Protein-structure-based prediction of animal model suitability for pharmacodynamic studies of subtype-selective estrogens.

CHEMMEDCHEM(2006)

引用 5|浏览4
暂无评分
摘要
Subtype-selective estrogens are of increasing importance as tools used to unravel physiological roles of the estrogen receptors, ER alpha and ER beta, in various species. Although human ER alpha and ER beta differ by only two amino acids within the binding pockets, we and others recently succeeded in generating subtype-selective agonists. We have proposed that the selectivity of the steroidal compounds 16 alpha-lactone-estradiol (16 alpha-LE2, hER alpha selective) and 8 beta-vinyl-estradiol (8 beta-VE2, hER beta selective) is based on the interaction of certain substituents of these compounds with essentially one amino acid in the respective ER binding pockets. For in vitro and ex vivo pharmacological experiments with these compounds we intended to use bovine tissues available from slaughterhouses in larger quantities. Using homology modeling techniques we determined that the amino acid conferring high hER beta-selectivity to 8 beta-VE2 is not exchanged between human and bovine ER alpha and bovine ER beta. Thus, we predicted our steroidal hER beta-selective compound to exhibit only weak agonistic activity at bER beta and that bovine tissue is therefore not suited for investigation of ER beta functions. The situation is presumably identical for pig, sheep, an the common marmoset, whereas rats, mice, and rhesus macaques are appropriate animal models to study pharmacological effects of 8 beta-VE2 in vivo. This prediction was confirmed in transactivation studies assessing estradiol (E-2) and the two subtype-selective ligands on bovine ER beta and on a series of hER alpha and hER beta with mutations in their respective ligand-binding pockets. We have shown that the detailed understanding of the interactions of a compound with its target protein enables the identification of relevant species for pharmacological studies.
更多
查看译文
关键词
estrogen receptors,mutagenesis,protein structure
AI 理解论文
溯源树
样例
生成溯源树,研究论文发展脉络
Chat Paper
正在生成论文摘要