Combining chemodescriptors and biodescriptors in quantitative structure-activity relationship modeling.

In view of the wide distribution of halocarbons in our world, their toxicity is a public health concern. Previous work has shown that various measures of toxicity can be predicted with standard molecular descriptors. In our work, biodescriptors of the effect of halocarbons on the liver were obtained by exposing hepatocytes to 14 halocarbons and a control and by producing two-dimensional electrophoresis gels to assess the expressed proteome. The resulting spot abundances provide additional biological information that might be used in toxicity prediction. QSAR models were fitted via ridge regression to predict eight dependent toxicity measures: d37, arr, EC50MTT, EC50LDH, EC20SH, LECLP, LECROS, and LECCAT. Three predictor sets were used for each-the chemodescriptors alone, the biodescriptors alone, and the combined set of both chemo- and biodescriptors. The results differed somewhat from one dependent to another, but overall it was shown that better results could be obtained by using both chemo- and biodescriptors in the model than by using either chemo- or biodescriptors alone. The library of compounds used was small and quite homogeneous, so our immediate conclusions are correspondingly limited in scope, but we believe the underlying methodologies have broad applicability at the interface of chemical and biological descriptors.

Halogenated aliphatic toxicity QSARs employing metabolite descriptors.

The toxic effects from exposure to halogenated hydrocarbons (HAs), which are produced in large amounts and used in a variety of applications, are well-known. Previously, QSARs for the toxicity of a series of HAs in vitro have been studied extensively. In this work, using a composite toxicity metric calculated from a set of five in vitro hepatotoxicity endpoints determined for 20 HAs, we find that QSARs derived using quantum descriptors calculated from the neutral HA species are statistically similar to QSARs calculated from HA metabolites. In most cases, QSARs derived using descriptors calculated from both neutral HAs and metabolites are statistically superior to those derived using either neutral-HA descriptors or metabolite descriptors. However, to properly utilize metabolite descriptors, multiple QSARs, each of which utilizes a set of HAs that form unique metabolites, must be derived and toxicity values calculated therefrom must be averaged. These average toxicity values agree better with experiment than those calculated from the neutral-HA QSARs.