Development and Validation of a UHPLC-MS/MS Method for the Simultaneous Quantification of Candesartan and Bisoprolol Together with Other 16 Antihypertensive Drugs in Plasma Samples
Bibliographic record
Abstract
Antihypertensive pharmacological therapy is often characterized by a coadministration of different classes of drugs. Therefore, analytical methods allowing the simultaneous quantification of many drugs are needed for therapeutic drug monitoring (TDM) purposes in this context. In particular, TDM represents a useful tool to discriminate poor adherence from real cases of resistant hypertension. For this reason, the aim of this study is to validate, following the Food and Drug Administration (FDA) and the European Medicines Agency (EMA) guidelines, an ultrahigh-performance liquid chromatography coupled with tandem mass spectrometry (UHPLC-MS/MS) method for the simultaneous quantification of 18 antihypertensive drugs in human plasma. A LX-50 coupled with a QSight 220 UHPLC-MS/MS system with electrospray ionization and multiple reaction monitoring mode was used, after a binary gradient separation (13 min) on a reverse-phase Acquity UPLC HSS T3 [1.8 μm, 2.1 mm × 150 mm] column. Method validation showed a stable and acceptable matrix effect, recovery, high accuracy, and precision, assessing the eligibility of this method for routine use in the clinical context.
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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 | 0.003 |
| 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) | 0.001 | 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".