In silico design of novel proton-pump inhibitors with reduced adverse effects.
10.1007/s11684-018-0630-3
- Author:
Xiaoyi LI
1
;
Hong KANG
2
;
Wensheng LIU
3
;
Sarita SINGHAL
3
;
Na JIAO
1
;
Yong WANG
4
;
Lixin ZHU
5
;
Ruixin ZHU
6
Author Information
1. Department of Gastroenterology, Shanghai East Hospital, School of Life Sciences and Technology, Tongji University, Shanghai, 200092, China.
2. School of Biomedical Informatics, The University of Texas Health Science Center at Houston, 7000 Fannin St, Houston, TX, 77030, USA.
3. Digestive Diseases and Nutrition Center, Department of Pediatrics, The State University of New York at Buffalo, Buffalo, NY, 14260, USA.
4. Basic Medical College, Beijing University of Chinese Medicine, Beijing, 100029, China.
5. Digestive Diseases and Nutrition Center, Department of Pediatrics, The State University of New York at Buffalo, Buffalo, NY, 14260, USA. lixinzhu@buffalo.edu.
6. Department of Gastroenterology, Shanghai East Hospital, School of Life Sciences and Technology, Tongji University, Shanghai, 200092, China. rxzhu@tongji.edu.cn.
- Publication Type:Journal Article
- Keywords:
adverse effect;
pKa calculation;
pharmacological mechanism;
proton-pump inhibitor;
toxicological mechanism
- MeSH:
Computer Simulation;
Drug Design;
Humans;
Proton Pump Inhibitors;
toxicity;
Toxicological Phenomena
- From:
Frontiers of Medicine
2019;13(2):277-284
- CountryChina
- Language:English
-
Abstract:
The development of new proton-pump inhibitors (PPIs) with less adverse effects by lowering the pKa values of nitrogen atoms in pyrimidine rings has been previously suggested by our group. In this work, we proposed that new PPIs should have the following features: (1) number of ring II = number of ring I + 1; (2) preferably five, six, or seven-membered heteroatomic ring for stability; and (3) 1 < pKa1 < 4. Six molecular scaffolds based on the aforementioned criteria were constructed, and R groups were extracted from compounds in extensive data sources. A virtual molecule dataset was established, and the pKa values of specific atoms on the molecules in the dataset were calculated to select the molecules with required pKa values. Drug-likeness screening was further conducted to obtain the candidates that significantly reduced the adverse effects of long-term PPI use. This study provided insights and tools for designing targeted molecules in silico that are suitable for practical applications.
