title: Computational toxicology in drug development.
abstract: Computational tools for predicting toxicity have been envisaged for their potential to considerably impact the attrition rate of compounds in drug discovery and development. In silico techniques like knowledge-based expert systems (quantitative) structure activity relationship tools and modeling approaches may therefore help to significantly reduce drug development costs by succeeding in predicting adverse drug reactions in preclinical studies. It has been shown that commercial as well as proprietary systems can be successfully applied in the pharmaceutical industry. As the prediction has been exhaustively optimized for early safety-relevant endpoints like genotoxicity, future activities will now be directed to prevent the occurrence of undesired toxicity in patients by making these tools more relevant to human disease.
authors.affiliation: F. Hoffmann-La Roche Ltd., Non-Clinical Drug Safety, Basel CH-4070, Switzerland. wolfgang.muster@roche.com
authors.forename: Wolfgang
authors.initials: W
authors.lastname: Muster
authors.forename: Alexander
authors.initials: A
authors.lastname: Breidenbach
authors.forename: Holger
authors.initials: H
authors.lastname: Fischer
authors.forename: Stephan
authors.initials: S
authors.lastname: Kirchner
authors.forename: Lutz
authors.initials: L
authors.lastname: Müller
authors.forename: Axel
authors.initials: A
authors.lastname: Pähler
mesh_terms: D019295:Computational Biology; D015195:Drug Design; D006801:Humans; D008958:Models, Molecular; D008968:Molecular Conformation; D021281:Quantitative Structure-Activity Relationship; D014116:Toxicology / Q000379:methods*