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

Clicks: 1
ID: 328119
2026
Free PDF
Article Quality & Performance Metrics
Overall Quality
Not rated
Combines reader engagement with the AI quality analysis. This article has not been analysed, so there is no overall score — reader engagement is measured and shown alongside.
AI Quality Assessment
Not analyzed
Readership in this journal

Ranked #131 of 133 articles by views in toxicological sciences : an official journal of the society of toxicology

Most read Least read

Bar heights use a square-root scale. Only the 120 most-read articles are drawn; the journal has 133 in total.

Mint this article as an NFT
Not yet minted

Create a permanent, verifiable on-chain record of this article on the Scimatic Network. The NFT is held in your Journament account, and you can withdraw it to your own wallet at any time.

5 SUSD one-off · no wallet required
Sign in to mint
Abstract
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 adverse outcomes 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 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.
Reference Key
openalex_W7163340638 Use this key to autocite in the manuscript while using SciMatic Manuscript Manager or Thesis Manager
Authors Dorian Rollin, Chenyu Shen, Ksenia J. Groh, Marissa Kosnik
Journal toxicological sciences : an official journal of the society of toxicology
Year 2026
DOI
10.1093/toxsci/kfag122
URL
https://doi.org/10.1093/toxsci/kfag122
Keywords Keywords not found

Citations

No citations found. To add a citation, contact the admin at info@scimatic.org

Comments

No comments yet. Be the first to comment on this article.