Past, present and future of haemophilia gene therapy: From vectors and transgenes to known and unknown outcomes
Bibliographic record
Abstract
Since the 1960s, the pace of innovation in haemophilia treatment has been fast and furious and occasionally with unintended consequences. As newer technologies are harnessed to better treat, and potentially cure, haemophilias A and B, an understanding of their underlying scientific principles and their benefits and risks are essential for all stakeholders. This review summarizes the starts and stops of introducing FVIII and FIX transgenes clinically, beginning 20 years ago. Lessons from earlier nonclinical and clinical experiments have been utilized to improve vector selection, vector design, promoter/enhancer cis control regions and codon-optimized transgenes to trigger in vivo clinical FVIII and FIX levels in the near-normal to normal ranges. Many known and unknown questions remain, and some, based upon benefit and risk, should be answered during larger phase 3 clinical trials. Prior clinical outcomes in haemophilia trials have not been standardized, making between-trial comparisons difficult. Going forward, haemophilia gene therapy clinical trials should utilize a standard set of core outcomes, to facilitate comparisons to other gene- and protein-based therapies. These outcomes will be more important as the field moves beyond the first-generation gene therapies into more complex vectors that may address the shortcomings of first-generation vectors and offer greater benefits to the patient.
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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 | 0.002 | 0.002 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.000 |
| Bibliometrics | 0.002 | 0.002 |
| Science and technology studies | 0.000 | 0.002 |
| Scholarly communication | 0.002 | 0.004 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.001 | 0.003 |
| Insufficient payload (model declined to judge) | 0.002 | 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".