Pharmacogenetic perspectives in improving pharmacokinetic profiles for efficient bioequivalence trials with highly variable drugs: a review

Conducting bioequivalence trials under traditional crossover study designs without exposing a large number of healthy volunteers to demonstrate two highly variable (%coefficient of variability greater than 30) test/reference (branded) drug products in different formulations to meet the standard 90% confidence interval criteria of relevant pharmacokinetic metrics between 0.80 and 1.25 and to maintain the consumer risk smaller than 5% has been a challenging task. Genetic polymorphisms encoding key drug-metabolizing enzymes can significantly influence absorption, distribution, metabolism and elimination of many highly variable generic drugs after administration. This article briefly reviews the case studies and examples of utilizing pharmacogenetic screening approaches in the recent literature to alleviate the resources and ethical burden of recruiting larger numbers of subjects in bioequivalence trials needed to perform pharmacokinetic studies for formulations of highly variable drug products without widening the bioequivalence acceptance limits.

Method of Estimating the Equivalence of Dissolution Profiles: a Modern View (Review)

Introduction. One of the purposes of dissolution profile comparison is to establish the equivalence of dissolution profiles of the studied drug and the comparison drug.Text. According to the current regulatory documents, the main tool for quantitative confirmation of equivalence of drug release profiles is the calculation of the similarity factor (f 2). However, it does not consider the form of dissolution profiles, incomplete release of the drug substance, time correlation, and is not susceptible to the «outliers», which leads to false positive results. Special attention should be paid to the dissolution of drugs with high variability, which is not eliminated by either increasing the sample or changing the sampling scheme. If f 2 is not used, it is necessary to use model-dependent and model-independent methods that are statistically correct, and their use is sufficiently justified (difference factor f 1 , Weibull distribution function, comparison of release degrees at different time points (according to the student's t-criterion). However, these models have an empirical nature that calls into question the application of such methods. Multivariate analysis is widely discussed in the literature and can be used to compare the similarity of dissolution with the assumption that the data has a normal distribution. The most common methods for checking similarity of dissolution profiles for highly variable drugs are the Mahalanobis distance test and the bootstrap for f 2. There is a document of EMA about suitability of the Mahalanobis distance as a tool to assess the comparability of drug dissolution profiles and to a larger extent to emphasise the importance of confidence intervals to quantify the uncertainty around the point estimate of the chosen metric. The bootstrap methodology for f 2 does not provide a clear understanding of the application to dissolution profile comparison for incomplete-release drugs, particularly in biorelevant environments. The «T2EQ» function, based on the Mahalanobis distance for highly variable drugs (Hoffelder), gives undefined results in practice.Conclusion. The topic of equivalence of dissolution profiles requires discussion, since it is shown that the convergence factor is outdated and cannot be adequately applied. The use of modern methods does not have a clear regulatory confirmation by the regulatory authority. In the published scientific literature, several statistical methods have been explored and compared for their design and performance. It is necessary to develop a clear plan (decision treeы) for conducting the procedure for equivalence of dissolution profiles, employing a range of statistical methods.

Methods to control the empirical type I error rate in average bioequivalence tests for highly variable drugs

Average bioequivalence tests are used in clinical trials to determine whether a generic drug has the same effect as an original drug in the population. For highly variable drugs whose intra-subject variances of direct drug effects are high, extra criteria are needed in bioequivalence studies. Currently used average bioequivalence tests for highly variable drugs recommended by the European Medicines Agency and the US Food and Drug Administration use sample estimators in the null hypotheses of interest. They cannot control the empirical type I error rate, so the consumer's risk is higher than the predetermined level. In this paper, we propose two new statistically sound methods that can control the empirical type I error rate without involving any sample estimators in the null hypotheses. In the proposed methods, we consider the average level of direct drug effects and the intra-subject variance of the direct drug effects. The first proposed method tests the latter parameter first to determine whether a product should be regarded as a highly variable drug, and then tests the former using corresponding bioequivalence limits. The second proposed method tests these two parameters simultaneously to capture the bioequivalence region. Extensive simulations are done to compare these methods. The simulation results show that the proposed methods have good performance on controlling the empirical type I error rate. The proposed methods are useful for pharmaceutical manufacturers and regulators.