Limited Sampling Modeling for Estimation of Phenotypic Metrics for CYP Enzymes and the ABCB1 Transporter Using a Cocktail Approach.

Plasma concentration data points (n = 2,640) from 16 healthy adults were used to develop and validate limited sampling strategies (LSS) for estimation of phenotypic metrics for CYP enzymes and the ABCB1 transporter, using a cocktail of subtherapeutic doses of the selective probes caffeine (CYP1A2), metoprolol (CYP2D6), midazolam (CYP3A), losartan (CYP2C9), omeprazole (CYP2C19), and fexofenadine (ABCB1). All-subsets linear regression modelling was applied to estimate the AUC for caffeine, fexofenadine, and midazolam, and the AUC ratio of metoprolol: α-OH metoprolol and omeprazole:5-OH omeprazole. LSS-derived metrics were compared with the parameters' 'best estimates' obtained by non-compartmental analysis using all plasma concentration data points. The correlation coefficient ( ) was used to identify the LSS equations that provided the best fit for timed plasma samples, and the jack-knife statistics was used as an additional validation procedure for the LSS models. Single time-point LSS models provided values greater than 0.95 ( > 0.95) for the AUC ratio of metoprolol:α-OH metoprolol and omeprazole:5-OH omeprazole, whereas 2 time-point models were required for > 0.95 for the AUC of caffeine, fexofenadine, and midazolam. Increasing the number of sampling points to three led to minor increases in and/or the bias or prediction of the estimates. In conclusion, the LSS models provided accurate prediction of phenotypic indices for CYP1A2, CYP2C19, CYP2D6, CYP3A, and ABCB1, when using subtherapeutic doses of selective probes for these enzymes and transporter.