Gunde Egeskov Jensen, Nikolai Georgiev Nikolov, Karin Dreisig, Anne Marie Vinggaard and Jay Russel Niemela
For the development of QSAR models for Androgen Receptor (AR) antagonism, a training set based on reporter gene data from Chinese hamster ovary (CHO) cells was constructed. The training set is composed of data from the literature as well as new data for 51 cardiovascular drugs screened for AR antagonism in our laboratory. The data set represents a wide range of chemical structures and various functions. Twelve percent of the screened drugs were AR antagonisms; three out of six statins showed AR antagonism, two showed cytotoxicity and one was negative. The newly identified AR antagonisms are: Lovastatin, Simvastatin, Mevastatin, Amiodarone, Docosahexaenoic acid and Dilazep. A total of 874 (231 positive, 643 negative) chemicals constitute the training set for the model. The Case Ultra expert system was used to construct the QSAR model. The model was cross-validated (leave-groups-out) with a concordance of 78.4%, a specificity of 86.1% and a sensitivity of 57.9%. The model was run on a set of 51,240 EINECS chemicals, and 74% were within the domain of the model. Approximately 9.2% of the chemicals in domain of the model were predicted active for AR antagonism. Case Ultra identified common alerts among different chemicals. By comparing biophores (alerts in positive chemicals) and biophobes (alerts in negative chemicals), it appears that chlorine (Cl) and bromine (Br) enhance AR antagonistic effect whereas nitrogen (N) seems to decrease the effect. A specific study of benzophenones and benzophenone derivatives indicate that a radical with a “high” number of atoms in 4-position and/or other positions generally decrease the anti-androgenic effect.