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A Systems Biology Approach for Identifying Hepatotoxicant Groups Based on Similarity in Mechanisms of Action and Chemical Structure

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In Silico Methods for Predicting Drug Toxicity

Part of the book series: Methods in Molecular Biology ((MIMB,volume 1425))

Abstract

When evaluating compound similarity, addressing multiple sources of information to reach conclusions about common pharmaceutical and/or toxicological mechanisms of action is a crucial strategy. In this chapter, we describe a systems biology approach that incorporates analyses of hepatotoxicant data for 33 compounds from three different sources: a chemical structure similarity analysis based on the 3D Tanimoto coefficient, a chemical structure-based protein target prediction analysis, and a cross-study/cross-platform meta-analysis of in vitro and in vivo human and rat transcriptomics data derived from public resources (i.e., the diXa data warehouse). Hierarchical clustering of the outcome scores of the separate analyses did not result in a satisfactory grouping of compounds considering their known toxic mechanism as described in literature. However, a combined analysis of multiple data types may hypothetically compensate for missing or unreliable information in any of the single data types. We therefore performed an integrated clustering analysis of all three data sets using the R-based tool iClusterPlus. This indeed improved the grouping results. The compound clusters that were formed by means of iClusterPlus represent groups that show similar gene expression while simultaneously integrating a similarity in structure and protein targets, which corresponds much better with the known mechanism of action of these toxicants. Using an integrative systems biology approach may thus overcome the limitations of the separate analyses when grouping liver toxicants sharing a similar mechanism of toxicity.

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Acknowledgments

This work was supported by the European Commission under its 7th Framework Programme with the Grant “diXa” (283775) and by the European Chemical Industry Council - Long-Range Research Initiative (CEFIC-LRI) with the Grant “DECO2” (AIMT4-UM).

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Authors and Affiliations

  1. Department of Cell Biology-Inspired Tissue Engineering, MERLN Institute, Maastricht University, Universiteitssingel 40, Maastricht, 6229ER, The Netherlands

    Dennie G. A. J. Hebels

  2. Department of Toxicogenomics, Maastricht University, Universiteitssingel 40, Maastricht, 6229ER, The Netherlands

    Dennie G. A. J. Hebels, Danyel G. J. Jennen & Jos C. S. Kleinjans

  3. Department of Computational Molecular Biology, Bioinformatics Group, Max Planck Institute for Molecular Genetics, Ihnestr. 63-73, Berlin, 14195, Germany

    Axel Rasche & Ralf Herwig

  4. Division of Medicinal Chemistry, Leiden Academic Centre for Drug Research, Leiden University, Leiden, 2300 RA, The Netherlands

    Gerard J. P. van Westen

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  1. Dennie G. A. J. Hebels
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  2. Axel Rasche
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  3. Ralf Herwig
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  4. Gerard J. P. van Westen
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  5. Danyel G. J. Jennen
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  6. Jos C. S. Kleinjans
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Correspondence to Dennie G. A. J. Hebels .

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Editors and Affiliations

  1. Mario Negri Inst for Pharm Rsrch, IRCCS, Milan, Italy

    Emilio Benfenati

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Hebels, D.G.A.J., Rasche, A., Herwig, R., van Westen, G.J.P., Jennen, D.G.J., Kleinjans, J.C.S. (2016). A Systems Biology Approach for Identifying Hepatotoxicant Groups Based on Similarity in Mechanisms of Action and Chemical Structure. In: Benfenati, E. (eds) In Silico Methods for Predicting Drug Toxicity. Methods in Molecular Biology, vol 1425. Humana Press, New York, NY. https://doi.org/10.1007/978-1-4939-3609-0_15

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  • DOI: https://doi.org/10.1007/978-1-4939-3609-0_15

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  • Publisher Name: Humana Press, New York, NY

  • Print ISBN: 978-1-4939-3607-6

  • Online ISBN: 978-1-4939-3609-0

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