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Record W3091032186 · doi:10.18433/jpps30865

Pharmacokinetics and Toxicokinetics Roles of Membrane Transporters at Kidney Level

2020· review· en· W3091032186 on OpenAlexvenueno aff
Jéssica Veiga-Matos, Fernando Remião, Ana Motales

Bibliographic record

VenueJournal of Pharmacy & Pharmaceutical Sciences · 2020
Typereview
Languageen
FieldMedicine
TopicDrug Transport and Resistance Mechanisms
Canadian institutionsnot available
Fundersnot available
KeywordsEffluxMultidrug resistance-associated protein 2TransporterMultidrug Resistance-Associated ProteinsATP-binding cassette transporterAbcg2Solute carrier familyOrganic cation transport proteinsOrganic anion transporter 1ChemistryBiochemistryOrganic anion-transporting polypeptideXenobioticMembrane transport proteinBiologyGene

Abstract

fetched live from OpenAlex

Transporters are large membrane proteins, which control the passage of various compounds through biological membranes. These proteins are divided into uptake and efflux transporters and play an important role in the toxicokinetics of many endobiotics and xenobiotics. The uptake transporters facilitate the absorption of these compounds from the blood into the proximal tubular cells, while the efflux transporters eliminate these compounds into tubular fluid (urine). Overall, the uptake is performed by the superfamily solute carrier (SLC) transporters, which are, mostly, located in the basolateral membrane. The organic anion transporters (OATs; SLC22), the organic cation transporters (OCTs; SLC22), the organic cation/carnitine transporters (OCTNs), and the organic anion transporting polypeptides (OATP; SLC21/SLCO) are some examples of uptake transporters of the SLC superfamily. On the other hand, the superfamily ATP-binding cassette (ABC) transporters carry out the elimination of the substances through the apical membrane of the proximal tubular cells. The multidrug resistance proteins 1 (MDR; ABCB), the multi resistance protein (MRP2; ABCC) and the breast cancer resistance protein (BCRP, ABCG) along with the multidrug and toxin extrusion (MATE), which is an SLC transporter, carry out the substance efflux of the cell, However, uptake transporters seem to be more efficient than efflux transporters, leading to an accumulation of compounds in proximal tubular cells and, consequently, to renal damage. The accumulation of compounds can also occur due to variations in the number of transporters that exist due to differences in sex, age, genetic polymorphisms and epigenetics. Furthermore, some substances can inhibit, induce or, eventually, activate these transporters, with consequent drug-drug interactions (DDIs) as a result of alterations on the toxicokinetics of xenobiotics, leading to an increase of their accumulation and, consequently, to renal damage. These compounds may be exogenous, such as antibiotics, antivirals, cisplatin, metals, herbicides, mycotoxins and drugs; or endogenous, like uric acid, bile acids, bilirubin conjugates and conjugated steroids. Thus, in this review, we will focus on the accumulation of exogenous compounds due to variations on renal transporters and the consequent biological effects caused by them.

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)0.001
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 score0.004
Threshold uncertainty score0.012

Distilled classifier scores by category (both heads)

CategoryCodexGemma
Metaresearch0.0010.002
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0010.001
Bibliometrics0.0010.001
Science and technology studies0.0000.000
Scholarly communication0.0020.001
Open science0.0000.001
Research integrity0.0010.002
Insufficient payload (model declined to judge)0.0040.001

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 head0.148
GPT teacher head0.422
Teacher spread0.274 · 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

Citations31
Published2020
Admission routes1
Has abstractyes

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