The role of the upper sample size limit in two-stage bioequivalence designs

An In Vitro-In Vivo Simulation Approach for the Prediction of Bioequivalence

The aim of this study was to develop a new in vitro–in vivo simulation (IVIVS) approach in order to predict the outcome of a bioequivalence study. The predictability of the IVIVS procedure was evaluated through its application in the development process of a new generic product of amlodipine/irbesartan/hydrochlorothiazide. The developed IVIVS methodology is composed of three parts: (a) mathematical description of in vitro dissolution profiles, (b) mathematical description of in vivo kinetics, and (c) development of joint in vitro–in vivo simulations. The entire programming was done in MATLAB^® and all created scripts were validated through other software. The IVIVS approach can be implemented for any number of subjects, clinical design, variability and can be repeated for thousands of times using Monte Carlo techniques. The probability of success of each scenario is recorded and finally, an overall assessment is made in order to select the most suitable batch. Alternatively, if the IVIVS shows reduced probability of BE success, the R&D department is advised to reformulate the product. In this study, the IVIVS approach predicted successfully the BE outcome of the three drugs. During the development of generics, the IVIVS approach can save time and expenses.

A Three-Treatment Two-Stage Design for Selection of a Candidate Formulation and Subsequent Demonstration of Bioequivalence

This paper introduces a two-stage bioequivalence design involving the selection of one out of two candidate formulations at an initial stage and quantifies the overall power (chance of ultimately showing bioequivalence) in a range of scenarios with CVs ranging from 0.1 to 1. The methods introduced are derivates of the methods invented in 2008 by Diane Potvin and co-workers (Pharm Stat. 7(4): 245-262, 2008). The idea is to test the two candidate formulations independently in an initial stage, making a selection of one of these formulations basis of the observed point estimates, and to run, when necessary, a second stage of the trial with pooling of data. Alpha levels are identified which are shown to control the maximum type I error at 5%. Results, expressed as powers and sample sizes, are also published for scenarios where the two formulations are far apart in terms of the match against the reference (one GMR being 0.80, the other GMR being 0.95) and in scenarios where the two test formulations have an actual better match (one GMR being 0.90, the other GMR being 0.95). The methods seem to be compliant with wording of present guidelines from EMA, FDA, WHO, and Health Canada. Therefore the work presented here may be useful for companies developing drugs whose approval hinges on in vivo proof of bioequivalence and where traditional in vitro screening methods (such as dissolution trials) may have poor ability to predict the best candidate.

Hematocrit effect on dried blood spots in adults: a computational study and theoretical considerations

Dried blood spots (DBS) are formed by deposition of a small amount of blood on specific adsorbent paper and its physical drying. DBS are employed as a sampling method in several fields of life sciences and drug research. A concern about DBS is the so-called 'Hematocrit (Ht) effect', as a different Ht leads, due to different viscosity, to different spot size, affecting assay bias. Solutions have been proposed, including the correction of quantified concentrations with a suitable correction factor. In order to quantitatively assess Ht impact on the DBS measurements, a computational approach was developed and implemented in R^® language. First, the % relative error was modeled with respect to Ht. Then, Monte Carlo simulations were performed in virtual men/women populations with different Ht levels and the % relative error in relation to the Ht used for calibrators was quantified. An upper level for % relative error being a 'tolerable contribution' of Ht effect to % total analytical error was finally suggested, defining, for the first time, a potential Ht range for analysis of adults' samples, where correction of concentrations of unknown samples may be omitted. Such tolerable level for % relative error may be defined in each laboratory, also based on experimental parameters (type of paper and blood volume). Using a Ht calibration value representing the study population is fully rationalized, leading to reduced probability for concentration corrections. Regulatory criteria for bioanalysis can thus be targeted, moving towards wider utilization of DBS in human pharmacokinetic and clinical trials.

Two-stage designs in bioequivalence trials

PURPOSE

The aim of this study is to assess the current status of non-fixed sample size designs in bioequivalence trials with a focus on two-stage adaptive approaches.

METHODS

We searched PubMed and Google Scholar from inception to October 2014. Regulatory guidelines were obtained from the public domain. Different methods were compared by Monte Carlo simulations for their impact on the patient's and producer's risks.

RESULTS

Add-on designs, group sequential designs and adaptive two-stage sequential designs are currently accepted to demonstrate bioequivalence in various regulations. All three approaches may inflate the patient's risk if applied inconsiderately. Direct transfer of methods developed for superiority testing to bioequivalence is not warranted. Published two-stage frameworks maintain the type I error and generally the desired power. Adaptation based on the observed T/R ratio observed in the first stage should be applied with caution. Monte Carlo simulations are an efficient tool to explore the operating characteristics of methods.

CONCLUSIONS

Validated two-stage frameworks can be applied without requiring the sponsor to perform own simulations-which could further improve power based on additional assumptions. Two-stage designs are both ethical and economical alternatives to fixed sample designs.