Introduction/Aim. Despite the fact that proton pump inhibitors are widely used for the inhibition of gastric acid secretion, recent studies have revealed certain long-term side effects. Due to acidic environment in the stomach, it is challenging to design new competitive inhibitors of gastric proton pump with more potent inhibition of gastric acid secretion to conventional drugs. The aim of this in silico study was to assess the potential of designed vonoprazan derivatives to inhibit the gastric proton pump using molecular docking study. Methods. The distribution-based design of the vonoprazan derivatives was carried out by optimization of the distribution coefficient at physiological pH and pKa values. A molecular docking study was performed using the protein structure of gastric proton pump (PDB ID: 5YLU) in complex with vonoprazan in AutoDock Vina software. Results. According to the estimated values of docking scores, derivatives 11, 21, and 25 showed the highest binding affinity to gastric proton pump. Compounds 3, 13, 14, 16, 17, 20, 22, and 23 formed the highest number of significant binding interactions with the active site of proton pump. Conclusion. Based on the obtained binding parameters, it can be concluded that derivatives 14 and 23 achieved the highest number of significant binding interactions (16 and 15, respectively) with concomitant lower values of the docking scores (-9.2 and-9.3 kcal/mol) compared to vonoprazan as a binding control. Based on the binding assessment criteria, these two compounds represent the molecules with the strongest inhibitory potential towards gastric proton pump.
References
1.
Buntrock RE. ChemOffice Ultra 7.0. Journal of Chemical Information and Computer Sciences. 2002;42(6):1505–6.
2.
Sabe VT, Ntombela T, Jhamba LA, Maguire GEM, Govender T, Naicker T, et al. Current trends in computer aided drug design and a highlight of drugs discovered via computational techniques: A review. European Journal of Medicinal Chemistry. 2021;224:113705.
3.
Marvinsketch. DISPLAYING and characterizing chemical structures, substructures, and reactions. 2010;
4.
Daina A, Michielin O, Zoete V. SwissADME: a free web tool to evaluate pharmacokinetics, drug-likeness and medicinal chemistry friendliness of small molecules. Scientific Reports. 7(1).
5.
Lipinski CA, Lombardo F, Dominy BW, Feeney PJ. Experimental and computational approaches to estimate solubility and permeability in drug discovery and development settings 1PII of original article: S0169-409X(96)00423-1. The article was originally published in Advanced Drug Delivery Reviews 23 (1997) 3–25. 1. Advanced Drug Delivery Reviews. 2001;46(1–3):3–26.
6.
Ghose AK, Viswanadhan VN, Wendoloski JJ. Prediction of Hydrophobic (Lipophilic) Properties of Small Organic Molecules Using Fragmental Methods: An Analysis of ALOGP and CLOGP Methods. The Journal of Physical Chemistry A. 1998;102(21):3762–72.
7.
Egan WJ, Merz, KM, Baldwin JJ. Prediction of Drug Absorption Using Multivariate Statistics. Journal of Medicinal Chemistry. 2000;43(21):3867–77.
8.
Veber DF, Johnson SR, Cheng HY, Smith BR, Ward KW, Kopple KD. Molecular Properties That Influence the Oral Bioavailability of Drug Candidates. Journal of Medicinal Chemistry. 2002;45(12):2615–23.
9.
Muegge I, Heald SL, Brittelli D. Simple Selection Criteria for Drug-like Chemical Matter. Journal of Medicinal Chemistry. 2001;44(12):1841–6.
10.
Daina A, Zoete V. A BOILED‐Egg To Predict Gastrointestinal Absorption and Brain Penetration of Small Molecules. ChemMedChem. 2016;11(11):1117–21.
11.
Pires DEV, Blundell TL, Ascher DB. pkCSM: Predicting Small-Molecule Pharmacokinetic and Toxicity Properties Using Graph-Based Signatures. Journal of Medicinal Chemistry. 2015;58(9):4066–72.
12.
Velesinović A, Nikolić G. Protein-protein interaction networks and protein-ligand docking: Contemporary insights and future perspectives. Acta Facultatis Medicae Naissensis. 2021;38(1):5–17.
13.
Morris GM, Huey R, Lindstrom W, Sanner MF, Belew RK, Goodsell DS, et al. AutoDock4 and AutoDockTools4: Automated docking with selective receptor flexibility. Journal of Computational Chemistry. 2009;30(16):2785–91.
14.
Biovia DS, Berman HM, Westbrook J. 17.2, San Diego: Dassault Systèmes. J Chem Phys. 2000;10:21–9991.
15.
Trott O, Olson AJ. AutoDock Vina: Improving the speed and accuracy of docking with a new scoring function, efficient optimization, and multithreading. Journal of Computational Chemistry. 2010;31(2):455–61.
16.
Schrödinger L, DeLano W. The PyMOL Molecular Graphics System, Version 2.0.
17.
Nishida H, Arikawa Y, Hirase K, Imaeda T, Inatomi N, Hori Y, et al. Identification of a novel fluoropyrrole derivative as a potassium-competitive acid blocker with long duration of action. Bioorganic & Medicinal Chemistry. 2017;25(13):3298–314.
18.
Comer JEA. High‐Throughput Measurement of log D and pK a. Methods and Principles in Medicinal Chemistry. 2003. p. 21–45.
19.
Muh’d M baba, Uzairu A, Shallangwa GA, Uba S. Molecular docking and quantitative structure-activity relationship study of anti-ulcer activity of quinazolinone derivatives. Journal of King Saud University - Science. 2020;32(1):657–66.
20.
Noor A, Qazi NG, Nadeem H, Khan A ullah, Paracha RZ, Ali F, et al. Synthesis, characterization, anti-ulcer action and molecular docking evaluation of novel benzimidazole-pyrazole hybrids. Chemistry Central Journal. 2017;11(1).
21.
Andrews PR, Craik DJ, Martin JL. Functional group contributions to drug-receptor interactions. Journal of Medicinal Chemistry. 1984;27(12):1648–57.
22.
Hopkins AL, Keserü GM, Leeson PD, Rees DC, Reynolds CH. The role of ligand efficiency metrics in drug discovery. Nature Reviews Drug Discovery. 2014;13(2):105–21.
23.
Abe K, Irie K, Nakanishi H, Suzuki H, Fujiyoshi Y. Crystal structures of the gastric proton pump. Nature. 2018;556(7700):214–8.
24.
Oshima T, Miwa H. Potent Potassium-competitive Acid Blockers: A New Era for the Treatment of Acid-related Diseases. Journal of Neurogastroenterology and Motility. 2018;24(3):334–44.
25.
Barocelli E. Histamine in the control of gastric acid secretion: a topic review. Pharmacological Research. 2003;47(4):299–304.
26.
SACHS G, SHIN JM, HOWDEN CW. Review article: the clinical pharmacology of proton pump inhibitors. Alimentary Pharmacology & Therapeutics. 2006;23(s2):2–8.
27.
Castellana C, Pecere S, Furnari M, Telese A, Matteo MV, Haidry R, et al. Side effects of long-term use of proton pump inhibitors: practical considerations. Polish Archives of Internal Medicine. 2021;
28.
Son M, Park IS, Kim S, Ma HW, Kim JH, Kim TI, et al. Novel Potassium-Competitive Acid Blocker, Tegoprazan, Protects Against Colitis by Improving Gut Barrier Function. Frontiers in Immunology. 13.
29.
Hunt RH, Scarpignato C. Potassium-Competitive Acid Blockers (P-CABs): Are They Finally Ready for Prime Time in Acid-Related Disease? Clinical and Translational Gastroenterology. 2015;6(10):e119.
30.
Shin JM, Cho YM, Sachs G. Chemistry of Covalent Inhibition of the Gastric (H+, K+)-ATPase by Proton Pump Inhibitors. Journal of the American Chemical Society. 2004;126(25):7800–11.
31.
Strand DS, Kim D, Peura DA. 25 Years of Proton Pump Inhibitors: A Comprehensive Review. Gut and Liver. 2017;11(1):27–37.
32.
Chey WD, Mody RR, Izat E. Patient and Physician Satisfaction with Proton Pump Inhibitors (PPIs): Are There Opportunities for Improvement? Digestive Diseases and Sciences. 2010;55(12):3415–22.
33.
Tanaka S, Morita M, Yamagishi T, Madapally HV, Hayashida K, Khandelia H, et al. Structural Basis for Binding of Potassium-Competitive Acid Blockers to the Gastric Proton Pump. Journal of Medicinal Chemistry. 2022;65(11):7843–53.
34.
Inatomi N, Matsukawa J, Sakurai Y, Otake K. Potassium-competitive acid blockers: Advanced therapeutic option for acid-related diseases. Pharmacology & Therapeutics. 2016;168:12–22.
35.
Abe K, Yamamoto K, Irie K, Nishizawa T, Oshima A. Gastric proton pump with two occluded K+ engineered with sodium pump-mimetic mutations. Nature Communications. 12(1).
36.
Lee JS, Cho JY, Song H, Kim EH, Hahm KB. Revaprazan, a novel acid pump antagonist, exerts anti-inflammatory action against Helicobacter pylori-induced COX-2 expression by inactivating Akt signaling. Journal of Clinical Biochemistry and Nutrition. 2012;51(2):77–83.
37.
Garnock-Jones KP. Vonoprazan: First Global Approval. Drugs. 2015;75(4):439–43.
38.
Sugano K. Vonoprazan fumarate, a novel potassium-competitive acid blocker, in the management of gastroesophageal reflux disease: safety and clinical evidence to date. Therapeutic Advances in Gastroenterology. 2018;11.
39.
Takahashi N, Take Y. Tegoprazan, a Novel Potassium-Competitive Acid Blocker to Control Gastric Acid Secretion and Motility. The Journal of Pharmacology and Experimental Therapeutics. 2018;364(2):275–86.
40.
Akazawa Y, Fukuda D, Fukuda Y. Vonoprazan-based therapy forHelicobacter pylorieradication: experience and clinical evidence. Therapeutic Advances in Gastroenterology. 2016;9(6):845–52.
41.
Otake K, Sakurai Y, Nishida H, Fukui H, Tagawa Y, Yamasaki H, et al. Characteristics of the Novel Potassium-Competitive Acid Blocker Vonoprazan Fumarate (TAK-438). Advances in Therapy. 2016;33(7):1140–57.
42.
Wang MS, Gong Y, Zhuo LS, Shi XX, Tian YG, Huang CK, et al. Distribution- and Metabolism-Based Drug Discovery: A Potassium-Competitive Acid Blocker as a Proof of Concept. Research. 2022;2022.
43.
Veselinović J, Veselinović A, Toropov A, Toropova A, Damnjanović I, Nikolić G. Monte Carlo Method Based QSAR Modeling of Coumarin Derivates as Potent HIV‐1 Integrase Inhibitors and Molecular Docking Studies of Selected 4‐phenyl Hydroxycoumarins. Acta Facultatis Medicae Naissensis. 2014;31(2):95–103.
The statements, opinions and data contained in the journal are solely those of the individual authors and contributors and not of the publisher and the editor(s). We stay neutral with regard to jurisdictional claims in published maps and institutional affiliations.
,
