Immune Responses and Immunosuppressive Strategies for Adeno-Associated Virus-Based Gene Therapy for Treatment of Central Nervous System Disorders: Current Knowledge and Approaches
Bibliographic record
Abstract
Adeno-associated viruses (AAVs) are being increasingly used as gene therapy vectors in clinical studies especially targeting central nervous system (CNS) disorders. Correspondingly, host immune responses to the AAV capsid or the transgene-encoded protein have been observed in various clinical and preclinical studies. Such immune responses may adversely impact patients' health, prevent viral transduction, prevent repeated dosing strategies, eliminate transduced cells, and pose a significant barrier to the potential effectiveness of AAV gene therapy. Consequently, multiple immunomodulatory strategies have been used in attempts to limit immune-mediated responses to the vector, enable readministration of AAV gene therapy, prevent end-organ toxicity, and increase the duration of transgene-encoded protein expression. Herein we review the innate and adaptive immune responses that may occur during CNS-targeted AAV gene therapy as well as host- and treatment-specific factors that could impact the immune response. We also summarize the available preclinical and clinical data on immune responses specifically to CNS-targeted AAV gene therapy and discuss potential strategies for incorporating prophylactic immunosuppression regimens to circumvent adverse immune responses.
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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 | 0.000 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.000 |
| Bibliometrics | 0.002 | 0.001 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.001 | 0.000 |
| Research integrity | 0.001 | 0.002 |
| Insufficient payload (model declined to judge) | 0.002 | 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".