Development and Validation of an Anion Exchange High-Performance Liquid Chromatography Method for Analysis of Empty Capsids and Capsids Encapsidating Genetic Material in a Purified Preparation of Recombinant Adeno-Associated Virus Serotype 5
Bibliographic record
Abstract
The development of various manufacturing platforms and analytical technologies has substantially contributed to successfully translating the recombinant adeno-associated viral vector from the laboratory to the clinic. The active deployment of these analytical technologies for process and product characterization has helped define critical quality attributes and improve the quality of the clinical grade material. In this article, we report an anion exchange high-performance liquid chromatography (AEX-HPLC) method for relative and as well as absolute quantification of empty capsids (EC) and capsids encapsidating genetic material (CG) in purified preparations of adeno-associated virus (AAV) using serotype 5 as a model. The selection of optimal chromatographic buffer composition and step-gradient elution protocol offered baseline separation of EC and CG in the form of two peaks, as validated with the respective reference standards. The native amino acid fluorescence-based detection offered excellent linearity with a correlation coefficient of 0.9983 over two-log dilutions of the sample. The limit of detection and limit of quantification values associated with the total AAV5 capsid assay are 3.1E + 09 and 9.5E + 09, respectively. AEX-HPLC showed method comparability with the analytical ultracentrifugation (AUC) method for determination of relative proportions of EC and CG, supporting the reported HPLC method as an easy-to-access alternative to AUC with operational simplicity. Moreover, rapid and easy adaptation of this method to AAV8 material also demonstrated the robustness of the proposed approach.
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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 | 0.003 | 0.002 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.001 |
| Bibliometrics | 0.001 | 0.000 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.000 | 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".