Literature DB >> 18218286

Two recombinant human interferon-beta 1a pharmaceutical preparations produce a similar transcriptional response determined using whole genome microarray analysis.

A E Sterin Prync1, P Yankilevich, P R Barrero, R Bello, L Marangunich, A Vidal, M Criscuolo, L Benasayag, A L Famulari, R O Domínguez, M A Kauffman, R A Diez.   

Abstract

OBJECTIVES: Recombinant human interferon-beta (IFN-b) is a well-established treatment for multiple sclerosis (MS). The regulatory process for marketing authorization of biosimilars is currently under debate in certain countries. In the EU, EMEA has clearly defined the process including overarching and product-specific guidelines, which includes clinical testing. Biosimilarity needs to be based on comparability criteria, including at least molecular characterization, biological activity relevant for the therapeutic effect and relative bioavailability ("bioequivalence"). In the case of such complex diseases as MS, where the effect of treatment is not so directly measurable, in vitro tools can provide additional data to support comparability. Genomic microarrays assays might be useful to compare multisource biopharmaceuticals. The aim of the present study was to compare the pharmacodynamic genomic effects (in terms of transcriptional regulation) of two recombinant human IFN-I(2)1a preparations on lymphocytes of multiple sclerosis patients using a whole genome microarray assay.
METHODS: We performed an ex vivo whole genome expression profiling of the effect of two preparations of IFN-I(2)1a on non-adherent mononuclears from five relapsing-remitting MS patients analyzing microarrays (CodeLink Human Whole Genome). Patients blood was drawn, PBMCs isolated and cultured in three different conditions: culture medium (control), 1,000 U/ml of IFN-I(2)1a (BLA- (STOFERON, Bio Sidus) and 1,000 U/ml of IFN-I(2)1a (REBIF, Serono) RNA was purified from non-adherent cells (mostly lymphocytes), amplified and hybridized. Raw data were generated by CodeLink proprietary software. Data normalization, quality control and analysis of differential gene expression between treatments were done using linear model for microarray data. Functional annotation analysis of IFN-I(2)1a MS treatment transcription was done using DAVID.
RESULTS: Out of the approximately 45,000 human sequences examined, no evidence of differential regulation was found when both treatments were compared (minimum adjusted p-value > 0.999). The IFN-I(2)1a effect differentially regulated the expression of 868 genes. The expression of standard markers such as GTP cyclohidrolase, MxA, and OAS isoenzymes A and B changed as a consequence of the action of IFN-I(2)1a.
CONCLUSIONS: This exhaustive and highly sensitive assay did not show differences in the genomic expression profile of these two products under the assayed experimental conditions. These results suggest that this technology might be useful for the initial comparison of biosimilars, being part of a comprehensive comparability program that includes clinical testing.

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Year:  2008        PMID: 18218286     DOI: 10.5414/cpp46064

Source DB:  PubMed          Journal:  Int J Clin Pharmacol Ther        ISSN: 0946-1965            Impact factor:   1.366


  3 in total

1.  Conformation and dynamics of biopharmaceuticals: transition of mass spectrometry-based tools from academe to industry.

Authors:  Igor A Kaltashov; Cedric E Bobst; Rinat R Abzalimov; Steven A Berkowitz; Damian Houde
Journal:  J Am Soc Mass Spectrom       Date:  2009-10-29       Impact factor: 3.109

2.  cDNA targets improve whole blood gene expression profiling and enhance detection of pharmocodynamic biomarkers: a quantitative platform analysis.

Authors:  Mark L Parrish; Chris Wright; Yarek Rivers; David Argilla; Heather Collins; Brendan Leeson; Andrey Loboda; Michael Nebozhyn; Matthew J Marton; Serguei Lejnine
Journal:  J Transl Med       Date:  2010-09-25       Impact factor: 5.531

3.  Excessive biologic response to IFNβ is associated with poor treatment response in patients with multiple sclerosis.

Authors:  Richard A Rudick; M R Sandhya Rani; Yaomin Xu; Jar-Chi Lee; Jie Na; Jennifer Shrock; Anupama Josyula; Elizabeth Fisher; Richard M Ransohoff
Journal:  PLoS One       Date:  2011-05-13       Impact factor: 3.240

  3 in total

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