METHODS: Warfarin relies on regular monitoring of International Normalized Ratio which is a standardized test to measure prothrombin time and appropriate dose adjustment. Pharmacometabonomics is a novel scientific field which deals with identification and quantification of the metabolites present in the metabolome using spectroscopic techniques such as Nuclear Magnetic Resonance (NMR). Pharmacometabonomics helps to indicate perturbation in the levels of metabolites in the cells and tissues due to drug or ingestion of any substance. NMR is one of the most widely-used spectroscopic techniques in metabolomics because of its reproducibility and speed.
RESULTS: There are many factors that influence the metabolism of warfarin, making changes in drug dosage common, and clinical factors like drug-drug interactions, dietary interactions and age explain for the most part the variability in warfarin dosing. Some studies have showed that pharmacogenetic testing for warfarin dosing does not improve health outcomes, and around 26% of the variation in warfarin dose requirements remains unexplained yet.
CONCLUSION: Many recent pharmacometabonomics studies have been conducted to identify novel biomarkers of drug therapies such as paracetamol, aspirin and simvastatin. Thus, a technique such as NMR based pharmacometabonomics to find novel biomarkers in plasma and urine might be useful to predict warfarin outcome.
BIOLOGICAL SIGNIFICANCE: This paper reports the application of comparative proteomic and metabolomic approaches to reveal the molecular basis for important phenotypic changes Leishmania parasites that are deficient in glucose uptake. Leishmania cause a very significant disease burden across the world and there are few effective drugs available for control. This work shows that proteomics and metabolomics can produce complementary data that advance understanding of parasite metabolism and highlight potential new targets for chemotherapy.
METHODS: The case-control portion of the study was conducted in nine UK centers with men ages 50-69 years who underwent prostate-specific antigen screening for prostate cancer within the Prostate Testing for Cancer and Treatment (ProtecT) trial. Two data sources were used to appraise causality: a genome-wide association study (GWAS) of metabolites in 24,925 participants and a GWAS of prostate cancer in 44,825 cases and 27,904 controls within the Association Group to Investigate Cancer Associated Alterations in the Genome (PRACTICAL) consortium.
RESULTS: Thirty-five metabolites were strongly associated with prostate cancer (P < 0.0014, multiple-testing threshold). These fell into four classes: (i) lipids and lipoprotein subclass characteristics (total cholesterol and ratios, cholesterol esters and ratios, free cholesterol and ratios, phospholipids and ratios, and triglyceride ratios); (ii) fatty acids and ratios; (iii) amino acids; (iv) and fluid balance. Fourteen top metabolites were proxied by genetic variables, but MR indicated these were not causal.
CONCLUSIONS: We identified 35 circulating metabolites associated with prostate cancer presence, but found no evidence of causality for those 14 testable with MR. Thus, the 14 MR-tested metabolites are unlikely to be mechanistically important in prostate cancer risk.
IMPACT: The metabolome provides a promising set of biomarkers that may aid prostate cancer classification.