alexa Assessing Hepatoxicants based on High-throughput Quantitative SILAC Proteomics and Causal Biological Networks
ISSN: 0974-276X

Journal of Proteomics & Bioinformatics
Open Access

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Research Article

Assessing Hepatoxicants based on High-throughput Quantitative SILAC Proteomics and Causal Biological Networks

Ahmed Enayetallah1, Sashi Nadanaciva2, Paul Ajuh3, Cristina Vázquez Martín3, Amy Wheat3, Angus Lamond3, Daniel Ziemek4 and Karen L. Leach2,5*

1Translational Medicine, Biogen Idec, Cambridge, Massachusetts, USA

2Compound Safety Prediction, Pfizer Global Research and Development, Pfizer, Inc, Groton, Connecticut, USA

3Dundee Cell Products, Dundee, Scotland

4Computational Sciences Center of Emphasis, Pfizer Global Research and Development, Pfizer, Inc, Berlin, Germany

5Pfizer Global Research and Development, Centers for Therapeutic Innovation, Boston, Massachusetts, USA

*Corresponding Author:
Karen L. Leach
Compound Safety Prediction
Pfizer Global Research and Development
Pfizer, Inc, Groton, Connecticut, USA
Tel: +1 860-441-4100
E-mail: [email protected]

Received date: January 27, 2015; Accepted date: February 17, 2015; Published date: February 25, 2015

Citation: Enayetallah A, Nadanaciva S, Ajuh P, Martín CV, Wheat A, et al. (2015) Assessing Hepatoxicants based on High-throughput Quantitative SILAC Proteomics and Causal Biological Networks. J Proteomics Bioinform 8:049-059. doi:10.4172/jpb.1000352

Copyright: © 2015 Enayetallah A, et al. This is an open-access article distributed under the terms of the Creative Commons Attribution License, which permits unrestricted use, distribution, and reproduction in any medium, provided the original author and source are credited.

 
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Abstract

Drug-induced liver injury can result in the termination of compounds in preclinical development, as well as withdrawal of marketed drugs. Identification of the signaling pathways and proteins involved in injury is an important step towards establishing assays that could be utilized to identify potential hepatotoxicants early in the drug discovery process. In this study we used high throughput quantitative mass spectrometry proteomics involving Stable Isotope Labeling with Amino acids in Cell culture (SILAC) leveraged by a recently developed systems biology approach (Causal Reasoning Engine, CRE) to investigate the effects of hepatotoxic compounds on the cellular proteome of HepG2 cells. Cells were treated with various concentrations of nefazadone, nimesulide, nomifensine, or glafenine, all of which cause hepatotoxicity in humans. Buspirone and rosiglitazone were used as comparator compounds not associated with hepatotoxicity. In comparison to more traditional proteomic analysis tools CRE results provided detailed molecular hypotheses that condense into biological networks and collectively explain a significant number of the measured protein changes. The CRE hypotheses demonstrate that magnitude of response is not necessarily the differentiating factor between DILI and non-DILI compounds but rather the biological processes implicated. Differentiating CRE molecular hypotheses implicate lipid and glucose metabolism, inflammation, oxidative stress and DNA damage as consistent major in vitro discriminating factors. Overall, this paper provides evidence that SILAC data coupled with the CRE analysis method can provide context and new insight into variation of stress responses in hepatotoxic versus non-hepatotoxic compounds even within the same therapeutic class.

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