Potential of helper-dependent Adenoviral vectors in CRISPR-cas9-mediated lung gene therapy
Bibliographic record
Abstract
Since CRISPR/Cas9 was harnessed to edit DNA, the field of gene therapy has witnessed great advances in gene editing. New avenues were created for the treatment of diseases such as Cystic Fibrosis (CF). CF is caused by mutations in the Cystic Fibrosis Transmembrane Conductance Regulator (CFTR) gene. Despite the success of gene editing with the CRISPR/Cas9 in vitro, challenges still exist when using CRISPR/Cas9 in vivo to cure CF lung disease. The delivery of CRISPR/Cas9 into lungs, as well as the difficulty to achieve the efficiency required for clinical efficacy, has brought forth new challenges. Viral and non-viral vectors have been shown to deliver DNA successfully in vivo, but the sustained expression of CFTR was not adequate. Before the introduction of Helper-Dependent Adenoviral vectors (HD-Ad), clinical trials of treating pulmonary genetic diseases with first-generation viral vectors have shown limited efficacy. With the advantages of larger capacity and lower immunogenicity of HD-Ad, together with the versatility of the CRISPR/Cas9 system, delivering CRISPR/Cas9 to the airway with HD-Ad for lung gene therapy shows great potential. In this review, we discuss the status of the application of CRISPR/Cas9 in CF gene therapy, the existing challenges in the field, as well as new hurdles introduced by the presence of CRISPR/Cas9 in the lungs. Through the analysis of these challenges, we present the potential of CRISPR/Cas9-mediated lung gene therapy using HD-Ad vectors with Cystic Fibrosis lung disease as a model of therapy.
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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 | 0.001 |
| 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".