From Fresh Frozen Plasma to First-in-human: Bringing Coagulation Factor V Deficiency into Therapeutic Trials
Bibliographic record
Abstract
Abstract: Congenital factor V (FV) deficiency, affecting approximately 1 in 1 million individuals worldwide, remains among the rare bleeding disorders (RBDs) without a licensed factor-specific replacement therapy. While other RBDs have successfully transitioned from plasma-based treatment to approved factor concentrates-exemplified by factor X deficiency's progression to US Food & Drug Administration (FDA)-approved Coagadex and two FDA-approved concentrates for factor XIII deficiency-FV deficiency treatment has remained unchanged for decades, relying solely on plasma and platelet transfusions. Two promising therapeutic candidates have emerged: a human plasma-derived FV concentrate demonstrating in vitro correction of severe deficiency, and an engineered activated FV (superFVa) showing potent hemostatic activity in preclinical models. This commentary outlines a pragmatic pathway to clinical trials, leveraging proven development strategies from other RBDs, existing registry infrastructure, and regulatory incentives for rare diseases. We propose phased trials combining pharmacodynamic endpoints with clinical outcomes, enabling feasible enrollment while generating decision-grade evidence. The time has come to extend modern therapeutic development to FV deficiency.
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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 | 0.004 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.002 | 0.001 |
| Bibliometrics | 0.002 | 0.001 |
| Science and technology studies | 0.000 | 0.001 |
| Scholarly communication | 0.002 | 0.003 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.002 | 0.004 |
| Insufficient payload (model declined to judge) | 0.005 | 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".