In prior works, this group demonstrated the feasibility of valid adaptive sequential designs for crossover bioequivalence studies. In this paper, we extend the prior work to optimize adaptive sequential designs over a range of geometric mean test/reference ratios (GMRs) of 70-143% within each of two ranges of intra-subject coefficient of variation (10-30% and 30-55%). These designs also introduce a futility decision for stopping the study after the first stage if there is sufficiently low likelihood of meeting bioequivalence criteria if the second stage were completed, as well as an upper limit on total study size. The optimized designs exhibited substantially improved performance characteristics over our previous adaptive sequential designs. Even though the optimized designs avoided undue inflation of type I error and maintained power at ≥ 80%, their average sample sizes were similar to or less than those of conventional single stage designs.

Optimal adaptive sequential designs for crossover bioequivalence studies

Bioequivalence (BE) studies are most often conducted as crossover trials, and therefore establishing their required sample size necessitates specification of the within-person variance. Given that this specification is often difficult in practice, there has been great interest in recent years in the use of adaptive designs for BE trials. However, while numerous methods for this have now been presented, their focus has been solely on two-treatment BE studies. In some instances, it will be desired to incorporate more than a single test and reference formulation into a BE trial. It would therefore be useful to establish methodology for the design of adaptive multi-treatment BE trials, to acquire the benefits in the two-treatment setting in this more complex situation. Here, we achieve this for three-treatment studies by extending previously proposed designs for two-treatment trials. First, we discuss the additional design considerations that arise when multiple comparisons are made. Next, an extensive simulation study is employed to compare the performance of the proposed procedures. With this, we demonstrate that two-stage designs with desirable statistical operating characteristics can be readily identified for three-treatment BE trials. Supplementary materials for this article are available online.

Two-Stage Adaptive Designs for Three-Treatment Bioequivalence Studies

To demonstrate bioequivalence between two drug formulations, a pilot trial is often conducted prior to a pivotal trial to assess feasibility and gain preliminary information about the treatment effect. Due to the limited sample size, it is not recommended to perform significance tests at the conventional 5% level using pilot data to determine if a pivotal trial should take place. Whilst some authors suggest to relax the significance level, a Bayesian framework provides an alternative for informing the decision-making. Moreover, a Bayesian approach also readily permits possible incorporation of pilot data in priors for the parameters that underpin the pivotal trial.We consider two-sequence, two-period crossover designs that compare test (T) and reference (R) treatments. We propose a robust Bayesian hierarchical model, embedded with a scaling factor, to elicit a Go/No-Go decision using predictive probabilities. Following a Go decision, the final analysis to formally establish bioequivalence can leverage both the pilot and pivotal trial data jointly. A simulation study is performed to evaluate trial operating characteristics.Compared with conventional procedures, our proposed method improves the decision-making to correctly allocate a Go decision in scenarios of bioequivalence. By choosing an appropriate threshold, the probability of correctly (incorrectly) making a No-Go (Go) decision can be ensured at a desired target level. Using both pilot and pivotal trial data in the final analysis can result in a higher chance of declaring bioequivalence. The false positive rate can be maintained in situations when T and R are not bioequivalent.The proposed methodology is novel and effective in different stages of bioequivalence assessment. It can greatly enhance the decision-making process in bioequivalence trials, particularly in situations with a small sample size. 

A Bayesian approach to pilot-pivotal trials for bioequivalence assessment

Abstract Objective The study aimed to develop a rapid, simple and sensitive LC/ESI-MS/MS method to measure prazosin concentration in human plasma and apply bedside sampling in bioequivalence study of two prazosin tablets to resolve the adverse effect of orthostatic hypotension. Significance The LC/ESI-MS/MS prazosin method was highly sensitive and selective. Bedside sampling reduced the orthostatic hypotension incidence and subject dropout rate. Methods After sample preparation, prazosin and terazosin (IS) were detected on mass spectrometer operating in multiple reaction monitoring mode using positive ionization. Mobile phase flow rate was set at 0.40 mL/min with sample run time of 1.75 min. The bioanalytical method was validated as per EMEA and FDA guidelines. Bedside sampling was performed in bioequivalence study for the first 4 h after dosing. The three primary pharmacokinetic parameters, C max, AUC0- t and AUC0-∞ and 90% confidence interval were determined. Results The small injection volume of 1 μL minimized instrumentation contamination and prolonged the analytical column lifespan. Linearity was obtained between 0.5 and 30.0 ng/mL, with coefficient of determination, r 2 ≥ 0.99. The mean extraction recovery of prazosin and IS was >92%, with precision value (CV, %) ≤ 10.3%. Only two orthostatic hypotension adverse events were reported. The two prazosin formulations were found to be bioequivalent. Conclusion The LC/ESI-MS/MS method has shown robustness and reliability exemplified by the incurred sample re-analysis result. Bedside sampling should be proposed for bioequivalence or pharmacokinetic studies of drugs demonstrating adverse event of orthostatic hypotension.

https://figshare.com/articles/journal_contribution/Simple_and_high_sample_throughput_LC_ESI-MS_MS_method_for_bioequivalence_study_of_prazosin_a_drug_with_risk_of_orthostatic_hypotension/21201160

Simple and high sample throughput LC/ESI-MS/MS method for bioequivalence study of prazosin, a drug with risk of orthostatic hypotension

In 2010 the European Medicines Agency allowed a two-stage design in bioequivalence studies. However, in the public domain there are mainly articles describing the theoretical and statistical base for the application of the two-stage design. One of the reasons seems to be the lack of practical guidance for the Sponsors on when and how the two-stage design can be beneficial in bioequivalence studies. Different variants with positive and negative outcomes have been evaluated, including a pivotal study, pilot + pivotal study and two-stage study. The scientific perspective on the two-stage bioequivalence study has been confronted with the industrial one. Key information needed to conduct a bioequivalence study – such as in vitro data and pharmacokinetics – have been listed and organized into a decision scheme. Advantages and disadvantages of the two-stage design have been summarized. The use of the two-stage design in bioequivalence studies seems to be a beneficial alternative to the 2 × 2 crossover study. Basic information on the properties of the active substance and the characteristics of the drug form are needed to make an initial decision to carry out the two-stage study.

/pdf/10th-anniversary-of-a-two-stage-design-in-bioequivalence-why-4btuac0tqy.pdf

10th Anniversary of a Two-Stage Design in Bioequivalence. Why Has it Still Not Been Implemented?

Sample size reestimation in a crossover, bioequivalence study can be a useful adaptive design tool, particularly when the intrasubject variability of the drug formulation under investigation is not well understood. When sample size reestimation is done based on an interim estimate of the intrasubject variability and bioequivalence is tested using the pooled estimate of intrasubject variability, type 1 error inflation will occur. Type 1 error inflation is caused by the pooled estimate being a biased estimator of the intrasubject variability. The type 1 error inflation and bias of the pooled estimator of variability are well characterized in the setting of a two-arm, parallel study. The purpose of this work is to extend this characterization to the setting of a crossover, bioequivalence study with sample size reestimation and to propose an estimator of the intrasubject variability that will prevent type 1 error inflation.

Controlling type 1 error rate for sequential, bioequivalence studies with crossover designs.

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Controlling type 1 error rate for sequential, bioequivalence studies with crossover designs.