Chemistry-Based Modeling on Phenotype-Based Drug-Induced Liver Injury Annotation: From Public to Proprietary Data

Drug-induced liver
injury (DILI) is an important safety concern
and a major reason to remove a drug from the market. Advancements
in recent machine learning methods have led to a wide range of in
silico models for DILI predictive methods based on molecule chemical
structures (fingerprints). Existing publicly available DILI data sets
used for model building are based on the interpretation of drug labels
or patient case reports, resulting in a typical binary clinical DILI
annotation. We developed a novel phenotype-based annotation to process
hepatotoxicity information extracted from repeated dose in vivo preclinical
toxicology studies using INHAND annotation to provide a more informative
and reliable data set for machine learning algorithms. This work resulted
in a data set of 430 unique compounds covering diverse liver pathology
findings which were utilized to develop multiple DILI prediction models
trained on the publicly available data (TG-GATEs) using the compound’s
fingerprint. We demonstrate that the TG-GATEs compounds DILI labels
can be predicted well and how the differences between TG-GATEs and
the external test compounds (Johnson & Johnson) impact the model
generalization performance.