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Frontiers of Medicine

ISSN 2095-0217

ISSN 2095-0225(Online)

CN 11-5983/R

Postal Subscription Code 80-967

2018 Impact Factor: 1.847

Front. Med.    2019, Vol. 13 Issue (2) : 277-284    https://doi.org/10.1007/s11684-018-0630-3
RESEARCH ARTICLE |
In silico design of novel proton-pump inhibitors with reduced adverse effects
Xiaoyi Li1, Hong Kang2, Wensheng Liu3, Sarita Singhal3, Na Jiao1, Yong Wang4, Lixin Zhu3,5(), Ruixin Zhu1()
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. Genome, Environment and Microbiome Community of Excellence, The State University of New York at Buffalo, Buffalo, NY 14214, USA
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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.

Keywords proton-pump inhibitor      adverse effect      pharmacological mechanism      toxicological mechanism      pKa calculation     
Corresponding Authors: Lixin Zhu,Ruixin Zhu   
Just Accepted Date: 24 April 2018   Online First Date: 31 May 2018    Issue Date: 28 March 2019
 Cite this article:   
Xiaoyi Li,Hong Kang,Wensheng Liu, et al. In silico design of novel proton-pump inhibitors with reduced adverse effects[J]. Front. Med., 2019, 13(2): 277-284.
 URL:  
http://academic.hep.com.cn/fmd/EN/10.1007/s11684-018-0630-3
http://academic.hep.com.cn/fmd/EN/Y2019/V13/I2/277
Fig.1  Key steps of PPI pharmacology. PPIs are made as a prodrug. PPIs are not reactive at neutral pH. PPIs are hydrophobic and freely move across membranes. PPIs become protonated, carry a positive charge, and cannot go across plasma membranes once they reach an acidic environment. Protonation triggers a 3-step transformation that results in the sulfinamide form of PPI, which is the active form of the drug. Sulfinamide forms a di-sulfide bond with another chemical with a free thio group. The covalent bond formation between PPI and H,K inactivates H,K-ATPase and inhibits acid secretion.
Fig.2  Structure of PPI. PPI can be split into two parts. The first part is the substituted pyridine, and the second part is the substituted benzimidazoles (or substituted imidazopyridines in several cases).
Fig.3  Designed molecular scaffolds. Six different molecular scaffolds of PPIs are designed based on the analysis on PPIs pharmacology and toxicology.
Fig.4  Relationship network of the first five R groups in the databases. The lines indicate from which database a certain molecule is generated, and the thickness of the line reflects the log value of the number of certain molecule in the corresponding database.
Fig.5  pKa1 distributions of virtual molecules generated from R groups of 10 different databases. The pKa1 distribution significantly varies in different R group databases and scaffolds.
Fig.6  Distributions of pKa values and structural similarity of the generated virtual molecules from different scaffolds. (A) The distributions of pKa values of nitrogen atom on pyrimidine ring. (B) The PCA analysis on the FingerPrints of 115 511 virtual molecules. FingerPrints are described by MACCS from RDkit, and different colors indicate the pKa value.
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