更新于：2024-11-01

17β-HSD1 x STS

更新于：2024-11-01

基本信息

关联

项与 17β-HSD1 x STS 相关的药物

DSHIs (ElexoPharm)

靶点

17β-HSD1 x STS

作用机制

17β-HSD1抑制剂 [+1]

在研机构

ELEXOPHARM GmbH [+1]

原研机构

Universität des Saarlandes [+1]

在研适应症

子宫内膜异位症

非在研适应症-

最高研发阶段临床前

首次获批国家/地区-

首次获批日期1800-01-20

100 项与 17β-HSD1 x STS 相关的临床结果

登录后查看更多信息

100 项与 17β-HSD1 x STS 相关的转化医学

登录后查看更多信息

0 项与 17β-HSD1 x STS 相关的专利（医药）

登录后查看更多信息

项与 17β-HSD1 x STS 相关的文献（医药）

2023-07-13·Journal of medicinal chemistry1区 · 医学

Potent Dual Inhibitors of Steroid Sulfatase and 17β-Hydroxysteroid Dehydrogenase Type 1 with a Suitable Pharmacokinetic Profile for In Vivo Proof-of-Principle Studies in an Endometriosis Mouse Model.

1区 · 医学

Article

作者: Salah, Mohamed ; Frotscher, Martin ; Rudzitis-Auth, Jeannette ; Abdelsamie, Ahmed S ; Tahoun, Mariam ; van Koppen, Chris J ; Laschke, Matthias W ; Stotz, Lisa

Treating estrogen-dependent diseases like endometriosis with drugs suppressing local estrogen activation may be superior to existing endocrine therapies. Steroid sulfatase (STS) and 17β-hydroxysteroid dehydrogenase type 1 (17β-HSD1) are key enzymes of local estrogen activation. We describe the rational design, synthesis, and biological profilation of furan-based compounds as a novel class of dual STS/17β-HSD1 inhibitors (DSHIs). In T47D cells, compound 5 showed irreversible inhibition of STS and potent, reversible inhibition of 17β-HSD1. It was selective over 17β-HSD2 and displayed high metabolic stabilities in human and mouse liver S9 fractions. No effect on cell viability was detected up to 31 μM (HEK293) and 23 μM (HepG2), respectively, and there was no activation of the aryl hydrocarbon receptor (AhR) up to 3.16 μM. Single daily application to mice revealed steady-state plasma levels high enough to make this compound eligible for an in vivo proof-of-principle study in a mouse endometriosis model.

2022-09-08·Journal of Medicinal Chemistry1区 · 医学

Dual Targeting of Steroid Sulfatase and 17β-Hydroxysteroid Dehydrogenase Type 1 by a Novel Drug-Prodrug Approach: A Potential Therapeutic Option for the Treatment of Endometriosis

1区 · 医学

Article

作者: Tahoun, Mariam ; Abdelsamie, Ahmed S. ; Mohamed, Abdelrahman ; Hawner, Manuel ; Frotscher, Martin ; Salah, Mohamed

A novel approach for the dual inhibition of steroid sulfatase (STS) and 17β-hydroxysteroid dehydrogenase type 1(17β HSD1) by a single drug was explored, starting from in-house 17β HSD1 inhibitors via masking their phenolic OH group with a sulfamate ester. The sulfamates were intentionally designed as drugs for the inhibition of STS and, at the same time, prodrugs for 17β-HSD1 inhibition ("drug-prodrug approach"). The most promising sulfamates 13, 16, 18-20, 22-24, 36, and 37 showed nanomolar IC₅₀ values for STS inhibition in a cellular assay and their corresponding phenols displayed potent 17β-HSD1 inhibition in cell-free and cellular assays, high selectivity over 17β-HSD2, reasonable metabolic stability, and low estrogen receptor α affinity. A close relationship was found between the liberation of the phenolic compound by sulfamate hydrolysis and 17β-HSD1 inactivation. These results showed that the envisaged drug-prodrug concept was successfully implemented. The novel compounds constitute a promising class of therapeutics for the treatment of endometriosis and other estrogen-dependent diseases.

2022-09-01·The Journal of Steroid Biochemistry and Molecular Biology

Differential proteomics analysis of JEG-3 and JAR placental cell models and the effect of androgen treatment

Article

作者: Sathyanarayana, Sheela ; Kruger, Laken ; Paquette, Alison ; Prasad, Bhagwat ; Mettu, Vijaya Saradhi ; Yue, Guihua

The placenta is a vital fetal organ that plays an important role in maintaining fetal sex hormone homeostasis. Xenobiotics can alter placental sex-steroidogenic enzymes and transporters, including enzymes such as aromatase (CYP19A1) and the hydroxysteroid dehydrogenases (HSDs) but studying how compounds disrupt in vivo placental metabolism is complex. Utilizing high-throughput in vitro models is critical to predict the disruption of placental sex-steroidogenic enzymes and transporters, particularly by drug candidates in the early stages of drug discovery. JAR and JEG-3 cells are the most common, simple, and cost-effective placental cell models that are capable of high-throughput screening, but how well they express the sex-steroidogenic enzymes and transporters is not well known. Here, we compared the proteomes of JAR and JEG-3 cells in the presence and absence of physiologically relevant concentrations of dehydroepiandrosterone (DHEA, 8 µM) and testosterone (15 nM) to aid the characterization of sex-steroidogenic enzymes and transporters in these cell models. Global proteomics analysis detected 2931 and 3449 proteins in JAR cells and JEG-3 cells, respectively. However, dramatic differences in sex-steroidogenic enzymes and transporters were observed between these cells. In particular, the basal expression of steroid sulfatase (STS), HSD17B1, and HSD17B7 were unique to JEG-3 cells. JEG-3 cells also showed significantly higher protein levels of aldo-keto reductase (AKR) 1A1 and AKR1B1, while JAR cells showed significantly higher levels of HSD17B4 and HSDB12. Aldehyde dehydrogenase (ALDH) 3A2 and HSD17B11 enzymes as well as the transporters sterol O-acyltransferase (SOAT) 1 and ATP binding cassette subfamily G2 (ABCG2) were comparable between the cell lines, whereas sulfotransferases (SULTs) were uniquely present within JAR cells. Androgen treatments significantly lowered HSD17B11, HSD17B4, HSD17B12, and ALDH3A2 levels in JAR cells. DHEA treatment significantly raised the level of HSD17B1 by 51 % in JEG-3 cells, whereas CYP19A1 was increased to significant levels in both JAR and JEG-3 cells after androgen treatments. The proteomics data were supported by a complementary targeted metabolomics analysis of culture media in the DHEA (8 µM) and testosterone (15 nM) treated groups. This study has indicated that untreated JEG-3 cells express more sex-steroidogenic enzymes and transporters. Nevertheless, JEG-3 and JAR cells are unique and their respective proteomics data can be used to select the best model depending on the hypothesis.