Two-stage designs in bioequivalence trials

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.

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.

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

PURPOSE

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.

METHODS

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.

RESULTS

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.

CONCLUSION

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.

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

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.

Design and inference for 3-stage bioequivalence testing with serial sampling data

A bioequivalence test is to compare bioavailability parameters, such as the maximum observed concentration (Cmax ) or the area under the concentration-time curve, for a test drug and a reference drug. During the planning of a bioequivalence test, it requires an assumption about the variance of Cmax or area under the concentration-time curve for the estimation of sample size. Since the variance is unknown, current 2-stage designs use variance estimated from stage 1 data to determine the sample size for stage 2. However, the estimation of variance with the stage 1 data is unstable and may result in too large or too small sample size for stage 2. This problem is magnified in bioequivalence tests with a serial sampling schedule, by which only one sample is collected from each individual and thus the correct assumption of variance becomes even more difficult. To solve this problem, we propose 3-stage designs. Our designs increase sample sizes over stages gradually, so that extremely large sample sizes will not happen. With one more stage of data, the power is increased. Moreover, the variance estimated using data from both stages 1 and 2 is more stable than that using data from stage 1 only in a 2-stage design. These features of the proposed designs are demonstrated by simulations. Testing significance levels are adjusted to control the overall type I errors at the same level for all the multistage designs.

A comparison of group sequential and fixed sample size designs for bioequivalence trials with highly variable drugs

PURPOSE

A drug is defined as highly variable if its intra-individual coefficient of variation (CV) is greater than or equal to 30%. In such a case, bioequivalence may be assessed by means of methods that take the (high) variability into account. The Scaled Average Bioequivalence (SABE) approach is such a procedure and represents the recommendations of FDA. The aim of this investigation is to compare the performance characteristics of classical group sequential designs (GSD) and fixed design settings for three-period crossover bioequivalence studies with highly variable drugs, where the SABE procedure is utilized.

METHODS

Monte Carlo simulations were performed to assess type I error rate, power, and average sample size for GSDs with Pocock's and O'Brien-Fleming's stopping rules and various timings of the interim analysis and for fixed design settings.

RESULTS

Based on our investigated scenarios, the GSDs show comparable properties with regard to power and type I error rate as compared to the corresponding fixed designs. However, due to an advantage in average sample size, the most appealing design is Pocock's approach with interim analysis after 50% information fraction.

CONCLUSIONS

Due to their favorable performance characteristics, two-stage GSDs are an appealing alternative to fixed sample designs when assessing bioequivalence in highly variable drugs.

Two-stage designs versus European scaled average designs in bioequivalence studies for highly variable drugs: Which to choose?

The usual approach to determine bioequivalence for highly variable drugs is scaled average bioequivalence, which is based on expanding the limits as a function of the within-subject variability in the reference formulation. This requires separately estimating this variability and thus using replicated or semireplicated crossover designs. On the other hand, regulations also allow using common 2 × 2 crossover designs based on two-stage adaptive approaches with sample size reestimation at an interim analysis. The choice between scaled or two-stage designs is crucial and must be fully described in the protocol. Using Monte Carlo simulations, we show that both methodologies achieve comparable statistical power, though the scaled method usually requires less sample size, but at the expense of each subject being exposed more times to the treatments. With an adequate initial sample size (not too low, eg, 24 subjects), two-stage methods are a flexible and efficient option to consider: They have enough power (eg, 80%) at the first stage for non-highly variable drugs, and, if otherwise, they provide the opportunity to step up to a second stage that includes additional subjects.

Pharmacokinetics and pharmacogenetics of capecitabine and its metabolites following replicate administration of two 500 mg tablet formulations

PURPOSE

To describe concentration versus time profiles of capecitabine and its metabolites 5'-DFUR, 5'-DFCR and 5-FU, depending on tablet formulation and on frequent and/or relevant genetic polymorphisms of cytidine deaminase, dihydropyrimidine dehydrogenase, thymidylate synthase and methylenetetrahydrofolate reductase (MTHFR).

METHODS

In 46 cancer patients on chronic capecitabine treatment, who voluntarily participated in the study, individual therapeutic doses were replaced on four consecutive mornings by the study medication. The appropriate number of 500 mg test (T) or reference (R) capecitabine tablets was given in randomly allocated sequences TRTR or RTRT (replicate design). Average bioavailability was assessed by ANOVA.

RESULTS

Thirty female and 16 male patients suffering from gastrointestinal or breast cancer (mean age 53.4 years; mean dose 1739 mg) were included. The T/R ratios for AUC0-t(last) and C max were 96.7 % (98 % CI 90.7-103.2 %) and 87.2 % (98 % CI 74.9-101.5 %), respectively. Within-subject variability for AUC0-t(last) and C max (coefficient of variation for R) was 16.5 and 30.2 %, respectively. Similar results were seen for all metabolites. No serious adverse events occurred. For the MTHFR C677T (rs1801133) genotype, an increasing number of 677C alleles showed borderline correlation with an increasing elimination half-life of capecitabine (p = 0.043).

CONCLUSIONS

The extent of absorption was similar for T and R, but the rate of absorption was slightly lower for T. While such differences are not considered as clinically relevant, formal bioequivalence criteria were missed. A possible, probably indirect role of the MTHFR genotype in pharmacokinetics of capecitabine and/or 5-FU should be investigated in further studies.