Antibody Delivery Mediated by Recombinant Adeno-associated Virus for the Treatment of Various Chronic and Infectious Diseases
Bibliographic record
Abstract
Monoclonal antibodies (mAbs) based-therapies are currently one of the most successful strategies to treat immune disorders, cancer and infectious diseases. Vectors derived from adenoassociated virus (AAV) are very attractive to deliver the genes coding the mAbs because they allow long-term expression thus, reducing the number of administrations. They can also penetrate biological barriers such as the blood-brain-barrier to transduce cells localized in immunoprivileged organs. Recent animal studies with AAV have demonstrated the capacity of AAV to deliver sufficient quantity of antibodies to confer an efficient immunoprotection against chronic and infectious diseases for several months to years. The treatment was successfully applied either for prophylaxis or therapeutic use, depending on the disease and its progression. In this review, we discuss the advantages and the limitations of AAV for mAb and immunoadhesin delivery. Recent advances in vector design and antibody engineering are also presented. Optimization of the vector design can improve the kinetic and the level of mAbs expression whereas protein engineering can enhance transgene product properties. Furthermore, an exhaustive review of pre-clinical studies for chronic diseases including Alzheimer disease, amyotrophic lateral sclerosis and cancer is presented as well as for infectious diseases.
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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) | 0.001 | 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 | 0.001 |
| Insufficient payload (model declined to judge) | 0.003 | 0.002 |
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".