Print Email Facebook Twitter Predicting protein retention in ion-exchange chromatography using an open source QSPR workflow Title Predicting protein retention in ion-exchange chromatography using an open source QSPR workflow Author Neijenhuis, T. (TU Delft BT/Bioprocess Engineering) Le Bussy, Olivier (GSK Vaccines, Rixensart) Geldhof, Geoffroy (GSK Vaccines, Rixensart) Klijn, M.E. (TU Delft BT/Bioprocess Engineering) Ottens, M. (TU Delft BT/Design and Engineering Education) Date 2024 Abstract Protein-based biopharmaceuticals require high purity before final formulation to ensure product safety, making process development time consuming. Implementation of computational approaches at the initial stages of process development offers a significant reduction in development efforts. By preselecting process conditions, experimental screening can be limited to only a subset. One such computational selection approach is the application of Quantitative Structure Property Relationship (QSPR) models that describe the properties exploited during purification. This work presents a novel open-source Python tool capable of extracting a range of features from protein 3D models on a local computer allowing total transparency of the calculations. As open-source tool, it also impacts initial investments in constructing a QSPR workflow for protein property prediction for third parties, making it widely applicable within the field of bioprocess development. The focus of current calculated molecular features is projection onto the protein surface by constructing surface grid representations. Linear regression models were trained with the calculated features to predict chromatographic retention times/volumes. Model validation shows a high accuracy for anion and cation exchange chromatography data (cross-validated R2 of 0.87 and 0.95). Hence, these models demonstrate the potential of the use of QSPR to accelerate process design. Subject chromatographyprotein featuresQuantitative Structure Activity Relationship (QSAR)Quantitative Structure Property Relationship (QSPR)retention prediction To reference this document use: http://resolver.tudelft.nl/uuid:cf3f497d-32d5-4c84-aeb0-404314965385 DOI https://doi.org/10.1002/biot.202300708 ISSN 1860-6768 Source Biotechnology Journal, 19 (3) Part of collection Institutional Repository Document type journal article Rights © 2024 T. Neijenhuis, Olivier Le Bussy, Geoffroy Geldhof, M.E. Klijn, M. Ottens Files PDF Biotechnology_Journal_-_2 ... n_open.pdf 1.01 MB Close viewer /islandora/object/uuid:cf3f497d-32d5-4c84-aeb0-404314965385/datastream/OBJ/view