Open AccessRevealing Adverse Outcome Pathways from Public High-Throughput Screening Data to Evaluate New Toxicants by a Knowledge-Based Deep Neural Network Approach
Author(s) -
Heather L Ciallella,
Daniel P. Russo,
Lauren M. Aleksunes,
Fabian A. Grimm,
Hao Zhu
Publication year - 2021
Publication title -
environmental science and technology
Language(s) - English
Resource type - Journals
SCImago Journal Rank - 2.851
H-Index - 397
eISSN - 1520-5851
pISSN - 0013-936X
DOI - 10.1021/acs.est.1c02656
Subject(s) - adverse outcome pathway , estrogen receptor , estrogen , estrogen receptor alpha , artificial neural network , computational biology , high throughput screening , throughput , nuclear receptor , computer science , chemistry , biology , artificial intelligence , bioinformatics , biochemistry , endocrinology , transcription factor , gene , breast cancer , genetics , cancer , telecommunications , wireless
Traditional experimental testing to identify endocrine disruptors that enhance estrogenic signaling relies on expensive and labor-intensive experiments. We sought to design a knowledge-based deep neural network (k-DNN) approach to reveal and organize public high-throughput screening data for compounds with nuclear estrogen receptor α and β (ERα and ERβ) binding potentials. The target activity was rodent uterotrophic bioactivity driven by ERα/ERβ activations. After training, the resultant network successfully inferred critical relationships among ERα/ERβ target bioassays, shown as weights of 6521 edges between 1071 neurons. The resultant network uses an adverse outcome pathway (AOP) framework to mimic the signaling pathway initiated by ERα and identify compounds that mimic endogenous estrogens (i.e., estrogen mimetics). The k-DNN can predict estrogen mimetics by activating neurons representing several events in the ERα/ERβ signaling pathway. Therefore, this virtual pathway model, starting from a compound's chemistry initiating ERα activation and ending with rodent uterotrophic bioactivity, can efficiently and accurately prioritize new estrogen mimetics (AUC = 0.864-0.927). This k-DNN method is a potential universal computational toxicology strategy to utilize public high-throughput screening data to characterize hazards and prioritize potentially toxic compounds.