Multiplexed Gene Expression as a Characterization of Bioactivity for Interferon Beta (IFN-β) Biosimilar Candidates: Impact of Innate Immune Response Modulating Impurities (IIRMIs).

Multiplexed Gene Expression as a Characterization of Bioactivity for Interferon Beta (IFN-β) Biosimilar Candidates: Impact of Innate Immune Response Modulating Impurities (IIRMIs).

Publication date: Feb 08, 2019

Recombinant human interferon-β (rhIFN-β) therapy is the first-line treatment in relapsing-remitting forms of multiple sclerosis (MS). The mechanism of action underlying its therapeutic activity is only partially understood as IFN-βs induce the expression of over 1000 genes modifying multiple immune pathways. Currently, assessment of potency for IFN-β products is based on their antiviral effect, which is not linked to its therapeutic effect. Here, we explore the use of a multiplexed gene expression system to more broadly characterize IFN-β bioactivity. We find that MM6 cells stimulated with US-licensed rhIFN-βs induce a dose-dependent and reproducible pattern of gene expression. This pattern of gene expression was used to compare the bioactivity profile of biosimilar candidates with the corresponding US-licensed rhIFN-β products, Rebif and Betaseron. While the biosimilar candidate for Rebif matched the pattern of gene expression, there were differences in the expression of a subset of interferon-inducible genes including CXCL-10, CXCL-11, and GBP1 induced by the biosimilar candidate for Betaseron. Assessment of product impurities in both products suggested that the difference was rooted in the presence of innate immune response modulating impurities (IIRMIs) in the licensed product. These studies indicate that determining the expression levels for an array of reporter genes that monitor different pathways can be informative as part of the demonstration of biosimilarity or comparability for complex immunomodulatory products such as IFN-β, but the sensitivity of each gene to potential impurities in the product should be examined to fully understand the results.



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