Translational Success Stories
Bibliographic record
Abstract
Anticoagulants are the cornerstone of therapy for conditions associated with arterial and venous thrombosis. Direct thrombin inhibitors (DTIs) are anticoagulants that bind to thrombin and block its enzymatic activity. The bivalent parenteral DTIs hirudin and bivalirudin were based on the observation that the salivary extracts of medicinal leeches prevented blood from clotting. Key events that facilitated the subsequent development of small molecule active site inhibitors, such as argatroban, were the observation that fibrinopeptide A had antithrombotic properties and determination of the crystal structure of thrombin. Hirudin and argatroban have found their niche for the treatment of patients with heparin-induced thrombocytopenia, whereas bivalirudin is approved as an alternative to heparin for patients undergoing percutaneous coronary intervention. The development of orally active direct thrombin inhibitors was challenging because of the need to convert water-soluble, poorly absorbable, active site inhibitors into fat-soluble prodrugs that were then transformed back to the active drug after intestinal absorption. Dabigatran etexilate was the first new oral anticoagulant to be approved for long-term anticoagulant treatment in 6 decades. This Review highlights the development of DTIs as a translational success story; an example in which the combination of scientific ingenuity, structure-based design, and rigorous clinical trials has created a new class of anticoagulants that has improved patient care.
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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 | 0.007 | 0.016 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.002 | 0.002 |
| Science and technology studies | 0.001 | 0.004 |
| Scholarly communication | 0.007 | 0.010 |
| Open science | 0.002 | 0.005 |
| Research integrity | 0.005 | 0.007 |
| Insufficient payload (model declined to judge) | 0.048 | 0.016 |
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".