GSFM: A genome-scale functional module transformation to represent drug efficacy for in silico drug discovery.
10.1016/j.apsb.2024.08.017
- Author:
Saisai TIAN
1
;
Xuyang LIAO
2
;
Wen CAO
1
;
Xinyi WU
3
;
Zexi CHEN
4
;
Jinyuan LU
5
;
Qun WANG
6
;
Jinbo ZHANG
7
;
Luonan CHEN
4
;
Weidong ZHANG
1
Author Information
1. Department of Phytochemistry, School of Pharmacy, Second Military Medical University, Shanghai 200433, China.
2. State Key Laboratory for Quality Ensurance and Sustainable Use of Dao-di Herbs, Institute of Medicinal Plant Development, Chinese Academy of Medical Sciences and Peking Union Medical College, Beijing 100193, China.
3. School of Pharmacy, Henan University, Kaifeng 475004, China.
4. Key Laboratory of Systems Biology, Shanghai Institutes for Biological Sciences, Chinese Academy of Sciences, Shanghai 200031, China.
5. School of Pharmacy, Anhui University of Chinese Medicine, Hefei 230012, China.
6. Shanghai Frontiers Science Center of TCM Chemical Biology, Institute of Interdisciplinary Integrative Medicine Research, Shanghai University of Traditional Chinese Medicine, Shanghai 201203, China.
7. Department of pharmacy, Tianjin Rehabilitation Center of Joint Logistics Support Force, Tianjin 300110, China.
- Publication Type:Journal Article
- Keywords:
Data transformation strategy;
Drug efficacy;
GSFM;
In silico drug discovery;
Multi-dimensions activities
- From:
Acta Pharmaceutica Sinica B
2025;15(1):133-150
- CountryChina
- Language:English
-
Abstract:
Pharmacotranscriptomic profiles, which capture drug-induced changes in gene expression, offer vast potential for computational drug discovery and are widely used in modern medicine. However, current computational approaches neglected the associations within gene‒gene functional networks and unrevealed the systematic relationship between drug efficacy and the reversal effect. Here, we developed a new genome-scale functional module (GSFM) transformation framework to quantitatively evaluate drug efficacy for in silico drug discovery. GSFM employs four biologically interpretable quantifiers: GSFM_Up, GSFM_Down, GSFM_ssGSEA, and GSFM_TF to comprehensively evaluate the multi-dimension activities of each functional module (FM) at gene-level, pathway-level, and transcriptional regulatory network-level. Through a data transformation strategy, GSFM effectively converts noisy and potentially unreliable gene expression data into a more dependable FM active matrix, significantly outperforming other methods in terms of both robustness and accuracy. Besides, we found a positive correlation between RSGSFM and drug efficacy, suggesting that RSGSFM could serve as representative measure of drug efficacy. Furthermore, we identified WYE-354, perhexiline, and NTNCB as candidate therapeutic agents for the treatment of breast-invasive carcinoma, lung adenocarcinoma, and castration-resistant prostate cancer, respectively. The results from in vitro and in vivo experiments have validated that all identified compounds exhibit potent anti-tumor effects, providing proof-of-concept for our computational approach.
