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

Stephen Goldrick; Haneen Alosert; Clare Lovelady; Nicholas J Bond; Tarik Senussi; Diane Hatton; John Klein; Matthew Cheeks; Richard Turner; James Savery; Suzanne S Farid

doi:10.3389/fbioe.2023.1160223

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

Front Bioeng Biotechnol. 2023 Jun 5:11:1160223. doi: 10.3389/fbioe.2023.1160223. eCollection 2023.

Authors

Affiliations

¹ Department of Biochemical Engineering, University College London, London, United Kingdom.
² Cell Culture and Fermentation Science, Biopharmaceuticals Development, R&D, AstraZeneca, Cambridge, United Kingdom.
³ Analytical Sciences, Biopharmaceuticals Development, R&D, AstraZeneca, Cambridge, United Kingdom.
⁴ Data Science and Modelling, Biopharmaceuticals Development, R&D, AstraZeneca, Cambridge, United Kingdom.
⁵ Purification Process Sciences, Biopharmaceuticals Development, R&D, AstraZeneca, Cambridge, United Kingdom.

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 CLD ₄, 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 (MI _CL) 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 CLD ₄ 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 CLD ₄ 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. CLD ₄ identified sub-optimal process conditions leading to increased levels of trisulfide bond that would not be identified through conventional cell line development methodologies. CLD ₄ 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.

Keywords: Industry 4.0; cell line development; data analytics; machine learning; natural language generation.

Grants and funding

This research is associated with the joint UCL-AstraZeneca Centre of Excellence for predictive multivariate decision support tools in the bioprocessing sector and financial support from AstraZeneca and UCL is gratefully acknowledged. Financial support from the UK Engineering and Physical Sciences Research Council (EPSRC) and AstraZeneca for the Engineering Doctorate studentship for HA is also gratefully acknowledged (Grant Ref: EP/ S021868/1). This research is aligned with the EPSRC Future Targeted Healthcare Manufacturing Hub hosted by UCL Biochemical Engineering (Grant Ref: EP/P006485/1).