Unveiling the metabolic fate of drugs through metabolic reaction-based molecular networking.
10.1016/j.apsb.2025.03.050
- Author:
Haodong ZHU
1
;
Xupeng TONG
2
;
Qi WANG
1
;
Aijing LI
1
;
Zubao WU
1
;
Qiqi WANG
1
;
Pei LIN
1
;
Xinsheng YAO
1
;
Liufang HU
1
;
Liangliang HE
1
;
Zhihong YAO
1
Author Information
1. State Key Laboratory of Bioactive Molecules and Druggability Assessment; International Cooperative Laboratory of Traditional Chinese Medicine Modernization and Innovative Drug Development of Ministry of Education (MOE) of China; Guangdong Basic Research Center of Excellence for Natural Bioactive Molecules and Discovery of Innovative Drugs; Guangdong Province Key Laboratory of Pharmacodynamic Constituents of TCM and New Drugs Research, College of Pharmacy, Jinan University, Guangzhou 510632, China.
2. Hangzhou Chenfeng Qingxing Technology Co., Ltd., Hangzhou 310000, China.
- Publication Type:Journal Article
- Keywords:
Drug metabolism;
Endogenous interference elimination;
LC–MS;
MRMN;
MS2 feature degradation improvement;
Prototypes and metabolites “one-pot” annotation;
Redundant ions identification;
YaoLab online platform
- From:
Acta Pharmaceutica Sinica B
2025;15(6):3210-3225
- CountryChina
- Language:English
-
Abstract:
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.
