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Record W2184006226

A pilot study to evaluate the pharmacokinetics of sibutramine in healthy subjects under fasting and fed conditions.

2004· article· en· W2184006226 on OpenAlexaff
Zohreh Abolfathi, Jean Couture, François Vallée, Marc Lebel, Mario Tanguay, Eric Masson

Bibliographic record

VenuePubMed · 2004
Typearticle
Languageen
FieldMedicine
TopicPharmacology and Obesity Treatment
Canadian institutionsMicropharma (Canada)
Fundersnot available
KeywordsSibutramineMetabolitePharmacokineticsBioavailabilityPharmacologyActive metaboliteCrossover studyMedicineChemistryInternal medicineWeight lossObesity
DOInot available

Abstract

fetched live from OpenAlex

PURPOSE: The purpose of this study was to characterize the food effect on the pharmacokinetics of sibutramine and its pharmacologically active metabolites. METHODS: This was an open label, single dose, crossover study completed by six healthy males. A single dose of sibutramine 15 mg was administered orally under fasting and fed conditions. Plasma concentrations of sibutramine and its metabolites were determined by LC/MS/MS: Non-compartmental pharmacokinetics and statistical analysis were performed using SAS. RESULTS: The food intake increased significantly AUCs and C(max) of sibutramine and its M1 metabolite, but did not affect M2 metabolite. When sibutramine was administered with food, the T(max) was delayed by 2 to 4 hours for sibutramine, M1 and M2 metabolites as stated in the literature. CONCLUSIONS: The results of this study indicate that sibutramine is measurable using a sensitive bioanalytical method. In contrast with what is reported in the product monograph, this study demonstrated that the bioavailability of sibutramine and M1 metabolite was significantly increased with administration with food. The results confirmed lack of food effect on the pharmacokinetics of M2 metabolite. These relatively large food effect observed for sibutramine and M1 metabolite, could have implication for the efficacy and safety of the drug.

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 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)0.000
metaresearch head score (Gemma)0.000
Version: codex-gemma-dda1882f352aValidation status: machine_predicted_unvalidated
Candidate categoriesnone
Consensus categoriesnone
DomainCandidate signal: none · Consensus signal: none
Study designCandidate signal: Observational · Consensus signal: Observational
GenreCandidate signal: Empirical · Consensus signal: Empirical
Teacher disagreement score0.115
Threshold uncertainty score0.271

Codex and Gemma teacher scores by category

CategoryCodexGemma
Metaresearch0.0000.000
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 head0.074
GPT teacher head0.358
Teacher spread0.284 · 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 teacher head, not a consensus.

The models applied no category: nothing in the taxonomy fit this work.
Study designObservational
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

Citations25
Published2004
Admission routes1
Has abstractyes

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