Predicting the toxic potential of drugs and chemicals in silico: a model for the peroxisome proliferator-activated receptor gamma (PPAR gamma)

Abstract

Poor pharmacokinetics, side effects and compound toxicity are frequent causes of late-stage failures in drug development. A safe in silico identification of adverse effects triggered by drugs and chemicals would therefore be highly desirable as it not only bears economical potential but also spawns a variety of ecological benefits: sustainable resource management, reduction of animal models and possibly less risky clinical trials as in silico studies are typically based on human proteins. In the recent past, our laboratory has developed a 6D-QSAR concept and validated a series of "virtual test kits" based on the aryl hydrocarbon, estrogen, androgen, thyroid, and glucocorticoid receptor as well as on the enzyme cytochrome P450 3A4. The test kits were trained using a representative selection of 610 substances and validated with 188 compounds different therefrom. These models were subsequently compiled into a database for the virtual screening of drugs and environmental chemicals. In this account, we report the validation of a model for the peroxisome proliferator-activated receptor gamma (PPAR gamma). Its receptor surrogate is based on the experimental structure of the protein and 95 tyrosine-based compounds. The simulation reached a cross-validated r(2)=0.832 (75 training ligands) and yielded a predictive r(2)=0.723 (20 test compounds). The model was challenged by a series of scramble tests as well as with the prediction of a few structurally different compounds.