Antigen-specific nanoparticle tolerance reduced inflammation at the target tissue and increased target protein expression in recombinant adeno-associated virus (AAV)-mediated gene therapy
Bibliographic record
Abstract
Abstract Recombinant Adeno-associated virus (AAV)-mediated gene therapy is an attractive approach for treating diseases where expression of an endogenous protein is defective or absent. While AAV is considered to be non-integrating and weakly immunogenic, AAV delivery elicits antigen (Ag)-specific T cells targeting transduced cells, and neutralizing antibodies inhibiting secondary administration. Effector T cell responses and vector dilution lead to decreased protein expression within target tissue, thereby requiring periodic AAV re-dosing with AAV to sustain the therapeutic benefit. Treatment with Ag-containing biodegradable poly(lactide-co-glycolide) (PLGA) nanoparticles (denoted as CNPs) represents a putative co-treatment allowing for decreased therapeutic AAV dose and frequency of AAV re-dosing to maintain therapeutic benefit. In the present studies, AAV8-eGFP was administered intramuscularly or intravenously in the presence or absence of B cell depletion. Prophylactic or therapeutic treatment with CNP(VP1 + eGFP) allowed for higher levels of eGFP expression within the target tissues following primary and secondary AAV8-eGFP dosing. Additionally, Ag-specific CNP(VP1 + eGFP) treatment inhibited the increase in effector CD8+ T cells within the target tissue following secondary AAV8-eGFP administration. Taken together, co-treatment of gene therapy patients with Ag-specific CNPs may allow for enhanced transgene expression over time, while allowing for AAV re-dosing.
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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 | 0.000 | 0.000 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.001 | 0.000 |
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".