025-72
6 · The paper itself

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.

Indexed as

Anti-Bacterial AgentsArtificial IntelligenceBacteriaBioprospectingDrug Resistance, BacterialBacterial InfectionsComputational BiologyDrug DiscoveryHumansMachine LearningAnti-Bacterial Agentsantimicrobial resistancebioactive compoundsnovel antibiotic developmenttherapeutic strategies

Identifiers

PMID41429182
PMCPMC12721875

What OpenQuestion holds

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LicenceCC BY
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Registered trials

None linked

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.