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Record W2095964129 · doi:10.1002/cjoc.200990130

Development and Validation of Selective Spectrophotometric Methods for the Determination of Pregabalin in Pharmaceutical Preparation

2009· article· en· W2095964129 on OpenAlex

Why this work is in the frame

A frame that forgets how it found something cannot be audited. These are the routes that admitted this work.

affAt least one author lists a Canadian institution in the pinned OpenAlex snapshot.

Bibliographic record

VenueChinese Journal of Chemistry · 2009
Typearticle
Languageen
FieldChemistry
TopicAnalytical Methods in Pharmaceuticals
Canadian institutionsTellabs (Canada)
Fundersnot available
KeywordsChemistryNinhydrinColor reactionTetracyanoquinodimethaneChromatographyAmine gas treatingDimethylformamidePregabalinCombinatorial chemistryOrganic chemistrySolventMolecule

Abstract

fetched live from OpenAlex

Abstract Three simple, quick and sensitive methods are described for the spectrophotometric determination of pregabalin (Pgb) in pharmaceutical preparations. Among them, the first two methods are based on the reaction of Pgb as n‐electron donors with 2,3‐dichloro‐5,6‐dicyano‐1,4‐benzoquinone (DDQ) and 7,7,8,8‐tetracyanoquinodimethane (TCNQ) as π‐acceptors to give highly colored complex species. The colored products were quantitated spectrophotometrically at 494 and 841 nm for DDQ and TCNQ, respectively. Optimization of the different experimental conditions was conducted. Beer's law was obeyed in the concentration ranges 2.0–30.0 and 1.5–10 g·mL −1 for DDQ and TCNQ methods, respectively. The third method is based on the interaction of ninhydrin (NN) with primary amine present in the pregabaline. This reaction produces a blue coloured product in N , N ‐dimethylformamide (DMF) medium, which absorbs maximally at 573 nm. Beer's law was found in the concentration range 40.0–180.0 µg·mL −1 . The methods were applied successfully to the determination of this drug in pharmaceutical dosage forms.

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.

Full frame distilled prediction

Teacher imitation

Not calibrated prevalence, not ground truth. Human validation pending. Learned from the 10,348 direct Codex labels and 10,348 direct Gemma labels. Candidate is the union of thresholded teacher heads; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels or direct frontier model labels.

metaresearch head score (Codex)0.002
metaresearch head score (Gemma)0.003
Version: codex-gemma-dda1882f352aValidation 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: Empirical
Teacher disagreement score0.087
Threshold uncertainty score0.372

Codex and Gemma teacher scores by category

CategoryCodexGemma
Metaresearch0.0020.003
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0000.000
Bibliometrics0.0000.000
Science and technology studies0.0000.000
Scholarly communication0.0000.000
Open science0.0000.000
Research integrity0.0000.000
Insufficient payload (model declined to judge)0.0000.000

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 head0.046
GPT teacher head0.469
Teacher spread0.423 · 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