Development of a scalable and robust AEX method for enriched rAAV preparations in genome-containing VCs of serotypes 5, 6, 8, and 9
Bibliographic record
Abstract
Removal of empty capsids from adeno-associated virus (AAV) manufacturing lots remains a critical step in the downstream processing of AAV clinical-grade batches. Because of similar physico-chemical characteristics, the AAV capsid populations totally lacking or containing partial viral DNA are difficult to separate from the desired vector capsid populations. Based on minute differences in density, ultracentrifugation remains the most effective separation method and has been extensively used at small scale but has limitations associated with availabilities and operational complexities in large-scale processing. In this paper, we report a scalable, robust, and versatile anion-exchange chromatography (AEX) method for removing empty capsids and subsequent enrichment of vectors of AAV serotypes 5, 6, 8, and 9. On average, AEX resulted in about 9-fold enrichment of AAV5 in a single step containing 80% ± 5% genome-containing vector capsids, as verified and quantified by analytical ultracentrifugation. The optimized process was further validated using AAV6, AAV8, and AAV9, resulting in over 90% vector enrichment. The AEX process showed comparable results not only for vectors with different transgenes of different sizes but also for AEX runs under different geometries of chromatographic media. The herein-reported sulfate-salt-based AEX process can be adapted to different AAV serotypes by appropriately adjusting elution conditions to achieve enriched vector preparations.
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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) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.001 | 0.000 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.001 | 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".