Regression modeling strategy for prediction of AUC of evogliptin, a novel dipeptidyl peptidase IV inhibitor in humans, using single dose PK data

• Published in: International journal of pharmacokinetics Vol. 3; no. 1; pp. 23 - 38
• Main Authors: Giri, Poonam, Joshi, Shuchi, Srinivas, Nuggehally R
• Format: Journal Article
• Language: English
• Published: London Future Science Ltd 01.02.2018; Newlands Press
• Subjects: Bland-Altman; Burns; Clinical trials; correlation co-efficient; Diabetes; Drug dosages; Enzymes; healthy subjects; limited regression model; prediction of AUC; Regression analysis; root mean square error; United States > US; Japan
• ISSN: 2053-0846; 2053-0854; 2053-0846
• DOI: 10.4155/ipk-2017-0015

To develop a limited regression model of evogliptin for prediction of AUC data for internal (within study) and external studies. Regression analyses (linear/power/polynomial) were performed in multitiered approach using paired peak plasma concentration (C ) versus AUC data of evogliptin. For all models, correlation co-efficient (r) and root mean square error (%RMSE) were used in predicting internal/external data. Bland-Altman analysis was performed for all the models. Limited power model showed highest predictability (r = >0.98 and ≤ 15.5% RMSE), followed by linear model (r = >0.98 and ≤20.5% RMSE) and polynomial (r = >0.96 and ≤27.0% RMSE). Bland-Altman plots confirmed acceptable bias and precision. Limited regression models were successfully developed for prediction of AUC of evogliptin.