Perspectives and Insights on Antihemorrhagic Drugs: From Development to Submission of New Drug Submission (NDS) to Health Canada
Bibliographic record
Abstract
Hemophilia A and B, characterized by deficiencies in clotting factors VIII and IX, respectively, pose significant challenges due to spontaneous bleeding episodes that impair quality of life. Traditional factor replacement therapies, while effective, are limited by short half-lives, intravenous administration, and inhibitor risks. Emerging non-factor antihemorrhagic drugs, such as marstacimab, concizumab, fitusiran, SerpinPC, and Mim8, offer innovative prophylactic solutions for patients without inhibitors. These therapies utilize novel mechanisms, including inhibition of natural anticoagulants and factor VIII mimetic activity, enabling subcutaneous delivery and extended dosing intervals. This article explores their clinical applications, chemical properties, packaging, safety profiles, and regulatory pathways for New Drug Submission (NDS) in Canada. By integrating emerging technologies and addressing regulatory considerations, these therapies promise to transform hemophilia care, with recommendations for robust post-market surveillance and equitable access (Ozelo & Yamaguti-Hayakawa, 2022; Mannucci, 2023).
Fetched live from OpenAlex and de-inverted. Abstracts are not stored in this database: the inverted indexes are 8.6 GB of the frame’s 9.3 GB of text, and the host has 13 GB free.
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 | 0.005 | 0.008 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.003 | 0.002 |
| Science and technology studies | 0.004 | 0.006 |
| Scholarly communication | 0.012 | 0.003 |
| Open science | 0.002 | 0.002 |
| Research integrity | 0.004 | 0.003 |
| Insufficient payload (model declined to judge) | 0.022 | 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".