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
In silico design of novel proton-pump inhibitors with reduced adverse effects
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.
proton-pump inhibitor / adverse effect / pharmacological mechanism / toxicological mechanism / pKa calculation
[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
|
/
〈 | 〉 |