Evaluation of Biomarkers to Detect Early Prothrombotic Imbalance in Rat Models of Hypercoagulability Induced by Thromboplastin Infusion and Hypofibrinolysis Induced by Tranexamic Acid
Bibliographic record
Abstract
Thrombotic complications including myocardial infarction, stroke, venous thrombosis and pulmonary thromboembolism are common causes of drug attrition often discovered at late stages of drug development. Current nonclinical safety assessments include screening tests that detect hemorrhagic complications but do not identify conditions signaling a risk of thrombosis. Our study aimed to identify sensitive tests for detecting prothrombotic imbalance, without overt thrombosis, for use in early nonclinical drug safety assessments in rodents. Sprague Dawley rats were administered different doses of thromboplastin or tranexamic acid to induce variable intensity hypercoagulable or hypofibrinolytic states, respectively. A panel of functional and quantitative assays measuring hemostatic proteins and pathways were evaluated, in concert with traditional coagulation screening tests and blood cell counts. Profound changes were observed with different patterns of test abnormalities for the different stimuli. Measurements of D-dimer and thrombin antithrombin complex concentrations, plasminogen activator inhibitor-1 and Factor VIIa activity were among the most sensitive tests of hypercoagulability. In contrast, hypofibrinolysis was best characterized in a kinetic, turbidimetric assay. Traditional coagulation screening tests were relatively insensitive, and no single test defined the cause of prothrombotic imbalance. Our results demonstrate that customized biomarker panels can detect early drug-induced prothrombotic states in rats arising from distinct mechanisms.
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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 | 0.000 | 0.000 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.000 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.001 |
| Insufficient payload (model declined to judge) | 0.001 | 0.000 |
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".