Supplementation Strategies for Tuning Glycosylation of Monoclonal Antibodies and Enhancing Growth in Mammalian Cell Culture by Omics Analysis
Bibliographic record
Abstract
Two fundamental objectives of bioprocess engineering are to increase productivity and improve product quality. Glycosylation is a critical and variable factor in the quality of several therapeutic proteins, particularly those with immune-system interactions such as monoclonal antibodies (mAbs). In this thesis, supplementation of nutrients to growth medium of CHO1A7 mammalian cell cultures are examined towards enhancing cell growth, by increasing peak cell density, and improving product quality, by tuning glycosylation of EG2-hFc heavy-chain camelid antibodies. Targeted profiling via 1D-1H-NMR metabolomics and differential expression analysis via 2D-DIGE proteomics, elucidate factors to create a nutrient cocktail to enhance culture growth. Eight target nutrients corresponding with five identified metabolic systems for CHO cells including anaplerotic TCA-replenishment; NADH/NADPH replenishment; tetrahydrofolate cycle C1 cofactor conversions; limitations to lipid synthesis; and redox modulation; resulted in a ~75% improvement to peak cell densities. Towards improving product quality, nucleotide-sugar precursors, capable of shifting glycan distributions were supplemented to growth medium to tune glycosylation of EG2-hFc towards a single G0 glycoform. Growth inhibition from glucosamine-based precursors was mitigated given a priori knowledge from metabolomic analysis of the system – identifying cytosolic acetyl-CoA as a sensitive metabolic pool for CHO1A7 cell growth. Additional nucleotide-sugar precursor nutrients were examined to better resolve conflicting reports of effects to glycan distributions. These conflicts are subsequently attributed to five key factors: differences across cell platforms; differences between glycan sites of expressed proteins; the fermentation and sampling timeline; glutamine levels; finally, no standardized metrics for reporting shifts in glycan distributions with respect to controls.
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How this classification was reachedexpand
Full frame machine prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. The Gemma side is a direct model label for every work in the frame, read from the title-only record. The Codex side is a classifier learned from the 10,348 direct Codex labels and calibrated to design-weighted sample rates; fields without enough sample support carry no Codex call. Candidate is the union of the two sides; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels.
Distilled classifier scores by category (both heads)
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.001 | 0.000 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.000 | 0.001 |
| Research integrity | 0.000 | 0.001 |
| Insufficient payload (model declined to judge) | 0.001 | 0.000 |
Machine scores (provisional)
The two teacher heads of the student model, read on this work. A score orders the frame for review; it never asserts a category, and the validation status ships verbatim with every row.
Baseline scores from an immature model (maturity gate not passed, 7 training rounds). Scores rank; they never assert a category.
score_only:v0-immature-baseline · verbatim from the scoring run: score_only means the number may rank works, and no category label ships from itClassification
machine, unvalidatedMachine predicted; a candidate call from one source (direct Gemma or distilled Codex), not a consensus.
How this classification was reached, model by model and score by score, is at the end of the page under "How this classification was reached".