Structural optimization on a virtual screening hit of smoothened receptor

摘要

The Hedgehog (Hh) pathway plays a critical role during embryonic development by controlling cell patterning, growth and migration. In adults, the function of Hh pathway is curtailed to tissue repair and maintenance. Aberrant reactivation of Hh signaling has been linked to tumorigenesis in various cancers, such as basal cell carcinoma (BCC) and medulloblastoma. The Smoothened (Smo) receptor, a key component of the Hh pathway which is central to the signaling transduction, has emerged as an attractive therapeutic target for the treatment of human cancers. Taking advantage of the availability of several crystal structures of Smo in complex with different antagonists, we have previously conducted a molecular docking-based virtual screening to identify several compounds which exhibited significant inhibitory activity against the Hh pathway activation (IC50 < 10 mu M) in a Gli-responsive element (GRE) reporter gene assay. The most potent compound (ChemDiv ID C794-1677: 47 nM) showed comparable Hh signaling inhibition to the marketed drug vismodegib (46 nM). Herein, we report our structural optimization based on the virtual screening hit C794-1677. Our efforts are aimed to improve potency, decrease cLogP, and remove potentially metabolic labile/toxic pyrrole and aniline functionalities presented in C794-1677. The optimization led to the identification of numerous potent compounds exemplified by 25 (7.1 nM), which was 7 folds more potent compared with vismodegib. In addition, 25 was much less lipophilic compared with C794-1677 and devoid of the potentially metabolic labile/toxic pyrrole and aniline functional groups. Furthermore, 25 exhibited promising efficacy in inhibiting Gli1 mRNA expression in NIH3T3 cells with either wildtype Smo or D473H Smo mutant. These results represented significant improvement over the virtual screening hit C794-1677 and suggested that compound 25 can be used as a good starting point to support lead optimization. (C) 2019 Elsevier Masson SAS. All rights reserved.