Discovery of drug-like acetylcholinesterase inhibitors by rapid virtual screening of a 6.9million compound database.

Abstract

Cholinesterase inhibitors remain the mainstay of Alzheimer's disease treatment, and the search for new inhibitors with better efficacy and side effect profiles is ongoing. Virtual screening (VS) is a powerful technique for searching large compound databases for potential hits. This study used a sequential VS workflow combining ligand-based VS, molecular docking and physicochemical filtering to screen for central nervous system (CNS) drug-like acetylcholinesterase inhibitors (AChEIs) amongst the 6.9million compounds of the CoCoCo database. Eleven in silico hits were initially selected, resulting in the discovery of an AChEI with a Ki of 3.2µM. In vitro kinetics and in silico molecular dynamics experiments informed the selection of an additional seven analogues. This led to the discovery of two further AChEIs, with Ki values of 2.9µM and 0.65µM. All three compounds exhibited reversible, mixed inhibition of acetylcholinesterase. Importantly, the in silico physicochemical filter facilitated the discovery of CNS drug-like compounds, such that all three inhibitors displayed high in vitro blood-brain barrier model permeability.