Limited Sampling Strategy for Predicting the Area under Plasma Concentration-Time Curve of Nadolol in Healthy Subjects.

Abstract

Nadolol is a hydrophilic β-adrenoceptor blocker with a relatively long half-life and negligible metabolism. It is a substrate of P-glycoprotein and organic anion transporting polypeptide 1A2, and may serve as an in vivo probe drug for the assessment of drug-drug and food-drug interactions mediated by these transporters. In the present study, we aimed to develop limited sampling strategy (LSS) models for predicting the area under the plasma concentration-time curve (AUC_0-∞) of nadolol. Plasma concentration data (C_t) in healthy volunteers reported in four previous studies were randomly divided into a training dataset for model development (n = 15) and a test dataset for model validation (n = 16). By multiple linear regression analysis, we confirmed that four out of the eight models using two time points and all models using three time points met the acceptable criteria. In particular, the three time point models using (C₃, C₆, and C₂₄) and (C₄, C₈, and C₂₄) showed better predictive performances with r² values of 0.983 and 0.980, respectively. In drug interaction studies of nadolol with itraconazole, rifampicin, grapefruit juice, and green tea extract, both LSS models accurately predicted the AUC_0-∞ with percent mean absolute error ≤11% and percent root mean square error ≤12%. In addition, using digitized pharmacokinetic data of nadolol, both LSS models were further validated by predicting the AUC_0-∞ in different doses. The results suggest that the LSS models using three time points allow a reliable prediction of AUC_0-∞ of nadolol in healthy individuals.