Abstract
Regulatory approval of biosimilar products requires demonstration of analytical similarity of functional and structural attributes between the proposed biosimilar product and on-market reference product. The statistical framework for how to evaluate the analytical similarity data has recently been published and a U.S. regulatory guidance is expected soon. This paper illustrates the challenges and issues encountered by Hospira (a Pfizer company) in implementing this newly described statistical framework to support the analytical similarity assessments for biosimilar products. A simulation approach using multilevel (hierarchical) linear regression is also proposed to statistically derive shelf-life specification limits. The approach may be applicable when there is larger volume of data that can be generated as part of the analytical similarity assessment. The performance of the simulation approach is compared when there is a limited vs. sufficiently large sample size and when the quality attribute of interest has a low vs. high analytical variability. The proposed simulation approach to calculate shelf-life specification limits is also benchmarked against a commonly utilized approach in industry based on a fixed effect Analysis of Covariance (ANCOVA) model.
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Montes, R., Bernat, B., Srebalus-Barnes, C. (2016). Statistical Applications for Biosimilar Product Development. In: Lin, J., Wang, B., Hu, X., Chen, K., Liu, R. (eds) Statistical Applications from Clinical Trials and Personalized Medicine to Finance and Business Analytics. ICSA Book Series in Statistics. Springer, Cham. https://doi.org/10.1007/978-3-319-42568-9_20
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DOI: https://doi.org/10.1007/978-3-319-42568-9_20
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