A high-throughput method to computationally develop candidate adverse outcome pathways in humans: a proof of concept with insecticides and Parkinson's Disease.

Adverse outcome pathways (AOPs) describe stressor-non-specific sequences of events between a first molecular trigger (molecular initiating event, MIE), causally linked key events (KEs), and an adverse outcome (AO). AOPs are intended to aid in chemical toxicity testing as a new approach methodology. However, commonly used AOP development methods depend on manual curation, which is labor-intensive. As a result, there are still relatively few AOPs, and a huge number of toxicity mechanisms and possible AOs remain undescribed. Therefore, systematic and high-throughput approaches to predict new AOPs are needed. Here, we developed and implemented a data integration-based framework to generate new candidate AOPs using insecticides and Parkinson's Disease as a proof of concept. We integrated and statistically linked disconnected databases (e.g. Comparative Toxicogenomics Database, Human Protein Atlas, and Gene Ontology) to form MIE-KE (cell level)-KE (tissue level)-AO candidate AOPs. Through this systematic process, we generated 562,117 candidate AOPs, which we then scored using a weight-of-evidence (WoE) approach and prioritized 12,756 AOPs with a WoE >0.5. The prioritized AOPs describe varied mechanisms of toxicity related to e.g. MAPK, PTEN, and FGFR signaling pathways, with "increases phosphorylation of MAPK1" as the most frequent MIE. Through analysis of 100 random prioritized AOPs, we found 70% had external literature supporting their biological plausibility, and only 15% represented identifiably implausible associations. Our AOP-generating approach yields consistently structured AOPs and can complement existing and emerging development methods to expand AOP coverage across different stressors and outcomes.