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 proc...
| Main Authors: | , , , , , , , , , , , |
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| Format: | Article |
| Language: | English |
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Frontiers Media S.A.
2024-03-01
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| Series: | Frontiers in Toxicology |
| Subjects: | |
| Online Access: | https://www.frontiersin.org/articles/10.3389/ftox.2024.1347364/full |
| _version_ | 1797266216251817984 |
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| author | Ronnie Joe Bever Stephen W. Edwards Todor Antonijevic Mark D. Nelms Caroline Ring Danni Harris Scott G. Lynn David Williams Grace Chappell Rebecca Boyles Susan Borghoff Kristan J. Markey |
| author_facet | Ronnie Joe Bever Stephen W. Edwards Todor Antonijevic Mark D. Nelms Caroline Ring Danni Harris Scott G. Lynn David Williams Grace Chappell Rebecca Boyles Susan Borghoff Kristan J. Markey |
| author_sort | Ronnie Joe Bever |
| collection | DOAJ |
| description | 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. |
| first_indexed | 2024-04-25T00:57:10Z |
| format | Article |
| id | doaj.art-6f5a3717b10f4de28b9874309c93fd28 |
| institution | Directory Open Access Journal |
| issn | 2673-3080 |
| language | English |
| last_indexed | 2024-04-25T00:57:10Z |
| publishDate | 2024-03-01 |
| publisher | Frontiers Media S.A. |
| record_format | Article |
| series | Frontiers in Toxicology |
| spelling | doaj.art-6f5a3717b10f4de28b9874309c93fd282024-03-11T09:48:52ZengFrontiers Media S.A.Frontiers in Toxicology2673-30802024-03-01610.3389/ftox.2024.13473641347364Optimizing androgen receptor prioritization using high-throughput assay-based activity modelsRonnie Joe Bever0Stephen W. Edwards1Todor Antonijevic2Mark D. Nelms3Caroline Ring4Danni Harris5Scott G. Lynn6David Williams7Grace Chappell8Rebecca Boyles9Susan Borghoff10Kristan J. Markey11U.S. Environmental Protection Agency, Washington, DC, United StatesRTI International, Research Triangle Park, NC, United StatesToxStrategies, Katy, TX, United StatesRTI International, Research Triangle Park, NC, United StatesToxStrategies, Austin, TX, United StatesRTI International, Research Triangle Park, NC, United StatesU.S. Environmental Protection Agency, Washington, DC, United StatesRTI International, Research Triangle Park, NC, United StatesToxStrategies, Asheville, NC, United StatesRTI International, Research Triangle Park, NC, United StatesToxStrategies, Research Triangle Park, NC, United StatesU.S. Environmental Protection Agency, Washington, DC, United StatesIntroduction: 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.https://www.frontiersin.org/articles/10.3389/ftox.2024.1347364/fullendocrine disruptionandrogen receptorcomputational toxicologyhighthroughput screeningtiered testing |
| spellingShingle | Ronnie Joe Bever Stephen W. Edwards Todor Antonijevic Mark D. Nelms Caroline Ring Danni Harris Scott G. Lynn David Williams Grace Chappell Rebecca Boyles Susan Borghoff Kristan J. Markey Optimizing androgen receptor prioritization using high-throughput assay-based activity models Frontiers in Toxicology endocrine disruption androgen receptor computational toxicology highthroughput screening tiered testing |
| title | Optimizing androgen receptor prioritization using high-throughput assay-based activity models |
| title_full | Optimizing androgen receptor prioritization using high-throughput assay-based activity models |
| title_fullStr | Optimizing androgen receptor prioritization using high-throughput assay-based activity models |
| title_full_unstemmed | Optimizing androgen receptor prioritization using high-throughput assay-based activity models |
| title_short | Optimizing androgen receptor prioritization using high-throughput assay-based activity models |
| title_sort | optimizing androgen receptor prioritization using high throughput assay based activity models |
| topic | endocrine disruption androgen receptor computational toxicology highthroughput screening tiered testing |
| url | https://www.frontiersin.org/articles/10.3389/ftox.2024.1347364/full |
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