title: Computational toxicology: an overview of the sources of data and of modelling methods.
abstract: BACKGROUND
  Toxicology has the goal of ensuring the safety of humans, animals and the environment. Computational toxicology is an area of active development and great potential. There are tangible reasons for the emerging interest in this discipline from academia, industry, regulatory bodies and governments.
  
  
  RESULTS
  Pharmaceuticals, personal health care products, nutritional ingredients and products of the chemical industries are all potential hazards and need to be assessed. Toxicological tests for these products are costly, frequently use laboratory animals and are time-consuming. This delays end-user access to improved products or, conversely, the timely withdrawal of dangerous substances from the market. The aim of computational toxicology is to accelerate the assessment of potentially dangerous substances through in silico models.
  
  
  CONCLUSIONS
  In this review, we provide an overview of the development of models for computational toxicology. Addressing the significant divide between the experimental and computational worlds-believed to be a prime hindrance to computational toxicology-we briefly consider the fundamental issue of toxicological data and the assays they stem from. Different kinds of models that can be built using such data are presented: computational filters, models for specific toxicological endpoints and tools for the generation of testable hypotheses.
authors.affiliation: Unilever Centre for Molecular Science Informatics, Department of Chemistry, University of Cambridge, Lensfield Road, Cambridge CB2 1EW, UK. contact@flo.nigsch.com
authors.forename: Florian
authors.initials: F
authors.lastname: Nigsch
authors.forename: N J Maximilan
authors.initials: NJ
authors.lastname: Macaluso
authors.forename: John B O
authors.initials: JB
authors.lastname: Mitchell
authors.forename: Donatas
authors.initials: D
authors.lastname: Zmuidinavicius
mesh_terms: D000818:Animals; D019295:Computational Biology / Q000379:methods*; D016208:Databases, Factual; D064420:Drug-Related Side Effects and Adverse Reactions; D006801:Humans; D021281:Quantitative Structure-Activity Relationship; D012984:Software; D049490:Systems Biology; D043922:Toxicogenetics; D014116:Toxicology / Q000379:methods* / Q000639:trends