Identification of multi-target anti-cancer agents from TCM formula by in silico prediction and in vitro validation.
10.1016/S1875-5364(22)60180-8
- Author:
Bao-Yue ZHANG
1
;
Yi-Fu ZHENG
2
,
3
;
Jun ZHAO
1
;
De KANG
1
;
Zhe WANG
1
;
Lv-Jie XU
1
;
Ai-Lin LIU
4
;
Guan-Hua DU
5
Author Information
1. State Key Laboratory of Bioactive Substance and Function of Natural Medicines, Institute of Materia Medica, Chinese Academy of Medical Sciences and Peking Union Medical College, Beijing 100050, China.
2. State Key Laboratory of Bioactive Substance and Function of Natural Medicines, Institute of Materia Medica, Chinese Academy of Medical Sciences and Peking Union Medical College, Beijing 100050, China
3. School of Life Sciences, Tsinghua University, Beijing 100084, China.
4. State Key Laboratory of Bioactive Substance and Function of Natural Medicines, Institute of Materia Medica, Chinese Academy of Medical Sciences and Peking Union Medical College, Beijing 100050, China. Electronic address: murielle.liuailin@imm.ac.cn.
5. State Key Laboratory of Bioactive Substance and Function of Natural Medicines, Institute of Materia Medica, Chinese Academy of Medical Sciences and Peking Union Medical College, Beijing 100050, China. Electronic address: murielle.dugh@imm.ac.cn.
- Publication Type:Journal Article
- Keywords:
Cancer;
Multi-target;
Nave Bayesian;
Recursive partitioning;
TCM formulae;
mt-QSAR model
- MeSH:
Antineoplastic Agents/pharmacology*;
Bayes Theorem;
Drugs, Chinese Herbal/chemistry*;
Humans;
Medicine, Chinese Traditional;
Neoplasms/drug therapy*
- From:
Chinese Journal of Natural Medicines (English Ed.)
2022;20(5):332-351
- CountryChina
- Language:English
-
Abstract:
Cancer is a complex disease associated with multiple gene mutations and malignant phenotypes, and multi-target drugs provide a promising therapy idea for the treatment of cancer. Natural products with abundant chemical structure types and rich pharmacological characteristics could be ideal sources for screening multi-target antineoplastic drugs. In this paper, 50 tumor-related targets were collected by searching the Therapeutic Target Database and Thomson Reuters Integrity database, and a multi-target anti-cancer prediction system based on mt-QSAR models was constructed by using naïve Bayesian and recursive partitioning algorithm for the first time. Through the multi-target anti-cancer prediction system, some dominant fragments that act on multiple tumor-related targets were analyzed, which could be helpful in designing multi-target anti-cancer drugs. Anti-cancer traditional Chinese medicine (TCM) and its natural products were collected to form a TCM formula-based natural products library, and the potential targets of the natural products in the library were predicted by multi-target anti-cancer prediction system. As a result, alkaloids, flavonoids and terpenoids were predicted to act on multiple tumor-related targets. The predicted targets of some representative compounds were verified according to literature review and most of the selected natural compounds were found to exert certain anti-cancer activity in vitro biological experiments. In conclusion, the multi-target anti-cancer prediction system is very effective and reliable, and it could be further used for elucidating the functional mechanism of anti-cancer TCM formula and screening for multi-target anti-cancer drugs. The anti-cancer natural compounds found in this paper will lay important information for further study.
