Limited sampling strategy models for estimating the AUC of gliclazide in Chinese healthy volunteers

Abstract

The aim of this work is to reduce the cost of required sampling for the estimation of the area under the gliclazide plasma concentration versus time curve within 60h (AUC0-60t ). The limited sampling strategy (LSS) models were established and validated by the multiple regression model within 4 or fewer gliclazide concentration values. Absolute prediction error (APE), root of mean square error (RMSE) and visual prediction check were used as criterion. The results of Jack-Knife validation showed that 10 (25.0%) of the 40 LSS based on the regression analysis were not within an APE of 15% using one concentration-time point. 90.2, 91.5 and 92.4% of the 40 LSS models were capable of prediction using 2, 3 and 4 points, respectively. Limited sampling strategies were developed and validated for estimating AUC0-60t of gliclazide. This study indicates that the implementation of an 80mg dosage regimen enabled accurate predictions of AUC0-60t by the LSS model. This study shows that 12, 6, 4, 2h after administration are the key sampling times. The combination of (12, 2h), (12, 8, 2h) or (12, 8, 4, 2h) can be chosen as sampling hours for predicting AUC0-60t in practical application according to requirement.