Ronnie Joe Bever, Stephen W. Edwards, T. Antonijevic, M. Nelms, Caroline Ring, Danni Harris, Scott G. Lynn, David Williams, Grace Chappell, Rebecca Boyles, Susan Borghoff, K. Markey
{"title":"利用基于高通量检测的活性模型优化雄激素受体的优先排序","authors":"Ronnie Joe Bever, Stephen W. Edwards, T. Antonijevic, M. Nelms, Caroline Ring, Danni Harris, Scott G. Lynn, David Williams, Grace Chappell, Rebecca Boyles, Susan Borghoff, K. Markey","doi":"10.3389/ftox.2024.1347364","DOIUrl":null,"url":null,"abstract":"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.","PeriodicalId":73111,"journal":{"name":"Frontiers in toxicology","volume":null,"pages":null},"PeriodicalIF":3.6000,"publicationDate":"2024-03-11","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":"0","resultStr":"{\"title\":\"Optimizing androgen receptor prioritization using high-throughput assay-based activity models\",\"authors\":\"Ronnie Joe Bever, Stephen W. Edwards, T. Antonijevic, M. Nelms, Caroline Ring, Danni Harris, Scott G. Lynn, David Williams, Grace Chappell, Rebecca Boyles, Susan Borghoff, K. 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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. 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引用次数: 0
摘要
简介:利用高通量筛选检测数据建立的计算模型有望为美国环保署的内分泌干扰素筛选计划(EDSP)确定测试化学品的优先次序并进行筛选。这项工作的目的是展示一种数据处理方法,用于从一个更大的综合模型中确定最佳的最小检测电池,提供一种统一的方法来评估与雄激素受体(AR)通路模型相比未来最小检测电池的性能,并将化学聚类分析纳入这一评估。尽管 AR 通路模型中的一些检测方法已不再由原始供应商提供,但这种方法仍可用于未来对最小检测模型进行优先排序和筛选的评估:我们比较了之前发表的两个模型,发现扩展的 14 种检测模型对拮抗剂的敏感性更高,而原始的 11 种检测模型对激动剂的敏感性略高。然后,我们对原始 AR 通路模型中的检测子集进行了研究,以优化整体测试策略,使成本最小化,同时在广泛的化学空间内保持灵敏度:对 14 项检测模型衍生的子集模型中的关键检测项目进行了评估,确定了三项预测拮抗作用的关键检测项目和两项预测激动作用的关键检测项目。要以高灵敏度(95%)预测激动和拮抗作用,至少需要九种检测方法。不过,以基于化学结构的群组为指导的检测工作流程可以减少每种化学物质所需的平均检测次数,方法是根据化学物质的结构,根据其成为 AR 激动剂的可能性来选择检测方法。我们的研究结果表明,多阶段测试工作流程可以提供 95% 的灵敏度,而所需资源仅为运行原始完整模型中所有检测方法所需资源的 48%。通过结合硅学活性预测,还可以进一步减少资源:这项工作展示了一种数据驱动的方法,该方法结合了化学品聚类以及同时考虑拮抗和激动机制,从而更有效地筛选化学品。本案例研究为优先排序和筛选策略提供了概念验证,可用于未来的分析,以最大限度地减少预测 AR 活性所需的试验总数,从而最大限度地增加可测试化学品的数量,并根据数据驱动来确定化学品的优先排序,以便在 EDSP 框架下进行进一步筛选。
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.