We demonstrate a computational network model that integrates 18 in vitro, high-throughput screening assays measuring estrogen receptor (ER) binding, dimerization, chromatin binding, transcriptional activation, and ER-dependent cell proliferation. The network model uses activity patterns across the in vitro assays to predict whether a chemical is an ER agonist or antagonist, or is otherwise influencing the assays through a manner dependent on the physics and chemistry of the technology platform ("assay interference"). The method is applied to a library of 1812 commercial and environmental chemicals, including 45 ER positive and negative reference chemicals. Among the reference chemicals, the network model correctly identified the agonists and antagonists with the exception of very weak compounds whose activity was outside the concentration range tested. The model agonist score also correlated with the expected potency class of the active reference chemicals. Of the 1812 chemicals evaluated, 111 (6.1%) were predicted to be strongly ER active in agonist or antagonist mode. This dataset and model were also used to begin a systematic investigation of assay interference. The most prominent cause of false-positive activity (activity in an assay that is likely not due to interaction of the chemical with ER) is cytotoxicity. The model provides the ability to prioritize a large set of important environmental chemicals with human exposure potential for additional in vivo endocrine testing. Finally, this model is generalizable to any molecular pathway for which there are multiple upstream and downstream assays available.

The field of toxicology is on the cusp of a major transformation in how the safety and hazard of chemicals are evaluated for potential effects on human health and the environment. Brought on by the recognition of the limitations of the current paradigm in terms of cost, time, and throughput, combined with the ever increasing power of modern biological tools to probe mechanisms of chemical-biological interactions at finer and finer resolutions, 21st century toxicology is rapidly taking shape. A key element of the new approach is a focus on the molecular and cellular pathways that are the targets of chemical interactions. By understanding toxicity in this manner, we begin to learn how chemicals cause toxicity, as opposed to merely what diseases or health effects they might cause. This deeper understanding leads to increasing confidence in identifying which populations might be at risk, significant susceptibility factors, and key influences on the shape of the dose-response curve. The U. S. Environmental Protection Agency (EPA) initiated the ToxCast, or "toxicity forecaster", program 5 years ago to gain understanding of the strengths and limitations of the new approach by starting to test relatively large numbers (hundreds) of chemicals against an equally large number of biological assays. Using computational approaches, the EPA is building decision support tools based on ToxCast in vitro screening results to help prioritize chemicals for further investigation, as well as developing predictive models for a number of health outcomes. This perspective provides a summary of the initial, proof of concept, Phase I of ToxCast that has laid the groundwork for the next phases and future directions of the program.

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