Evidence map›Paper›PMID 42340977›Full record

ReviewPLoS pathogens2026

Beyond resistance: Emerging methods to dissect drug responses in Leishmania.

Beatriz Cristina Dias de Oliveira, Jorge Arias Del Angel, Nicole Herrmann May, Michael P Barrett, Malgorzata Anna Domagalska, Tom Beneke

Abstract readReview
In one paragraph

Review in PLoS pathogens, 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

6 authors.

Beatriz Cristina Dias de OliveiraDepartment of Cell and Developmental Biology, Biocenter, University of Würzburg, Würzburg, Germany.
Jorge Arias Del AngelDepartment of Cell and Developmental Biology, Biocenter, University of Würzburg, Würzburg, Germany.
Nicole Herrmann MayDepartment of Cell and Developmental Biology, Biocenter, University of Würzburg, Würzburg, Germany.
Michael P BarrettSchool of Infection & Immunity, College of Medical, Veterinary and Life Sciences, University of Glasgow, Glasgow, United Kingdom.
Malgorzata Anna DomagalskaExperimental Parasitology Unit, Department of Biomedical Sciences, Institute of Tropical Medicine, Antwerp, Belgium.
Tom BenekeDepartment of Cell and Developmental Biology, Biocenter, University of Würzburg, Würzburg, Germany.ORCID https://orcid.org/0000-0001-9117-2649

Funding

No grant is acknowledged in the PubMed record.

6 · The paper itself

Abstract

Treatment failure and relapse remain major challenges in leishmaniasis despite available chemotherapies. Historically, these outcomes have been interpreted through the lens of classical drug resistance driven by heritable genetic mutations. However, drug responses are increasingly recognised to extend beyond resistance and include distinct but related phenomena such as hypersensitivity, tolerance, and persistence. Dissecting this range of responses in Leishmania requires approaches that capture both heritable genetic variation and dynamic cellular states. Here, we highlight how emerging genomics and perturb-omics technologies can resolve mechanisms underlying diverse drug responses. We emphasise that their impact depends on state-aware experimental design, including calibrated drug-selection, varied exposure regimens, and strategies to isolate rare persister populations. Together, these approaches provide a framework to move beyond resistance-centric models towards a more comprehensive understanding of parasite drug response.

Indexed as

Antiprotozoal AgentsDrug ResistanceLeishmaniaLeishmaniasisAnimalsGenomicsHumansAntiprotozoal Agents

Identifiers

PMID42340977
PMCPMC13293385

What OpenQuestion holds

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Registered trials

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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.