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Record W2020919429 · doi:10.1517/17425250903176439

The effect of oral lipids and circulating lipoproteins on the metabolism of drugs

2009· review· en· W2020919429 on OpenAlexaff
Jigar P. Patel, Dion R. Brocks

Bibliographic record

VenueExpert Opinion on Drug Metabolism & Toxicology · 2009
Typereview
Languageen
FieldMedicine
TopicDrug Transport and Resistance Mechanisms
Canadian institutionsUniversity of Alberta
Fundersnot available
KeywordsBioavailabilityDrugMetabolismLipid metabolismDrug metabolismChemistryPharmacologyLipoproteinEnzymeBiochemistrySmall intestineTriglycerideCholesterolBiology

Abstract

fetched live from OpenAlex

The oral bioavailability of many lipophilic drugs is known to increase when coadministered with fatty meals. Although such a phenomenon is typically ascribed to increased solubilization and absorption of drug, in some cases this increase in systemic exposure may be in part due to the influence of lipids on the presystemic metabolism of the affected drug. Oral lipids on their absorption may interfere with the drug metabolizing enzymes expressed in the small intestine and/or liver. Fatty acids incorporated in dietary triglyceride can modulate the expression and activity of drug metabolizing enzymes within the small intestine. Lipoproteins, which are the major carriers of lipids in the systemic circulation, can become associated with lipophilic drugs. Such a combination may influence the metabolism of lipophilic drugs through limiting their uptake into the cells thereby decreasing their metabolism. In a contrary manner, an increased uptake and metabolism of lipoprotein-bound drug may be facilitated by lipoprotein receptors mediated uptake. The components of lipoproteins may also modulate the expression or activity of hepatic and extrahepatic drug metabolizing enzymes.

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)0.002
metaresearch head score (Gemma)0.000
Version: codex-gemma-dda1882f352aValidation status: machine_predicted_unvalidated
Candidate categoriesMeta-epidemiology (narrow)
Consensus categoriesnone
DomainCandidate signal: none · Consensus signal: none
Study designCandidate signal: Not applicable · Consensus signal: none
GenreCandidate signal: Review · Consensus signal: Review
Teacher disagreement score0.987
Threshold uncertainty score1.000

Codex and Gemma teacher scores by category

CategoryCodexGemma
Metaresearch0.0020.000
Meta-epidemiology (narrow)0.0010.000
Meta-epidemiology (broad)0.0040.001
Bibliometrics0.0000.001
Science and technology studies0.0000.001
Scholarly communication0.0000.000
Open science0.0010.000
Research integrity0.0010.001
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 head0.027
GPT teacher head0.333
Teacher spread0.306 · 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 designNot applicable
Domainnot available
GenreReview

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

Citations35
Published2009
Admission routes1
Has abstractyes

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