title: Systems Toxicology: Systematic Approach to Predict Toxicity.
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.
authors.affiliation: Unit of Computational Medicine, Department of Medicine, Karolinska Institutet and Center for Molecular Medicine, Karolinska University Hospital, Science for Life Laboratories, Stockholm, Sweden.
authors.forename: Narsis A
authors.initials: NA
authors.lastname: Kiani
authors.forename: Ming-Mei
authors.initials: MM
authors.lastname: Shang
authors.forename: Jesper
authors.initials: J
authors.lastname: Tegner
mesh_terms: D019295:Computational Biology; D003198:Computer Simulation; D055808:Drug Discovery; D064420:Drug-Related Side Effects and Adverse Reactions; D006801:Humans; D004364:Pharmaceutical Preparations
chemical_list: D004364:Pharmaceutical Preparations
keywords: System pharmacology; drug adverse effects; network analysis.; predictive modeling