A Pharmacometabonomic
Approach To Predicting Metabolic
Phenotypes and Pharmacokinetic Parameters of Atorvastatin in Healthy
Volunteers
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Abstract
Genetic
polymorphism and environment each influence individual
variability in drug metabolism and disposition. It is preferable to
predict such variability, which may affect drug efficacy and toxicity,
before drug administration. We examined individual differences in
the pharmacokinetics of atorvastatin by applying gas chromatography–mass
spectrometry-based metabolic profiling to predose plasma samples from
48 healthy volunteers. We determined the level of atorvastatin in
plasma using liquid chromatography–tandem mass spectrometry.
With the endogenous molecules, which showed a good correlation with
pharmacokinetic parameters, a refined partial least-squares model
was calculated based on predose data from a training set of 36 individuals
and exhibited good predictive capability for the other 12 individuals
in the prediction set. In addition, the model was successfully used
to predictively classify individual pharmacokinetic responses into
subgroups. Metabolites such as tryptophan, alanine, arachidonic acid,
2-hydroxybutyric acid, cholesterol, and isoleucine were indicated
as candidate markers for predicting by showing better predictive capability
for explaining individual differences than a conventional physiological
index. These results suggest that a pharmacometabonomic approach offers
the potential to predict individual differences in pharmacokinetics
and therefore to facilitate individualized drug therapy