<i>In silico</i> design of novel proton-pump inhibitors with reduced adverse effects

doi:10.1007/s11684-018-0630-3

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 Zhu^3,⁵(

), 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

Download: PDF(1769 KB) HTML
Export: BibTeX | EndNote | Reference Manager | ProCite | RefWorks

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 Author(s): 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:

https://academic.hep.com.cn/fmd/EN/10.1007/s11684-018-0630-3
https://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.

1	CMartín de Argila. Safety of potent gastric acid inhibition. Drugs 2005; 65(Suppl 1): 97–104 pmid: 16335863
2	DJHetzel, J Dent, WDReed, FMNarielvala, MMackinnon, JHMcCarthy, BMitchell, BRBeveridge, BHLaurence, GGGibson, AKGrant, DJCShearman, RWhitehead, PJBuckle. Healing and relapse of severe peptic esophagitis after treatment with omeprazole. Gastroenterology 1988; 95(4): 903–912 https://doi.org/10.1016/0016-5085(88)90162-X pmid: 3044912
3	THagiwara, K Mukaisho, TNakayama, HSugihara, THattori. 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 https://doi.org/10.1136/gut.2010.207662 pmid: 21097844
4	WLiu, SS Baker, JTrinidad, ALBurlingame, RDBaker, JGForte, LPVirtuoso, NKEgilmez, LZhu. Inhibition of lysosomal enzyme activities by proton pump inhibitors. J Gastroenterol 2013; 48(12): 1343–1352 https://doi.org/10.1007/s00535-013-0774-5 pmid: 23478938
5	GPolimeni, P Cutroneo, AGallo, SGallo, ESpina, APCaputi. Rabeprazole and psychiatric symptoms. Ann Pharmacother 2007; 41(7): 1315–1317 https://doi.org/10.1345/aph.1K134 pmid: 17609230
6	ESarzynski, C Puttarajappa, YXie, MGrover, HLaird-Fick. Association between proton pump inhibitor use and anemia: a retrospective cohort study. Dig Dis Sci 2011; 56(8): 2349–2353 https://doi.org/10.1007/s10620-011-1589-y pmid: 21318590
7	HABowlby, GR Dickens. Angioedema and urticaria associated with omeprazole confirmed by drug rechallenge. Pharmacotherapy 1994; 14(1): 119–122 https://doi.org/10.1002/j.1875-9114.1994.tb02796.x pmid: 8159596
8	DWu, T Qiu, QZhang, HKang, S Yuan, LZhu, RZhu. Systematic toxicity mechanism analysis of proton pump inhibitors: an in silico study. Chem Res Toxicol 2015; 28(3): 419–430 https://doi.org/10.1021/tx5003782 pmid: 25626140
9	G.Landrum Rdkit: a software suite for cheminformatics, computational chemistry, and predictive modeling. 2013
10	LSettimo, K Bellman, RMKnegtel. Comparison of the accuracy of experimental and predicted pKa values of basic and acidic compounds. Pharm Res 2014; 31(4): 1082–1095 https://doi.org/10.1007/s11095-013-1232-z pmid: 24249037
11	CALipinski, F Lombardo, BWDominy, PJFeeney. 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 https://doi.org/10.1016/S0169-409X(00)00129-0 pmid: 11259830
12	LOlbe, E Carlsson, PLindberg. A proton-pump inhibitor expedition: the case histories of omeprazole and esomeprazole. Nat Rev Drug Discov 2003; 2(2): 132–139 https://doi.org/10.1038/nrd1010 pmid: 12563304
13	VLaw, C Knox, YDjoumbou, TJewison, ACGuo, Y Liu, AMaciejewski, DArndt, MWilson, VNeveu, ATang, G Gabriel, CLy, SAdamjee, ZTDame, BHan, Y Zhou, DSWishart. DrugBank 4.0: shedding new light on drug metabolism. Nucleic Acids Res 2014; 42(Database issue D1): D1091–D1097 https://doi.org/10.1093/nar/gkt1068 pmid: 24203711
14	CYChen. TCM Database@Taiwan: the world’s largest traditional Chinese medicine database for drug screening in silico. PLoS One 2011; 6(1): e15939 https://doi.org/10.1371/journal.pone.0015939 pmid: 21253603
15	FZhu, Z Shi, CQin, LTao, X Liu, FXu, LZhang, YSong, X Liu, JZhang, BHan, P Zhang, YChen. Therapeutic target database update 2012: a resource for facilitating target-oriented drug discovery. Nucleic Acids Res 2012; 40(Database issue): D1128–D1136 https://doi.org/10.1093/nar/gkr797 pmid: 21948793
16	JLReymond, L Ruddigkeit, LBlum, Rvan Deursen. The enumeration of chemical space. Wiley Interdiscip Rev Comput Mol Sci 2012; 2(5): 717–733 https://doi.org/10.1002/wcms.1104
17	LRuddigkeit, R van Deursen, LCBlum, JLReymond. Enumeration of 166 billion organic small molecules in the chemical universe database GDB-17. J Chem Inf Model 2012; 52(11): 2864–2875 https://doi.org/10.1021/ci300415d pmid: 23088335
18	DSWishart, T Jewison, ACGuo, MWilson, CKnox, Y Liu, YDjoumbou, RMandal, FAziat, EDong, S Bouatra, ISinelnikov, DArndt, JXia, P Liu, FYallou, TBjorndahl, RPerez-Pineiro, REisner, FAllen, VNeveu, RGreiner, AScalbert. HMDB 3.0 — The Human Metabolome Database in 2013. Nucleic Acids Res 2013; 41(Database issue): D801–D807 pmid: 23161693
19	MKanehisa, S Goto, YSato, MFurumichi, MTanabe. KEGG for integration and interpretation of large-scale molecular data sets. Nucleic Acids Res 2012; 40(Database issue D1): D109–D114 https://doi.org/10.1093/nar/gkr988 pmid: 22080510
20	AWiener, M Shudler, ALevit, MYNiv. BitterDB: a database of bitter compounds. Nucleic Acids Res 2012; 40(Database issue): D413–D419 https://doi.org/10.1093/nar/gkr755 pmid: 21940398
21	JAhmed, T Meinel, MDunkel, MSMurgueitio, RAdams, CBlasse, AEckert, SPreissner, RPreissner. 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 https://doi.org/10.1093/nar/gkq910 pmid: 20952398
22	DWishart, D Arndt, APon, TSajed, ACGuo, Y Djoumbou, CKnox, MWilson, YLiang, JGrant, YLiu, SA Goldansaz, SMRappaport. T3DB: the toxic exposome database. Nucleic Acids Res 2015; 43(Database issue): D928–D934 https://doi.org/10.1093/nar/gku1004 pmid: 25378312
23	ELim, A Pon, YDjoumbou, CKnox, S Shrivastava, ACGuo, VNeveu, DSWishart. T3DB: a comprehensively annotated database of common toxins and their targets. Nucleic Acids Res 2010; 38(Database issue suppl_1): D781–D786 https://doi.org/10.1093/nar/gkp934 pmid: 19897546
24	HSong, Q Chu, FYan, YYang, W Han, XZheng. 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 https://doi.org/10.1111/jgh.13278 pmid: 26699443
25	JCShelley, A Cholleti, LLFrye, JRGreenwood, MRTimlin, MUchimaya. 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 https://doi.org/10.1007/s10822-007-9133-z pmid: 17899391
26	JJKlicić, RA Friesner, SYLiu, WCGuida. 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 https://doi.org/10.1021/jp012533f
27	GTBalogh, B Gyarmati, BNagy, LMolnar, GMKeseru. Comparative evaluation of in silico pK(a) prediction tools on the Gold Standard Dataset. QSAR Comb Sci 2009; 28(10): 1148–1155 https://doi.org/10.1002/qsar.200960036
28	SVellay, Miller N Latimer, GPaillard. Interactive text mining with Pipeline Pilot: a bibliographic web-based tool for PubMed. Infect Disord Drug Targets 2009; 9(3): 366–374 https://doi.org/10.2174/1871526510909030366 pmid: 19519489
29	MRupp, R Körner, IV. Tetko. Predicting the pKa of small molecule. Comb Chem High Throughput Screen 2011; 14(5): 307–327 https://doi.org/10.2174/138620711795508403 pmid: 21470178
30	CGrüber, V Buss. 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 https://doi.org/10.1016/0045-6535(89)90503-1
31	WPWalters, MT Stahl, MAMurcko. Virtual screening — an overview. Drug Discov Today 1998; 3(4): 160–178 https://doi.org/10.1016/S1359-6446(97)01163-X
32	GRBickerton, GV Paolini, JBesnard, SMuresan, ALHopkins. Quantifying the chemical beauty of drugs. Nat Chem 2012; 4(2): 90–98 https://doi.org/10.1038/nchem.1243 pmid: 22270643
33	STian, J Wang, YLi, DLi, L Xu, THou. The application of in silico drug-likeness predictions in pharmaceutical research. Adv Drug Deliv Rev 2015; 86: 2–10 https://doi.org/10.1016/j.addr.2015.01.009 pmid: 25666163
34	CDurand, KC Willett, ARDesilets. Proton pump inhibitor use in hospitalized patients: is overutilization becoming a problem? Clin Med Insights Gastroenterol 2012; 5: CGast. S9588 https://doi.org/10.4137/CGast.S9588 pmid: 24833936

[1]	FMD-17115-OF-ZRX_suppl_1	Download
[2]	FMD-17115-OF-ZRX_suppl_2	Download

[1]	Hudan Pan, Yanfang Zheng, Zhongqiu Liu, Zhongwen Yuan, Rutong Ren, Hua Zhou, Ying Xie, Liang Liu. Deciphering the pharmacological mechanism of Guan-Jie-Kang in treating rat adjuvant-induced arthritis using omics analysis[J]. Front. Med., 2019, 13(5): 564-574.

Viewed

Full text

Abstract

Cited

Shared

Discussed