Development of a Rapid Adeno-Associated Virus (AAV) Identity Testing Platform through Comprehensive Mass Analysis of Full-length AAV Capsid Proteins
Bibliographic record
Abstract
Adeno-associated viruses (AAVs) are commonly used as vectors for the delivery of gene therapy targets. Characterization of AAV capsid proteins (VPs) and their post-translational modifications (PTMs) has become a critical attribute monitored to evaluate product quality. To accommodate the growing use of AAV delivery systems, rapid analytical methods that comply with good manufacturing practice (GMP) standards are needed. As shown in previous studies, mass protein liquid chromatography-mass spectrometry (LC-MS) analysis of full-length AAV VPs provides both quick and reliable serotype identification as well as proteoform information of each VP. However, for AAV2, separation of VP2 and VP1 has proven difficult to obtain, with successful separation only achieved when using mobile phase modifiers incompatible with MS. This incompatibility hinders in-depth AAV VP characterization by suppressing ion signal, thus reducing VP proteoform identifications through the generation of low intensity MS spectra. Using AAV2 as a test case, we demonstrate how coupling hydrophilic interaction liquid chromatography (HILIC) with MS compatible difluoroacetic acid (DFA) as a mobile phase modifier, achieves complete separation of the three AAV VPs while also generating high quality MS spectra. Employing increased MS resolving power enabled improved identification of VP proteoforms whose PTMs were confirmed using peptide mapping. The LC-MS workflow was further transformed to develop an assay using GMP compliant software capable of rapid AAV serotype profiling. Incorporated into this method is the ability to perform serotype PTM characterization if desired. Such a platform provides product quality control capabilities that are easily accessible within a regulatory setting.
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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 | 0.001 |
| 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".