Publications by authors named "Amita Puranik"

SARS-CoV-2 spike protein is an essential component of numerous protein-based vaccines for COVID-19. The receptor-binding domain of this spike protein is a promising antigen with ease of expression in microbial hosts and scalability at comparatively low production costs. This study describes the production, purification, and characterization of RBD of SARS-CoV-2 protein, which is currently in clinical trials, from a commercialization perspective.

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The therapeutic and immunological properties of biopharmaceuticals are governed by the glycoforms contained in them. Thus, bioinformatics tools capable of performing comprehensive characterization of glycans are significantly important to the biopharma industry. The primary structural elucidation of glycans using mass spectrometry is tricky and tedious in terms of spectral interpretation.

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The growing complexity of novel biopharmaceutical formats, such as Fc-fusion proteins, in increasingly competitive environment has highlighted the need of high-throughput analytical platforms. Multi-attribute method (MAM) is an emerging analytical technology that utilizes liquid chromatography coupled with mass spectrometry to monitor critical quality attributes (CQAs) in biopharmaceuticals. MAM is intended to supplement or replace the conventional chromatographic and electrophoretic approaches used for quality control and drug release purpose.

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Analytical and functional characterization of batches of biologics/biosimilar products are imperative towards qualifying them for pre-clinical and clinical investigations. Several orthogonal strategies are employed to characterize the functional attributes of these drugs. However, the use of conventional techniques for online monitoring of functional attributes is not feasible.

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Biopharmaceuticals, such as monoclonal antibodies, are considered as life-saving drugs for autoimmune diseases, cancer and infectious diseases. However, biotherapeutics tend to undergo chemical degradation during various stages of manufacturing. The conditions of chemical degradation, along with the physical degradation pathways, have a direct influence on the overall stability, safety and efficacy of these therapeutics.

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Principles of Industry 4.0 direct us to predict how pharmaceutical operations and regulations may exist with automation, digitization, artificial intelligence (AI), and real time data acquisition. Machine learning (ML), a sub-discipline of AI, involves the use of statistical tools to extract the desired information either through understanding the underlying patterns in the information or by development of mathematical relationships among the critical process parameters (CPPs) and critical quality attributes (CQAs) of biopharmaceuticals.

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Glycosylation has been shown to define the safety and efficacy of biopharmaceuticals, thus classified as a critical quality attribute. However, controlling glycan heterogeneity has always been a major challenge owing to the multivariate factors that govern the glycosylation process. Conventional approaches for controlling glycosylation such as gene editing and metabolic control have succeeded in obtaining desired glycan profiles in accordance with the Quality by Design paradigm.

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Biotherapeutics, such as mAbs and fusion proteins, are a major and rapidly growing class of pharmaceuticals. Majority of the biopharmaceuticals are glycoproteins, wherein about 1 to 30% of their molecular weight (MW) are contributed by the glycans. Determination of MW of heavily glycosylated proteins, such as Fc-fusion proteins, is seriously hampered by the physicochemical characteristics and heterogeneity of the attached carbohydrates.

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