4.5 Article

Investigation and prediction of protein precipitation by polyethylene glycol using quantitative structure-activity relationship models

期刊

JOURNAL OF BIOTECHNOLOGY
卷 241, 期 -, 页码 87-97

出版社

ELSEVIER
DOI: 10.1016/j.jbiotec.2016.11.014

关键词

Polyethylene glycol; Precipitation Quantitative structure activity relationship (QSAR); Semi-mechanistic modeling; Monoclonal antibody

资金

  1. German Federal Ministry of Education and Research (BMBF) [0315640B]
  2. Lonza Biologics PLC

向作者/读者索取更多资源

Precipitation of proteins is considered to be an effective purification method for proteins and has proven its potential to replace costly chromatography processes. Besides salts and polyelectrolytes, polymers, such as polyethylene glycol (PEG), are commonly used for precipitation applications under mild conditions. Process development, however, for protein precipitation steps still is based mainly on heuristic approaches and high-throughput experimentation due to a lack of understanding of the underlying mechanisms. In this work we apply quantitative structure-activity relationships (QSARs) to model two parameters, the discontinuity point m* and the beta-Value, that describe the complete precipitation curve of a protein under defined conditions. The generated QSAR models are sensitive to the protein type, pH, and ionic strength. It was found that the discontinuity point m* is mainly dependent on protein molecular structure properties and electrostatic surface properties, whereas the beta-value is influenced by the variance in electrostatics and hydrophobicity on the protein surface. The models for m* and the beta-value exhibit a good correlation between observed and predicted data with a coefficient of determination of R-2 >= 0.90 and, hence, are able to accurately predict precipitation curves for proteins. The predictive capabilities were demonstrated for a set of combinations of protein type, pH, and ionic strength not included in the generation of the models and good agreement between predicted and experimental data was achieved. (C) 2016 Elsevier B.V. All rights reserved.

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