作者: Kapoor, Abhijeet ; Bolel, Priyanka ; Marugan, Juan J ; Wang, Amy Q ; Sarkar, Rhitajit ; Southall, Noel ; Gershengorn, Marvin C ; Klubo-Gwiezdzinska, Joanna ; Templin, Jay S ; Xu, Xin ; Neumann, Susanne ; Eliseeva, Elena ; Dulcey, Andrés E

AbstractContextThyroid-stimulating hormone (or thyrotropin) receptor (TSHR) could be a selective target for small molecule ligands to treat thyroid cancer (TC).ObjectiveWe report a novel, orally efficacious ligand for TSHR that exhibits proliferation inhibitory activity against human TC in vitro and in vivo, and inhibition of metastasis in vivo.MethodsA35 (NCATS-SM4420; NCGC00241808) was selected from a sublibrary of >200 TSHR ligands. Cell proliferation assays including BrdU incorporation and WST-1, along with molecular docking studies were done. In vivo activity of A35 was assessed in TC cell–derived xenograft (CDX) models with immunocompromised (NSG) mice. Formalin-fixed, paraffin-embedded sections of tumor and lung tissues were observed for the extent of cell death and metastasis.ResultsA35 was shown to stimulate cAMP production in some cell types by activating TSHR but not in TC cells, MDA-T32, and MDA-T85. A35 inhibited proliferation of MDA-T32 and MDA-T85 in vitro and in vivo, and pulmonary metastasis of MDA-T85F1 in mice. In vitro, A35 inhibition of proliferation was reduced by a selective TSHR antagonist. Inhibition of CDX tumor growth without decreases in mouse weights and liver function showed A35 to be efficacious without apparent toxicity. Lastly, A35 reduced levels of Ki67 in the tumors and metastatic markers in lung tissues.ConclusionWe conclude that A35 is a TSHR-selective inhibitor of TC cell proliferation and metastasis, and suggest that A35 may be a promising lead drug candidate for the treatment of differentiated TC in humans.

2023-06-19·Chemical research in toxicology

Machine Learning Model for Screening Thyroid Stimulating Hormone Receptor Agonists Based on Updated Datasets and Improved Applicability Domain Metrics.

Article

作者: Liu, Wenjia ; Wang, Haobo ; Wang, Zhongyu ; Tang, Weihao ; Chen, Jingwen

Machine learning (ML) models for screening endocrine-disrupting chems. (EDCs), such as TSH receptor (TSHR) agonists, are essential for sound management of chems.Previous models for screening TSHR agonists were built on imbalanced datasets and lacked applicability domain (AD) characterization essential for regulatory application.Herein, an updated TSHR agonist dataset was built, for which the ratio of active to inactive compounds greatly increased to 1:2.6, and chem. spaces of structure-activity landscapes (SALs) were enhanced.Resulting models based on 7 mol. representations and 4 ML algorithms were proven to outperform previous ones.Weighted similarity d. (ρs) and weighted inconsistency of activities (IA) were proposed to characterize the SALs, and a state-of-the-art AD characterization methodol. ADSAL{ρs, IA} was established.An optimal classifier developed with PubChem fingerprints and the random forest algorithm, coupled with ADSAL{ρs ≥ 0.15, IA ≤ 0.65}, exhibited good performance on the validation set with the area under the receiver operating characteristic curve being 0.984 and balanced accuracy being 0.941 and identified 90 TSHR agonist classes that could not be found previously.The classifier together with the ADSAL{ρs, IA} may serve as efficient tools for screening EDCs, and the AD characterization methodol. may be applied to other ML models.

2022-10-23·Endocrinology

Modeling TSH Receptor Dimerization at the Transmembrane Domain

Article

作者: Latif, Rauf ; Mezei, Mihaly ; Davies, Terry F

AbstractBiophysical studies have established that the thyrotropin (TSH) receptor (TSHR) undergoes posttranslational modifications including dimerization. Following our earlier simulation of a TSHR–transmembrane domain (TMD) monomer (called TSHR-TMD-TRIO) we have now proceeded with a molecular dynamics simulation (MD) of TSHR-TMD dimerization using this improved membrane-embedded model. The starting structure was the TMD protein with all extracellular and intracellular loops and internal waters, which was placed in the relative orientation of the model originally generated with Brownian dynamics. Furthermore, this model was embedded in a DPPC lipid bilayer further solvated with water and added salt. Data from the MD simulation studies showed that the dimeric subunits stayed in the same relative orientation and distance during the 1000 ns of study. Comparison of representative conformations of the individual monomers when dimerized with the conformations from the monomer simulation showed subtle differences as represented by the backbone root mean square deviations. Differences in the conformations of the ligand-binding sites, suggesting variable affinities for these “hot spots,” were also revealed by comparing the docking scores of 46 small-molecule ligands that included known TSHR agonists and antagonists as well as their derivatives. These data add further insight into the tendency of the TSHR-TMD to form dimeric and oligomeric structures and show that the differing conformations influence small-molecule binding sites within the TMD.

100 项与 TSHR target (Septerna) 相关的药物交易

登录后查看更多信息