ReviewCancer treatment and research2026
Clinical Trials and Translational Advances in Metabolic Targeting.
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.
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.
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.
Who cites it
0 citing papers in PubMed.
No citing paper in PubMed yet.
Corrections and comments
PubMed lists nothing against this paper. Absence here is not a guarantee, only a check that was made.
Authors and funding
4 authors.
Funding
No grant is acknowledged in the PubMed record.
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
Identifiers
42144529What OpenQuestion holds
Registered trials
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.