ADVANCING PHARMACOMETABOLOMICS: EMERGING TRENDS IN NEXT-GENERATION SEQUENCING AND ARTIFICIAL INTELLIGENCE
Metabolomics provides a broader understanding of biochemical processes and information about disease mechanisms, supports diagnosis, and personalised medicine. This review discusses the progress in metabolomics, biomarker development, disease diagnosis, and precision medicine. A systematic review of peer-reviewed articles and clinical studies evaluating experimental research was performed. Various analytical techniques, specifically nuclear magnetic resonance (NMR) spectroscopy and mass spectrometry (MS), were searched for their role in metabolic profiling. Additionally, various computational approaches, specifically artificial intelligence (AI) and bioinformatics, were reviewed to determine their implications on metabolite identification and metabolic pathway analysis. Clinical and environmental metabolomics case studies provided examples of true applications and the challenges they presented. Overall, the findings from this systematic review indicate that metabolomics adds value to early disease diagnosis and patient profiling concerning personalised treatment, while AI-facilitated analyses improve accuracy in biomarker identification and metabolic analysis of biochemical pathways. Furthermore, understanding the additional applications of metabolomics within nutrition, as well as its relevance in environmental health, contributes to the evolving landscape of dispositions underlying human health. Notably, barriers such as the complex nature of metabolomics, methodological consistency, and reproducibility hinder its wider clinical application. Metabolomics is changing the landscape of biomedical research and healthcare through the integration of genotype and phenotype relationships. As technologies for the analysis of biological samples advance along with computational tools, metabolomics will likely be at the forefront of personalised medicine and disease prevention. Future metabolomics research should focus on the standardisation of biological data, data integration, and interdisciplinary collaboration and ultimately minimise its neglect toward clinical impact.