Development and Validation of a Simple HPLC-UV Assay Method for Determination of Levetiracetam Concentrations in Human Plasma
Bibliographic record
Abstract
Levetiracetam (LEV) is a broad spectrum antiseizure medication that is used in various seizure types. There is evidence that therapeutic drug monitoring (TDM) of LEV is of value in selected patient populations, therefore determination of LEV plasma concentrations is essential. Herein we developed and validated a simple, reproducible, and practical method for the quantification of LEV concentrations in human plasma samples using high performance liquid chromatography (HPLC). Plasma samples (0.3 mL) deproteinization was done using acetonitrile. HPLC chromatographic separation of plasma samples was accomplished by reversed phase C18 column. The mobile phase constituted water and acetonitrile (90:10, v/v) ran at flow rate of 1 mL/min. Signal acquisition was conducted at a wavelength of 192 nm. Calibration curves showed excellent linearity (Correlation coefficient r2 > 0.99) over a concentration range of 3–80 μg/mL. Both inter and intraday assay accuracy and precision were less than 8% (except for the lowest limit of quantification was within 20%). Elution time was 15 min. The developed method excluded the use of buffers and utilized small volumes of plasma samples with simple mobile phase composition. Therefore, our method could be practically applied to routine TDM.
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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 | 0.005 | 0.005 |
| 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 | 0.002 |
| Insufficient payload (model declined to judge) | 0.001 | 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".