A transcriptomics data-driven gene space accurately predicts liver cytopathology and drug-induced liver injury

Research output: Contribution to journalArticleScientificpeer-review

Details

Original languageEnglish
Article number15932
Pages (from-to)1-15
JournalNature Communications
Volume8
Publication statusPublished - 3 Jul 2017
MoE publication typeA1 Journal article-refereed

Researchers

  • Pekka Kohonen
  • Juuso A. Parkkinen
  • Egon L. Willighagen
  • Rebecca Ceder
  • Krister Wennerberg
  • Samuel Kaski

  • Roland C. Grafström

Research units

  • Karolinska Institutet
  • Maastricht University
  • University of Helsinki

Abstract

Predicting unanticipated harmful effects of chemicals and drug molecules is a difficult and costly task. Here we utilize a 'big data compacting and data fusion' - concept to capture diverse adverse outcomes on cellular and organismal levels. The approach generates from transcriptomics data set a 'predictive toxicogenomics space' (PTGS) tool composed of 1,331 genes distributed over 14 overlapping cytotoxicity-related gene space components. Involving ∼2.5 × 108 data points and 1,300 compounds to construct and validate the PTGS, the tool serves to: explain dose-dependent cytotoxicity effects, provide a virtual cytotoxicity probability estimate intrinsic to omics data, predict chemically-induced pathological states in liver resulting from repeated dosing of rats, and furthermore, predict human drug-induced liver injury (DILI) from hepatocyte experiments. Analysing 68 DILI-annotated drugs, the PTGS tool outperforms and complements existing tests, leading to a hereto-unseen level of DILI prediction accuracy.

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