Serum Predose Metabolic Profiling for Prediction of Rosuvastatin Pharmacokinetic Parameters in Healthy Volunteers

Abstract

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.

Statin drugs enhance responses to immune checkpoint blockade in head and neck cancer models

“…The serum concentration achieved in humans after oral administration of statin drugs is in the nanomolar range, typically 500 nM or less, 26 27 41 and lower for rosuvastatin versus other statins. 41 To mimic these conditions, MOC1 cells were seeded into 96-well Agilent RTCA plates and allowed to adhere overnight prior to addition of statins or T cells. TIL were obtained from murine tumors and cultured ex vivo for 10-14 days with IL-2 prior to adding them to the wells containing tumor cells (figure 1A).…”

Pharmacokinetics in Pharmacometabolomics: Towards Personalized Medication

“…Concurrently, during the same year, attention shifted towards the cholesterol-lowering agent rosuvastatin, primarily driven by the adverse reactions it causes and the substantial inter-individual variations observed in its pharmacokinetic profiles. Anne et al [63] studied the blood concentration and PK parameters of rosuvastatin in healthy volunteers. They analyzed pre-dose plasma metabolites using non-targeted metabolomics, correlated differential metabolites with PK parameters, and established an elastic net linear regression machine learning model, which effectively pinpointed metabolites of significance for the prediction of both the area under the curve (AUC) and the maximum concentration (Cmax) of rosuvastatin.…”