Aldehyde oxidase mediated enantioselective metabolic health risk of dinotefuran

Chiral pollutants often pose significant differential environmental health risks.In this study,the biotransformation of chiral dinotefuran(DIN)and its enantioselective metabolic toxicity mechanisms have been systemically investigated.Firstly,reversedphase chromatography-high resolution mass spectrometry was developed to quantify the content of DIN R/S chiral enantiomer with pg level sensitivity,revealing a lower elimination rate constant(K_(e))of S-DIN(0.730 h^(-1))than R-DIN(0.746 h^(-1)).Secondly,the interaction mechanism between DIN metabolism and important endogenous bioactive molecules,such as aldehyde oxidase(AOX)and neurotransmitters,was revealed.The DIN nitro-group was converted into a guanidine group by the reducing site of nearby flavin adenine dinucleotide(FAD)in AOX with the preferred higher affinity of S-configuration.Meanwhile,the endogenous tryptophan(Trp)aldehyde metabolic intermediate,5-hydroxyindoleacetaldehyde(5-HIAL),provides a persistent electron donor for DIN reduction via the oxidation-catalyzed site in AOX,resulting in remarkable up-regulation of monoamine neurotransmitters such as serotonin and dopamine.Thirdly,the higher level of neurotransmitters further mediated dysregulation of oxylipin homeostasis via the serotonergic pathway,where S-DIN exhibited more pronounced liver lipid damage and environmental health risk with the accumulated lipid biomarkers,oxidized triglyceride(OxTG)and oxidized sphingomyelin(OxSM).This study elucidates the AOX-mediated enantioselectivity metabolic pathway of DIN,providing a new analytical method for chiral pollutants and paves the way for their health risk assessments.

Transfer learning enhanced graph neural network for aldehyde oxidase metabolism prediction and its experimental application

Aldehyde oxidase(AOX)is a molybdoenzyme that is primarily expressed in the liver and is involved in the metabolism of drugs and other xenobiotics.AOX-mediated metabolism can result in unexpected outcomes,such as the production of toxic metabolites and high metabolic clearance,which can lead to the clinical failure of novel therapeutic agents.Computational models can assist medicinal chemists in rapidly evaluating the AOX metabolic risk of compounds during the early phases of drug discovery and provide valuable clues for manipulating AOX-mediated metabolism liability.In this study,we developed a novel graph neural network called AOMP for predicting AOX-mediated metabolism.AOMP integrated the tasks of metabolic substrate/non-substrate classification and metabolic site prediction,while utilizing transfer learning from 13C nuclear magnetic resonance data to enhance its performance on both tasks.AOMP significantly outperformed the benchmark methods in both cross-validation and external testing.Using AOMP,we systematically assessed the AOX-mediated metabolism of common fragments in kinase inhibitors and successfully identified four new scaffolds with AOX metabolism liability,which were validated through in vitro experiments.Furthermore,for the convenience of the community,we established the first online service for AOX metabolism prediction based on AOMP,which is freely available at https://aomp.alphama.com.cn.