Farglitazar

In a search for effective PPAR-γ agonists, 110 clinical drugs were screened via molecular docking, and 9 drugs, including parecoxib, were selected for subsequent biological evaluation. Molecular docking of parecoxib to the ligand-binding domain of PPAR-γ showed high binding affinity and relevant binding conformation compared with the PPAR-γ ligand/antidiabetic drug rosiglitazone. Per the docking result, parecoxib showed the best PPAR-γ transactivation in Ac2F rat liver cells. Further docking simulation and a luciferase assay suggested parecoxib would be a selective (and partial) PPAR-γ agonist. PPAR-γ activation by parecoxib induced adipocyte differentiation in 3T3-L1 murine preadipocytes. Parecoxib promoted adipogenesis in a dose-dependent manner and enhanced the expression of adipogenesis transcription factors PPAR-γ, C/EBPα, and C/EBPβ. These data indicated that parecoxib might be utilized as a partial PPAR-γ agonist for drug repositioning study.

New approach methodologies (NAMs) that efficiently provide information about chemical hazard without using whole animals are needed to accelerate the pace of chemical risk assessments. Technological advancements in gene expression assays have made in vitro high-throughput transcriptomics (HTTr) a feasible option for NAMs-based hazard characterization of environmental chemicals. In this study, we evaluated the Templated Oligo with Sequencing Readout (TempO-Seq) assay for HTTr concentration-response screening of a small set of chemicals in the human-derived MCF7 cell model. Our experimental design included a variety of reference samples and reference chemical treatments in order to objectively evaluate TempO-Seq assay performance. To facilitate analysis of these data, we developed a robust and scalable bioinformatics pipeline using open-source tools. We also developed a novel gene expression signature-based concentration-response modeling approach and compared the results to a previously implemented workflow for concentration-response analysis of transcriptomics data using BMDExpress. Analysis of reference samples and reference chemical treatments demonstrated highly reproducible differential gene expression signatures. In addition, we found that aggregating signals from individual genes into gene signatures prior to concentration-response modeling yielded in vitro transcriptional biological pathway altering concentrations (BPACs) that were closely aligned with previous ToxCast high-throughput screening assays. Often these identified signatures were associated with the known molecular target of the chemicals in our test set as the most sensitive components of the overall transcriptional response. This work has resulted in a novel and scalable in vitro HTTr workflow that is suitable for high-throughput hazard evaluation of environmental chemicals.