Matricaria chamomilla L. contains antioxidant flavonoids that can have
their bioactivity enhanced by enzymatic hydrolysis of specific glycosyl groups. This study
implements an untargeted metabolomics approach based on ultra-performance liquid
chromatography coupled with electrospray ionisation quadrupole time-of-flight mass
spectrometry technique operating in MSE mode (UPLC-QTOF-MSE) and
spectrophotometric analysis of chamomile aqueous infusions, before and after hydrolysis by
hesperidinase and β-galactosidase. Several phenolic compounds were altered in the
enzymatically treated infusion, with the majority being flavonoid derivatives of apigenin,
esculetin, and quercetin. Although enzymatically modifying the infusion only led to a
small increase in antioxidant activity (DPPH• method), its inhibitory effect
on pancreatic lipase was of particular interest. The enzymatically treated infusion
exhibited a greater inhibitory effect (EC50 of 35.6 µM) than unmodified
infusion and kinetic analysis suggested mixed inhibition of pancreatic lipase. These
results are of great relevance due to the potential of enzymatically treated functional
foods in human health.
Comparative study of the effect of green and roasted water extracts of mate (Ilexparaguariensis) on glucosyltransferase activity of Streptococcusmutans,
Rosuvastatin is a well-known lipid-lowering agent generally used for hypercholesterolemia treatment and coronary artery disease prevention. There is a substantial inter-individual variability in the absorption of statins usually caused by genetic polymorphisms leading to a variation in the corresponding pharmacokinetic parameters, which may affect drug therapy safety and efficacy. Therefore, the investigation of metabolic markers associated with rosuvastatin inter-individual variability is exceedingly relevant for drug therapy optimization and minimizing side effects. This work describes the application of pharmacometabolomic strategies using liquid chromatography coupled to mass spectrometry to investigate endogenous plasma metabolites capable of predicting pharmacokinetic parameters in predose samples. First, a targeted method for the determination of plasma concentration levels of rosuvastatin was validated and applied to obtain the pharmacokinetic parameters from 40 enrolled individuals; then, predose samples were analyzed using a metabolomic approach to search for associations between endogenous metabolites and the corresponding pharmacokinetic parameters. Data processing using machine learning revealed some candidates including sterols and bile acids, carboxylated metabolites, and lipids, suggesting the approach herein described as promising for personalized drug therapy.
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