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Identification of in vitro and in vivo disconnects using transcriptomic data

Overview of attention for article published in BMC Genomics, August 2015
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Title
Identification of in vitro and in vivo disconnects using transcriptomic data
Published in
BMC Genomics, August 2015
DOI 10.1186/s12864-015-1726-7
Pubmed ID
Authors

Martin Otava, Ziv Shkedy, Willem Talloen, Geert R Verheyen, Adetayo Kasim

Abstract

Integrating transcriptomic experiments within drug development is increasingly advocated for the early detection of toxicity. This is partly to reduce costs related to drug failures in the late, and expensive phases of clinical trials. Such an approach has proven useful both in the study of toxicology and carcinogenicity. However, general lack of translation of in vitro findings to in vivo systems remains one of the bottle necks in drug development. This paper proposes a method for identifying disconnected genes between in vitro and in vivo toxicogenomic rat experiments. The analytical framework is based on the joint modeling of dose-dependent in vitro and in vivo data using a fractional polynomial framework and biclustering algorithm. Most disconnected genes identified belonged to known pathways, such as drug metabolism and oxidative stress due to reactive metabolites, bilirubin increase, glutathion depletion and phospholipidosis. We also identified compounds that were likely to induce disconnect in gene expression between in vitro and in vivo toxicogenomic rat experiments. These compounds include: sulindac and diclofenac (both linked to liver damage), naphtyl isothiocyanate (linked to hepatoxocity), indomethacin and naproxen (linked to gastrointestinal problem and damage of intestines). The results confirmed that there are important discrepancies between in vitro and in vivo toxicogenomic experiments. However, the contribution of this paper is to provide a tool to identify genes that are disconnected between the two systems. Pathway analysis of disconnected genes may improve our understanding of uncertainties in the mechanism of actions of drug candidates in humans, especially concerning the early detection of toxicity.

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Mendeley readers

Mendeley readers

The data shown below were compiled from readership statistics for 28 Mendeley readers of this research output. Click here to see the associated Mendeley record.

Geographical breakdown

Country Count As %
Chile 1 4%
Unknown 27 96%

Demographic breakdown

Readers by professional status Count As %
Researcher 8 29%
Student > Ph. D. Student 6 21%
Student > Master 5 18%
Student > Bachelor 3 11%
Student > Doctoral Student 2 7%
Other 2 7%
Unknown 2 7%
Readers by discipline Count As %
Agricultural and Biological Sciences 6 21%
Biochemistry, Genetics and Molecular Biology 5 18%
Chemical Engineering 2 7%
Environmental Science 2 7%
Immunology and Microbiology 2 7%
Other 7 25%
Unknown 4 14%
Attention Score in Context

Attention Score in Context

This research output has an Altmetric Attention Score of 1. This is our high-level measure of the quality and quantity of online attention that it has received. This Attention Score, as well as the ranking and number of research outputs shown below, was calculated when the research output was last mentioned on 03 May 2016.
All research outputs
#17,770,433
of 22,824,164 outputs
Outputs from BMC Genomics
#7,568
of 10,654 outputs
Outputs of similar age
#179,440
of 266,186 outputs
Outputs of similar age from BMC Genomics
#212
of 257 outputs
Altmetric has tracked 22,824,164 research outputs across all sources so far. This one is in the 19th percentile – i.e., 19% of other outputs scored the same or lower than it.
So far Altmetric has tracked 10,654 research outputs from this source. They receive a mean Attention Score of 4.7. This one is in the 23rd percentile – i.e., 23% of its peers scored the same or lower than it.
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We're also able to compare this research output to 257 others from the same source and published within six weeks on either side of this one. This one is in the 10th percentile – i.e., 10% of its contemporaries scored the same or lower than it.