An evaluation roadmap for critical quality attributes from tier 1 in analytical similarity assessment
Kejian Wu,
Haitao Pan,
Chen Li,
Qingbo Zhao,
Ling Wang and
Jielai Xia
PLOS ONE, 2018, vol. 13, issue 12, 1-14
Abstract:
Analytical similarity assessment of critical quality attributes (CQAs) serves as a foundation for the development of biosimilar products and facilitates an abbreviated subsequent clinical evaluation. In this study, we establish a statistical evaluation roadmap with statistical approaches for some selected CQAs from Tier 1, because they are most relevant to clinical outcomes and require the most rigorous statistical methods. In the roadmap, we incorporate 3 methods—ranking and tier assignment of quality attributes, the equivalence test, and the Mann–Whitney test for equivalence—that are important to determine analytical similarity between the reference and biosimilar products. For the equivalence test, we develop a power calculation formula based on the two one-sided tests procedure. Exact sample sizes can be numerically calculated. Then, we propose a flexible idea for selecting the number of reference lots (nR) and the number of biosimilar lots (nT) to adjust for serious unbalanced sample sizes. From results of extensive simulations under various parameter settings, we obtain a workable strategy to determine the optimum sample size combination (nT, nR) for the equivalence test of CQAs from Tier 1. R codes are provided to facilitate implementation of the roadmap and corresponding methods in practice.
Date: 2018
References: View complete reference list from CitEc
Citations:
Downloads: (external link)
https://journals.plos.org/plosone/article?id=10.1371/journal.pone.0208354 (text/html)
https://journals.plos.org/plosone/article/file?id= ... 08354&type=printable (application/pdf)
Related works:
This item may be available elsewhere in EconPapers: Search for items with the same title.
Export reference: BibTeX
RIS (EndNote, ProCite, RefMan)
HTML/Text
Persistent link: https://EconPapers.repec.org/RePEc:plo:pone00:0208354
DOI: 10.1371/journal.pone.0208354
Access Statistics for this article
More articles in PLOS ONE from Public Library of Science
Bibliographic data for series maintained by plosone ().