Evidence map›Paper›PMID 22511348›Full record

ArticleDrug metabolism and disposition: the biological fate of chemicals2012

Probabilistic orthology analysis of the ATP-binding cassette transporters: implications for the development of multiple drug resistance phenotype.

Ciaran Fisher, Tanya Coleman, Nick Plant

Abstract read
In one paragraph

Article in Drug metabolism and disposition: the biological fate of chemicals, 2012. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 1 paper.

0numbers the graph read from it
0cells of the map it votes in
1citing papers in PubMed
–field-weighted citation impact
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

1 citing paper in PubMed.

  1. Article
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

3 authors.

Ciaran FisherCentre for Toxicology, Faculty of Health and Medical Sciences, University of Surrey, Guildford, GU2 7XH, UK.
Tanya Coleman
Nick Plant

Funding

Biotechnology and Biological Sciences Research Council BB/E527671/1
6 · The paper itself

Abstract

Drug transporters are rapidly becoming recognized as central to determining a chemical's fate within the body. This action is a double-edged sword, protecting the body from toxicants, but also potentially leading to reduced clinical efficacy of drugs through multiple drug resistance phenotype. To examine the interrelationship of this superfamily, we have constructed phylogenetic trees over an extended evolutionary distance representing each of the seven subfamilies. In addition, using protein sequences from species important in the design and evaluation of novel chemicals, namely human, macaque, rat, mouse, and dog, we have undertaken probabilistic orthology analysis to examine speciation probabilities within this phylogeny. These data allow us to accurately predict orthologous sequences across these species, an important confirmatory step with implications for cross-species extrapolation of data during drug safety testing. Finally, we present the first complete phylogeny for subfamilies within humans constructed using the entire coding sequences, at both the DNA and protein levels. We demonstrate for the first time that genes associated with the multiple drug resistance phenotype cluster separately from other genes within the same subfamily, suggestive of a conserved, fundamental, difference in these proteins. Such work may help guide future studies on the mechanisms underlying multiple drug resistance as well as the development of novel therapeutic approaches to mitigate against its development.

Indexed as

AnimalsATP-Binding Cassette TransportersBiological TransportDogsDrug-Related Side Effects and Adverse ReactionsDrug Resistance, MultipleHumansMacacaMicePhylogenyRatsATP-Binding Cassette Transporters

Identifiers

PMID22511348
PMCPMC3382838

What OpenQuestion holds

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Registered trials

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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.