Limited Sampling Strategy for Accurate Prediction of Pharmacokinetics of Saroglitazar: A 3-point Linear Regression Model Development and Successful Prediction of Human Exposure.

PURPOSE: Our aim was to develop and validate the extrapolative performance of a regression model using a limited sampling strategy for accurate estimation of the area under the plasma concentration versus time curve for saroglitazar.

METHODS: Healthy subject pharmacokinetic data from a well-powered food-effect study (fasted vs fed treatments; n = 50) was used in this work. The first 25 subjects' serial plasma concentration data up to 72 hours and corresponding AUC0-t (ie, 72 hours) from the fasting group comprised a training dataset to develop the limited sampling model. The internal datasets for prediction included the remaining 25 subjects from the fasting group and all 50 subjects from the fed condition of the same study. The external datasets included pharmacokinetic data for saroglitazar from previous single-dose clinical studies. Limited sampling models were composed of 1-, 2-, and 3-concentration-time points' correlation with AUC0-t of saroglitazar. Only models with regression coefficients (R2 ) >0.90 were screened for further evaluation. The best R2 model was validated for its utility based on mean prediction error, mean absolute prediction error, and root mean square error. Both correlations between predicted and observed AUC0-t of saroglitazar and verification of precision and bias using Bland-Altman plot were carried out.

FINDINGS: None of the evaluated 1- and 2-concentration-time points models achieved R2 > 0.90. Among the various 3-concentration-time points models, only 4 equations passed the predefined criterion of R2 > 0.90. Limited sampling models with time points 0.5, 2, and 8 hours (R2 = 0.9323) and 0.75, 2, and 8 hours (R2 = 0.9375) were validated. Mean prediction error, mean absolute prediction error, and root mean square error were <30% (predefined criterion) and correlation (r) was at least 0.7950 for the consolidated internal and external datasets of 102 healthy subjects for the AUC0-t prediction of saroglitazar. The same models, when applied to the AUC0-t prediction of saroglitazar sulfoxide, showed mean prediction error, mean absolute prediction error, and root mean square error <30% and correlation (r) was at least 0.9339 in the same pool of healthy subjects.

IMPLICATIONS: A 3-concentration-time points limited sampling model predicts the exposure of saroglitazar (ie, AUC0-t ) within predefined acceptable bias and imprecision limit. Same model was also used to predict AUC0-∞ . The same limited sampling model was found to predict the exposure of saroglitazar sulfoxide within predefined criteria. This model can find utility during late-phase clinical development of saroglitazar in the patient population.