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|Title: ||Identification of in vitro and in vivo disconnects using transcriptomic data|
|Authors: ||OTAVA, Martin|
Verheyen, Geert R.
|Issue Date: ||2015|
|Citation: ||BMC GENOMICS, 16|
|Abstract: ||Background: 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.
Results: 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).
Conclusion: 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.|
|Notes: ||Correspondence: firstname.lastname@example.org|
|ISI #: ||000359526500003|
|Type: ||Journal Contribution|
|Validation: ||ecoom, 2016|
|Appears in Collections: ||Research publications|
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