UCL Discovery
UCL home » Library Services » Electronic resources » UCL Discovery

Next-generation cell line selection methodology leveraging data lakes, natural language generation and advanced data analytics

Goldrick, S; Alosert, H; Lovelady, C; Bond, NJ; Senussi, T; Hatton, D; Klein, J; ... Farid, SS; + view all (2023) Next-generation cell line selection methodology leveraging data lakes, natural language generation and advanced data analytics. Frontiers in Bioengineering and Biotechnology , 11 , Article 1160223. 10.3389/fbioe.2023.1160223. Green open access

[thumbnail of fbioe-11-1160223.pdf]
Preview
Text
fbioe-11-1160223.pdf - Published Version

Download (5MB) | Preview

Abstract

Cell line development is an essential stage in biopharmaceutical development that often lies on the critical path. Failure to fully characterise the lead clone during initial screening can lead to lengthy project delays during scale-up, which can potentially compromise commercial manufacturing success. In this study, we propose a novel cell line development methodology, referenced as CLD4, which involves four steps enabling autonomous data-driven selection of the lead clone. The first step involves the digitalisation of the process and storage of all available information within a structured data lake. The second step calculates a new metric referenced as the cell line manufacturability index (MICL) quantifying the performance of each clone by considering the selection criteria relevant to productivity, growth and product quality. The third step implements machine learning (ML) to identify any potential risks associated with process operation and relevant critical quality attributes (CQAs). The final step of CLD4 takes into account the available metadata and summaries all relevant statistics generated in steps 1–3 in an automated report utilising a natural language generation (NLG) algorithm. The CLD4 methodology was implemented to select the lead clone of a recombinant Chinese hamster ovary (CHO) cell line producing high levels of an antibody-peptide fusion with a known product quality issue related to end-point trisulfide bond (TSB) concentration. CLD4 identified sub-optimal process conditions leading to increased levels of trisulfide bond that would not be identified through conventional cell line development methodologies. CLD4 embodies the core principles of Industry 4.0 and demonstrates the benefits of increased digitalisation, data lake integration, predictive analytics and autonomous report generation to enable more informed decision making.

Type: Article
Title: Next-generation cell line selection methodology leveraging data lakes, natural language generation and advanced data analytics
Location: Switzerland
Open access status: An open access version is available from UCL Discovery
DOI: 10.3389/fbioe.2023.1160223
Publisher version: https://doi.org/10.3389/fbioe.2023.1160223
Language: English
Additional information: © 2023 Goldrick, Alosert, Lovelady, Bond, Senussi, Hatton, Klein, Cheeks, Turner, Savery and Farid. This is an open-access article distributed under the terms of the Creative Commons Attribution License (CC BY). The use, distribution or reproduction in other forums is permitted, provided the original author(s) and the copyright owner(s) are credited and that the original publication in this journal is cited, in accordance with accepted academic practice. No use, distribution or reproduction is permitted which does not comply with these terms.
Keywords: Industry 4.0, cell line development, data analytics, machine learning, natural language generation
UCL classification: UCL
UCL > Provost and Vice Provost Offices > UCL BEAMS
UCL > Provost and Vice Provost Offices > UCL BEAMS > Faculty of Engineering Science
UCL > Provost and Vice Provost Offices > UCL BEAMS > Faculty of Engineering Science > Dept of Biochemical Engineering
URI: https://discovery.ucl.ac.uk/id/eprint/10172917
Downloads since deposit
44Downloads
Download activity - last month
Download activity - last 12 months
Downloads by country - last 12 months

Archive Staff Only

View Item View Item