Optimizing androgen receptor prioritization using high-throughput assay-based activity models

Introduction: Computational models using data from high-throughput screening assays have promise for prioritizing and screening chemicals for testing under the U.S. Environmental Protection Agency's Endocrine Disruptor Screening Program (EDSP). The purpose of this work was to demonstrate a data processing method for the determination of optimal minimal assay batteries from a larger comprehensive model, to provide a uniform method of evaluating the performance of future minimal assay batteries compared with the androgen receptor (AR) pathway model, and to incorporate chemical cluster analysis into this evaluation. Although several of the assays in the AR pathway model are no longer available through the original vendor, this approach could be used for future evaluations of minimal assay models for prioritization and screening. Methods: We compared two previously published models and found that an expanded 14-assay model had higher sensitivity for antagonists, whereas the original 11-assay model had slightly higher sensitivity for agonists. We then investigated subsets of assays in the original AR pathway model to optimize overall testing strategies that minimize cost while maintaining sensitivity across a broad chemical space. Results and Discussion: Evaluation of the critical assays across subset models derived from the 14-assay model identified three critical assays for predicting antagonism and two critical assays for predicting agonism. A minimum of nine assays is required for predicting agonism and antagonism with high sensitivity (95%). However, testing workflows guided by chemical structure-based clusters can reduce the average number of assays needed per chemical by basing the assays selected for testing on the likelihood of a chemical being an AR agonist, according to its structure. Our results show that a multi-stage testing workflow can provide 95% sensitivity while requiring only 48% of the resources required for running all assays from the original full models. The resources can be reduced further by incorporating in silico activity predictions. Conclusion: This work illustrates a data-driven approach that incorporates chemical clustering and simultaneous consideration of antagonism and agonism mechanisms to more efficiently screen chemicals. This case study provides a proof of concept for prioritization and screening strategies that can be utilized in future analyses to minimize the overall number of assays needed for predicting AR activity, which will maximize the number of chemicals that can be tested and allow data-driven prioritization of chemicals for further screening under the EDSP.

Toward an AOP Network-Based Tiered Testing Strategy for the Assessment of Thyroid Hormone Disruption

A growing number of environmental pollutants are known to adversely affect the thyroid hormone system, and major gaps have been identified in the tools available for the identification, and the hazard and risk assessment of these thyroid hormone disrupting chemicals. We provide an example of how the adverse outcome pathway (AOP) framework and associated data generation can address current testing challenges in the context of fish early life stage tests, and fish tests in general. We demonstrate how a suite of assays covering biological processes involved in the underlying toxicological pathways can be implemented in a tiered screening and testing approach for thyroid hormone disruption, using the levels of assessment of the OECD's Conceptual Framework for the Testing and Assessment of Endocrine Disrupting Chemicals as a guide.

Surrogate species selection for assessing potential adverse environmental impacts of genetically engineered insect-resistant plants on non-target organisms

Most regulatory authorities require that developers of genetically engineered insect-resistant (GEIR) crops evaluate the potential for these crops to have adverse impacts on valued non-target organisms (NTOs), i.e., organisms not intended to be controlled by the trait. In many cases, impacts to NTOs are assessed using surrogate species, and it is critical that the data derived from surrogates accurately predict any adverse impacts likely to be observed from the use of the crop in the agricultural context. The key is to select surrogate species that best represent the valued NTOs in the location where the crop is going to be introduced, but this selection process poses numerous challenges for the developers of GE crops who will perform the tests, as well as for the ecologists and regulators who will interpret the test results. These issues were the subject of a conference “Surrogate Species Selection for Assessing Potential Adverse Environmental Impacts of Genetically Engineered Plants on Non-Target Organisms” convened by the Center for Environmental Risk Assessment, ILSI Research Foundation. This report summarizes the proceedings of the conference, including the presentations, discussions and the points of consensus agreed to by the participants.