Unveiling the metabolic fate of drugs through metabolic reaction-based molecular networking

Unveiling the metabolic fate of drugs through metabolic reaction-based molecular networking

Effective annotation of in vivo drug metabolites using liquid chromatography-mass spectrometry (LC–MS) remains a formidable challenge. Herein, a metabolic reaction-based molecular networking (MRMN) strategy is introduced, which enables the “one-pot” discovery of prototype drugs and their metabolites...

Full description

Saved in:
Bibliographic Details
Main Authors: Haodong Zhu, Xupeng Tong, Qi Wang, Aijing Li, Zubao Wu, Qiqi Wang, Pei Lin, Xinsheng Yao, Liufang Hu, Liangliang He, Zhihong Yao
Format: Article
Language:English
Published: Elsevier 2025-06-01
Series:Acta Pharmaceutica Sinica B
Subjects:
Drug metabolism
MRMN
LC–MS
MS2 feature degradation improvement
Redundant ions identification
Endogenous interference elimination
Online Access:http://www.sciencedirect.com/science/article/pii/S2211383525002199
Tags: Add Tag
No Tags, Be the first to tag this record!
Description
Summary:Effective annotation of in vivo drug metabolites using liquid chromatography-mass spectrometry (LC–MS) remains a formidable challenge. Herein, a metabolic reaction-based molecular networking (MRMN) strategy is introduced, which enables the “one-pot” discovery of prototype drugs and their metabolites. MRMN constructs networks by matching metabolic reactions and evaluating MS2 spectral similarity, incorporating innovations and improvements in feature degradation of MS2 spectra, exclusion of endogenous interference, and recognition of redundant nodes. A minimum 75% correlation between structural similarity and MS2 similarity of neighboring metabolites was ensured, mitigating false negatives due to spectral feature degradation. At least 79% of nodes, 49% of edges, and 97% of subnetworks were reduced by an exclusion strategy of endogenous ions compared to the Global Natural Products Social Molecular Networking (GNPS) platform. Furthermore, an approach of redundant ions identification was refined, achieving a 10%–40% recognition rate across different samples. The effectiveness of MRMN was validated through a single compound, plant extract, and mixtures of multiple plant extracts. Notably, MRMN is freely accessible online at https://yaolab.network, broadening its applications.
ISSN:2211-3835

Similar Items