Prime Editing for Human Gene Therapy: Where Are We Now?
Bibliographic record
Abstract
Gene therapy holds tremendous potential in the treatment of inherited diseases. Unlike traditional medicines, which only treat the symptoms, gene therapy has the potential to cure the disease by addressing the root of the problem: genetic mutations. The discovery of CRISPR/Cas9 in 2012 paved the way for the development of those therapies. Improvement of this system led to the recent development of an outstanding technology called prime editing. This system can introduce targeted insertions, deletions, and all 12 possible base-to-base conversions in the human genome. Since the first publication on prime editing in 2019, groups all around the world have worked on this promising technology to develop a treatment for genetic diseases. To date, prime editing has been attempted in preclinical studies for liver, eye, skin, muscular, and neurodegenerative hereditary diseases, in addition to cystic fibrosis, beta-thalassemia, X-linked severe combined immunodeficiency, and cancer. In this review, we portrayed where we are now on prime editing for human gene therapy and outlined the best strategies for correcting pathogenic mutations by prime editing.
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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 | 0.002 |
| 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".