Optimization of YF17D-Vectored Zika Vaccine Production by Employing Small-Molecule Viral Sensitizers to Enhance Yields
Bibliographic record
Abstract
Background: Modern viral vector production needs to consider process intensification for higher yields from smaller production volumes. However, innate antiviral immunity triggered in the producer cell may limit virus replication. While commonly used cell lines (e.g., Vero or E1A-immortalised cells) are already compromised in antiviral pathways, the redundancy of innate signaling complicates host cell optimization by genetic engineering. Small molecules that are hypothesized to target antiviral pathways (Viral Sensitizers, VSEs) added to the culture media offer a versatile alternative to genetic modifications to increase permissiveness and, thus, viral yields across multiple cell lines. Methods: To explore how the yield for a chimeric Zika vaccine candidate (YF-ZIK) could be further be increased in an intensified bioprocess, we used spin tubes or an Ambr15 high-throughput microbioreactor system as scale-down models to optimize the dosing for eight VSEs in three host cell lines (AGE1.CR.pIX, BHK-21, and HEK293-F) based on their tolerability. Results: Addition of VSEs to an already optimized infection process significantly increased infectious titers by up to sevenfold for all three cell lines tested. The development of multi-component VSE formulations using a design of experiments approach allowed further synergistic titer increases in AGE1.CR.pIX cells. Scale-up to 1 L stirred-tank bioreactors and 3D-printed mimics of 200 or 2000 L reactors resulted in up to threefold and eightfold increases, respectively. Conclusions: Addition of single VSEs or combinations thereof allowed a further increase in YF-ZIK titers beyond the yield of an already optimized, highly intensified process. The described approach validates the use of VSEs and can be instructive for optimizing other virus production processes.
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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 | 0.001 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.001 |
| Insufficient payload (model declined to judge) | 0.001 | 0.001 |
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".