Investigation of the Potential of Bile Acid Methyl Esters as Inhibitors of Aldo-keto Reductase 1C2: Insight from Molecular Docking, Virtual Screening, Experimental Assays and Molecular Dynamics

Abstract

Human aldo-keto reductase 1C isoforms (AKR1C1-C4) catalyze reduction of endogenous and exogenous compounds, including therapeutic drugs, and are associated with chemotherapy resistance. AKR1C2 is involved in metastatic processes and is a target for the treatment of various cancers. Here we used molecular docking to explore the potential of a series of eleven bile acid methyl esters as AKR1C2 inhibitors. Autodock 4.2 ranked 10 of the 11 test compounds above a decoy set generated based on ursodeoxycholic acid, a known AKR1C2 inhibitor, while 5 of these 10 ranked above 94 % of decoys in Autodock Vina. Seven inactives reported in the literature not to inhibit AKR1C2 ranked below the decoy threshold: 5 of these are specific inhibitors of AKR1C3, a related isoform. Using the same parameters, Autodock Vina identified steroidal analogs of AKR1C substrates, bile acids, and AKR1C inhibitors in the top 5 % of a virtual screen of a natural product library. In experimental assays, 6 out of 11 of the tested bile acid methyl esters inhibited >50 % of AKR1C2 activity, while 2 compounds were strong AKR1C3 inhibitors. Potential off-target interactions with the glucocorticoid receptor were measured using a yeast-based fluorescence assay, where results suggest that the methyl ester could interfere with binding. The top ranking compound based on docking and experimental results showed dose-dependent inhibition of AKR1C2 with an IC50 of ∼3.6 μM. Molecular dynamics simulations (20 ns) were used to explore potential interactions between a bile acid methyl ester and residues in the AKR1C2 active site. Our molecular docking results identify AKR1C2 as a target for bile acid methyl esters, which combined with virtual screening results could provide new directions for researchers interested in synthesis of AKR1C inhibitors.

Keywords: aldo-keto reductases; cancer; drug design; molecular dynamics; molecular modelling.