Identfication of Potent LXRβ-Selective Agonists without LXRα Activation by In Silico Approaches

Activating Liver X receptors (LXRs) represents a promising therapeutic option for dyslipidemia. However, activating LXR&alpha
may cause undesired lipogenic effects. Discovery of highly LXR&beta
selective agonists without LXR&alpha
activation were indispensable for dyslipidemia. In this study, in silico approaches were applied to develop highly potent LXR&beta
selective agonists based on a series of newly reported 3-(4-(2-propylphenoxy)butyl)imidazolidine-2,4-dione-based LXR&alpha
/&beta
dual agonists. Initially, Kohonen and stepwise multiple linear regression SW-MLR were performed to construct models for LXR&beta
agonists and LXR&alpha
agonists based on the structural characteristics of LXR&alpha
dual agonists, respectively. The obtained LXR&beta
agonist model gave a good predictive ability (R2train = 0.837, R2test = 0.843, Q2LOO = 0.715), and the LXR&alpha
agonist model produced even better predictive ability (R2train = 0.968, R2test = 0.914, Q2LOO = 0.895). Also, the two QSAR models were independent and can well distinguish LXR&beta
and LXR&alpha
activity. Then, compounds in the ZINC database met the lower limit of structural similarity of 0.7, compared to the 3-(4-(2-propylphenoxy)butyl)imidazolidine-2,4-dione scaffold subjected to our QSAR models, which resulted in the discovery of ZINC55084484 with an LXR&beta
prediction value of pEC50 equal to 7.343 and LXR&alpha
prediction value of pEC50 equal to &minus
1.901. Consequently, nine newly designed compounds were proposed as highly LXR&beta
selective agonists based on ZINC55084484 and molecular docking, of which LXR&beta
prediction values almost exceeded 8 and LXR&alpha
prediction values were below 0.