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.

醛氧化酶（Aldehyde oxidase, AOX）是一类钼黄素酶，近年来受到学界广泛关注。此前在临床试验阶段，其在外源性物质代谢中的作用常未被及时识别，进而对潜在新药的研发进程造成负面影响。此外，药物化学家为提升候选化合物对细胞色素P450（cytochrome P450）活性的稳定性所做的尝试，进一步增加了新化合物易受AOX亲核攻击的风险，这也凸显了AOX在药物代谢中的核心作用。因此，亟需开发全新的预测策略，以评估新化学实体对AOX酶的潜在作用倾向，从而提升研发效率、降低研发成本，并优化实验研究的优先级。本研究提出了当前最前沿的计算方法，可用于预测新化合物对人源AOX（human AOX, hAOX）的作用倾向，适用于药物设计与药代动力学优化。该方法基于大规模均质实验数据集开发，相关数据集也作为补充材料公开。