Evidence map›Paper›PMID 42387986›Full record

ReviewCancer reports (Hoboken, N.J.)2026

Advancing Cancer Cachexia Drug Development: Leveraging Biomarkers and Functional Endpoints to Optimize Trial Design.

Nada O Othman, Hadir Habib, Doaa M Almeldin, Kyrillus S Shohdy

Abstract readReview
In one paragraph

Review in Cancer reports (Hoboken, N.J.), 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

4 authors.

Nada O OthmanDepartment of Clinical Oncology, Cairo University, Cairo, Egypt.
Hadir HabibDepartment of Clinical Oncology, Cairo University, Cairo, Egypt.
Doaa M AlmeldinDepartment of Clinical Oncology, Cairo University, Cairo, Egypt.
Kyrillus S ShohdySchool of Cancer Sciences, University of Glasgow, Glasgow, UK.ORCID 0000-0001-8627-3605

Funding

Cancer Research UK SEBCATP-2023/100009
6 · The paper itself

Abstract

backgroundCancer-associated cachexia is a complex, multistage metabolic disorder that markedly contributes to morbidity and mortality in patients with advanced cancer, yet effective pharmacological interventions are lacking. Cachexia drug development has expanded significantly in recent years, with multiple repurposed agents and novel therapies targeting specific pathways entering clinical trials. However, clinical benefit has been limited not only by a lack of promising agents but also, to a large extent, by suboptimal trial design. RECENT

findingsThis article reviews the current evidence to highlight the core principles required to modernize cachexia clinical trials. We emphasize that both repurposed drugs and novel agents have a role, but multimodal approaches integrating pharmacologic intervention with nutritional support and exercise are essential for therapeutic success. Early intervention, guided by predictive biomarkers such as C-reactive protein and Interleukin-6, and relatively short study durations focused on rapid clinical benefit are critical considerations.

conclusionWe demonstrate the importance of moving beyond weight-based endpoints alone toward functional primary endpoints. Selection of appropriate control arms is crucial and should include multimodal interventions, while trial designs must account for patient heterogeneity and concurrent anticancer therapies.

Indexed as

CachexiaClinical Trials as TopicDrug DevelopmentNeoplasmsResearch DesignAntineoplastic AgentsBiomarkersBiomarkers, TumorHumansAntineoplastic AgentsBiomarkersBiomarkers, Tumorcancer cachexiadrug developmenttrial design

Identifiers

PMID42387986
PMCPMC13323836

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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.