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A joint modeling approach for uncovering associations between gene expression, bioactivity and chemical structure in early drug discovery to guide lead selection and genomic biomarker development

Author

Listed:
  • Perualila-Tan Nolen
  • Shkedy Ziv

    (Interuniversity Institute for Biostatistics and Statistical Bioinformatics (I-BioStat), Center for Statistics, Hasselt University, 3590 Diepenbeek, Belgium)

  • Kasim Adetayo

    (Wolfson Research Institute for Health and Wellbeing, TS17 6BH Durham University, UK)

  • Talloen Willem
  • Verbist Bie
  • Göhlmann Hinrich W.H.

    (Janssen Pharmaceutica NV, 2340, Beerse, Belgium)

  • QSTAR Consortium

    (http://qstar-consortium.org)

Abstract

The modern drug discovery process involves multiple sources of high-dimensional data. This imposes the challenge of data integration. A typical example is the integration of chemical structure (fingerprint features), phenotypic bioactivity (bioassay read-outs) data for targets of interest, and transcriptomic (gene expression) data in early drug discovery to better understand the chemical and biological mechanisms of candidate drugs, and to facilitate early detection of safety issues prior to later and expensive phases of drug development cycles. In this paper, we discuss a joint model for the transcriptomic and the phenotypic variables conditioned on the chemical structure. This modeling approach can be used to uncover, for a given set of compounds, the association between gene expression and biological activity taking into account the influence of the chemical structure of the compound on both variables. The model allows to detect genes that are associated with the bioactivity data facilitating the identification of potential genomic biomarkers for compounds efficacy. In addition, the effect of every structural feature on both genes and pIC50 and their associations can be simultaneously investigated. Two oncology projects are used to illustrate the applicability and usefulness of the joint model to integrate multi-source high-dimensional information to aid drug discovery.

Suggested Citation

  • Perualila-Tan Nolen & Shkedy Ziv & Kasim Adetayo & Talloen Willem & Verbist Bie & Göhlmann Hinrich W.H. & QSTAR Consortium, 2016. "A joint modeling approach for uncovering associations between gene expression, bioactivity and chemical structure in early drug discovery to guide lead selection and genomic biomarker development," Statistical Applications in Genetics and Molecular Biology, De Gruyter, vol. 15(4), pages 291-304, August.
    • Handle: RePEc:bpj:sagmbi:v:15:y:2016:i:4:p:291-304:n:2
    DOI: 10.1515/sagmb-2014-0086
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    References listed on IDEAS

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    1. Smyth Gordon K, 2004. "Linear Models and Empirical Bayes Methods for Assessing Differential Expression in Microarray Experiments," Statistical Applications in Genetics and Molecular Biology, De Gruyter, vol. 3(1), pages 1-28, February.
    2. Kasim Adetayo & Lin Dan & Van Sanden Suzy & Clevert Djork-Arné & Bijnens Luc & Göhlmann Hinrich & Amaratunga Dhammika & Hochreiter Sepp & Shkedy Ziv & Talloen Willem, 2010. "Informative or Noninformative Calls for Gene Expression: A Latent Variable Approach," Statistical Applications in Genetics and Molecular Biology, De Gruyter, vol. 9(1), pages 1-31, January.
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