Overcoming the Challenges Imposed by Humoral Immunity to AAV Vectors to Achieve Safe and Efficient Gene Transfer in Seropositive Patients
Bibliographic record
Abstract
(AveXis, now Novartis) place vectors derived from adeno-associated viruses (AAV) among the best options for gene transfer in multiple tissues. Despite these successes, limitations remain to the application of this therapeutic modality in a wider population. AAV was originally identified as a promising virus to derive gene therapy vectors because, despite infecting humans, it was not associated with any evident disease. Thee large proportion of AAV infections in the human population is now revealing as a limitation because after exposure to wild-type AAV, anti-AAV antibodies develops and may neutralize the vectors derived from the virus. Injection of AAV in humans is generally well-tolerated although the immune system can activate after the recognition of AAV vectors capsid and genome. The formation of high-titer neutralizing antibodies to AAV after the first injection precludes vector re-administration. Thus, both pre-existing and post-treatment humoral responses to AAV vectors greatly limit a wider application of this gene transfer modality. Different methods were suggested to overcome this limitation. The extensive preclinical data available and the large clinical experience in the control of AAV vectors immunogenicity are key to clinical translation and to demonstrate the safety and efficacy of these methods and ultimately bring a curative treatment to patients.
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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 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.000 | 0.001 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.001 | 0.003 |
| Insufficient payload (model declined to judge) | 0.004 | 0.003 |
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".