Reproducibility Probability Estimation and RP-Testing for Some Nonparametric Tests

Several reproducibility probability (RP)-estimators for the binomial, sign, Wilcoxon signed rank and Kendall tests are studied. Their behavior in terms of MSE is investigated, as well as their performances for RP-testing. Two classes of estimators are considered: the semi-parametric one, where RP-estimators are derived from the expression of the exact or approximated power function, and the non-parametric one, whose RP-estimators are obtained on the basis of the nonparametric plug-in principle. In order to evaluate the precision of RP-estimators for each test, the MSE is computed, and the best overall estimator turns out to belong to the semi-parametric class. Then, in order to evaluate the RP-testing performances provided by RP estimators for each test, the disagreement between the RP-testing decision rule, i.e., "accept H0 if the RP-estimate is lower than, or equal to, 1/2, and reject H0 otherwise", and the classical one (based on the critical value or on the p-value) is obtained. It is shown that the RP-based testing decision for some semi-parametric RP estimators exactly replicates the classical one. In many situations, the RP-estimator replicating the classical decision rule also provides the best MSE

Statistical considerations in biosimilar assessment using biosimilarity index

10.4172/jbb.1000160Journal of Bioequivalence and Bioavailability5

On the glycosylation aspects of biosimilarity

The recent expiration of several protein therapeutics opened the door for biosimilar development. Biosimilars are biologic medical products that are similar but not identical copies of already-authorized protein therapeutics. Critical quality attributes (CQA), such as post-translational modifications of recombinant biotherapeutics, are important for the clinical efficacy and safety of both the innovative biologics and their biosimilar counterparts. Here, we summarize biosimilarity CQAs, considering the regulatory guidelines and the statistical aspects (e.g., biosimilarity index) and then discuss glycosylation as one of the important attributes of biosimilarity. Finally, we introduced the ‘Glycosimilarity Index’, which is based on the averaged biosimilarity criterion

Biosimilars in dermatology: identifying myths and knowledge gaps

Biosimilars are beginning to gain regulatory approval in the United States. Biosimilars are structurally near identical to the innovator and must demonstrate identical pharmacokinetics via the same binding affinity and biological function on assays. However, biologics are so complex that even the innovator company cannot produce exact duplicates; there is batch-to-batch variation. The International Psoriasis Council has outlined a biosimilarity index, which aims to standardize preclinical definitions of biosimilarity. Such an index, paired with post-approval monitoring, could provide a transparent, quantitative definition of biosimilarity. Such an index could increase trust in biosimilar medicines and the preclinical assessment process without increasing costs. As preclinical analyses are critical to biosimilar approval, manufacturers should devote proportionate resources to completing them. Dermatologists, who might reflexively look for indication-specific clinical data, might also shift their focus to preclinical variables. Finally, it should be noted that biosimilars provide more evidence of similarity than we have for different batches of the innovator product. Thus, any clinical testing standards, or lack thereof, for different batches of innovator products should also apply to biosimilars

A Tolerance Interval Approach to Assessing the Biosimilarity of Follow-On Biologics

<p>With many important biologic products due to lose patent protection in the next few years, the development of follow-on biologics has received much attention from both sponsors and regulatory authorities. Biologics are often produced in living systems. The living systems used to produce biologics are highly complex and could be sensitive to very minor changes in the manufacturing process. According to the guideline published by the European Medicines Agency, biosimilar products are similar, not identical, to the innovator products they seek to copy. Therefore, in developing a biosimilar, it is important to assess the similarity between it and the innovator product. In this article, we consider a two-arm, parallel design with a reference biological product and a biosimilar. Then we construct a biosimilarity index for assessing the degree of similarity based on the tolerance limits. The acceptance criterion is proposed to judge whether the biosimilar is similar to the reference product. We also address the determination of the number of subjects to ensure that the occurring probability of biosimilarity criterion is maintained at a desired level, say 80 or 90%.</p

A tolerance interval approach to assessing the biosimilarity of follow-on biologics

[[abstract]]With many important biologic products due to lose patent protection in the next few years, the development of follow-on biologics has received much attention from both sponsors and regulatory authorities. Biologics are often produced in living systems. The living systems used to produce biologics are highly complex and could be sensitive to very minor changes in the manufacturing process. According to the guideline published by the European Medicines Agency, biosimilar products are similar, not identical, to the innovator products they seek to copy. Therefore, in developing a biosimilar, it is important to assess the similarity between it and the innovator product. In this article, we consider a two-arm, parallel design with a reference biological product and a biosimilar. Then we construct a biosimilarity index for assessing the degree of similarity based on the tolerance limits. The acceptance criterion is proposed to judge whether the biosimilar is similar to the reference product. We also address the determination of the number of subjects to ensure that the occurring probability of biosimilarity criterion is maintained at a desired level, say 80 or 90%

NOVEL BAYESIAN ADAPTIVE CLINICAL TRIAL DESIGNS IN EARLY PHASES

Early phase, or phase I and phase II, trials are the first step in testing new medicines that have been developed in the lab. The main goal of phase I clinical trials is to establish the recommended dose of new drugs for phase II trials. For the cytotoxic drugs, the goal is to find maximum tolerated dose (MTD). The guiding principle for dose escalation in phase I trials is to avoid exposing too many patients to subtherapeutic doses while preserving safety and maintaining rapid accrual. Therefore, dose escalation methods, especially Bayesian designs, are recommended to be used in phase I trials. There are many proposed Bayesian phase I adaptive designs, among them, continual reassessment method (CRM) is the firstly proposed pioneered Bayesian design. The CRM needs pre-specification of a series of prior guesses of toxicity probabilities of each investigated doses, known as the skeleton, using a parametric model, and then continuously updates the estimate of the dose-toxicity curve based on accumulating data. By using a dose-toxicity model, the CRM efficiently pools data across doses and adaptively makes the decision of dose assignment and selection. Two chapters of the thesis devote to development of the CRM design (chapter 2) and to extend the CRM design (chapter 3). Specifically, chapter 2 deals with the issue of skeleton pre-specification in the CRM design. We propose an automatic way to generate multiple skeletons for Bayesian model averaging CRM (BMA-CRM), an extension of robust version of the CRM, to avoid arbitrary specification of skeletons with improving performances compared to the original CRM and BMA-CRM designs. Chapter 3 deals with bridging studies, or follow-up trials. The emergence of bridging studies is due to different ethnic populations with different responses to a same drug and consequentially attaining different MTDs. Therefore, conventional one-size-fit-all paradigm cannot work. But, despite variations among different ethnic populations, their drug responses still show somewhat similarities. Commonly, a landmark trial has been conducted and a MTD dose has also been established for a certain population. Thus, independent conducting a trial for a new population of ignoring information of the landmark trial is wasteful. Therefore, challenges of the bridging studies include: how to effectively use/borrow information of the historical landmark trial, and how to design trials to accommodate heterogeneities of different populations. In this chapter, we develop a novel design, Bridging-CRM, B-CRM, to borrow the landmark trial information based on a proposed mixture estimator and the CRM framework, and to acknowledge different populations\u27 heterogeneities of using the idea of multiple skeletons. Chapter 4 focuses on phase II design for biosimilar drug development. Biosimilar is a term that describes the equivalence of a generic version to an innovator\u27s biologic drug product; biosimilars are close, but not exact copies of biologic drugs already on the market. Guidelines for statistical methods to establish biosimilarity remain nonspecific because of the newness of biosimilars. It is therefore of high urgency to develop appropriate and reliable statistical methodologies for developing biosimilars. Some statistical methods have been proposed to assess biosimilarity, but none of them proposed designs in this field. However, biosimilar trials come with several challenges that are beyond the scope of the conventional randomized comparative trial design. First, when a biosimilar is ready to be tested in a randomized trial, the innovative reference drug has been in the market for many years and a huge amount of data on that drug has accumulated. It is critical to incorporate these rich historical data into the biosimilar trial design to improve trial efficiency. Another challenge when designing biosimilar trials is determining how to quantify and monitor the biosimilar during the trial. To address these issues, in chapter 4, we develop a new approach, the \textit{calibrated power prior} (CPP), to allow the design to adaptively borrow information from the historical data according to the congruence between the historical data and the data collected in the current trial. We also propose the \textit{Bayesian biosimilarity index} (BBI) to assess the similarity between the biosimilar and the innovative reference drug. In our design, we evaluate the BBI in a group sequential fashion based on the accumulating interim data, and stop the trial early once there is enough information to conclude or reject the similarity