Evidence map›Paper›PMID 42144529›Full record

ReviewCancer treatment and research2026

Clinical Trials and Translational Advances in Metabolic Targeting.

Hariharan Thirumalai Vengateswaran, Mohammad Habeeb, Huay Woon You, Ciniraj Raveendran

Abstract readReview
PubMed Publisher
In one paragraph

Review in Cancer treatment and research, 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.

Hariharan Thirumalai VengateswaranDepartment of Pharmaceutics, Crescent School of Pharmacy, B.S. Abdur Rahman Crescent Institute of Science and Technology, Chennai, India.
Mohammad HabeebDepartment of Pharmaceutics, Crescent School of Pharmacy, B.S. Abdur Rahman Crescent Institute of Science and Technology, Chennai, India.
Huay Woon YouPusat PERMATA@Pintar Negara, Universiti Kebangsaan Malaysia, Bangi, Malaysia.
Ciniraj RaveendranDepartment of Radiation Oncology, Government Medical College Thiruvananthapuram, Thiruvananthapuram, India.

Funding

No grant is acknowledged in the PubMed record.

6 · The paper itself

Abstract

Targeting metabolic pathways provides novel therapeutic prospects as metabolic reprogramming is becoming more widely recognized as a characteristic of a variety of disorders. Despite preclinical research and clinical trials demonstrating the potential of metabolic treatments, the complicated nature and flexibility of cellular metabolic networks prevent their advancement into effective treatments. Compared to conventional methods, metabolism-based treatment may be able to interfere with important functions in diseased cells and their surroundings, allowing for more individualized and accurate treatments. However, there are still challenges associated with overcoming variability and resistance, which highlights the necessity of advanced single-cell analysis methods. Personalizing metabolic therapies and optimizing treatment plans for individual patients are becoming feasible with the integration of artificial intelligence (AI) and machine learning (ML). Furthermore, patient stratification depends significantly on the application of metabolomics and advanced imaging methods, which allow for a more accurate decision of treatment modalities. This chapter provides a comprehensive overview of recent clinical trials and translational advancements in metabolic targeting, with a focus on small-molecule inhibitors and multi-omics technologies. New developments in metabolic research, from mechanistic understanding to clinical applications, are transforming the biomedical treatment while offering novel opportunities for disease management rather than traditional approaches.

Indexed as

Clinical Trials as TopicMetabolic Networks and PathwaysMolecular Targeted TherapyNeoplasmsTranslational Research, BiomedicalArtificial IntelligenceHumansMetabolic ReprogrammingMetabolomicsArtificial intelligenceCancerClinical trialsMetabolomics

Identifiers

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.