Evidence map›Paper›PMID 42613358›Full record

Reviewnpj antimicrobials and resistance2026

Antimicrobial peptides for tuberculosis in a post-antibiotic era: from in vitro promise to therapeutic potential.

Gama Dominic, Yasmin Neves Vieira Sabino, Mustasim Famous, Omololu Ebenezer Fagunwa, Rachel M Wheatley, Ralf Bauer, Suzanne Hingley-Wilson, Linda B Oyama

Abstract readReview
In one paragraph

Review in npj antimicrobials and resistance, 2026. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Not yet cited in PubMed.

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

0 citing papers in PubMed.

No citing paper in PubMed yet.

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

8 authors.

Gama DominicInstitute for Global Food Security, School of Biological Sciences, Queen's University Belfast, Belfast, UK. gdominic01@qub.ac.uk.
Yasmin Neves Vieira SabinoInstitute for Global Food Security, School of Biological Sciences, Queen's University Belfast, Belfast, UK.
Mustasim FamousInstitute for Global Food Security, School of Biological Sciences, Queen's University Belfast, Belfast, UK.
Omololu Ebenezer FagunwaInstitute for Global Food Security, School of Biological Sciences, Queen's University Belfast, Belfast, UK.
Rachel M WheatleyInstitute for Global Food Security, School of Biological Sciences, Queen's University Belfast, Belfast, UK.
Ralf BauerUniversity of Strathclyde, Technology & Innovation Centre, Glasgow, UK.
Suzanne Hingley-WilsonSchool of Biosciences, University of Surrey, Guildford, Surrey, UK. s.hingley-wilson@surrey.ac.uk.
Linda B OyamaInstitute for Global Food Security, School of Biological Sciences, Queen's University Belfast, Belfast, UK. l.oyama@qub.ac.uk.

Funding

Biotechnology and Biological Sciences Research Council Reference Number - BB/X010902/1
6 · The paper itself

Abstract

Tuberculosis remains the leading cause of death from a single infectious pathogen, driven by prolonged treatment regimens and escalating drug resistance. Antimicrobial peptides represent a promising alternative therapeutic class with diverse mechanisms and limited resistance emergence. This review distinguishes anti-mycobacterial from anti-Mycobacterium tuberculosis peptides and systematically analyses 22 candidates curated in the APD6 alongside additional literature reports. We evaluate their structural and physicochemical determinants of activity, mechanistic evidence in vitro and in vivo, and synergy with conventional agents. Finally, we discuss rational design strategies, machine learning-guided optimisation, and translational barriers that must be overcome to realise peptide-based tuberculosis therapeutics.

Identifiers

PMID42613358
PMCPMC13487149

What OpenQuestion holds

Textmetadata
Read underepoch 390

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.