title: Data mining the NCI60 to predict generalized cytotoxicity.
abstract: Elimination of cytotoxic compounds in the early and later stages of drug discovery can help reduce the costs of research and development. Through the application of principal components analysis (PCA), we were able to data mine and prove that approximately 89% of the total log GI 50 variance is due to the nonspecific cytotoxic nature of substances. Furthermore, PCA led to the identification of groups of structurally unrelated substances showing very specific toxicity profiles, such as a set of 45 substances toxic only to the Leukemia_SR cancer cell line. In an effort to predict nonspecific cytotoxicity on the basis of the mean log GI 50, we created a decision tree using MACCS keys that can correctly classify over 83% of the substances as cytotoxic/noncytotoxic in silico, on the basis of the cutoff of mean log GI 50 = -5.0. Finally, we have established a linear model using least-squares in which nine of the 59 available NCI60 cancer cell lines can be used to predict the mean log GI 50. The model has R (2) = 0.99 and a root-mean-square deviation between the observed and calculated mean log GI 50 (RMSE) = 0.09. Our predictive models can be applied to flag generally cytotoxic molecules in virtual and real chemical libraries, thus saving time and effort.
authors.affiliation: Department of Medicinal Chemistry, College of Pharmacy, University of Michigan, Ann Arbor, Michigan 48109, USA.
authors.forename: Adam C
authors.initials: AC
authors.lastname: Lee
authors.forename: Kerby
authors.initials: K
authors.lastname: Shedden
authors.forename: Gustavo R
authors.initials: GR
authors.lastname: Rosania
authors.forename: Gordon M
authors.initials: GM
authors.lastname: Crippen
mesh_terms: D045744:Cell Line, Tumor; D003663:Decision Trees; D004354:Drug Screening Assays, Antitumor; D006801:Humans; D016247:Information Storage and Retrieval