Affinity chromatography for vaccines manufacturing: Finally ready for prime time?
Bibliographic record
Abstract
• This paper reviews the success of affinity chromatography at lab scale for vaccine purification and assesses the challenges preventing its industrial implementation. • Review of recent advancements in ligand development and chromatographic media indicates opportunity to bring affinity chromatography from lab bench to large-scale manufacturing. • It is suggested that when combined with single-use technology, affinity chromatography can significantly simplify process architecture, improve productivity and flexibility, and reduce cost of goods. Affinity chromatography is among the most powerful separation techniques, achieving the finest separation with high yields even in the most challenging feed streams. Incorporating affinity chromatography in vaccine purification has long been attempted by researchers to improve unit yield and purity with the secondary goal of reducing the number of downstream process operations. Despite the success in laboratory-scale proof of concept, implementation of this technique in pilot or cGMP manufacturing has rarely been realised due to technical and economic challenges in design and manufacturing of ideal ligands as well as availability of high-productivity chromatography media. This paper reviews evolving technologies in engineered ligands and chromatography media that are encouraging companies to re-visit the possible use of affinity chromatography in larger scale vaccine purification. It is postulated that commercial-scale implementation of high throughput single-use affinity chromatography can significantly simplify process architecture, improve productivity and flexibility, and reduce cost of goods.
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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) | 0.001 | 0.001 |
| Bibliometrics | 0.002 | 0.001 |
| Science and technology studies | 0.000 | 0.001 |
| Scholarly communication | 0.002 | 0.003 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.002 | 0.003 |
| Insufficient payload (model declined to judge) | 0.005 | 0.005 |
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".