In vitro evaluation of pyrethroid-mediated changes on neuronal burst parameters using microelectrode arrays

Integration of toxicodynamic and toxicokinetic new approach methods into a weight-of-evidence analysis for pesticide developmental neurotoxicity assessment: A case-study with DL- and L-glufosinate

DL-glufosinate ammonium (DL-GLF) is a registered herbicide for which a guideline Developmental Neurotoxicity (DNT) study has been conducted. Offspring effects included altered brain morphometrics, decreased body weight, and increased motor activity. Guideline DNT studies are not available for its enriched isomers L-GLF acid and L-GLF ammonium; conducting one would be time consuming, resource-intensive, and possibly redundant given the existing DL-GLF DNT. To support deciding whether to request a guideline DNT study for the L-GLF isomers, DL-GLF and the L-GLF isomers were screened using in vitro assays for network formation and neurite outgrowth. DL-GLF and L-GLF isomers were without effects in both assays. DL-GLF and L-GLF (1-100 μM) isomers increased mean firing rate of mature networks to 120-140% of baseline. In vitro toxicokinetic assessments were used to derive administered equivalent doses (AEDs) for the in vitro testing concentrations. The AED for L-GLF was ∼3X higher than the NOAEL from the DL-GLF DNT indicating that the available guideline study would be protective of potential DNT due to L-GLF exposure. Based in part on the results of these in vitro studies, EPA is not requiring L-GLF isomer guideline DNT studies, thereby providing a case study for a useful application of DNT screening assays.

Concentration-response evaluation of ToxCast compounds for multivariate activity patterns of neural network function

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 rat 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 distribution of neurotoxicants with known pharmacology within the clusters and found that compounds segregated differentially. Collectively, 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.

Neurotoxicity of pesticides

Pesticides are unique environmental contaminants that are specifically introduced into the environment to control pests, often by killing them. Although pesticide application serves many important purposes, including protection against crop loss and against vector-borne diseases, there are significant concerns over the potential toxic effects of pesticides to non-target organisms, including humans. In many cases, the molecular target of a pesticide is shared by non-target species, leading to the potential for untoward effects. Here, we review the history of pesticide usage and the neurotoxicity of selected classes of pesticides, including insecticides, herbicides, and fungicides, to humans and experimental animals. Specific emphasis is given to linkages between exposure to pesticides and risk of neurological disease and dysfunction in humans coupled with mechanistic findings in humans and animal models. Finally, we discuss emerging techniques and strategies to improve translation from animal models to humans.

Application of Microelectrode Array Approaches to Neurotoxicity Testing and Screening

Neurotoxicity can be defined by the ability of a drug or chemical to alter the physiology, biochemistry, or structure of the nervous system in a manner that may negatively impact the health or function of the individual. Electrophysiological approaches have been utilized to study the mechanisms underlying neurotoxic actions of drugs and chemicals for over 50 years, and in more recent decades, high-throughput patch-clamp approaches have been utilized by the pharmaceutical industry for drug development. The use of microelectrode array recordings to study neural network electrophysiology is a relatively newer approach, with commercially available systems becoming available only in the early 2000s. However, MEAs have been rapidly adopted as a useful approach for neurotoxicity testing. In this chapter, I will review the use of MEA approaches as they have been applied to the field of neurotoxicity testing, especially as they have been applied to the need to screen large numbers of chemicals for neurotoxicity and developmental neurotoxicity. In addition, I will also identify challenges for the field that when addressed will improve the utility of MEA approaches for toxicity testing.

In silico identification of protein targets for chemical neurotoxins using ToxCast in vitro data and read-across within the QSAR toolbox

This study evaluates the application of QSAR Toolbox and ToxCast screening data to identify neurological targets for pyrethroids.

There are many mechanisms of neurotoxicity that are initiated by the interaction of chemicals with different neurological targets. Under the U.S. Environmental Protection Agency's ToxCast program, the biological activity of thousands of chemicals was screened in biochemical and cell-based assays in a high-throughput manner. Two hundred sixteen assays in the ToxCast screening database were identified as targeting a total of 123 proteins having neurological functions according to the Gene Ontology database. Data from these assays were imported into the Organization for Economic Co-operation and Development QSAR Toolbox and used to predict neurological targets for chemical neurotoxins. Two sets of data were generated: one set was used to classify compounds as active or inactive and another set, composed of AC₅₀s for only active compounds, was used to predict AC₅₀ values for unknown chemicals. Chemical grouping and read-across within the QSAR Toolbox were used to identify neurologic targets and predict interactions for pyrethroids, a class of compounds known to elicit neurotoxic effects in humans. The classification prediction results showed 79% accuracy while AC₅₀ predictions demonstrated mixed accuracy compared with the ToxCast screening data.