Accelerated vaccine process development by orthogonal protein characterization
Bibliographic record
Abstract
The COVID-19 pandemic altered the vaccine development paradigm with accelerated timelines from concept through clinical safety and efficacy. Characterization and release assays for vaccine programs were developed under similar time constraints to support bioprocess development, scaleup and formulation. During the development of these vaccines, SARS-CoV-2 variants of concern (VOCs) emerged requiring integration of additional antigens into the target product profile. Biochemical testing to support the addition of new antigen variants (identity, quantity, antigenicity/potency) needed substantial re-development. Here we present a reversed-phase high-performance liquid-chromatography method for antigen purity with orthogonal identification characterization comprising of Simple Wes and liquid-chromatography tandem mass spectrometry (LC/MS/MS) to support accelerated process development for recombinant protein vaccines. This suite of assays was deployed to support rapid, scientific decision-making enabling the transition from completion of a placebo-controlled dose-ranging Phase 2 study to the start of the global Phase 3 safety and efficacy trial in less than 2 weeks.
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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 | 0.002 | 0.002 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 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 | 0.001 |
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".