High Concentrations of Tranexamic Acid Inhibit Ionotropic Glutamate Receptors
Bibliographic record
Abstract
BACKGROUND: The antifibrinolytic drug tranexamic acid is structurally similar to the amino acid glycine and may cause seizures and myoclonus by acting as a competitive antagonist of glycine receptors. Glycine is an obligatory co-agonist of the N-methyl-D-aspartate (NMDA) subtype of glutamate receptors. Thus, it is plausible that tranexamic acid inhibits NMDA receptors by acting as a competitive antagonist at the glycine binding site. The aim of this study was to determine whether tranexamic acid inhibits NMDA receptors, as well as α-amino-3-hydroxy-5-methyl-4-isoxazolepropionic acid and kainate subtypes of ionotropic glutamate receptors. METHODS: Tranexamic acid modulation of NMDA, α-amino-3-hydroxy-5-methyl-4-isoxazolepropionic acid, and kainate receptors was studied using whole cell voltage-clamp recordings of current from cultured mouse hippocampal neurons. RESULTS: Tranexamic acid rapidly and reversibly inhibited NMDA receptors (half maximal inhibitory concentration = 241 ± 45 mM, mean ± SD; 95% CI, 200 to 281; n = 5) and shifted the glycine concentration-response curve for NMDA-evoked current to the right. Tranexamic acid also inhibited α-amino-3-hydroxy-5-methyl-4-isoxazolepropionic acid receptors (half maximal inhibitory concentration = 231 ± 91 mM; 95% CI, 148 to 314; n = 5 to 6) and kainate receptors (half maximal inhibitory concentration = 90 ± 24 mM; 95% CI, 68 to 112; n = 5). CONCLUSIONS: Tranexamic acid inhibits NMDA receptors likely by reducing the binding of the co-agonist glycine and also inhibits α-amino-3-hydroxy-5-methyl-4-isoxazolepropionic acid and kainate receptors. Receptor blockade occurs at high millimolar concentrations of tranexamic acid, similar to the concentrations that occur after topical application to peripheral tissues. Glutamate receptors in tissues including bone, heart, and nerves play various physiologic roles, and tranexamic acid inhibition of these receptors may contribute to adverse drug effects.
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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 | 0.000 | 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 | 0.001 |
| Insufficient payload (model declined to judge) | 0.003 | 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".