MétaCan
Menu
Back to cohort
Record W2027255015 · doi:10.1038/clpt.2013.74

Emerging Transporters of Clinical Importance: An Update From the International Transporter Consortium

2013· review· en· W2027255015 on OpenAlexaff
Kathleen M. Hillgren, Dietrich Keppler, A. Zur, Kathleen M. Giacomini, Bruno Stieger, Carol E. Cass, L Zhang

Bibliographic record

VenueClinical Pharmacology & Therapeutics · 2013
Typereview
Languageen
FieldMedicine
TopicDrug Transport and Resistance Mechanisms
Canadian institutionsUniversity of Alberta
FundersNational Institute of General Medical Sciences
KeywordsMultidrug resistance-associated protein 2TransporterBile Salt Export PumpAbcg2PharmacologyEffluxDrugATP-binding cassette transporterCholestasisPharmacokineticsChemistryMedicineBiochemistryInternal medicineGene

Abstract

fetched live from OpenAlex

The International Transporter Consortium (ITC) has recently described seven transporters of particular relevance to drug development. Based on the second ITC transporter workshop in 2012, we have identified additional transporters of emerging importance in pharmacokinetics, interference of drugs with transport of endogenous compounds, and drug-drug interactions (DDIs) in humans. The multidrug and toxin extrusion proteins (MATEs, gene symbol SLC47A) mediate excretion of organic cations into bile and urine. MATEs are important in renal DDIs. Multidrug resistance proteins (MRPs or ABCCs) are drug and conjugate efflux pumps, and impaired activity of MRP2 results in conjugated hyperbilirubinemia. The bile salt export pump (BSEP or ABCB11) prevents accumulation of toxic bile salt concentrations in hepatocytes, and BSEP inhibition or deficiency may cause cholestasis and liver injury. In addition, examples are presented on the roles of nucleoside and peptide transporters in drug targeting and disposition.

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)0.002
metaresearch head score (Gemma)0.002
Version: metacan-v3-hybrid-931329e0061cValidation status: machine_predicted_unvalidated
Candidate categoriesnone
Consensus categoriesnone
DomainCandidate signal: none · Consensus signal: none
Study designCandidate signal: Not applicable · Consensus signal: none
GenreCandidate signal: Review · Consensus signal: Review
Teacher disagreement score0.005
Threshold uncertainty score0.018

Distilled classifier scores by category (both heads)

CategoryCodexGemma
Metaresearch0.0020.002
Meta-epidemiology (narrow)0.0010.000
Meta-epidemiology (broad)0.0020.001
Bibliometrics0.0040.005
Science and technology studies0.0000.001
Scholarly communication0.0020.004
Open science0.0010.001
Research integrity0.0020.003
Insufficient payload (model declined to judge)0.0050.003

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 head0.219
GPT teacher head0.506
Teacher spread0.287 · 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 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

Citations345
Published2013
Admission routes1
Has abstractyes

Explore more

Same venueClinical Pharmacology & TherapeuticsSame topicDrug Transport and Resistance MechanismsFrench-language works237,207