Metabolomics is an analytical approach to metabolism and involves quantitative and comparative
analysis of low-molecular-weight metabolites in body fluids or cellular/tissues extracts. Owing
to its ability to reveal disease-specific metabolic patterns or metabolic changes produced in response to
a therapeutic intervention; it is gaining widespread applications virtually in all aspects of biomedical
and pharmaceutical research pertaining to human healthcare management. It has also started playing a
strategic role in pharmacological and toxicological research for evaluating therapeutic efficacy/safety of
promising drug candidates either alone or in conjunction with other omics tools such as genomics, transcriptomics
and proteomics. The metabolic profiling capabilities of nuclear magnetic resonance (NMR)
spectroscopy along with pattern recognition methods have successfully been applied for identifying a
diagnostic panel of biomarkers, evaluating drug efficacy/safety, screening toxicity and disease mechanism.
Particularly, the interest in applying NMR-based metabolomics for the assessment of therapeutic
efficacy and safety is increasing among drug researchers and drug regulators owing to its nondestructive,
non-selective and minimal sample preparation requirement. On top of this, it offers the potential
for high-throughput (i.e. >100 samples a day is attainable) and provides highly reproducible results.
In this review, we will discuss some of the recent developments related to NMR based metabolomics
followed by some recent literature examples to highlight its potential in (a) the evaluation of
therapeutic efficacy and safety of lead discovery compounds, (b) monitoring disease status and recovery
after treatment and (c) identification and evaluation of biomarkers of systemic/organ-specific toxicity.
Additionally, the review will also highlight its role to facilitate clinical trial testing and improve
post-approval drug monitoring.
Keywords: Nuclear magnetic resonance, Metabolomics, Pharmacometabolomics, Toxicometabolomics, Clinical metabolomics,
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