From Experiments to a Fast Easy-to-Use Computational Methodology to Predict Human Aldehyde Oxidase Selectivity and Metabolic Reactions

Aldehyde oxidase (AOX) is a molibdo-flavoenzyme
that has raised
great interest in recent years, since its contribution in xenobiotic
metabolism has not always been identified before clinical trials,
with consequent negative effects on the fate of new potential drugs.
The fundamental role of AOX in metabolizing xenobiotics is also due
to the attempt of medicinal chemists to stabilize candidates toward
cytochrome P450 activity, which increases the risk for new compounds
to be susceptible to AOX nucleophile attack. Therefore, novel strategies
to predict the potential liability of new entities toward the AOX
enzyme are urgently needed to increase effectiveness, reduce costs,
and prioritize experimental studies. In the present work, we present
the most up-to-date computational method to predict liability toward
human AOX (hAOX), for applications in drug design
and pharmacokinetic optimization. The method was developed using a
large data set of homogeneous experimental data, which is also disclosed
as Supporting Information.