Machine learning enhanced process design in protein a chromatography
File(s)
Published version
Author(s)
Galeazzi, Andrea
Sachio, Steven
Edwards, Elizabeth
Hilton, David
Papathanasiou, Maria M
Type
Journal Article
Abstract
Quality by Digital Design (QbDD) employs in-silico experimentation to reduce wet-lab reliance and accelerate development. Design space identification is critical for QbDD to overcome bottlenecks and streamline process design. Traditional design space identification methods require costly, high-fidelity models. This work introduces a machine learning-enhanced design space identification approach that utilises wet-lab data. A transfer learning framework is developed under limited data availability by leveraging synthetic datasets created using off-the-shelf mechanistic models. An artificial neural network is constructed and used to classify feasible design regions under high, moderate, and low data availability (HDA, MDA, LDA). Results show strong performance in HDA for the data-driven method, with transfer learning improving accuracy in MDA and being essential in LDA. This approach demonstrates machine learning’s potential to enable cost- and time-efficient process design in early-stage biopharmaceutical development.
Date Issued
2025-09-27
Date Acceptance
2025-07-04
Citation
Journal of Chromatography A, 2025, 1759
ISSN
0021-9673
Publisher
Elsevier
Journal / Book Title
Journal of Chromatography A
Volume
1759
Copyright Statement
© 2025 The Authors. Published by Elsevier B.V. This is an open access article under the CC BY license (http://creativecommons.org/licenses/by/4.0).
License URL
Identifier
https://www.ncbi.nlm.nih.gov/pubmed/40701119
PII: S0021-9673(25)00539-4
Subjects
Biochemical Research Methods
Biochemistry & Molecular Biology
Chemistry
Chemistry, Analytical
Design space identification
Life Sciences & Biomedicine
Machine learning
PHARMACEUTICAL QUALITY
Physical Sciences
Protein A
Quality by digital design
Science & Technology
Transfer learning
Publication Status
Published
Coverage Spatial
Netherlands
Article Number
466193
Date Publish Online
2025-07-21