Evidence map›Paper›PMID 23494981›Full record

ArticleClinical pharmacokinetics2013

Disposition pathway-dependent approach for predicting organic anion-transporting polypeptide-mediated drug-drug interactions.

Zhe-Yi Hu

Abstract read
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In one paragraph

Article in Clinical pharmacokinetics, 2013. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 5 papers.

0numbers the graph read from it
0cells of the map it votes in
5citing papers in PubMed
0.9field-weighted citation impact, top 24% of its field
1 · What the graph read from it

What it found

Each row is one number read from the abstract, on the scale the paper reported it, with its interval. Left of the dashed line favours the treatment, right favours the comparator. Under each row is the sentence it came from. New to these charts? A ten-minute tutorial.

The abstract states no effect estimate the extractor could read, or names no intervention and outcome on the map, so this paper lights no cell and moves no belief. It is still indexed, cited and linked below.

2 · The registry

The trial behind it

Trials whose registry record cites this paper, or whose number appears in the abstract. A trial that started after this paper was published is citing it as background, not reporting it.

Neither the registry nor the abstract names a trial number. If this is a trial report, that itself is worth knowing.

3 · Its place in the literature

Who cites it

5 citing papers in PubMed, 11 citations in OpenAlex.

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4 · The record

Corrections and comments

PubMed lists nothing against this paper. Absence here is not a guarantee, only a check that was made.

5 · Who and what money

Authors and funding

1 author at 1 institution in 1 country.

Zhe-Yi HuDepartment of Clinical Pharmacy, College of Pharmacy, University of Tennessee, 881 Madison Ave, Room 328, Memphis, TN 38163, USA. zhu13@uthsc.edu
University of Tennessee at Knoxville · US

Funding

No grant is acknowledged in the PubMed record.

6 · The paper itself

Abstract

BACKGROUND AND

objectiveOrganic anion-transporting polypeptide (OATP)-mediated drug-drug interactions (DDIs) are among the most important classes of clinically relevant DDIs. Accurate prediction of the OATP-mediated DDIs is not successful due to the sequential disposition pathways of OATP substrates in humans. Intestinal and hepatic uptake transporters, efflux transporters, and cytochrome P450 (CYP) enzymes are often involved in the sequential disposition pathways of typical OATP substrates. The aim of this proof-of-concept study is to develop and validate a novel approach which can be used to predict OATP-mediated DDIs with significantly increased accuracy and decreased false-negatives.

methodsThe feasibility of using a disposition pathway-dependent prediction (DPDP) approach to predict the ratios of the area under the plasma concentration-time curve (AUC(R)) in the presence and absence of the inhibitor was investigated. A total of 62 clinical DDI studies were included in this feasibility study. The disposition pathways governing the outcome of DDIs were first identified for each substrate using the information within learning sets, and then substrate-specific algorithms were used to predict the DDI risks of the external validation set (51 DDIs).

resultsThe method predicted AUC(R) within 50-200 % for 50 studies (98 %), and the false-negative rate was 9.8 %. The DPDP approach showed significant improvement over an existing approach and was used to forecast the magnitude of 198 DDIs that have not been studied.

conclusionThis approach can be used to avoid unnecessary clinical DDI studies during new drug development.

Indexed as

Drug InteractionsModels, BiologicalTissue DistributionDrug-Related Side Effects and Adverse ReactionsForecastingHumansOrganic Anion TransportersPharmaceutical PreparationsSubstrate SpecificityOrganic Anion TransportersPharmaceutical Preparations

Identifiers

PMID23494981
OpenAlexW2073930425

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Read under generation 80e0d062 · epoch 390. Bibliography from PubMed, PubMed Central and OpenAlex; grants from NIH RePORTER; trial links from ClinicalTrials.gov; estimates, votes and beliefs from the OpenQuestion graph.