Correlation of elimination fraction area under the curve with total body clearance.

This study attempted to determine the area under the curve ([Formula: see text]), which corresponds to the sum of all elimination processes correlated with the total clearance value. The study attempted to determine the [Formula: see text] based on the coordinates known from classic non-compartmental pharmacokinetics for a single administration of the drug. 318 pharmacokinetics profiles were used for the analysis, obtained from 220 healthy subjects over ten studies. Pharmacokinetic calculations were performed with the use of Phoenix™ WinNonlin(®) 6.3. The leave-one-out (LOO) method was used for model cross-validation. Squared cross-validated correlation coefficient (Q (2)) parameter and the difference between Q (2) and R (2) were calculated as a measure of the internal performance and model predictive ability. A high correlation between the clearance value and [Formula: see text] was demonstrated (R (2) > 0.65). However, only in the case of four studies was it possible to validate the linear model using the leave-one-out validation procedure (R (2) > 0.86). The present study proposed a method of graphical and mathematical determination of the area under the curve for the drug elimination process after a single dose of the drug. Furthermore, the concept of calculating the statistical moments and mean elimination time (MET) only for elimination processes based on [Formula: see text] was presented. The result of this work is also a new method of determining the half-life of elimination phase based on the MET value.

Evaluation of sampling spacing in pharmacokinetic studies using six sigma method.

Key elements of pharmacokinetics (PK) studies include both, the number of sampling points (NSP) as well as the spacing between the sampling points (SSP). Optimization of the SSP is discussed in guidelines of all key regulatory agencies (RA). Those however, provide only very general rules on how to properly distribute the NSPs in proposed PK studies. Here we demonstrate that the six sigma (SX) method can be effectively used to assess the quality of SSPs. We have tested a modified SX method analyzing 466 PK profiles from 16 studies including a total of 368 healthy volunteers. Non-compartmental modeling was used to estimate PK parameters. The arithmetic means of minimum and maximum values of SX obtained for each subject in all studies were 1.97 and 3.83, respectively. The method described here allows comparing quality of studies performed at different centers, even if they cover different chemical entities. We propose that the SX values can be used to assess quality of PK studies, what is consistent with recommendations of the RAs.