A review of the rationale for gene therapy for hemophilia A with inhibitors: one-shot tolerance and treatment?
Bibliographic record
Abstract
The therapeutic landscape for people living with hemophilia A (PwHA) has changed dramatically in recent years, but many clinical challenges remain, including the development of inhibitory antibodies directed against factor VIII (FVIII) that occur in approximately 30% of people with severe hemophilia A. Emicizumab, an FVIII mimetic bispecific monoclonal antibody, provides safe and effective bleeding prophylaxis for many PwHA, but clinicians still explore therapeutic strategies that result in immunologic tolerance to FVIII to enable effective treatment with FVIII for problematic bleeding events. This immune tolerance induction (ITI) to FVIII is typically accomplished through repeated long-term exposure to FVIII using a variety of protocols. Meanwhile, gene therapy has recently emerged as a novel ITI option that provides an intrinsic, consistent source of FVIII. As gene therapy and other therapies now expand therapeutic options for PwHA, we review the persistent unmet medical needs with respect to FVIII inhibitors and effective ITI in PwHA, the immunology of FVIII tolerization, the latest research on tolerization strategies, and the role of liver-directed gene therapy to mediate FVIII ITI.
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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 | 0.002 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.003 | 0.001 |
| Bibliometrics | 0.002 | 0.002 |
| Science and technology studies | 0.000 | 0.001 |
| Scholarly communication | 0.001 | 0.002 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.002 | 0.003 |
| 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".