Regression modeling strategy for prediction of AUC of evogliptin, a novel dipeptidyl peptidase IV inhibitor in humans, using single dose PK data. (7th March 2018)

• Record Type: Journal Article
• Authors: Giri, Poonam; Joshi, Shuchi; Srinivas, Nuggehally R
• Is Part Of: International journal of pharmacokinetics. Volume 3:Number 1(2018)
• Page Start: 23
• Page End: 38
• Publication Date: 2018-03-07
• Subjects: Bland–Altman -- correlation co-efficient -- healthy subjects -- limited regression model -- prediction of AUC -- root mean square error; Pharmacokinetics -- Periodicals; 615.705
• DOI: 10.4155/ipk-2017-0015
• Languages: English
• ISSNs: 2053-0846

Abstract

Aim: To develop a limited regression model of evogliptin for prediction of AUC data for internal (within study) and external studies.Method: Regression analyses (linear/power/polynomial) were performed in multitiered approach using paired peak plasma concentration (Cmax ) 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.Results: 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.Conclusion: Limited regression models were successfully developed for prediction of AUC of evogliptin.