ArticleBiotechnology progress2018
Chromatographic parameter determination for complex biological feedstocks.
Article in Biotechnology progress, 2018. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 5 papers.
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Who cites it
5 citing papers in PubMed.
- Deep Learning in Therapeutic Antibody Development.Methods in molecular biology (Clifton, N.J.) · 2022Article
- The use of predictive models to develop chromatography-based purification processes.Frontiers in bioengineering and biotechnology · 2022Review
- White paper on high-throughput process development for integrated continuous biomanufacturing.Biotechnology and bioengineering · 2021Review
- Proteomics-based method to comprehensively model the removal of host cell protein impurities.Biotechnology progressArticle
- From protein structure to an optimized chromatographic capture step using multiscale modeling.Biotechnology progressArticle
Corrections and comments
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Authors and funding
7 authors.
Funding
No grant is acknowledged in the PubMed record.
Abstract
The application of mechanistic models for chromatography requires accurate model parameters. Especially for complex feedstocks such as a clarified cell harvest, this can still be an obstacle limiting the use of mechanistic models. Another commonly encountered obstacle is a limited amount of sample material and time to determine all needed parameters. Therefore, this study aimed at implementing an approach on a robotic liquid handling system that starts directly with a complex feedstock containing a monoclonal antibody. The approach was tested by comparing independent experimental data sets with predictions generated by the mechanistic model using all parameters determined in this study. An excellent agreement between prediction and experimental data was found verifying the approach. Thus, it can be concluded that RoboColumns with a bed volume of 200 μL can well be used to determine isotherm parameters for predictions of larger scale columns. Overall, this approach offers a new way to determine crucial model input parameters for mechanistic modelling of chromatography for complex biological feedstocks. © 2018 The Authors Biotechnology Progress published by Wiley Periodicals, Inc. on behalf of American Institute of Chemical Engineers Biotechnol. Prog., 34:1006-1018, 2018.
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Registered trials
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