Publications by authors named "Pranavan Thillaivinayagalingam"

Surface plasmon resonance (SPR) permits the quantitative analysis of therapeutic antibody concentrations and impurities including bacteria, Protein A, Protein G and small molecule ligands leached from chromatography media. The use of surface plasmon resonance has gained popularity within the biopharmaceutical industry due to the automated, label free, real time interaction that may be exploited when using this method. The application areas to assess protein interactions and develop analytical methods for biopharmaceutical downstream process development, quality control, and in-process monitoring are reviewed.

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The high therapeutic and financial value offered by polyclonal antibodies and their fragments has prompted extensive commercialization for the treatment of a wide range of acute clinical indications. Large-scale manufacture typically includes antibody-specific chromatography steps that employ custom-made affinity matrices to separate product-specific IgG from the remainder of the contaminating antibody repertoire. The high cost of such matrices necessitates efficient process design in order to maximize their economic potential.

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Rapid analyses of chromatographic steps within a biopharmaceutical manufacturing process are often desirable to evaluate column performance, provide mass balance data and to permit accurate calculations of yields and recoveries. Using SPR (surface plasmon resonance) biosensor (Biacore) technology, we have developed a sandwich immunoassay to quantify polyclonal anti-digoxin Fab fragments used for the production of the FDA (Food and Drug Administration)-approved biotherapeutic DigiFab. The results show that specific Fab may be quantified in all affinity process streams and accurate yield and mass balance data calculated.

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This paper describes the effect of several variables on the affinity process for the production of the FDA approved biotherapeutic product Digoxin Immune Fab (Ovine) (DigiFab, Protherics Inc., TN, USA). The study considers the effects of column re-use on matrix capacity and on the subsequent recovery of the antibody product, and the impact of varying column loading on matrix performance.

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