A hemophilia A mouse model for the in vivo assessment of emicizumab function
Bibliographic record
Abstract
The bispecific antibody emicizumab is increasingly used for hemophilia A treatment. However, its specificity for human factors IX and X (FIX and FX) has limited its in vivo functional analysis to primate models of acquired hemophilia. Here, we describe a novel mouse model that allows emicizumab function to be examined. Briefly, FVIII-deficient mice received IV emicizumab 24 hours before tail-clip bleeding was performed. A second infusion with human FIX and FX, administered 5 minutes before bleeding, generated consistent levels of emicizumab (0.7-19 mg/dL for 0.5-10 mg/kg doses) and of both FIX and FX (85 and 101 U/dL, respectively, after dosing at 100 U/kg). Plasma from these mice display FVIII-like activity in assays (diluted activated partial thromboplastin time and thrombin generation), similar to human samples containing emicizumab. Emicizumab doses of 1.5 mg/kg and higher significantly reduced blood loss in a tail-clip-bleeding model using FVIII-deficient mice. However, reduction was incomplete compared with mice treated with human FVIII concentrate, and no difference in efficacy between doses was observed. From this model, we deducted FVIII-like activity from emicizumab that corresponded to a dose of 4.5 U of FVIII per kilogram (ie, 9.0 U/dL). Interestingly, combined with a low FVIII dose (5 U/kg), emicizumab provided enough additive activity to allow complete bleeding arrest. This model could be useful for further in vivo analysis of emicizumab.
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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 | 0.000 |
| Meta-epidemiology (narrow) | 0.002 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.002 | 0.001 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.001 | 0.000 |
| 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".