Alosert, Haneen;
(2024)
Big data analytics streamlining
therapeutic drug development from
micro-scale to bench-scale cell culture.
Doctoral thesis (Eng.D), UCL (University College London).
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Abstract
The primary objective of this research was to investigate the bottlenecks of cell line development (CLD) to reduce process development (PD) timelines and increase product yields. The available literature shows studies performed on data measured during a single CLD screening stage yet failed to show work consolidating the data across all CLD stages to identify their impact on cell line performance. This was undertaken by applying statistical multivariate data analysis (MVDA) tools to different CLD and PD stages to identify hidden impacts on clonal cell lines. The first research question addressed predicting top clonal cell line selection using different MVDA tools such as K-means clustering and multi-criteria decision making. The primary findings proposed a new data driven approach to identify top clones, shifting away from subjective results such as operator observations classified as meta-data. Decision classification trees were also used to show evidence that using cell growth variables such as Viable Cell Density (VCD) can predict endpoint titre. Furthermore, the research also featured the effect of screening platforms on cell line selection, ultimately recommending the Hamilton 350 μL system to use in comparison to the Biomek 1 mL system. The second question focused on the impact of processing scale, where an unexplained difference in titre found between two different controlled reactor systems at 15 mL and 5 L scales were assessed. The results identified that low temperature, efficient lactate metabolism, and high cell densities notably influenced product concentration from 15 mL to 5 L scale. The final Question focused on the impact of transfected vector type on cell line performance throughout CLD screening. The results demonstrated intron containing (gDNA) vectors better performed against intron-less (cDNA) vectors across all tested scales and conditions. gDNA cell lines showed evidence of higher titre and more efficient lactate profiles compared to cDNA clonal cell lines. Finally, results also highlighted an approach that predicts low or high endpoint lactate concentration levels at an earlier culture timepoint using cumulative pH error.
| Type: | Thesis (Doctoral) |
|---|---|
| Qualification: | Eng.D |
| Title: | Big data analytics streamlining therapeutic drug development from micro-scale to bench-scale cell culture |
| Open access status: | An open access version is available from UCL Discovery |
| Language: | English |
| Additional information: | Copyright © The Author 2023. Original content in this thesis is licensed under the terms of the Creative Commons Attribution-NonCommercial 4.0 International (CC BY-NC 4.0) Licence (https://creativecommons.org/licenses/by-nc/4.0/). Any third-party copyright material present remains the property of its respective owner(s) and is licensed under its existing terms. Access may initially be restricted at the author’s request. |
| Keywords: | MVDA, PCA, Kmeans Clustering, Decision Classification tree, Manufacturability Index, Cell line development (CLD), Vector construct design, cDNA & gDNA, Cumulative pH error |
| 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/10192312 |
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