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Record W2114005512 · doi:10.1093/chromsci/bmt103

A Rapid and Sensitive HPLC Assay of Some Concomitant Anti-Migraine Drugs

2013· article· en· W2114005512 on OpenAlexaff
Mamdouh R. Rezk, Adel M. Michael, Hayam M. Lotfy, Ayman O.S. El‐Kadi, M. Shehata

Bibliographic record

VenueJournal of Chromatographic Science · 2013
Typearticle
Languageen
FieldChemistry
TopicFree Radicals and Antioxidants
Canadian institutionsUniversity of Alberta
Fundersnot available
KeywordsChemistryChromatographyZolmitriptanNaproxenHigh-performance liquid chromatographyDosage formAnalytePharmacokineticsPharmacologySumatriptanReceptor

Abstract

fetched live from OpenAlex

This work describes a simple and sensitive method for simultaneous determination of zolmitriptan, naproxen and propranolol in their dosage forms using HPLC. The drugs were separated isocratically on a Zorbax C8 (4.6 × 250 mm with 5 µm particle size) column using a mobile phase composed of 20 mM phosphate citrate buffer [0.1% TEA (pH 3.1)]:methanol:THF (5:3:2, by volumes). The detection was accomplished fluorometrically setting the excitation wavelength at 280 nm and emission wavelength at 360 nm. The method was validated over a linearity range of 100-900 ng/mL for zolmitriptan, 50-300 ng/mL for naproxen and 100-800 ng/mL for propranolol. The assay was successfully applied to the determination of the studied drugs in pharmaceutical dosage forms without interference from tablet excipients with high specificity. The method can be applied successfully in the future for the pharmacokinetic study of these drugs in the human plasma with high accuracy especially that LOQs of zolmitriptan and propranolol in the proposed method cover their Cmax.

Fetched live from OpenAlex and de-inverted. Abstracts are not stored in this database: the inverted indexes are 8.6 GB of the frame’s 9.3 GB of text, and the host has 13 GB free.

How this classification was reachedexpand

Full frame machine prediction

Teacher imitation

Not 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.

metaresearch head score (Codex)0.001
metaresearch head score (Gemma)0.001
Version: metacan-v3-hybrid-931329e0061cValidation status: machine_predicted_unvalidated
Candidate categoriesnone
Consensus categoriesnone
DomainCandidate signal: none · Consensus signal: none
Study designCandidate signal: Bench or experimental · Consensus signal: Bench or experimental
GenreCandidate signal: Empirical · Consensus signal: none
Teacher disagreement score0.002
Threshold uncertainty score0.005

Distilled classifier scores by category (both heads)

CategoryCodexGemma
Metaresearch0.0010.001
Meta-epidemiology (narrow)0.0010.001
Meta-epidemiology (broad)0.0010.001
Bibliometrics0.0020.001
Science and technology studies0.0000.000
Scholarly communication0.0010.001
Open science0.0010.001
Research integrity0.0010.001
Insufficient payload (model declined to judge)0.0010.001

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.

Opus teacher head0.007
GPT teacher head0.222
Teacher spread0.214 · how far apart the two teachers sit on this one work
Validation statusscore_only:v0-immature-baseline · verbatim from the scoring run: score_only means the number may rank works, and no category label ships from it

Classification

machine, unvalidated

Machine predicted; a candidate call from one source (direct Gemma or distilled Codex), not a consensus.

The models applied no category: nothing in the taxonomy fit this work.
Study designBench or experimental
Domainnot available
GenreEmpirical

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".

Quick stats

Citations7
Published2013
Admission routes1
Has abstractyes

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