In 2016, the Frank R. Lautenberg Chemical Safety for the 21st Century Act became the first US legislation to advance chemical safety evaluations by utilizing novel testing approaches that reduce the testing of vertebrate animals. Central to this mission is the advancement of computational toxicology and artificial intelligence approaches to implementing innovative testing methods. In the current big data era, the terms volume (amount of data), velocity (growth of data), and variety (the diversity of sources) have been used to characterize the currently available chemical, in vitro, and in vivo data for toxicity modeling purposes. Furthermore, as suggested by various scientists, the variability (internal consistency or lack thereof) of publicly available data pools, such as PubChem, also presents significant computational challenges. The development of novel artificial intelligence approaches based on public massive toxicity data is urgently needed to generate new predictive models for chemical toxicity evaluations and make the developed models applicable as alternatives for evaluating untested compounds. In this procedure, traditional approaches (e.g., QSAR) purely based on chemical structures have been replaced by newly designed data-driven and mechanism-driven modeling. The resulting models realize the concept of adverse outcome pathway (AOP), which can not only directly evaluate toxicity potentials of new compounds, but also illustrate relevant toxicity mechanisms. The recent advancement of computational toxicology in the big data era has paved the road to future toxicity testing, which will significantly impact on the public health.
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.
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