Solid Dose Form of Metformin with Ethyl Eicosapentaenoic Acid Does Not Improve Metformin Plasma Availability

Background: The purpose of the study was to investigate effects of ethyl eicosapentaenoic acid on pharmacokinetics of metformin. Pharmacokinetic profiles of metformin and ethyl eicosapentaenoic acid when delivered separately or together in solid dose form were investigated and compared to determine whether the solid dose resulted in an altered metforminpharmacokinetics when given with or without food. Methods: A single-center, open-label, repeated dose study investigated the pharmacokinetic (PK) profile of metformin when administered in solid dose form with ethyl eicosapentaenoic acid compared to co-administration with icosapent ethyl, an ester of eicosapentaenoic acid and ethyl alcohol used to treat severe hypertriglyceridemia with metformin hydrochloride. Non-compartmental PK methods were used to compare area under the plasma concentration curve (AUC) and maximum plasma concentration (Cmax) between patients randomized to either the ester or separate medications group under both fasting and fed conditions. Results: Using these two PK parameters, results showed that metformin availability was higher under fasting conditions when delivered separately from icosapent ethyl. There were no group differences in the fed condition. Conclusions: The solid dose form of metformin and ethyl eicosapentaenoic acid did not improve the pharmacokinetics of metformin in terms of plasma availability, suggesting that little is to be gained over the separate administration of ethyl eicosapentaenoic acid and metformin hydrochloride.

A Simple Chi-Square Statistic for Testing Homogeneity of Zero-Inflated Distributions

Zero-inflated distributions are common in statistical problems where there is interest in testing homogeneity of two or more independent groups. Often, the underlying distribution that has an inflated number of zero-valued observations is asymmetric, and its functional form may not be known or easily characterized. In this case, comparisons of the groups in terms of their respective percentiles may be appropriate as these estimates are nonparametric and more robust to outliers and other irregularities. The median test is often used to compare distributions with similar but asymmetric shapes but may be uninformative when there are excess zeros or dissimilar shapes. For zero-inflated distributions, it is useful to compare the distributions with respect to their proportion of zeros, coupled with the comparison of percentile profiles for the observed non-zero values. A simple chi-square test for simultaneous testing of these two components is proposed, applicable to both continuous and discrete data. Results of simulation studies are reported to summarize empirical power under several scenarios. We give recommendations for the minimum sample size which is necessary to achieve suitable test performance in specific examples.

Use of Pearson’s Chi-Square for Testing Equality of Percentile Profiles across Multiple Populations

In large sample studies where distributions may be skewed and not readily transformed to symmetry, it may be of greater interest to compare different distributions in terms of percentiles rather than means. For example, it may be more informative to compare two or more populations with respect to their within population distributions by testing the hypothesis that their corresponding respective 10th, 50th, and 90th percentiles are equal. As a generalization of the median test, the proposed test statistic is asymptotically distributed as Chi-square with degrees of freedom dependent upon the number of percentiles tested and constraints of the null hypothesis. Results from simulation studies are used to validate the nominal 0.05 significance level under the null hypothesis, and asymptotic power properties that are suitable for testing equality of percentile profiles against selected profile discrepancies for a variety of underlying distributions. A pragmatic example is provided to illustrate the comparison of the percentile profiles for four body mass index distributions.