Identifying Neurophysiological Signatures of Neurotoxicant Action

Identifying Neurophysiological Signatures of Neurotoxicant Action

Safer Chemicals

Office of Research and Development (ORD), Center for Computational Toxicology and Exposure (CCTE)

Research Triangle Park, NC

3-12 months

This project will utilize data from microelectrode array recordings of neural networks and ToxCast endpoints to identify signatures acute and developmental neurotoxicity. It will improve prediction of these important endpoints by identifying neurophysiological and biochemical "fingerprints" for known compounds that can then be applied to previously untested compounds.

Recent technological advancements have made it possible to screen thousands of compounds for effects on neural network function using microelectrode array recordings (MEAs). These recordings provide a high-content assessment of how compounds alter temporal and spatial patterns of action potential spiking and bursting in networks of interconnected neurons. We have recently completed screening over 1000 compounds from the ToxCast Phase I and II libraries for their acute effects on neural network function in primary cortical neurons, and have rescreened 384 of these compounds at multiple concentrations. In addition, we have also characterized the activity of more than 150 compounds on the ontogeny of neural network development, which provides information on the potential of compounds to cause developmental neurotoxicity. For all of these studies, we have conducted an analysis of these data that includes at least 10 different endpoints describing the patterns of neural network activity and how it is altered by compound exposure. This goal of this GRIP project is to use computational techniques to identify patterns of activity caused by different classes of neurotoxicants, based either on their pharmacologic (e.g. GABA-A or NMDA receptor antagonist) or phenotypic responses (e.g. excitatory, seizurogenic, sedative). Data from the MEA recordings will also be combined with results from other ToxCast assays to aid or improve upon the identification of "compound fingerprints" related to acute and developmental neurotoxicity.

This project will foster the professional development of the intern allowing them to apply computational approaches to one of the largest datasets of compound effects on neurophysiological function available. They will join a multidisciplinary team and interact with biologists, toxicologists, mathematicians and data scientists. In the process of doing so, they will learn data analysis approaches, database analysis and management, and use of different programming languages such as R. The student will also have the opportunity to observe MEA recordings and, if time permits, help design experiments to confirm the computational models.

Carstens.Kelly@epa.gov