Session 1: Bioequivalence and Biosimilars
Organizers: Victoria Chang (Boehringer-Ingelheim) and Yi Tsong (FDA)
Chair: Yi Tsong (FDA)
Victoria Chang (Boehringer-Ingelheim) “Sample Size Determination for a Three-Arm Equivalence Trial of Poisson and Negative Binomial Responses”
Assessing equivalence or similarity has drawn much attention recently as the US pharmaceutical industry is under threat from biologics patent cliff. To claim equivalence between the test treatment and the reference treatment when assay sensitivity is well-established from historical data, one has to demonstrate both superiority of the test treatment over placebo and equivalence between the test treatment and the reference treatment. Thus, there is urgency for practitioners to derive a practical way to calculate sample size for a three-arm equivalence trial. In this paper, we derive power function and discuss sample size requirement for a three-arm equivalence trial with Poisson and negative binomial clinical endpoints as an extension to the prior research on continuous endpoints. In addition, we examine the effect of the dispersion parameter on the power and the sample size by varying its coefficient from small to large. In extensive numerical studies, we demonstrate that required sample size heavily depends on the dispersion parameter. Therefore, misusing a Poisson model for negative binomial data may easily lose power up to 20%, depending on the value of the dispersion parameter.
Meiyu Shen (FDA) “Distributional assumptions for AUC, Cmax and Tmax”
In a typical pharmacokinetic bioequivalence study with a single dose administration, one of the drug products is a reference formulation and the other a test formulation. Each subject is administered both formulations in a randomized two-period crossover design. A concentration-time profile is determined for each subject given each formulation. Each single concentration-time profile can be modeled by a pharmacokinetic compartmental model. Many software programs exist for estimating the pharmacokinetic parameters such as the absorption rate, the volume of distribution, etc. Then, AUC, Cmax, and Tmax can be obtained from the fitted pharmacokinetic model. In spite of these elaborate pharmacokinetic models, the AUC, Cmax, and Tmax are obtained from the nonparametric method for bioequivalence assessment. In practice, the univariate response variables such as log(AUC) and log(Cmax) are often assumed to follow a normal distribution without much experimental data support. For instance, an investigation of observed pharmacokinetic studies was based on numbers of subjects from 29 to 69 and so the power of the Shapiro-Wilk test to detect departures from either distribution (lognormal or normal) may have been limited. In this presentation, we investigate the normality assumption of log(AUC) or log(Cmax) using pharmacokinetic compartmental models typically used to describe concentration profiles over time. In particular, if data is generated using the simplest pharmacokinetic models (namely one and two compartment models), will it ultimately lead to deciding which distribution of log(AUC), log(Cmax), or log(Tmax) is most plausible?
Jean Pan (Amgen) “Statistical Considerations in Biosimilar Clinical Development”
Clinical development for a biosimilar product is aimed at demonstrating similarity to a reference biologic product. It is not intended to prove clinical safety and efficacy all over again. With this understanding, there are specific challenges to the design and analysis of biosimilar clinical studies. In this talk we will discuss several statistical strategies and challenges for biosimilar clinical studies, including selection of endpoints, determination of margins, and evaluation of the totality-of-evidence. Experiences from working with regulatory agencies on clinical development of some biosimilar molecules will be shared from a statistical perspective.
Cassie (Xiaoyu) Dong (FDA) “Statistical Approaches to Demonstrate Analytical Similarity of Quality Attributes”
In conventional equivalence testing, the equivalence margin is usually fixed. E.g. (80%, 125%) in PK studies. However, such a fixed margin may not be suitable for highly variable medicines or for testing quality attributes of biologics. Considering those practical issues, we proposed to establish the equivalence margin as a constant times the variability of the reference product. This constant is obtained by achieving a given power with a pre-specified samples sizes and the true mean difference. With this equivalence margin, test statistics of the equivalence testing on the mean values need to be carefully derived and examined. When the variability of the reference product is a known constant, we developed an exact t-statistics. When the reference variability is unknown, we need to consider the variability of the sample variance when we conduct the hypothesis testing. We developed approximate approaches, confidence interval approaches, and exact statistics. We investigated type I error rate, power function of our proposed statistical methods for each scenario.
