In silico design of novel proton-pump inhibitors with reduced adverse effects

Xiaoyi Li, Hong Kang, Wensheng Liu, Sarita Singhal, Na Jiao, Yong Wang, Lixin Zhu, Ruixin Zhu

PDF(1769 KB)
PDF(1769 KB)
Front. Med. ›› 2019, Vol. 13 ›› Issue (2) : 277-284. DOI: 10.1007/s11684-018-0630-3
RESEARCH ARTICLE

In silico design of novel proton-pump inhibitors with reduced adverse effects

Author information +
History +

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

Cite this article

Download citation ▾
Xiaoyi Li, Hong Kang, Wensheng Liu, Sarita Singhal, Na Jiao, Yong Wang, Lixin Zhu, Ruixin Zhu. In silico design of novel proton-pump inhibitors with reduced adverse effects. Front. Med., 2019, 13(2): 277‒284 https://doi.org/10.1007/s11684-018-0630-3

References

[1]
Martín de Argila C. Safety of potent gastric acid inhibition. Drugs 2005; 65(Suppl 1): 97–104
Pubmed
[2]
Hetzel DJ, Dent J, Reed WD, Narielvala FM, Mackinnon M, McCarthy JH, Mitchell B, Beveridge BR, Laurence BH, Gibson GG, Grant AK, Shearman DJC, Whitehead R, Buckle PJ. Healing and relapse of severe peptic esophagitis after treatment with omeprazole. Gastroenterology 1988; 95(4): 903–912
CrossRef Pubmed Google scholar
[3]
Hagiwara T, Mukaisho K, Nakayama T, Sugihara H, Hattori T. Long-term proton pump inhibitor administration worsens atrophic corpus gastritis and promotes adenocarcinoma development in Mongolian gerbils infected with Helicobacter pylori. Gut 2011; 60(5): 624–630
CrossRef Pubmed Google scholar
[4]
Liu W, Baker SS, Trinidad J, Burlingame AL, Baker RD, Forte JG, Virtuoso LP, Egilmez NK, Zhu L. Inhibition of lysosomal enzyme activities by proton pump inhibitors. J Gastroenterol 2013; 48(12): 1343–1352
CrossRef Pubmed Google scholar
[5]
Polimeni G, Cutroneo P, Gallo A, Gallo S, Spina E, Caputi AP. Rabeprazole and psychiatric symptoms. Ann Pharmacother 2007; 41(7): 1315–1317
CrossRef Pubmed Google scholar
[6]
Sarzynski E, Puttarajappa C, Xie Y, Grover M, Laird-Fick H. Association between proton pump inhibitor use and anemia: a retrospective cohort study. Dig Dis Sci 2011; 56(8): 2349–2353
CrossRef Pubmed Google scholar
[7]
Bowlby HA, Dickens GR. Angioedema and urticaria associated with omeprazole confirmed by drug rechallenge. Pharmacotherapy 1994; 14(1): 119–122
CrossRef Pubmed Google scholar
[8]
Wu D, Qiu T, Zhang Q, Kang H, Yuan S, Zhu L, Zhu R. Systematic toxicity mechanism analysis of proton pump inhibitors: an in silico study. Chem Res Toxicol 2015; 28(3): 419–430
CrossRef Pubmed Google scholar
[9]
Landrum G. Rdkit: a software suite for cheminformatics, computational chemistry, and predictive modeling. 2013
[10]
Settimo L, Bellman K, Knegtel RM. Comparison of the accuracy of experimental and predicted pKa values of basic and acidic compounds. Pharm Res 2014; 31(4): 1082–1095
CrossRef Pubmed Google scholar
[11]
Lipinski CA, Lombardo F, Dominy BW, Feeney PJ. Experimental and computational approaches to estimate solubility and permeability in drug discovery and development settings. Adv Drug Deliv Rev 2001; 46(1-3): 3–26
CrossRef Pubmed Google scholar
[12]
Olbe L, Carlsson E, Lindberg P. A proton-pump inhibitor expedition: the case histories of omeprazole and esomeprazole. Nat Rev Drug Discov 2003; 2(2): 132–139
CrossRef Pubmed Google scholar
[13]
Law V, Knox C, Djoumbou Y, Jewison T, Guo AC, Liu Y, Maciejewski A, Arndt D, Wilson M, Neveu V, Tang A, Gabriel G, Ly C, Adamjee S, Dame ZT, Han B, Zhou Y, Wishart DS. DrugBank 4.0: shedding new light on drug metabolism. Nucleic Acids Res 2014; 42(Database issue D1): D1091–D1097
CrossRef Pubmed Google scholar
[14]
Chen CY. TCM Database@Taiwan: the world’s largest traditional Chinese medicine database for drug screening in silico. PLoS One 2011; 6(1): e15939
CrossRef Pubmed Google scholar
[15]
Zhu F, Shi Z, Qin C, Tao L, Liu X, Xu F, Zhang L, Song Y, Liu X, Zhang J, Han B, Zhang P, Chen Y. Therapeutic target database update 2012: a resource for facilitating target-oriented drug discovery. Nucleic Acids Res 2012; 40(Database issue): D1128–D1136
CrossRef Pubmed Google scholar
[16]
Reymond JL, Ruddigkeit L, Blum L, van Deursen R. The enumeration of chemical space. Wiley Interdiscip Rev Comput Mol Sci 2012; 2(5): 717–733
CrossRef Google scholar
[17]
Ruddigkeit L, van Deursen R, Blum LC, Reymond JL. Enumeration of 166 billion organic small molecules in the chemical universe database GDB-17. J Chem Inf Model 2012; 52(11): 2864–2875
CrossRef Pubmed Google scholar
[18]
Wishart DS, Jewison T, Guo AC, Wilson M, Knox C, Liu Y, Djoumbou Y, Mandal R, Aziat F, Dong E, Bouatra S, Sinelnikov I, Arndt D, Xia J, Liu P, Yallou F, Bjorndahl T, Perez-Pineiro R, Eisner R, Allen F, Neveu V, Greiner R, Scalbert A. HMDB 3.0 — The Human Metabolome Database in 2013. Nucleic Acids Res 2013; 41(Database issue): D801–D807
Pubmed
[19]
Kanehisa M, Goto S, Sato Y, Furumichi M, Tanabe M. KEGG for integration and interpretation of large-scale molecular data sets. Nucleic Acids Res 2012; 40(Database issue D1): D109–D114
CrossRef Pubmed Google scholar
[20]
Wiener A, Shudler M, Levit A, Niv MY. BitterDB: a database of bitter compounds. Nucleic Acids Res 2012; 40(Database issue): D413–D419
CrossRef Pubmed Google scholar
[21]
Ahmed J, Meinel T, Dunkel M, Murgueitio MS, Adams R, Blasse C, Eckert A, Preissner S, Preissner R. CancerResource: a comprehensive database of cancer-relevant proteins and compound interactions supported by experimental knowledge. Nucleic Acids Res 2011; 39(Database issue suppl_1): D960–D967
CrossRef Pubmed Google scholar
[22]
Wishart D, Arndt D, Pon A, Sajed T, Guo AC, Djoumbou Y, Knox C, Wilson M, Liang Y, Grant J, Liu Y, Goldansaz SA, Rappaport SM. T3DB: the toxic exposome database. Nucleic Acids Res 2015; 43(Database issue): D928–D934
CrossRef Pubmed Google scholar
[23]
Lim E, Pon A, Djoumbou Y, Knox C, Shrivastava S, Guo AC, Neveu V, Wishart DS. T3DB: a comprehensively annotated database of common toxins and their targets. Nucleic Acids Res 2010; 38(Database issue suppl_1): D781–D786
CrossRef Pubmed Google scholar
[24]
Song H, Chu Q, Yan F, Yang Y, Han W, Zheng X. Red pitaya betacyanins protects from diet-induced obesity, liver steatosis and insulin resistance in association with modulation of gut microbiota in mice. J Gastroenterol Hepatol 2016; 31(8): 1462–1469
CrossRef Pubmed Google scholar
[25]
Shelley JC, Cholleti A, Frye LL, Greenwood JR, Timlin MR, Uchimaya M. Epik: a software program for pK( a ) prediction and protonation state generation for drug-like molecules. J Comput Aided Mol Des 2007; 21(12): 681–691
CrossRef Pubmed Google scholar
[26]
Klicić JJ, Friesner RA, Liu SY, Guida WC. Accurate prediction of acidity constants in aqueous solution via density functional theory and self-consistent reaction field methods. J Phys Chem A 2002; 106(7): 1327–1335
CrossRef Google scholar
[27]
Balogh GT, Gyarmati B, Nagy B, Molnar L, Keseru GM. Comparative evaluation of in silico pK(a) prediction tools on the Gold Standard Dataset. QSAR Comb Sci 2009; 28(10): 1148–1155
CrossRef Google scholar
[28]
Vellay S, Miller Latimer N, Paillard G. Interactive text mining with Pipeline Pilot: a bibliographic web-based tool for PubMed. Infect Disord Drug Targets 2009; 9(3): 366–374
CrossRef Pubmed Google scholar
[29]
Rupp M, Körner R, V. Tetko I. Predicting the pKa of small molecule. Comb Chem High Throughput Screen 2011; 14(5): 307–327
CrossRef Pubmed Google scholar
[30]
Grüber C, Buss V. Quantum-mechanically calculated properties for the development of quantitative structure-activity relationships (QSAR’S). pKa-values of phenols and aromatic and aliphatic carboxylic acids. Chemosphere 1989; 19(10-11): 1595–1609
CrossRef Google scholar
[31]
Walters WP, Stahl MT, Murcko MA. Virtual screening — an overview. Drug Discov Today 1998; 3(4): 160–178
CrossRef Google scholar
[32]
Bickerton GR, Paolini GV, Besnard J, Muresan S, Hopkins AL. Quantifying the chemical beauty of drugs. Nat Chem 2012; 4(2): 90–98
CrossRef Pubmed Google scholar
[33]
Tian S, Wang J, Li Y, Li D, Xu L, Hou T. The application of in silico drug-likeness predictions in pharmaceutical research. Adv Drug Deliv Rev 2015; 86: 2–10
CrossRef Pubmed Google scholar
[34]
Durand C, Willett KC, Desilets AR. Proton pump inhibitor use in hospitalized patients: is overutilization becoming a problem? Clin Med Insights Gastroenterol 2012; 5: CGast. S9588
CrossRef Pubmed Google scholar

Acknowledgements

This work was supported by National Natural Science Foundation of China (Nos. 31200986 and 41530105) (to RZ), Natural Science Foundation, the Shanghai Committee of Science and Technology (No. 16ZR1449800) (to RZ), the Fundamental Research Funds for the Central Universities (Nos. 10247201546 and 2000219083) (to RZ), a departmental start-up fund (to LZ), the Peter and Tommy Fund, Inc., Buffalo, NY (to LZ), Funds from the University at Buffalo Community of Excellence in Genome, Environment and Microbiome (GEM) (to LZ) and UTHealth Innovation for Cancer Prevention Research Training Program Post-doctoral Fellowship (Cancer Prevention and Research Institute of Texas, grant #RP160015) (to HK). The funders had no role in study design, data collection and analysis, decision to publish, or preparation of the manuscript.

Compliance with ethics guidelines

Xiaoyi Li, Hong Kang, Wensheng Liu, Sarita Singhal, Na Jiao, Yong Wang, Lixin Zhu, and Ruixin Zhu declare that they have no conflict of interest. This manuscript does not contain any studies with human or animal subjects.

Electronic Supplementary Material

Supplementary material is available in the online version of this article at https://doi.org/10.1007/s11684-018-0630-3 and is accessible for authorized users.

RIGHTS & PERMISSIONS

2018 Higher Education Press and Springer-Verlag GmbH Germany, part of Springer Nature
AI Summary AI Mindmap
PDF(1769 KB)

Accesses

Citations

Detail

Sections
Recommended

/