Citation:

Kosnik, M., J. Strickland, S. Marvel, D. Wallis, K. Wallace, A. Richard, D. Reif, AND T. Shafer. Concentration-Response Evaluation of ToxCast Compounds for Multivariate Activity Patterns of Neural Network Function. Archives of Toxicology. Springer, New York, NY, 94:469-484, (2020). https://doi.org/10.1007/s00204-019-02636-x

Impact/Purpose:

Thousands of chemicals in the environment have not been adequately characterized for their potential toxicities, including neurotoxicity. Because testing these compounds with conventional animal-based studies is too costly and time consuming, novel, high-throughput, low-cost methods are needed to screen and prioritize chemicals for toxicity testing, including for neurotoxicity. The studies conducted in the attached manuscript demonstrate an approach to screening chemicals for their ability to disrupt the function of interconnected neural networks in vitro. The work is a follow up study to Strickland et al., 2018 (Archives of Toxicology 2018 92, 487-500), and examines concentration-response effects for over 380 compounds on neural network function. Further, it explores how to best make use of multi-parameter data provided by MEA recordings of neural networks and demonstrates that different classes of chemicals can be grouped together based on their pattern of disruption of network activity. In addition, these patterns correspond to different structural features of the compounds tested. As such, this approach can be a useful addition to large-scale screening and predictive toxicity approaches such as those taken by the EPA’s ToxCast Program. This work will be of interest to chemical screening under the Toxic Substances Control Act (TSCA).

Description:

The US Environmental Protection Agency’s ToxCast program has generated toxicity data for thousands of chemicals but does not adequately assess potential neurotoxicity. Networks of neurons grown on microelectrode arrays (MEAs) offer an efficient approach to screen compounds for neuroactivity and distinguish between compound effects on firing, bursting, and connectivity patterns. Previously, single-concentrations of the ToxCast Phase II library were screened for effects on mean firing rate (MFR) in primary cortical networks. Here, we expand this approach by retesting 384 of those compounds (including 222 active in the previous screen) in concentration-response across 43 network activity parameters to evaluate neural network function. Using hierarchical clustering and machine learning methods on the full suite of chemical-parameter response data, we identified 15 network activity parameters crucial in characterizing activity of 237 compounds that were response actives (“hits”). Recognized neurotoxic compounds in this network function assay were often more potent compared to other ToxCast assays. Of these chemical-parameter responses, we identified three k-means clusters of chemical-parameter activity (i.e. multivariate MEA response patterns). Next, we evaluated the MEA clusters for enrichment of chemical features using a subset of ToxPrint chemotypes, revealing chemical structural features that distinguished the MEA clusters. Finally, we assessed the distributions of known neurotoxicants within the clusters and found that the MEA clusters discriminated specific bioactivity mechanisms. Taken together, these results demonstrate that multivariate MEA activity patterns can efficiently screen for diverse chemical activities relevant to neurotoxicity, and that response patterns may have predictive value related to chemical structural features.

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