Differences in the clinically effective molar concentrations of four direct thrombin inhibitors explain their variable prothrombin time prolongation
Bibliographic record
Abstract
Four direct thrombin inhibitors (DTIs), lepirudin, bivalirudin, argatroban, and melagatran, differ in their ability to prolong the prothrombin time (PT). Paradoxically, the DTI in clinical use with the lowest affinity for thrombin (argatroban) causes the greatest PT prolongation. We compared the effects of these DTIs on various clotting assays and on inhibition of human and bovine factor Xa (FXa). On a mole-for-mole basis, lepirudin was most able to prolong the PT, activated partial thromboplastin time (APTT), and thrombin clotting time (TCT), whereas argatroban had the least effect. At concentrations that doubled the APTT (argatroban, 1 micromol/l; melagatran, 0.5 micromol/l; bivalirudin, 0.25 micromol/l; lepirudin, 0.06 micromol/l), the rank order for PT prolongation was: argatroban > melagatran > bivalirudin > lepirudin. Although the Ki's associated with inhibition of human FXa by melagatran (1.4 micromol/l) and argatroban (3.2 micromol/l) approach their therapeutic concentrations, inhibition of FXa did not appear to be a major contributor to PT prolongation, since argatroban also prolonged the PT of bovine plasma (despite a Ki for bovine FXa of 2,600 micromol/l). Only melagatran inhibited prothrombinase-bound FXa. We conclude that the differing effects of the DTIs on PT prolongation are primarily driven by their respective molar plasma concentrations required for clinical effect. DTIs with a relatively low affinity for thrombin require high plasma concentrations to double the APTT; these higher plasma concentrations, in turn, quench more of the thrombin generated in the PT, thereby more greatly prolonging the PT.
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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 | 0.003 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.000 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.000 | 0.001 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.002 | 0.001 |
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".