Predicting Adverse Drug Effects from Literature- and Database-Mined Assertions

Introduction Given that adverse drug effects (ADEs) have led to post-market patient harm and subsequent drug withdrawal, failure of candidate agents in the drug development process, and other negative outcomes, it is essential to attempt to forecast ADEs and other relevant drug–target–effect relationships as early as possible. Current pharmacologic data sources, providing multiple complementary perspectives on the drug–target–effect paradigm, can be integrated to facilitate the inference of relationships between these entities. Objective This study aims to identify both existing and unknown relationships between chemicals (C), protein targets (T), and ADEs (E) based on evidence in the literature. Materials and Methods Cheminformatics and data mining approaches were employed to integrate and analyze publicly available clinical pharmacology data and literature assertions interrelating drugs, targets, and ADEs. Based on these assertions, a C–T–E relationship knowledge base was developed. Known pairwise relationships between chemicals, targets, and ADEs were collected from several pharmacological and biomedical data sources. These relationships were curated and integrated according to Swanson’s paradigm to form C–T–E triangles. Missing C–E edges were then inferred as C–E relationships. Results Unreported associations between drugs, targets, and ADEs were inferred, and inferences were prioritized as testable hypotheses. Several C–E inferences, including testosterone → myocardial infarction, were identified using inferences based on the literature sources published prior to confirmatory case reports. Timestamping approaches confirmed the predictive ability of this inference strategy on a larger scale. Conclusions The presented workflow, based on free-access databases and an association-based inference scheme, provided novel C–E relationships that have been validated post hoc in case reports. With refinement of prioritization schemes for the generated C–E inferences, this workflow may provide an effective computational method for the early detection of potential drug candidate ADEs that can be followed by targeted experimental investigations.