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Record W2159722117 · doi:10.18433/j3zp42

The Effect of Natural Health Products and Traditional Medicines on the Activity of Human Hepatic Microsomal-Mediated Metabolism of Oseltamivir

2010· article· en· W2159722117 on OpenAlexaffvenue
Rui Liu, Teresa Tam, Jingqin Mao, Ammar Saleem, Anthony Krantis, John T. Arnason, Brian C. Foster

Bibliographic record

VenueJournal of Pharmacy & Pharmaceutical Sciences · 2010
Typearticle
Languageen
FieldMedicine
TopicHerbal Medicine Research Studies
Canadian institutionsHealth CanadaUniversity of Ottawa
Fundersnot available
KeywordsOseltamivirProdrugNatural productMicrosomeMetabolitePharmacologyChemistryBiotransformationDrugActive metaboliteTraditional medicineBiochemistryMedicineIn vitroEnzymeDiseaseCoronavirus disease 2019 (COVID-19)

Abstract

fetched live from OpenAlex

PURPOSE: Oseltamivir is a prodrug that requires metabolic activation but there is little information on whether natural health products interact to prevent the biotransformation by the carboxylesterase. METHODS: HPLC-DAD-ESI-MSD and fluorometric assays were used to determine if 50-pooled mixed gender human liver microsomes can mediate the formation of the active carboxylate metabolite and then if this reaction is affected by natural health products. RESULTS: Extracts from 6 traditional Cree botanicals, a commercially available Echinacea product, Goldenseal and a traditional Chinese medicine reduced the formation of the active drug. In addition to oseltamivir carboxylate we report the detection of two new metabolites which are derivatives of oseltamivir carboxylate, one of which is a metabonate formed as a result of methanol. CONCLUSIONS: In vitro studies would suggest that there is the potential for some natural health products used by patients in response to pandemic A/H1N1 to reduce drug efficacy. Further studies are required to determine if these potential interactions could be clinically significant.

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)0.007
metaresearch head score (Gemma)0.005
Version: codex-gemma-dda1882f352aValidation status: machine_predicted_unvalidated
Candidate categoriesScience and technology studies
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 score0.070
Threshold uncertainty score0.999

Codex and Gemma teacher scores by category

CategoryCodexGemma
Metaresearch0.0070.005
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0010.000
Bibliometrics0.0000.001
Science and technology studies0.0000.004
Scholarly communication0.0000.000
Open science0.0000.000
Research integrity0.0000.001
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 head0.080
GPT teacher head0.451
Teacher spread0.370 · 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.

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

Citations23
Published2010
Admission routes2
Has abstractyes

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