Computational-aided understanding of metabolic mechanism: A case study

This study presents an innovative computational method, combined with experimental validation, to predict and elucidate the metabolic pathways of SILA-123 , a novel FLT3 inhibitor. Using UFLC/Q-TOF MS, we identified 21 metabolites generated through key reactions (oxidation, reduction, hydrolysis, cleavage, deamination, and glucuronidation). Advanced computational techniques were applied to identify key metabolic enzymes, with results confirmed experimentally. Our findings represent a significant advancement in the field of metabolic prediction. By integrating computational methods with experimental data, we have established a robust framework that can be applied to other therapeutic compounds. This approach not only enhances our understanding of SILA-123 's metabolic pathways but also provides a novel strategy for guiding metabolic prediction.

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Publication Details

Journal
Results in Chemistry
Published
2025-06-16
DOI
https://doi.org/10.1016/j.rechem.2025.102453
Primary Topic
Microbial Metabolic Engineering and Bioproduction
Type
article
Field-Weighted Citation Impact
0.00
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article

Computational-aided understanding of metabolic mechanism: A case study

Q. L. Niu, Mingyu Bai, Shan‐Liang Sun, Nian‐Guang Li et al.
Results in Chemistry
Microbial Metabolic Engineering and Bioproduction
article

Computational-aided understanding of metabolic mechanism: A case study

Q. L. Niu, Mingyu Bai, Shan‐Liang Sun, Nian‐Guang Li, Chenxiao Shan, Yan-Cheng Yu, Zhi‐Hao Shi, Qian Liu, Zixuan Wang, Qingqing Li, Xiaolong Wang, Mengyuan Zhang, Ning Ding, Ning Huang, Jin Yang, Xing-Feng Ni, Z Zhao, Meng Sun
article en

Abstract

This study presents an innovative computational method, combined with experimental validation, to predict and elucidate the metabolic pathways of SILA-123 , a novel FLT3 inhibitor. Using UFLC/Q-TOF MS, we identified 21 metabolites generated through key reactions (oxidation, reduction, hydrolysis, cleavage, deamination, and glucuronidation). Advanced computational techniques were applied to identify key metabolic enzymes, with results confirmed experimentally. Our findings represent a significant advancement in the field of metabolic prediction. By integrating computational methods with experimental data, we have established a robust framework that can be applied to other therapeutic compounds. This approach not only enhances our understanding of SILA-123 's metabolic pathways but also provides a novel strategy for guiding metabolic prediction.

Results in ChemistryVol. 16
Nanjing University of Chinese Medicine (CN), China Pharmaceutical University (CN)
Zero hunger
Openalex Percentile: Top 87%
Microbial Metabolic Engineering and Bioproduction
DOIOpen Access PDFOpenAlex
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Computational-aided understanding of metabolic mechanism: A case study — Q. L. Niu, Mingyu Bai, et al. · Results in Chemistry (2025) | TGRS Research Map | TGRS