| Autor: |
Ciallella HL; Center for Computational and Integrative Biology, Rutgers University Camden, Camden, New Jersey 08103, United States., Russo DP; Center for Computational and Integrative Biology, Rutgers University Camden, Camden, New Jersey 08103, United States.; Department of Chemistry, Rutgers University Camden, Camden, New Jersey 08102, United States., Aleksunes LM; Department of Pharmacology and Toxicology, Ernest Mario School of Pharmacy, Rutgers University, Piscataway, New Jersey 08854, United States., Grimm FA; ExxonMobil Biomedical Sciences, Inc., Annandale, New Jersey 08801, United States., Zhu H; Center for Computational and Integrative Biology, Rutgers University Camden, Camden, New Jersey 08103, United States.; Department of Chemistry, Rutgers University Camden, Camden, New Jersey 08102, United States. |
| Abstrakt: |
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. |
