Metabolomics: A Tool Ahead for Understanding Molecular Mechanisms of Drugs and Diseases

Abstract

To refer to metabolomics as a new field is injustice to ancient doctors who used ants to diagnose the patients of diabetes having glycosuria. Measuring the levels of molecules in biological fluids believing them to be the representatives of biochemical pathways of carbohydrates, fats, proteins, nucleic acids or xenobiotic metabolism and deciphering meaningful data from it is what can be called as metabolomics, just as high glucose in urine suggests diabetes mellitus. Genomics, epigenetics, proteomics, transcriptomics finally converge to metabolomics, which are the signatures of mechanisms of bodily processes which is why understanding this science can have many applications. Just as a heap of stones does not make a house, having data of metabolite levels does not make it a science. Analyzing this data would help us in constructing biochemical pathways and their interactions. Analyzing the changes caused by a drug in the metabolite levels would help us in deriving the mechanisms by which the drug acts. Comparing metabolite levels in diseased with non-diseased, good-responders with poor-responders to a particular drug can help in identifying new markers of a disease or response to a drug respectively. Also, metabolite levels of an endogenous substrate can tell us the status of a person's metabolizing enzymes and help in drug dose titration. Generating hypothesis by identifying the new molecular markers and testing their utility in clinics seems to be the most promising approach in future. This review narrates the modes of quantifying and identifying metabolome, its proposed applications in diagnosis, monitoring and understanding the diseases and drug responses. We also intend to identify hindrances in using metabolomics in clinical studies or experiments.

Brain Metabolic Changes in Rats following Acoustic Trauma

“…Over the last decade, metabolomics has become one of the major “omics” tools in understanding disease pathology, identifying biomarkers, improving diagnosis and developing personalized therapy (see Botas et al, 2015; Shah et al, 2015 for reviews). In addition, brain metabolomics has been used to understand the pathology and identify potential markers for neurodegenerative diseases in both animal models and human post-mortem tissues due to the fact that metabolic changes in the brain are more likely to reflect disease etiology than those in peripheral biofluids (Pears et al, 2005; Salek et al, 2010; Graham et al, 2013; Fauvelle et al, 2015; Xu et al, 2016).…”

“…Hopkins (2008) argued that the “one gene, one drug, one disease” drug design philosophy might be the fundamental problem. This is because biological functions, or dysfunctions in disease status, are more likely to be a consequence of complex biochemical regulation processes driven by interactive networks within the genome (Chen et al, 2008), transcriptome (Iancu et al, 2014), proteome (Ebhardt et al, 2015), and metabolome (Shah et al, 2015). Targeting such dynamic network biology by identifying disease-causing networks rather than disease-causing genes, is likely to be a more effective approach for drug discovery (Roth et al, 2004; Hopkins, 2007, 2008; Kell and Goodacre, 2014).…”

Evaluation of the anti-inflammatory effects of volatile oils from processed products of Angelica sinensis radix by GC–MS-based metabolomics

“…Besides, it can be used in disease diagnosis, drug research and development, toxicology and drug action mechanism research (Want et al, 2011;Shah et al, 2015;Cao et al, 2015;Beyoğlu et al, 2013).…”

Direct infussion Electrospray Mass Spectrometry as a new non-invasive tool for serum metabolomics in induced-stress subjects

“…Data Information Management Systems (Bioinformatics), which has been already used in similar approaches in metabolomics applied to diseases 10,11,12,27,39 , allowed a faster and more effective filtering and selection of data.…”

“…Thus it is one of the tools to open up new prospects with great potential in the study of diseases 6,7 such as coronary artery disease 11 , in diagnostic protocols 8 , the search for biomarkers 9 and the development of drugs 10 . The metabolomic analysis of cerebrospinal fluid has enabled us to predict the clinical outcome of subarachnoid haemorrhage 12 .…”