Chemical characterization of anemia-inducing aniline-related substances and their application to the construction of a decision tree-based anemia prediction model.

Abstract Anemia is a well-observed toxicity of chemical substances, and aniline is a typical anemia-inducing substance. However, it remains unclear whether all aniline-like substances with various substituents could induce anemia. We thus investigated the physicochemical characteristics of anemia-inducing substances by decision tree analyses. Training and validation substances were selected from a publicly available database of rat repeated-dose toxicity studies, and discrimination models were constructed by decision tree and bootstrapping methods with molecular descriptors as explanatory variables. To improve the accuracy of discrimination, we individually evaluated the explanatory variables to modify them, established “prerules” that were applied before subjecting a substance to a decision tree by considering metabolism, such as azo reduction and N-dealkylation, and introduced the idea of “partly negative” evaluation for substances having multiple aniline-like substructures. The final model obtained showed 79.2% and 77.5% accuracy for the training and validation dataset, respectively. In addition, we identified some chemical properties that reduce the anemia inducibility of aniline-like substances, including the addition of a sulfonate or carboxy functional group and/or a bulky multiring structure to anilines. In conclusion, the present findings will provide a novel insight into the mechanistic understanding of chemically induced anemia and help to develop a prediction system.