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Metabolomics in Early-Phase Clinical Trials from 2010 to 2025

Oct 2026 · Metabolites · Vol 16, pp. 777 · 0 citations · 39 references

TL;DR

An international assessment of metabolomics integration in registered early-phase clinical trials provides an international assessment of metabolomics integration in registered early-phase clinical trials and concludes that metabolomics remains underutilised in early-phase clinical trials.

Abstract

Background/Objectives: Metabolomics has the potential to enhance the definition of medication response phenotypes in early-phase clinical trials, yet global uptake and distribution are not well characterised. This study provides an international assessment of metabolomics integration in registered early-phase clinical trials. Methods: Data were obtained from the World Health Organization’s International Clinical Trials Registry Platform (ICTRP). Early-phase trials (phase 0–II) conducted from 2010 to 2025 were analysed, and metabolomics studies were identified using predefined keyword searches across trial records. Results: A total of 183,041 early-phase trials were identified, of which 640 (0.35%) incorporated metabolomics. Uptake has increased modestly over time but remains low overall and is concentrated predominantly in high-income countries. The United Kingdom demonstrated the highest proportional integration (1.3%), while the People’s Republic of China (n = 237) and the United States of America (n = 90) contributed the greatest absolute number of trials with metabolomics. Among the included metabolomic studies: 187 (29.2%) were phase 0, 229 (35.8%) were phase I trials, 183 (28.6%) were phase II, and 41 (6.4%) were combined phase I/II studies. Metabolomics was most commonly included as a secondary, rather than primary outcome (49.7% vs. 25.3%). Conclusions: Metabolomics remains underutilised in early-phase clinical trials. Addressing barriers to implementation will be critical to enable broader integration of metabolomics into early-phase clinical trials and to integrate a multi-omic approach to precision medicine.

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