Statistical assessment of biosimilar products.

Biological products or medicines are therapeutic agents that are produced using a living system or organism. Access to these life-saving biological products is limited because of their expensive costs. Patents on the early biological products will soon expire in the next few years. This allows other biopharmaceutical/biotech companies to manufacture the generic versions of the biological products, which are referred to as follow-on biological products by the U.S. Food and Drug Administration (FDA) or as biosimilar medicinal products by the European Medicine Agency (EMEA) of the European Union (EU). Competition of cost-effective follow-on biological products with equivalent efficacy and safety can cut down the costs and hence increase patients' access to the much-needed biological pharmaceuticals. Unlike for the conventional pharmaceuticals of small molecules, the complexity and heterogeneity of the molecular structure, complicated manufacturing process, different analytical methods, and possibility of severe immunogenicity reactions make evaluation of equivalence (similarity) between the biosimilar products and their corresponding innovator product a great challenge for both the scientific community and regulatory agencies. In this paper, we provide an overview of the current regulatory requirements for approval of biosimilar products. A review of current criteria for evaluation of bioequivalence for the traditional chemical generic products is provided. A detailed description of the differences between the biosimilar and chemical generic products is given with respect to size and structure, immunogenicity, product quality attributed, and manufacturing processes. In addition, statistical considerations including design criteria, fundamental biosimilar assumptions, and statistical methods are proposed. The possibility of using genomic data in evaluation of biosimilar products is also explored.

Duke Scholars

Author Shein-Chung Chow Biostatistics & Bioinformatics, Division of Biostatistics