Systems toxicology: Systematic approach to predict toxicity

Narsis A. Kiani*, Ming Mei Shang, Jesper Tegner

*Corresponding author for this work

Research output: Contribution to journalArticlepeer-review

5 Scopus citations

Abstract

Drug discovery is complex and expensive. Numerous drug candidates fail late in clinical trials or even after being released to the market. These failures are not only due to commercial considerations and less optimal drug efficacies but, adverse reactions originating from toxic effects also constitute a major challenge. During the last two decades, significant advances have been made enabling the early prediction of toxic effects using in silico techniques. However, by design, these essentially statistical techniques have not taken the disease driving pathophysiological mechanisms into account. The complexity of such mechanisms in combination with their interactions with drugspecific properties and environmental and life-style related factors renders the task of predicting toxicity on a purely statistical basis which is an insurmountable challenge. In response to this situation, an interdisciplinary field has developed, referred to as systems toxicology, where the notion of a network is used to integrate and model different types of information to better predict drug toxicity. In this study, we briefly review the merits and limitations of such recent promising predictive approaches integrating molecular networks, chemical compound networks, and protein drug association networks.

Original languageEnglish (US)
Pages (from-to)6911-6917
Number of pages7
JournalCurrent Pharmaceutical Design
Volume22
Issue number46
DOIs
StatePublished - Dec 1 2016

Keywords

  • Drug adverse effects
  • Network analysis
  • Predictive modeling
  • System pharmacology

ASJC Scopus subject areas

  • Pharmacology
  • Drug Discovery

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