Fluorimetric quantitation of citalopram and escitalopram in plasma: developing an express method to monitor compliance in clinical trials
Bibliographic record
Abstract
BACKGROUND: Selective serotonin reuptake inhibitors (SSRIs) in general, and citalopram/escitalopram in particular, are widely used to treat clinical depression. However, SSRI bioavailability and non-compliance represent major issues, especially in the clinical trials setting. In this context, frequent drug-level measurements for compliance monitoring would be a desirable tool to improve clinical outcomes with SSRIs. However, the liquid chromatography techniques available are expensive, requiring excessive sample preparation, and suffer from high complexity. We sought to develop a rapid method for the measurement of citalopram/escitalopram levels in human plasma by fluorimetry. METHODS: A total of 34 frozen human plasma samples were thawed at room temperature and repeatedly centrifuged in cellulose to remove aggregates, proteins and solids. Fluorescence spectra were measured in the range 270-450 nm with excitation at 240 nm on a FluoroMax 3 spectrofluorimeter. Control samples contained known concentrations of SSRIs. RESULTS: SSRI absorbance spectra were recorded in the range 230-320 nm. The shape of the spectra and the absorbance of citalopram and escitalopram were very similar, with UV maximum absorbance at 239 nm. The maximum extinction coefficient was epsilon239=15,930 M-1 cm-1 for citalopram and epsilon239=13,630 M-1 cm-1 for escitalopram. The fluorescence spectra of SSRIs are unique and are characterized by the presence of two well-defined conjugated spectra with maxima at 300 and 382 nm. CONCLUSIONS: Fluorimetry is very suitable for assessment of plasma SSRI levels. This inexpensive and efficient technique can objectively and reliably quantify drug levels in biological fluids, thereby directly determining the level of patient adherence to the prescribed drug regimen. This method will be useful in a broad spectrum of applications, from compliance/bioavailability assessments in animal and human experiments to utilization in large-scale clinical trials.
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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 | 0.009 | 0.006 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.002 | 0.001 |
| Science and technology studies | 0.000 | 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) | 0.001 | 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".