Escitalopram, but Not Its Major Metabolites, Exhibits Antiplatelet Activity in Humans
Bibliographic record
Abstract
BACKGROUND: Clinical depression has been identified as an independent risk factor for increased mortality during follow-up in patients suffered from acute coronary events, whereas increased platelet activity has been proposed as one of the mechanisms for this association. Some evidence suggests that selective serotonin reuptake inhibitors and/or their metabolites exhibit potent antiplatelet properties. METHODS: We assessed the in vitro effects of preincubation with escalating (50-200 nmol/L) concentrations of escitalopram (ESC) S-desmethyl-citalopram (S-DCT), and S-di-desmethyl-citalopram, (S-DCT) on platelet aggregation through the expression of major surface receptors using flow cytometry and quantitatively using platelet function analyzers in 20 healthy volunteers. RESULTS: Pretreatment of blood samples with ESC with ESC resulted in a significant inhibition of platelet aggregation induced by ADP (P = 0.0001) and by collagen with the highest dose (P = 0.001). Surface platelet expressions of glycoprotein Ib (CD42) (P = 0.04), lysosome associated membrane protein-3 (CD63) (P = 0.02), and GP37 (CD165) (P = 0.03) was decreased in the ESC-pretreated samples. Closure time by the Platelet Function Analyzer-100 analyzer was prolonged for the 200 nmol/L dose (P = 0.02), indicating platelet inhibition under high shear conditions. Two major metabolites of ESC, namely S-DCT and S-DDCT, did not affect platelet activity. CONCLUSION: Escitalopram, but not its metabolites, exhibited selective inhibition of human platelet properties. The direct antiplatelet effect of ESC requires further prospective or ex vivo testing to determine the possible clinical advantage of this finding.
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How this classification was reachedexpand
Full frame distilled prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. Learned from the 10,348 direct Codex labels and 10,348 direct Gemma labels. Candidate is the union of thresholded teacher heads; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels or direct frontier model labels.
Codex and Gemma teacher scores by category
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.004 | 0.000 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.002 | 0.001 |
| Bibliometrics | 0.000 | 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 | 0.002 |
| Insufficient payload (model declined to judge) | 0.000 | 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 teacher head, 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".