Current status of haemophilia gene therapy
Bibliographic record
Abstract
After many reports of successful gene therapy studies in small and large animal models of haemophilia, we have, at last, seen the first signs of success in human patients. These very encouraging results have been achieved with the use of adeno-associated viral (AAV) vectors in patients with severe haemophilia B. Following on from these initial promising studies, there are now three ongoing trials of AAV-mediated gene transfer in haemophilia B all aiming to express the factor IX gene from the liver. Nevertheless, as discussed in the first section of this article, there are still a number of significant hurdles to overcome if haemophilia B gene therapy is to become more widely available. The second section of this article deals with the challenges relating to factor VIII gene transfer. While the recent results in haemophilia B are extremely encouraging, there is, as yet, no similar data for factor VIII gene therapy. It is widely accepted that this therapeutic target will be significantly more problematic for a variety of reasons including accommodating the larger factor VIII cDNA, achieving adequate levels of transgene expression and preventing the far more frequent complication of antifactor VIII immunity. In the final section of the article, the alternative approach of lentiviral vector-mediated gene transfer is discussed. While AAV-mediated approaches to transgene delivery have led the way in clinical haemophilia gene therapy, there are still a number of potential advantages of using an alternative delivery vehicle including the fact that ex vivo host cell transduction will avoid the likelihood of immune responses to the vector. Overall, these are exciting times for haemophilia gene therapy with the likelihood of further clinical successes in the near future.
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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 | 0.003 | 0.003 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.003 | 0.003 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.003 | 0.003 |
| Insufficient payload (model declined to judge) | 0.024 | 0.013 |
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".