Transgene-host cell interactions mediate significant influences on the production, stability, and function of recombinant canine FVIII
Bibliographic record
Abstract
Recombinant FVIII manufacturing is characterized by poor product stability and low yields. Codon-optimization of transgenes accelerates translation by exploiting the synonymous codon usage bias of a species. However, this can alter the performance of the final product. Additionally, the effects of transgene design across diverse cell types are not well understood and are of interest for next-generation protein and gene therapies. To investigate the effects of transgene design across different host cells, B-domain-deleted (BDD) and modified codon-optimized (CO-N6) transgenes were inserted via lentiviral delivery into cBOECs, HEK293T, and MDCK cells. The CO-N6 cFVIII transgene produced threefold more protein per transgene in HEK293T cells, and sixfold more protein in the two canine cell lines. However, pharmacokinetic analysis in hemophilia A dogs demonstrated that cFVIII produced from cBOECs transduced with the CO-N6 transgene had significantly reduced in vivo recovery. Furthermore, this product showed reduced in vitro stability and activity on thrombin activation versus the BDD product. This trend was reversed in HEK293T lines. Overall, our results demonstrate the need for an integrated approach that not only assesses protein expression levels but also considers the influence that host-cells have on preserving the molecular and biochemical properties of the naturally occurring FVIII.
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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 | 0.001 |
| Meta-epidemiology (narrow) | 0.000 | 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 | 0.000 |
| 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".