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The effect of formulation variables on protein stability and integrity of a model IgG4 monoclonal antibody and translation to formulation of a model ScFv

单克隆抗体 抗体 化学 溶解度 蛋白质稳定性 小分子 计算生物学 色谱法 生物化学 生物 免疫学 有机化学
作者
Evdoxia Gourbatsi,Jane F. Povey,C. Mark Smales
出处
期刊:Biotechnology Letters [Springer Science+Business Media]
卷期号:40 (1): 33-46 被引量:1
标识
DOI:10.1007/s10529-017-2443-x
摘要

There are a number of blockbuster monoclonal antibodies on the market used for the treatment of a variety of diseases. Although the formulation of many antibodies is achieved in 'platform' formulations, some are so difficult to formulate that it can result in an inability to develop a finished drug product. Further, a large number of antibody-inspired or-based molecules are now being developed and assessed for biotherapeutic purposes and less is understood around the required active protein drug concentrations, excipients and additives required in final product formulations.We investigated the effect of formulation variables (pH, buffer composition, glycine and NaCl concentration, time and temperature of accelerated stability studies) on antibody solubility/aggregation and activity using a Plackett-Burman Experimental Design approach. We then used the findings from this study and applied these to the formulation of a single chain variable fragment (ScFv) molecule. Our data shows that prediction of ScFc stability from a model monoclonal antibody could be achieved although further formulation optimization was required. Mass spectrometry analysis confirmed changes to the mass and hence authenticity of both the model antibody and ScFv under formulation conditions that did not provide appropriate conditions for protection of the molecules.The role of the different formulation conditions on maintaining protein integrity is described and using mass spectrometry shows that protein integrity is compromised under particular conditions. The implications for predicting successful formulations for protein molecules is discussed and how antibody formulations could be used to predict formulation components for novel antibody based molecules.
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