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Bever RJ, Edwards SW, Antonijevic T, Nelms MD, Ring C, Harris D, Lynn SG, Williams D, Chappell G, Boyles R, Borghoff S, Markey KJ. Optimizing androgen receptor prioritization using high-throughput assay-based activity models. Front Toxicol 2024; 6:1347364. [PMID: 38529103 PMCID: PMC10961702 DOI: 10.3389/ftox.2024.1347364] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [What about the content of this article? (0)] [Affiliation(s)] [Abstract] [Key Words] [Track Full Text] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 11/30/2023] [Accepted: 01/22/2024] [Indexed: 03/27/2024] Open
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.
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Affiliation(s)
- Ronnie Joe Bever
- U.S. Environmental Protection Agency, Washington, DC, United States
| | | | | | - Mark D. Nelms
- RTI International, Research Triangle Park, NC, United States
| | | | - Danni Harris
- RTI International, Research Triangle Park, NC, United States
| | - Scott G. Lynn
- U.S. Environmental Protection Agency, Washington, DC, United States
| | - David Williams
- RTI International, Research Triangle Park, NC, United States
| | | | - Rebecca Boyles
- RTI International, Research Triangle Park, NC, United States
| | - Susan Borghoff
- ToxStrategies, Research Triangle Park, NC, United States
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Ma L, Li X, Liu C, Yan W, Ma J, Petersen RB, Peng A, Huang K. Modelling Parkinson's Disease in C. elegans: Strengths and Limitations. Curr Pharm Des 2022; 28:3033-3048. [PMID: 36111767 DOI: 10.2174/1381612828666220915103502] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [What about the content of this article? (0)] [Affiliation(s)] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 03/29/2022] [Accepted: 08/08/2022] [Indexed: 01/28/2023]
Abstract
Parkinson's disease (PD) is a common neurodegenerative disease that affects the motor system and progressively worsens with age. Current treatment options for PD mainly target symptoms, due to our limited understanding of the etiology and pathophysiology of PD. A variety of preclinical models have been developed to study different aspects of the disease. The models have been used to elucidate the pathogenesis and for testing new treatments. These models include cell models, non-mammalian models, rodent models, and non-human primate models. Over the past few decades, Caenorhabditis elegans (C. elegans) has been widely adopted as a model system due to its small size, transparent body, short generation time and life cycle, fully sequenced genome, the tractability of genetic manipulation and suitability for large scale screening for disease modifiers. Here, we review studies using C. elegans as a model for PD and highlight the strengths and limitations of the C. elegans model. Various C. elegans PD models, including neurotoxin-induced models and genetic models, are described in detail. Moreover, met.
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Affiliation(s)
- Liang Ma
- Department of Pharmacy, Wuhan Mental Health Center, Wuhan, China.,Department of Pharmacy, Wuhan Hospital for Psychotherapy, Wuhan, China
| | - Xi Li
- Tongji School of Pharmacy, Tongji Medical College, Huazhong University of Science and Technology, Wuhan, China
| | - Chengyu Liu
- Department of Transfusion Medicine, Wuhan Hospital of Traditional Chinese and Western Medicine, Tongji Medical College, Huazhong University of Science and Technology, Wuhan, China
| | - Wanyao Yan
- Department of Pharmacy, Wuhan Fourth Hospital, Tongji Medical College, Huazhong University of Science and Technology, Wuhan, China
| | - Jinlu Ma
- Human Resources Department, Wuhan Mental Health Center, Wuhan, China.,Human Resources Department, Wuhan Hospital for Psychotherapy, Wuhan, China
| | - Robert B Petersen
- Foundational Sciences, Central Michigan University College of Medicine, Mount Pleasant, MI, USA
| | - Anlin Peng
- Department of Pharmacy, The Third Hospital of Wuhan, Tongren Hospital of Wuhan University, Wuhan, China
| | - Kun Huang
- Tongji School of Pharmacy, Tongji Medical College, Huazhong University of Science and Technology, Wuhan, China
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Shechter S, Thomas DR, Jans DA. Application of In Silico and HTS Approaches to Identify Nuclear Import Inhibitors for Venezuelan Equine Encephalitis Virus Capsid Protein: A Case Study. Front Chem 2020; 8:573121. [PMID: 33505952 PMCID: PMC7832173 DOI: 10.3389/fchem.2020.573121] [Citation(s) in RCA: 4] [Impact Index Per Article: 1.0] [Reference Citation Analysis] [What about the content of this article? (0)] [Affiliation(s)] [Abstract] [Key Words] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 06/16/2020] [Accepted: 10/12/2020] [Indexed: 01/16/2023] Open
Abstract
The development of new drugs is costly and time-consuming, with estimates of over $US1 billion and 15 years for a product to reach the market. As understanding of the molecular basis of disease improves, various approaches have been used to target specific molecular interactions in the search for effective drugs. These include high-throughput screening (HTS) for novel drug identification and computer-aided drug design (CADD) to assess the properties of putative drugs before experimental work begins. We have applied conventional HTS and CADD approaches to the problem of identifying antiviral compounds to limit infection by Venezuelan equine encephalitis virus (VEEV). Nuclear targeting of the VEEV capsid (CP) protein through interaction with the host nuclear import machinery has been shown to be essential for viral pathogenicity, with viruses incapable of this interaction being greatly attenuated. Our previous conventional HTS and in silico structure-based drug design (SBDD) screens were successful in identifying novel inhibitors of CP interaction with the host nuclear import machinery, thus providing a unique opportunity to assess the relative value of the two screening approaches directly. This focused review compares and contrasts the two screening approaches, together with the properties of the inhibitors identified, as a case study for parallel use of the two approaches to identify antivirals. The utility of SBDD screens, especially when used in parallel with traditional HTS, in identifying agents of interest to target the host-pathogen interface is highlighted.
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Affiliation(s)
- Sharon Shechter
- Shechter Computational Solutions, Andover, MA, United States.,Department of Chemistry, College of Sciences, University of Massachusetts Lowell, Lowell, MA, United States
| | - David R Thomas
- Nuclear Signalling Laboratory, Department of Biochemistry and Molecular Biology, Biomedical Discovery Institute, Monash University, Monash, VIC, Australia
| | - David A Jans
- Nuclear Signalling Laboratory, Department of Biochemistry and Molecular Biology, Biomedical Discovery Institute, Monash University, Monash, VIC, Australia
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