The hepatotoxic potential of protein kinase inhibitors predicted with Random Forest and Artificial Neural Networks

Protein kinases (PKs) play a role in many pivotal aspects of cellular function. Dysregulation and mutations of protein kinases are involved in the development of different diseases, which might be treated by inhibition of the corresponding kinase. Protein kinase inhibitors (PKIs) are generally well tolerated, but unexpected and serious adverse events on the heart, lung, kidney and liver were observed clinically. In this study, the structure-activity relationship of PKIs in relation to hepatotoxicity was investigated. A dataset of 165 PKIs was compiled and the probability of human hepatotoxicity with two different machine learning algorithms (Random Forest and Artificial Neural Networks) was analysed. The estimated probability of hepatotoxicity was generally high for single PKIs. However, depending on the target kinase of the PKI, a difference in hepatotoxic potential could be observed. The similarity of the PKIs to each other is caused by the conserved site of action of the protein kinases. Hepatotoxicity may therefore always be an issue in PKIs.