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Toxicogenomic module associations with pathogenesis: a network-based approach to understanding drug toxicity

J J Sutherland1, Y W Webster1, J A Willy1

  • 1Lilly Research Laboratories, Eli Lilly and Company, Lilly Corporate Center, Indianapolis IN, USA.

The Pharmacogenomics Journal
|April 26, 2017
PubMed

Insights

Network-based genomic analysis aids drug safety by predicting toxicity. This approach identifies early toxicity signals and novel mechanisms, improving prediction of clinical liabilities.

Area of Science:

  • Toxicogenomics
  • Systems Biology
  • Computational Biology

Background:

  • Nonclinical safety studies are crucial for predicting drug clinical liabilities.
  • Whole-genome transcriptional profiling faces challenges with limited treatment data.
  • Network-based genomic analysis offers a solution for complex safety assessments.

Purpose of the Study:

  • To apply co-expression network analysis to rat liver gene expression data for safety assessment.
  • To develop a visual representation of the transcriptome (TXG-MAP) organizing gene modules.
  • To predict toxicity mechanisms and phenotypes using network analysis.

Main Methods:

  • Co-expression network analysis of rat liver gene expression data.
  • Definition and organization of 415 gene modules into the TXG-MAP.
  • Integration of module associations with transcriptional activity to explain toxicity.
  • Comparison of network-based analysis with traditional histology and gene-level analysis.

Main Results:

  • Identified 415 gene modules with unique transcriptional control within the TXG-MAP.
  • Demonstrated that early network responses predict outcomes in longer studies.
  • Discovered a novel hepatotoxicity mechanism involving endoplasmic reticulum stress and Nrf2 activation.
  • Showcased translation of rat-derived cholestatic injury subtypes to human data.
  • Found that combined module and gene-level analysis significantly enhances phenotype-gene association identification.

Conclusions:

  • Network-based genomic analysis, specifically co-expression networks and the TXG-MAP, improves nonclinical safety assessment.
  • Early network responses provide valuable predictive information complementing traditional methods.
  • The approach identifies novel toxicity mechanisms and biomarkers, with translatable findings between species.
  • Combining module and gene-level analysis is superior to gene-level analysis alone for identifying phenotype-gene associations.

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