Generic placeholder image

Current Pharmaceutical Design


ISSN (Print): 1381-6128
ISSN (Online): 1873-4286

Review Article

Systems Toxicology: Systematic Approach to Predict Toxicity

Author(s): Narsis A. Kiani, Ming-Mei Shang and Jesper Tegner

Volume 22, Issue 46, 2016

Page: [6911 - 6917] Pages: 7

DOI: 10.2174/1381612822666161003115629

Price: $65


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.

Keywords: System pharmacology, drug adverse effects, predictive modeling, network analysis.

Rights & Permissions Print Export Cite as
© 2023 Bentham Science Publishers | Privacy Policy