In silico approaches to predicting cancer potency for risk assessment of genotoxic impurities in drug substances.

The current risk assessment approach for addressing the safety of very small concentrations of genotoxic impurities (GTIs) in drug substances is the threshold of toxicological concern (TTC). The TTC is based on several conservative assumptions because of the uncertainty associated with deriving an excess cancer risk when no carcinogenicity data are available for the impurity. It is a default approach derived from a distribution of carcinogens and does not take into account the properties of a specific chemical. The purpose of the study was to use in silico tools to predict the cancer potency (TD(50)) of a compound based on its structure. Structure activity relationship (SAR) models (classification/regression) were developed from the carcinogenicity potency database using MultiCASE and VISDOM. The MultiCASE classification models allowed the prediction of carcinogenic potency class, while the VISDOM regression models predicted a numerical TD(50). A step-wise approach is proposed to calculate predicted numerical TD(50) values for compounds categorized as not potent. This approach for non-potent compounds can be used to establish safe levels greater than the TTC for GTIs in a drug substance.

Analysis of rodent growth data in toxicology studies.

To evaluate compound-related effects on the growth of rodents, body weight and food consumption data are commonly collected either weekly or biweekly in toxicology studies. Body weight gain, food consumption relative to body weight, and efficiency of food utilization can be derived from body weight and food consumption for each animal in an attempt to better understand the compound-related effects. These five parameters are commonly analyzed in toxicology studies for each sex using a one-factor analysis of variance (ANOVA) at each collection point. The objective of this manuscript is to present an alternative approach to the evaluation of compound-related effects on body weight and food consumption data from both subchronic and chronic rodent toxicology studies. This approach is to perform a repeated-measures ANOVA on a selected set of parameters and analysis intervals. Compared with a standard one-factor ANOVA, this approach uses a statistical analysis method that has greater power and reduces the number of false-positive claims, and consequently provides a succinct yet comprehensive summary of the compound-related effects. Data from a mouse carcinogenicity study are included to illustrate this repeated-measures ANOVA approach to analyzing growth data in contrast with the one-factor ANOVA approach.