Browse
Submit Data
Databases
API
Help

Dataset Information

20 Views

0 Connections

0 Citations

0 Reanalyses

0 Downloads

Omics score: 0

Selecting a minimal set of androgen receptor assays for screening chemicals.

ABSTRACT: Screening certain environmental chemicals for their ability to interact with endocrine targets, including the androgen receptor (AR), is an important global concern. We previously developed a model using a battery of eleven in vitro AR assays to predict in vivo AR activity. Here we describe a revised mathematical modeling approach that also incorporates data from newly available assays and demonstrate that subsets of assays can provide close to the same level of predictivity. These subset models are evaluated against the full model using 1820 chemicals, as well as in vitro and in vivo reference chemicals from the literature. Agonist batteries of as few as six assays and antagonist batteries of as few as five assays can yield balanced accuracies of 95% or better relative to the full model. Balanced accuracy for predicting reference chemicals is 100%. An approach is outlined for researchers to develop their own subset batteries to accurately detect AR activity using assays that map to the pathway of key molecular and cellular events involved in chemical-mediated AR activation and transcriptional activity. This work indicates in vitro bioactivity and in silico predictions that map to the AR pathway could be used in an integrated approach to testing and assessment for identifying chemicals that interact directly with the mammalian AR.

SUBMITTER: Judson R

PROVIDER: S-EPMC8356084 | biostudies-literature |

REPOSITORIES: biostudies-literature

ACCESS DATA

Json Xml

Similar Datasets

On selecting a minimal set of in vitro assays to reliably determine estrogen agonist activity.

Project description:The US EPA is charged with screening chemicals for their ability to be endocrine disruptors through interaction with the estrogen, androgen and thyroid axes. The agency is exploring the use of high-throughput in vitro assays to use in the Endocrine Disruptor Screening Program (EDSP), potentially as replacements for lower-throughput in vitro and in vivo tests. The first replacement is an integrated computational and experimental model for estrogen receptor (ER) activity, to be used as an alternative to the EDSP Tier 1 in vitro ER binding and transactivation assays and the in vivo uterotrophic bioassay. The ER agonist model uses a set of 16 in vitro assays that incorporate multiple technologies and cell lines and probe multiple points in the ER pathway. Here, we demonstrate that subsets of assays with as few as 4 assays can predict the activity of all 1811 chemicals tested with accuracy equivalent to that of the full 16-assay model. The prediction accuracy against reference chemicals is higher than that of the full chemical set, partly because the larger set contains many chemicals that can cause a variety of types of assay interference There are multiple accurate assay subsets, allowing flexibility in the construction of a multiplexed assay battery. We also discuss the issue of challenging chemicals, i.e. those that can give false positive results in certain assays, and could hence be more problematic when only a few assays are used.

| S-EPMC5796525 | biostudies-other

Identifying environmental chemicals as agonists of the androgen receptor by using a quantitative high-throughput screening platform.

Project description:The androgen receptor (AR, NR3C4) is a nuclear receptor whose main function is acting as a transcription factor regulating gene expression for male sexual development and maintaining accessory sexual organ function. It is also a necessary component of female fertility by affecting the functionality of ovarian follicles and ovulation. Pathological processes involving AR include Kennedy's disease and Klinefelter's syndrome, as well as prostate, ovarian, and testicular cancer. Strict regulation of sex hormone signaling is required for normal reproductive organ development and function. Therefore, testing small molecules for their ability to modulate AR is a first step in identifying potential endocrine disruptors. We screened the Tox21 10K compound library in a quantitative high-throughput format to identify activators of AR using two reporter gene cell lines, AR ?-lactamase (AR-bla) and AR-luciferase (AR-luc). Seventy-five compounds identified through the primary assay were characterized as potential agonists or inactives through confirmation screens and secondary assays. Biochemical binding and AR nuclear translocation assays were performed to confirm direct binding and activation of AR from these compounds. The top seventeen compounds identified were found to bind to AR, and sixteen of them translocated AR from the cytoplasm into the nucleus. Five potentially novel or not well-characterized AR agonists were discovered through primary and follow-up studies. We have identified multiple AR activators, including known AR agonists such as testosterone, as well as novel/not well-known compounds such as prulifloxacin. The information gained from the current study can be directly used to prioritize compounds for further in-depth toxicological evaluations, as well as their potential to disrupt the endocrine system via AR activation.

| S-EPMC6135100 | biostudies-literature

Profiling 976 ToxCast chemicals across 331 enzymatic and receptor signaling assays.

Project description:Understanding potential health risks is a significant challenge due to the large numbers of diverse chemicals with poorly characterized exposures and mechanisms of toxicities. The present study analyzes 976 chemicals (including failed pharmaceuticals, alternative plasticizers, food additives, and pesticides) in Phases I and II of the U.S. EPA's ToxCast project across 331 cell-free enzymatic and ligand-binding high-throughput screening (HTS) assays. Half-maximal activity concentrations (AC50) were identified for 729 chemicals in 256 assays (7,135 chemical-assay pairs). Some of the most commonly affected assays were CYPs (CYP2C9 and CYP2C19), transporters (mitochondrial TSPO, norepinephrine, and dopaminergic), and GPCRs (aminergic). Heavy metals, surfactants, and dithiocarbamate fungicides showed promiscuous but distinctly different patterns of activity, whereas many of the pharmaceutical compounds showed promiscuous activity across GPCRs. Literature analysis confirmed >50% of the activities for the most potent chemical-assay pairs (54) but also revealed 10 missed interactions. Twenty-two chemicals with known estrogenic activity were correctly identified for the majority (77%), missing only the weaker interactions. In many cases, novel findings for previously unreported chemical-target combinations clustered with known chemical-target interactions. Results from this large inventory of chemical-biological interactions can inform read-across methods as well as link potential targets to molecular initiating events in adverse outcome pathways for diverse toxicities.

| S-EPMC3685188 | biostudies-literature

Using in vitro high throughput screening assays to identify potential endocrine-disrupting chemicals.

Project description:BACKGROUND: Over the past 20 years, an increased focus on detecting environmental chemicals that pose a risk of adverse effects due to endocrine disruption has driven the creation of the U.S. Environmental Protection Agency (EPA) Endocrine Disruptor Screening Program (EDSP). Thousands of chemicals are subject to the EDSP; thus, processing these chemicals using current test batteries could require millions of dollars and decades. A need for increased throughput and efficiency motivated the development of methods using in vitro high throughput screening (HTS) assays to prioritize chemicals for EDSP Tier 1 screening (T1S). OBJECTIVE: In this study we used U.S. EPA ToxCast HTS assays for estrogen, androgen, steroidogenic, and thyroid-disrupting mechanisms to classify compounds and compare ToxCast results to in vitro and in vivo data from EDSP T1S assays. METHOD: We implemented an iterative model that optimized the ability of endocrine-related HTS assays to predict components of EDSP T1S and related results. Balanced accuracy was used as a measure of model performance. RESULTS: ToxCast estrogen receptor and androgen receptor assays predicted the results of relevant EDSP T1S assays with balanced accuracies of 0.91 (p < 0.001) and 0.92 (p < 0.001), respectively. Uterotrophic and Hershberger assay results were predicted with balanced accuracies of 0.89 (p < 0.001) and 1 (p < 0.001), respectively. Models for steroidogenic and thyroid-related effects could not be developed with the currently published ToxCast data. CONCLUSIONS: Overall, results suggest that current ToxCast assays can accurately identify chemicals with potential to interact with the estrogenic and androgenic pathways, and could help prioritize chemicals for EDSP T1S assays.

| S-EPMC3546348 | biostudies-literature

Bioactivity Signatures of Drugs vs. Environmental Chemicals Revealed by Tox21 High-Throughput Screening Assays.

Project description:Humans are exposed to tens of thousands of chemicals over the course of a lifetime, yet there remains inadequate data on the potential harmful effects of these substances on human health. Using quantitative high-throughput screening (qHTS), we can test these compounds for potential toxicity in a more efficient and cost-effective way compared to traditional animal studies. Tox21 has developed a library of ~10,000 chemicals (Tox21 10K) comprising one-third approved and investigational drugs and two-thirds environmental chemicals. In this study, the Tox21 10K was screened in a qHTS format against a panel of 70 cell-based assays with 213 readouts covering a broad range of biological pathways. Activity profiles were compared with chemical structure to assess their ability to differentiate drugs from environmental chemicals, and structure was found to be a better predictor of which chemicals are likely to be drugs. Drugs and environmental chemicals were further analyzed for diversity in structure and biological response space and showed distinguishable, but not distinct, responses in the Tox21 assays. Inclusion of other targets and pathways to further diversify the biological response space covered by these assays is likely required to better evaluate the safety profile of drugs and environmental chemicals to prioritize for in-depth toxicological studies.

| S-EPMC7931954 | biostudies-literature

Characterization of a novel androgen receptor (AR) coregulator RIPK1 and related chemicals that suppress AR-mediated prostate cancer growth via peptide and chemical screening.

Project description:Using bicalutamide-androgen receptor (AR) DNA binding domain-ligand binding domain as bait, we observed enrichment of FxxFY motif-containing peptides. Protein database searches revealed the presence of receptor-interacting protein kinase 1 (RIPK1) harboring one FxxFY motif. RIPK1 interacted directly with AR and suppressed AR transactivation in a dose-dependent manner. Domain mapping experiments showed that the FxxFY motif in RIPK1 is critical for interactions with AR and the death domain of RIPK1 plays a crucial role in its inhibitory effect on transactivation. In terms of tissue expression, RIPK1 levels were markedly higher in benign prostate hyperplasia and non-cancerous tissue regions relative to the tumor area. With the aid of computer modeling for screening of chemicals targeting activation function 2 (AF-2) of AR, we identified oxadiazole derivatives as good candidates and subsequently generated a small library of these compounds. A number of candidates could effectively suppress AR transactivation and AR-related functions in vitro and in vivo with tolerable toxicity via inhibiting AR-peptide, AR-coregulator and AR N-C interactions. Combination of these chemicals with antiandrogen had an additive suppressive effect on AR transcriptional activity. Our collective findings may pave the way in creating new strategies for the development and design of anti-AR drugs.

| S-EPMC5642495 | biostudies-literature

A Mechanistic High-Content Analysis Assay Using a Chimeric Androgen Receptor That Rapidly Characterizes Androgenic Chemicals.

Project description:Human health is at risk from environmental exposures to a wide range of chemical toxicants and endocrine-disrupting chemicals (EDCs). As part of understanding this risk, the U.S. Environmental Protection Agency (EPA) has been pursuing new high-throughput in vitro assays and computational models to characterize EDCs. EPA models have incorporated our high-content analysis-based green fluorescent protein estrogen receptor (GFP-ER): PRL-HeLa assay, which allows direct visualization of ER binding to DNA regulatory elements. Here, we characterize a modified functional assay based on the stable expression of a chimeric androgen receptor (ARER), wherein a region containing the native AR DNA-binding domain (DBD) was replaced with the ERα DBD (amino acids 183-254). We demonstrate that the AR agonist dihydrotestosterone induces GFP-ARER nuclear translocation, PRL promoter binding, and transcriptional activity at physiologically relevant concentrations (<1 nM). In contrast, the AR antagonist bicalutamide induces only nuclear translocation of the GFP-ARER receptor (at μM concentrations). Estradiol also fails to induce visible chromatin binding, indicating androgen specificity. In a screen of reference chemicals from the EPA and the Agency for Toxic Substances and Disease Registry, the GFP-ARER cell model identified and mechanistically grouped activity by known (anti-)androgens based on the ability to induce nuclear translocation and/or chromatin binding. Finally, the cell model was used to identify potential (anti-)androgens in environmental samples in collaboration with the Houston Ship Channel/Galveston Bay Texas A&M University EPA Superfund Research Program. Based on these data, the chromatin-binding, in vitro assay-based GFP-ARER model represents a selective tool for rapidly identifying androgenic activity associated with drugs, chemicals, and environmental samples.

| S-EPMC7477889 | biostudies-literature

Identifying and designing chemicals with minimal acute aquatic toxicity.

Project description:Industrial ecology has revolutionized our understanding of material stocks and flows in our economy and society. For this important discipline to have even deeper impact, we must understand the inherent nature of these materials in terms of human health and the environment. This paper focuses on methods to design synthetic chemicals to reduce their intrinsic ability to cause adverse consequence to the biosphere. Advances in the fields of computational chemistry and molecular toxicology in recent decades allow the development of predictive models that inform the design of molecules with reduced potential to be toxic to humans or the environment. The approach presented herein builds on the important work in quantitative structure-activity relationships by linking toxicological and chemical mechanistic insights to the identification of critical physical-chemical properties needed to be modified. This in silico approach yields design guidelines using boundary values for physiochemical properties. Acute aquatic toxicity serves as a model endpoint in this study. Defining value ranges for properties related to bioavailability and reactivity eliminates 99% of the chemicals in the highest concern for acute aquatic toxicity category. This approach and its future implementations are expected to yield very powerful tools for life cycle assessment practitioners and molecular designers that allow rapid assessment of multiple environmental and human health endpoints and inform modifications to minimize hazard.

| S-EPMC4443366 | biostudies-literature

"MIATA"-minimal information about T cell assays.

Project description:Immunotherapy, especially therapeutic vaccination, has a great deal of potential in the treatment of cancer and certain infectious diseases such as HIV (Allison et al., 2006; Fauci et al., 2008; Feldmann and Steinman, 2005). Numerous vaccine candidates have been tested in patients with a variety of tumor types and chronic viral diseases. Often, the best way to assess the clinical potential of these vaccines is to monitor the induced T cell response, and yet there are currently no standards for reporting these results. This letter is an effort to address this problem.

| S-EPMC3762500 | biostudies-other

Identification of potential aryl hydrocarbon receptor ligands by virtual screening of industrial chemicals.

Project description:We have developed a virtual screening procedure to identify potential ligands to the aryl hydrocarbon receptor (AhR) among a set of industrial chemicals. AhR is a key target for dioxin-like compounds, which is related to these compounds' potential to induce cancer and a wide range of endocrine and immune system-related effects. The virtual screening procedure included an initial filtration aiming at identifying chemicals with structural similarities to 66 known AhR binders, followed by 3 enrichment methods run in parallel. These include two ligand-based methods (structural fingerprints and nearest neighbor analysis) and one structure-based method using an AhR homology model. A set of 6445 commonly used industrial chemicals was processed, and each step identified unique potential ligands. Seven compounds were identified by all three enrichment methods, and these compounds included known activators and suppressors of AhR. Only approximately 0.7% (41 compounds) of the studied industrial compounds was identified as potential AhR ligands and among these, 28 compounds have to our knowledge not been tested for AhR-mediated effects or have been screened with low purity. We suggest assessment of AhR-related activities of these compounds and in particular 2-chlorotrityl chloride, 3-p-hydroxyanilino-carbazole, and 3-(2-chloro-4-nitrophenyl)-5-(1,1-dimethylethyl)-1,3,4-oxadiazol-2(3H)-one.

| S-EPMC5773624 | biostudies-literature

OmicsDI is part of the ELIXIR infrastructure

OmicsDI is an Elixir interoperability service. Learn more ›

Tweets

OmicsDI Databases

PRIDE
PeptideAtlas
MassIVE
JPOST Repository
Physiome Model Repository

EGA
EVA
ENA
LINCS
PAXDB
Cell Collective

MetaboLights
Metabolomics Workbench
MetabolomeExpress
GNPS
BioModels
FAIRDOMHub

ArrayExpress
dbGaP
ExpressionAtlas
GEO
NODE

Information

Databases
Help
API
Contact us
Code on GitHub
Terms of use
Submit Data