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High throughput screening setup of a scale‐down device for membrane chromatography‐aggregate removal of monoclonal antibodies
Author(s) -
Stein Dominik,
Thom Volkmar,
Hubbuch Jürgen
Publication year - 2020
Publication title -
biotechnology progress
Language(s) - English
Resource type - Journals
SCImago Journal Rank - 0.572
H-Index - 129
eISSN - 1520-6033
pISSN - 8756-7938
DOI - 10.1002/btpr.3055
Subject(s) - scalability , throughput , computer science , elution , process development , transferability , quality by design , process (computing) , process engineering , chromatography , materials science , chemistry , machine learning , engineering , logit , telecommunications , database , particle size , wireless , operating system
Abstract In biopharmaceutical process development, resin‐based high throughput screening (HTS) is well known for overcoming experimental limitations by permitting automated parallel processing at miniaturized scale, which results in fast data generation and reduced feed consumption. For membrane adsorber (MA), HTS solutions have so far only been available to a partial extent. Three case studies were performed with the aim of aligning HTS applications for MAs with those established for column chromatography: Process parameter range determination, mechanistic modeling (MM), and scalability. In order to exploit the MA typically features, such as high mass transfer and easy scalability, for scalable high throughput process development, a scale‐down device (SDD) for MA was developed. Its applicability is confirmed for a monoclonal antibody aggregate removal step. The first case study explores the experimental application of the SDD developed. It uses bind and elute mode and variations of pH and salt concentration to obtain process operation windows for ion‐exchange MAs Sartobind® S and Q. In the second case study, we successfully developed a mechanistic model based on parameters obtained from the SDD–HTS setup. The results proved to validate the use of the SDD developed for parameter estimation and thus model‐based process development. The third case study shows the transferability and scalability of data from the SDD–HTS setup using both a direct scale factor and MM. Both approaches show good applicability with a deviation below 20% in the prediction of 10% dynamic breakthrough capacity and reliable scale‐up from 0.42 to 800 ml.