Potent, selective and cell penetrant inhibitors of SF-1 by functional ultra-high-throughput screening.

The steroidogenic factor 1 (SF-1, also known as NR5A1) is a transcription factor belonging to the nuclear receptor superfamily. Whereas most of the members of this family have been extensively characterized, the therapeutic potential and pharmacology of SF-1 still remains elusive. Described here is the identification and characterization of selective inhibitory chemical probes of SF-1 by a rational ultra-high-throughput screening (uHTS) strategy. A set of 64,908 compounds from the National Institute of Health's Molecular Libraries Small Molecule Repository was screened in a transactivation cell-based assay employing a chimeric SF-1 construct. Two analogous isoquinolinones, ethyl 2-[2-[2-(2,3-dihydro-1,4-benzodioxin-7-ylamino)-2-oxoethyl]-1-oxoisoquinolin-5-yl]oxypropanoate (SID7969543) and ethyl 2-[2-[2-(1,3-benzodioxol-5-ylmethylamino)-2-oxoethyl]-1-oxoisoquinolin-5-yl]oxypropanoate and (SID7970631), were identified as potent submicromolar inhibitors, yielding IC(50) values of 760 and 260 nM. The compounds retained their potency in a more physiologic functional assay employing the full-length SF-1 protein and its native response element, yielding IC(50) values of 30 and 16 nM, respectively. The selectivity of these isoquinolinones was confirmed via transactivation-based functional assays for RAR-related orphan receptor A (RORA), Herpes simplex virus transcriptional activator protein Vmw65 (VP16), and liver receptor homolog 1 (LRH-1). Their cytotoxicity, solubility, permeability and metabolic stability were also measured. These isoquinolinones represent valuable chemical probes to investigate the therapeutic potential of SF-1.

Hepatic transcriptional networks induced by exposure to 2,3,7,8-tetrachlorodibenzo-p-dioxin.

The environmental contaminant 2,3,7,8-tetrachlorodibenzo-p-dioxin (TCDD) serves as a prototype for a range of environmental toxicants and as a pharmacologic probe to study signal transduction by the aryl hydrocarbon receptor (AHR). Despite a detailed understanding of how TCDD exposure leads to the transcriptional up-regulation of cytochrome P450-dependent monooxygenases, we know little about how compounds like TCDD lead to a variety of AHR-dependent toxic end points such as liver pathology, terata, thymic involution, and cancer. Using an acute exposure protocol and the toxic response of the mouse liver as a model system, we have begun a detailed microarray analysis to describe the transcriptional changes that occur after various TCDD doses and treatment times. Through correlation analysis of time- and dose-dependent toxicological end points, we are able to identify coordinately responsive transcriptional events that can be defined as primary transcriptional events and downstream events that may represent mechanistically linked sequelae or that have potential as biomarkers of toxicity.

EDGE: a centralized resource for the comparison, analysis, and distribution of toxicogenomic information.

Transcriptional profiling via microarrays holds great promise for toxicant classification and hazard prediction. Unfortunately, the use of different microarray platforms, protocols, and informatics often hinders the meaningful comparison of transcriptional profiling data across laboratories. One solution to this problem is to provide a low-cost and centralized resource that enables researchers to share toxicogenomic data that has been generated on a common platform. In an effort to create such a resource, we developed a standardized set of microarray reagents and reproducible protocols to simplify the analysis of liver gene expression in the mouse model. This resource, referred to as EDGE, was then used to generate a training set of 117 publicly accessible transcriptional profiles that can be accessed at http://edge.oncology.wisc.edu/. The Web-accessible database was also linked to an informatics suite that allows on-line clustering and K-means analyses as well as Boolean and sequence-based searches of the data. We propose that EDGE can serve as a prototype resource for the sharing of toxicogenomics information and be used to develop algorithms for efficient chemical classification and hazard prediction.

Application of genomics to toxicology research.

Traditional models of toxicity have relied on dissecting chemical action into pharmacokinetic and pharmacodynamic processes. However, the integration of genomic information with toxicology will enhance our basic understanding of these processes and significantly change the way we apply toxicological information to risk assessment and regulatory problems. In this article, we summarize the application of gene expression information and polymorphism discovery to four areas in toxicology: toxicity testing, cross-species extrapolation, understanding mechanism of action, and susceptibility.