Screening for Developmental Neurotoxicity at the National Toxicology Program: The Future Is Here

Abstract

The National Toxicology Program (NTP) receives requests to evaluate chemicals with potential to cause adverse health effects, including developmental neurotoxicity (DNT). Some recent requests have included classes of chemicals such as flame retardants, polycyclic aromatic compounds, perfluoroalkyl substances, and bisphenol A analogs with approximately 20-50 compounds per class, many of which include commercial mixtures. However, all the compounds within a class cannot be tested using traditional DNT animal testing guideline studies due to resource and time limitations. Hence, a rapid and biologically relevant screening approach is required to prioritize compounds for further in vivo testing. Because neurodevelopment is a complex process involving multiple distinct cellular processes, one assay will unlikely address the complexity. Hence, the NTP sought to characterize a battery of in vitro and alternative animal assays to quantify chemical effects on a variety of neurodevelopmental processes. A culmination of this effort resulted in a NTP-hosted collaborative project with approximately 40 participants spanning across domains of academia, industry, government, and regulatory agencies; collaborators presented data on cell-based assays and alternative animal models that was generated using a targeted set of compounds provided by the NTP. The NTP analyzed the assay results using benchmark concentration (BMC) modeling to be able to compare results across the divergent assays. The results were shared with the contributing researchers on a private web application during the workshop, and are now publicly available. This article highlights the overview and goals of the project, and describes the NTP's approach in creating the chemical library, development of NTPs data analysis strategy, and the structure of the web application. Finally, we discuss key issues with emphasis on the utility of this approach, and knowledge gaps that need to be addressed for its use in regulatory decision making.

Figure 1.

The NTP91 compound library. A, Pie chart of the NTP compound library, by use category (percent of total). As shown in the figure, we had a fairly uniform distribution of drugs (blue), flame retardants (yellow), industrial compounds (green), polycyclic aromatic hydrocarbons (PAH, purple), and pesticides (pink). B, Heat map identifying whether the compound has (black bars) or has not (light gray bars) been evaluated in a variety of systems toxicity assays. Data were derived from the EPA Chemistry Dashboard and Leadscope. Color bars indicate (i) if the compound has evidence of in vivo DNT or NT by NTP literature review (orange) and (ii) use category (eg, flame retardant [yellow]) repro. dev., reproductive and developmental.

Figure 2.

Schematic of assays included in the battery by increasing biological complexity. The figure shows the primary assays that were covered in the initial collaboration from left to right in increasing level of biological complexity. A total of 80 assays were evaluated as part of the Tox21 effort that covered receptor-based cellular assays including mitochondrial activity, stress response pathways, and general cytotoxicity. Additionally, 137 assays were assessed in cell-free and Novascreen models based on ToxCast data that included target genes related to genes of axon guidance or other axon parameter. As we move toward the right, the models covered functional aspects of key events that included neuronal differentiation, outgrowth and neural network formation in human-derived iPSC cells, immortalized cell lines, and rat primary cultures. Finally, to incorporate whole organisms in screening, we measured complex behavior (locomotor activity) and terata in zebrafish and planaria. DT, developmental toxicity; DNT, developmental neurotoxicity, and NT, neurotoxicity.

Figure 3.

Salient features of web application DNT-DIVER. A, Description of each assay in the database, including model system, doses-tested, and endpoints measured. B, Plate map and response for each well on the plate. C, Exploration of vehicle control response variability; used for better understanding normalization techniques. D, Individual dose-response dataset and BMC curve fits. E, Summary of curve-fit data for multiple endpoints (here development and mortality endpoint are shown). F, Activity summary showing BMC estimate for both specific effect (colored point) and viability (black point). G, BMC heat map comparing chemicals and readouts from multiple laboratories; H, Principal component analysis (PCA) showing each chemical, colored by chemical class, in biological endpoint readout space (79 readouts reduced to 3 dimensions).