Effects of CYP3A4*1G and CYP3A5*3 polymorphisms on pharmacokinetics of tylerdipine hydrochloride in healthy Chinese subjects

<p></p><p>The aim of this analysis was to explore the influence of CYP3A4*1G and CYP3A5*3 polymorphisms on the pharmacokinetics of tylerdipine in healthy Chinese subjects.</p><p>A total of 64 and 63 healthy Chinese subjects were included and identified as the genotypes of CYP3A4*1G and CYP3A5*3, respectively. Plasma samples were collected for up to 120 h post-dose to characterize the pharmacokinetic profile following single oral dose of the drug (5, 15, 20, 25 and 30 mg). Plasma levels were measured by a high-performance liquid chromatography-mass spectrometry (LC-MS/MS). The pharmacokinetic parameters were calculated using non-compartmental method. The maximum concentration (<i>C</i>_max) and the area under the curve (AUC_0–24 h) were all corrected by the dose given.</p><p>In the wild-type group, the mean dose-corrected AUC_0–24 h was 1.35-fold larger than in CYP3A4*1G carriers (<i>p</i> = .018). Among the three CYP3A5 genotypes, there showed significantly difference (<i>p</i> = .008) in the <i>t</i>_1/2, but no significant difference was observed for the AUC_0–24 h and <i>C</i>_max. In subjects with the CYP3A5*3/*3 genotype, the mean <i>t</i>_1/2 was 1.35-fold higher than in CYP3A5*1/*1 group (<i>p</i> = .007). And the <i>t</i>_1/2 in CYP3A5*3 carriers also was 1.32-fold higher than in the wild-type group (<i>p</i> = .004).</p><p>CYP3A4*1G and CYP3A5*3 polymorphisms may influence tylerdipine pharmacokinetic in healthy Chinese subjects.</p><p></p> <p>The aim of this analysis was to explore the influence of CYP3A4*1G and CYP3A5*3 polymorphisms on the pharmacokinetics of tylerdipine in healthy Chinese subjects.</p> <p>A total of 64 and 63 healthy Chinese subjects were included and identified as the genotypes of CYP3A4*1G and CYP3A5*3, respectively. Plasma samples were collected for up to 120 h post-dose to characterize the pharmacokinetic profile following single oral dose of the drug (5, 15, 20, 25 and 30 mg). Plasma levels were measured by a high-performance liquid chromatography-mass spectrometry (LC-MS/MS). The pharmacokinetic parameters were calculated using non-compartmental method. The maximum concentration (<i>C</i>_max) and the area under the curve (AUC_0–24 h) were all corrected by the dose given.</p> <p>In the wild-type group, the mean dose-corrected AUC_0–24 h was 1.35-fold larger than in CYP3A4*1G carriers (<i>p</i> = .018). Among the three CYP3A5 genotypes, there showed significantly difference (<i>p</i> = .008) in the <i>t</i>_1/2, but no significant difference was observed for the AUC_0–24 h and <i>C</i>_max. In subjects with the CYP3A5*3/*3 genotype, the mean <i>t</i>_1/2 was 1.35-fold higher than in CYP3A5*1/*1 group (<i>p</i> = .007). And the <i>t</i>_1/2 in CYP3A5*3 carriers also was 1.32-fold higher than in the wild-type group (<i>p</i> = .004).</p> <p>CYP3A4*1G and CYP3A5*3 polymorphisms may influence tylerdipine pharmacokinetic in healthy Chinese subjects.</p

A Pharmacometabonomic Approach To Predicting Metabolic Phenotypes and Pharmacokinetic Parameters of Atorvastatin in Healthy Volunteers

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

Measured C_max and AUC of triptolide in rats plasma.

<p>AUC, area under the curve.</p>*<p>, **Statistically different from the Control, P<0.05 or P<0.01 (t test).</p>#<p>, ^##Statistically different from the CR, P<0.05 or P<0.01 (t test).</p

Prediction of the Pharmacokinetic Parameters of Triptolide in Rats Based on Endogenous Molecules in Pre-Dose Baseline Serum

<div><h3>Background</h3><p>Individual variances usually affect drug metabolism and disposition, and hence result in either ineffectiveness or toxicity of a drug. In addition to genetic polymorphism, the multiple confounding factors of lifestyles, such as dietary preferences, contribute partially to individual variances. However, the difficulty of quantifying individual diversity greatly challenges the realization of individualized drug therapy. This study aims at quantitative evaluating the association between individual variances and the pharmacokinetics.</p> <h3>Methodology/Principal Findings</h3><p>Molecules in pre-dose baseline serum were profiled using gas chromatography mass spectrometry to represent the individual variances of the model rats provided with high fat diets (HFD), routine chows and calorie restricted (CR) chows. Triptolide and its metabolites were determined using high performance liquid chromatography mass spectrometry. Metabonomic and pharmacokinetic data revealed that rats treated with the varied diets had distinctly different metabolic patterns and showed differential C_max values, AUC and drug metabolism after oral administration of triptolide. Rats with fatty chows had the lowest C_max and AUC values and the highest percentage of triptolide metabolic transformation, while rats with CR chows had the highest C_max and AUC values and the least percentage of triptolide transformation. Multivariate linear regression revealed that in baseline serum, the concentrations of creatinine and glutamic acid, which is the precursor of GSH, were linearly negatively correlated to C_max and AUC values. The glutamic acid and creatinine in baseline serum were suggested as the potential markers to represent individual diversity and as predictors of the disposal and pharmacokinetics of triptolide.</p> <h3>Conclusions/Significance</h3><p>These results highlight the robust potential of metabonomics in characterizing individual variances and identifying relevant markers that have the potential to facilitate individualized drug therapy.</p> </div

The body weight of rats fed with different diets for two weeks.

**<p>Statistically different from the control, P<0.01 (t test).</p

Chromatograms of GC/TOFMS and LC-IT-TOFMS.

<p>(A). Typical GC/TOFMS chromatograms of rat blood serum. (B). The LC-IT-TOF/MS-extracted ion chromatograms (EIC) of a bile sample from a rat treated with triptolide. P, parent drug; M1, GSH conjugate of triptolide; M2, mono-hydroxylated triptolide; M3–M6, dihydroxylated triptolide; M7, carboxylated triptolide; M8–M9, triptolide sulfate; M10, mono-hydroxylated triptolide coupled with GSH.</p

The plasma concentration–time curves of triptolide after oral administration of triptolide.

<p>(A) low dose (0.6 mg/kg), (B), high dose (1.8 mg/kg). •, CR diet; ▪, normal diet; ▴, high fat diets. (n = 4, Mean±SD).</p

Correlation between the measured and predicted values of PK parameters including both high and low doses of triptolide.

<p>(A) measured C_max vs. predicted C_max values; (B) measured AUC_{0−30 min} vs. predicted AUC_{0−30 min} values.</p