Group-by-Treatment Interaction Effects in Comparative Bioavailability Studies.

Comparative bioavailability studies often involve multiple groups of subjects for a variety of reasons, such as clinical capacity limitations. This raises questions about the validity of pooling data from these groups in the statistical analysis and whether a group-by-treatment interaction should be evaluated. We investigated the presence or absence of group-by-treatment interactions through both simulation techniques and a meta-study of well-controlled trials. Our findings reveal that the test falsely detects an interaction when no true group-by-treatment interaction exists. Conversely, when a true group-by-treatment interaction does exist, it often goes undetected. In our meta-study, the detected group-by-treatment interactions were observed at approximately the level of the test and, thus, can be considered false positives. Testing for a group-by-treatment interaction is both misleading and uninformative. It often falsely identifies an interaction when none exists and fails to detect a real one. This occurs because the test is performed between subjects in crossover designs, and studies are powered to compare treatments within subjects. This work demonstrates a lack of utility for including a group-by-treatment interaction in the model when assessing single-site comparative bioavailability studies, and the clinical trial study structure is divided into groups.

Pharmacokinetics of a Modified-Release Dexamphetamine Sulfate Formulation Following Single and Multiple Dosing in Healthy Adults: Comparative Bioavailability with Immediate-Release Dexamphetamine Sulfate, between Strengths, Assessment of Food and Meal Composition Effects.

Background: A modified-release dexamphetamine sulfate formulation (DEX-MR) is under development for the treatment of attention-deficit/hyperactivity disorder. Objective: We investigated the bioequivalence of once-daily DEX-MR to twice-daily immediate-release dexamphetamine sulfate (DEX-IR) after single and multiple dosing and between strengths, and effects of food and meal types. Method: Three randomized, open-label, crossover studies in healthy males were conducted. In the single-dose study, participants received DEX-MR 20 mg, DEX-MR 10 mg (20 mg dose), and twice-daily DEX-IR 10 mg under fasted conditions and after a high-fat, high-calorie breakfast. In the breakfast study, participants received DEX-MR 20 mg and twice-daily DEX-IR 10 mg after a normocaloric and a high-fat, high-calorie breakfast. In the multiple-dose study, participants received DEX-MR 20 mg and twice-daily DEX-IR 10 mg for seven days each. In the run-in period (five days), participants consumed a normocaloric breakfast; on profile days, participants consumed a normocaloric breakfast (day 6) or a high-fat, high-calorie breakfast (day 7). Results: Once-daily DEX-MR at a dose of 20 mg was bioequivalent to twice-daily DEX-IR 10 mg after single dosing under fasted and fed conditions and after multiple dosing under fed conditions. DEX-MR 10 mg and DEX-MR 20 mg were bioequivalent when administered as a single 20 mg dose. Food slightly reduced the rate and extent of absorption of DEX-MR and delayed the time to peak plasma concentration (tmax) by approximately two hours compared to the fasted state. Bioavailability of DEX-MR was comparable under different meal conditions (normocaloric vs. high-fat, high-calorie breakfast) both after single and multiple dosing. Conclusions: Bioequivalence of once-daily DEX-MR and twice-daily DEX-IR was established. 1×2 DEX-MR 10 mg was bioequivalent to 1×1 DEX-MR 20 mg. DEX-MR should be administered with/after a meal to achieve the targeted pharmacokinetic profile (delayed tmax). Bioavailability of DEX-MR is not affected by meal composition (i.e., fat and caloric content).

Critical Remarks on Reference-Scaled Average Bioequivalence.

PURPOSE: More than a decade ago the option to assess highly variable drugs / drug products by reference-scaled average bioequivalence was introduced in regulatory practice. Recommended approaches differ between jurisdictions and may lead to different conclusions even for the same data set. According to our knowledge, implemented methods have not been directly compared for their operating characteristics (Type I Error and power). METHODS: We performed Monte Carlo simulations to assess the consumer risk and the clinically relevant difference for the recommended regulatory settings. RESULTS: In all methods for reference-scaled average bioequivalence the Type I Error can be inflated with a consequently compromised consumer risk. Furthermore, the clinically relevant difference could vary between studies performed with the same reference product. CONCLUSIONS: Only average bioequivalence with fixed - widened - limits would both maintain the consumer risk and offer an unambiguously defined clinically not relevant difference. As long as such an approach is not implemented in regulatory practice, we recommend adjusting the level of the test a.

An iterative method to protect the type I error rate in bioequivalence studies under two-stage adaptive 2×2 crossover designs.

Bioequivalence studies are the pivotal clinical trials submitted to regulatory agencies to support the marketing applications of generic drug products. Average bioequivalence (ABE) is used to determine whether the mean values for the pharmacokinetic measures determined after administration of the test and reference products are comparable. Two-stage 2×2 crossover adaptive designs (TSDs) are becoming increasingly popular because they allow making assumptions on the clinically meaningful treatment effect and a reliable guess for the unknown within-subject variability. At an interim look, if ABE is not declared with an initial sample size, they allow to increase it depending on the estimated variability and to enroll additional subjects at a second stage, or to stop for futility in case of poor likelihood of bioequivalence. This is crucial because both parameters must clearly be prespecified in protocols, and the strategy agreed with regulatory agencies in advance with emphasis on controlling the overall type I error. We present an iterative method to adjust the significance levels at each stage which preserves the overall type I error for a wide set of scenarios which should include the true unknown variability value. Simulations showed adjusted significance levels higher than 0.0300 in most cases with type I error always below 5%, and with a power of at least 80%. TSDs work particularly well for coefficients of variation below 0.3 which are especially useful due to the balance between the power and the percentage of studies proceeding to stage 2. Our approach might support discussions with regulatory agencies.

Assessing goodness-of-fit for evaluation of dose-proportionality.

For the clinical development of a new drug, the determination of dose-proportionality is an essential part of the pharmacokinetic evaluations, which may provide early indications of non-linear pharmacokinetics and may help to identify sub-populations with divergent clearances. Prior to making any conclusions regarding dose-proportionality, the goodness-of-fit of the model must be assessed to evaluate the model performance. We propose the use of simulation-based visual predictive checks to improve the validity of dose-proportionality conclusions for complex designs. We provide an illustrative example and include a table to facilitate review by regulatory authorities.

Reference Datasets for Studies in a Replicate Design Intended for Average Bioequivalence with Expanding Limits.

In order to help companies qualify and validate the software used to evaluate bioequivalence trials in a replicate design intended for average bioequivalence with expanding limits, this work aims to define datasets with known results. This paper releases 30 reference datasets into the public domain along with proposed consensus results. A proposal is made for results that should be used as validation targets. The datasets were evaluated by seven different software packages according to methods proposed by the European Medicines Agency. For the estimation of CVwR and Method A, all software packages produced results that are in agreement across all datasets. Due to different approximations of the degrees of freedom, slight differences were observed in two software packages for Method B in highly incomplete datasets. All software packages were suitable for the estimation of CVwR and Method A. For Method B, different methods for approximating the denominator degrees of freedom could lead to slight differences, which eventually could lead to contrary decisions in very rare borderline cases.

Inflation of Type I Error in the Evaluation of Scaled Average Bioequivalence, and a Method for its Control.

PURPOSE: To verify previously reported findings for the European Medicines Agency's method for Average Bioequivalence with Expanding Limits (ABEL) for assessing highly variable drugs and to extend the assessment for other replicate designs in a wide range of sample sizes and CVs. To explore the properties of a new modified method which maintains the consumer risk ≤0.05 in all cases. METHODS: Monte-Carlo simulations of three different replicate designs covering a wide range of sample sizes and intra-subject variabilities were performed. RESULTS: At the switching variability of CV wR 30% the consumer risk is substantially inflated to up to 9.2%, which translates into a relative increase of up to 84%. The critical region of inflated type I errors ranges approximately from CV wR 25 up to 45%. The proposed method of iteratively adjusting α maintains the consumer risk at the desired level of ≤5% independent from design, variability, and sample size. CONCLUSIONS: Applying the European Medicines Agency's ABEL method at the nominal level of 0.05 inflates the type I error to an unacceptable degree, especially close to a CV wR of 30%. To control the type I error nominal levels ≤0.05 should be employed. Iteratively adjusting α is suggested to find optimal levels of the test.

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.

Reference datasets for bioequivalence trials in a two-group parallel design.

In order to help companies qualify and validate the software used to evaluate bioequivalence trials with two parallel treatment groups, this work aims to define datasets with known results. This paper puts a total 11 datasets into the public domain along with proposed consensus obtained via evaluations from six different software packages (R, SAS, WinNonlin, OpenOffice Calc, Kinetica, EquivTest). Insofar as possible, datasets were evaluated with and without the assumption of equal variances for the construction of a 90% confidence interval. Not all software packages provide functionality for the assumption of unequal variances (EquivTest, Kinetica), and not all packages can handle datasets with more than 1000 subjects per group (WinNonlin). Where results could be obtained across all packages, one showed questionable results when datasets contained unequal group sizes (Kinetica). A proposal is made for the results that should be used as validation targets.

Reference datasets for 2-treatment, 2-sequence, 2-period bioequivalence studies.

It is difficult to validate statistical software used to assess bioequivalence since very few datasets with known results are in the public domain, and the few that are published are of moderate size and balanced. The purpose of this paper is therefore to introduce reference datasets of varying complexity in terms of dataset size and characteristics (balance, range, outlier presence, residual error distribution) for 2-treatment, 2-period, 2-sequence bioequivalence studies and to report their point estimates and 90% confidence intervals which companies can use to validate their installations. The results for these datasets were calculated using the commercial packages EquivTest, Kinetica, SAS and WinNonlin, and the non-commercial package R. The results of three of these packages mostly agree, but imbalance between sequences seems to provoke questionable results with one package, which illustrates well the need for proper software validation.

Quantitative determination of methylphenidate in plasma by gas chromatography negative ion chemical ionisation mass spectrometry using o-(pentafluorobenzyloxycarbonyl)-benzoyl derivatives.

The use of a novel electrophoric derivatisation reagent, o-(pentafluorobenzyloxycarbonyl)-benzoyl chloride, for the quantitative determination of methylphenidate in plasma is described. The drug can be quantitatively measured down to 72 pg/mL plasma using only 250 µL of sample due to the extraordinary sensitivity of the derivatives under negative ion chemical ionisation mass spectrometry. Plasma samples were made alkaline with carbonate buffer and treated with extraction solvent n-hexane and reagent solution for 30 min, which, after concentration, was measured by GC-NICI-MS. The method is rapid as extraction and derivatisation occur in one single step. A stable isotope-labelled internal standard was used and its synthesis described. Full validation data are given to demonstrate the usefulness of the assay, including specificity, linearity, accuracy and precision, long-term stability, short-term stability, freeze-thaw stability, stock solution stability, autosampler stability, aliquot analysis, robustness, matrix effect, and prospective analytical batch size accuracy. The method has been successfully applied to pharmacokinetic profiling of the drug after oral application.