Development and Validation of Tranexamic Acid Determination in Human Plasma by HPLC-MS/MS method
Bibliographic record
Abstract
Introduction. Tranexamic acid is one of the most common drugs used to stop bleeding after trauma, in surgery and gynecology. The most common analytical method for the determination of this compound is reversed-phase high-performance liquid chromatography (HPLC). However, this compound belongs to the group of so-called poorly retained compounds due to its chemical structure. It is necessary to develop an analytical method that will allow the determination of tranexamic acid in human blood plasma with the least time, resource costs and without the use of specialized columns.Aim. The aim of this study is to develop a method for tranexamic acid in human plasma by high performance liquid chromatography with tandem mass-spectrometry (HPLC-MS/MS) for pharmacokinetic studies.Materials and methods. Determination of tranexamic acid in plasma by HPLC-MS/MS. The samples were processed by acetonitrile protein precipitation.Results and discussion. This method was validated by next parameters: selectivity, matrix effect, calibration curve, accuracy, precision, recovery, lower limit of quantification, carry-over effect and stability.Conclusion. The method of the determination of tranexamic acid in human plasma was developed and validated by HPLC-MS/MS. The linearity in plasma sample was achieved in the concentration range of 100.00–15000.00 ng/ml. Method could be applied to tranexamic acid determination in plasma for pharmacokinetics and bioequivalence studies.
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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 | 0.003 | 0.004 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.002 | 0.001 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.002 | 0.001 |
| Insufficient payload (model declined to judge) | 0.003 | 0.002 |
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".