DISCOVERY OF DONEPEZIL-LIKE COMPOUNDS AS POTENTIAL ACETYLCHOLINESTERASE INHIBITORS DETERMINED BY PHARMACOPHORE MAPPING-BASED VIRTUAL SCREENING AND MOLECULAR DOCKING

Abstract

Objective Alzheimer's disease (AD) is the most common cause of dementia in older people due to abnormalities in the cholinergic system. Acetylcholinesterase has an important role in the regulation of the cholinergic system. Therefore, targeting AChE is one of the most promising strategies for the treatment of AD. Although several approved drugs to treat AD, it is still needed to develop potential inhibitor candidates. Therefore, the aim of this study is to discover newly donepezillike natural compounds and their synthetic derivatives targeting acetylcholinesterase enzyme (AChE). Material and Method A pharmacophore model of a known drug, donepezil was generated. Using the pharmacophore mapping module of the Discovery Studio 2021 program, the chemical library containing natural products and synthetic derivatives was screened. The pharmacokinetics and drug-likeness properties of the screened compounds were predicted by ADMET and Lipinski and Veber’s rule. Some criteria were used as a filter. In addition, bioactive compounds of the database were screened. Then, molecular docking study was performed by using Glide/SP of Maestro (Schrödinger, Inc.) to determine the potential molecules. Results The binding energies were determined for hit compounds after molecular modeling studies. Furthermore, H-bonding, pi-pi stacking, pi-cation, and pi-alkyl interactions between the protein-ligand complex have been identified by various amino acid residues such as Tyr, Asp, His, Trp, Arg. The results show that the potential compounds are a promising candidate with binding energy compared to donepezil. The molecular modeling results indicate that new scaffolds may contribute to the discovery of new AChE inhibitors compared to a reference drug. Conclusion This study may lead to further studies and contribute to examination with in vitro analysis. The scaffolds can be used to design novel and effective inhibitors.

Keywords

• Donepezil
• Alzheimer’s Disease
• Pharmacophore
• Acetylcholinesterase
• Molecular Docking

FARMAKOFOR HARİTALAMA-ESASLI SANAL TARAMA VE MOLEKÜLER YERLEŞTİRME İLE BELİRLENEN POTANSİYEL ASETİLKOLİNESTERAZ İNHİBİTÖRLERİ OLARAK DONEPEZİL-BENZERİ BİLEŞİKLERİN KEŞFİ

Abstract

Amaç Alzheimer hastalığı yaşlı insanlarda kolinerjik sistemdeki anormalliklerden dolayı bunamanın en yaygın nedenidir. Asetilkolinesteraz kolinerjik sistemin düzenlenmesinde önemli bir role sahiptir. Bu nedenle, AChE'yi hedeflemek AH tedavisi için en umut verici stratejilerden biridir. AH tedavisi için onaylanmış birkaç ilaç olmasına rağmen potansiyel inhibitör adaylarının keşfedilmesine halen ihtiyaç vardır. Bu nedenle, bu çalışmanın amacı asetilkolinesteraz enzimini (AChE) hedef alan yeni donepezil benzeri doğal bileşiklerin ve bunların sentetik türevlerinin keşfedilmesidir. Gereç ve Yöntem Bilinen bir ilaç olan donepezilin farmakofor modeli oluşturulmuştur. Discovery Studio 2021 programının farmakofor haritalama modülü kullanılarak doğal ürün ve sentetik türevlerini içeren kimyasal kütüphanesi taranmıştır. Taranan bileşiklerin farmakokinetik ve ilaca benzer özellikleri ADMET ve Lipinski ve Veber kuralı ile tahmin edilmiştir. Filtre olarak bazı kriterler kullanılmıştır. Ayrıca veri tabanının biyoaktif bileşikleri taranmıştır. Daha sonra, potansiyel molekülleri belirlemek için Maestro Glide/SP (Schrödinger, Inc.) kullanılarak moleküler yerleştirme çalışması yapılmıştır. Bulgular Moleküler modelleme çalışmalarının ardından öncü bileşikler için bağlanma enerjileri belirlendi. Ayrıca, protein-ligand kompleksi arasındaki H-bağ, pi-pi istifleme, pi-katyon ve pi-alkil etkileşimleri, Tyr, Asp, His, Trp, Arg gibi çeşitli amino asit kalıntıları ile tanımlanmıştır. Sonuçlar, potansiyel bileşiklerin donepezil ile karşılaştırıldığında bağlanma enerjisi ile umut verici bir aday olduğunu göstermektedir. Moleküler modelleme sonuçları, yeni yapı iskelelerinin standart ilaca kıyasla yeni AChE inhibitörlerinin keşfedilmesine katkıda bulunabileceğini belirtmektedir. Sonuç Bu çalışma daha ileri çalışmalara yol açabilir ve in vitro analizlerle incelenmesine katkı sağlayabilir. Yapı iskeleleri, yeni ve etkili inhibitörlerin tasarlanması için kullanılabilir.

Keywords

• Alzheimer Hastalığı
• Farmakofor
• Asetilkolinesteraz
• Moleküler Yerleştirme
• Donepezil

References

1. 1. Huang W, Tang L, Shi Y, Huang S, Xu L, Sheng R, Wu P, Li J, Zhou N, Hu Y. Searching for the Multi-Target-Directed Ligands against Alzheimer's disease: discovery of quinoxaline-based hybrid compounds with AChE, H₃R and BACE 1 inhibitory activities. Bioorg Med Chem. 2011;19(23):7158-67. doi: 10.1016/j. bmc.2011.09.061.
2. 2. Hirbod K, Jalili-Baleh L, Nadri H, Ebrahimi SES, Moradi A, Pakseresht B, Foroumadi A, Shafiee A, Khoobi M. Coumarin derivatives bearing benzoheterocycle moiety: synthesis, cholinesterase inhibitory, and docking simulation study. Iran J Basic Med Sci. 2017;20(6):631-638. doi: 10.22038/IJBMS. 2017.8830.
3. 3. Khoobi M, Alipour M, Sakhteman A, Nadri H, Moradi A, Ghandi M, Emami S, Foroumadi A, Shafiee A. Design, synthesis, biological evaluation and docking study of 5-oxo-4,5-dihydropyrano[ 3,2-c]chromene derivatives as acetylcholinesterase and butyrylcholinesterase inhibitors. Eur J Med Chem. 2013;68:260-9. doi: 10.1016/j.ejmech.2013.07.038.
4. 4. Gupta S, Fallarero A, Järvinen P, Karlsson D, Johnson MS, Vuorela PM, Mohan CG. Discovery of dual binding site acetylcholinesterase inhibitors identified by pharmacophore modeling and sequential virtual screening techniques. Bioorg Med Chem Lett. 2011;21(4):1105-12. doi: 10.1016/j.bmcl.2010.12.131.
5. 5. Arumugam N, Almansour AI, Kumar RS, Kotresha D, Saiswaroop R, Venketesh S. Dispiropyrrolidinyl-piperidone embedded indeno[1,2-b]quinoxaline heterocyclic hybrids: Synthesis, cholinesterase inhibitory activity and their molecular docking simulation. Bioorg Med Chem. 2019;27(12):2621-2628. doi: 10.1016/j.bmc.2019.03.058.
6. 6. Montanari S, Scalvini L, Bartolini M, Belluti F, Gobbi S, Andrisano V, Ligresti A, Di Marzo V, Rivara S, Mor M, Bisi A, Rampa A. Fatty Acid Amide Hydrolase (FAAH), Acetylcholinesterase (AChE), and Butyrylcholinesterase (BuChE): Networked Targets for the Development of Carbamates as Potential Anti-Alzheimer's Disease Agents. J Med Chem. 2016;59(13):6387-406. doi: 10.1021/acs.jmedchem.6b00609.
7. 7. Tehrani MB, Rezaei Z, Asadi M, Behnammanesh H, Nadri H, Afsharirad F, Moradi A, Larijani B, Mohammadi-Khanaposhtani M, Mahdavi M. Design, Synthesis, and Cholinesterase Inhibition Assay of Coumarin-3-carboxamide-N-morpholine Hybrids as New Anti-Alzheimer Agents. Chem Biodivers. 2016;16(7):e1900144. doi: 10.1002/cbdv.201900144.
8. 8. Ferreira-Vieira TH, Guimaraes IM, Silva FR, Ribeiro FM. Alzheimer's disease: Targeting the Cholinergic System. Curr Neuropharmacol. 2016;14(1):101-15. doi: 10.2174/1570159x13666150716165726.

9. 9. Sharma K. Cholinesterase inhibitors as Alzheimer's therapeutics (Review). Mol Med Rep. 2019;20(2):1479-1487. doi: 10.3892/mmr.2019.10374.
10. 10. Howes MJ, Perry NS, Houghton PJ. Plants with traditional uses and activities, relevant to the management of Alzheimer's disease and other cognitive disorders. Phytother Res. 2003;17(1):1-18. doi: 10.1002/ptr.1280.
11. 11. Comert Onder F, Sahin K, Senturk M, Durdagi S, Ay M. Identifying highly effective coumarin-based novel cholinesterase inhibitors by in silico and in vitro studies. J Mol Graph Model. 2022;115:108210. doi: 10.1016/j.jmgm.2022.108210.
12. 12. Li P, Niu Y, Li S, Zu X, Xiao M, Yin L, Feng J, He J, Shen Y. Identification of an AXL kinase inhibitor in triple-negative breast cancer by structure-based virtual screening and bioactivity test. Chem Biol Drug Des. 2022;99(2):222-232. doi: 10.1111/ cbdd.13977.
13. 13. Lu SH, Wu JW, Liu HL, Zhao JH, Liu KT, Chuang CK, Lin HY, Tsai WB, Ho Y. The discovery of potential acetylcholinesterase inhibitors: a combination of pharmacophore modeling, virtual screening, and molecular docking studies. J Biomed Sci. 2011;18(1):8. doi: 10.1186/1423-0127-18-8.
14. 14. Qing X, Lee XY, De Raeymaecker J, Tame J, Zhang K, De Maeyer M, Voet A. Pharmacophore modeling: advances, limitations, and current utility in drug discovery. J Receptor, Ligand and Channel Res. 2014;7:81–92. doi:10.2147/JRLCR.S46843.
15. 15. Kaserer T, Beck KR, Akram M, Odermatt A, Schuster D. Pharmacophore Models and Pharmacophore-Based Virtual Screening: Concepts and Applications Exemplified on Hydroxysteroid Dehydrogenases. Molecules. 2015;20(12):22799-832. doi:10.3390/molecules201219880.
16. 16. Lakra N, Matore BW, Banjare P, Singh R, Singh J, Roy PP. Pharmacophore based virtual screening of cholinesterase inhibitors: search of new potential drug candidates as antialzheimer agents. In Silico Pharmacol. 2022 Sep 29;10(1):18. doi: 10.1007/s40203-022-00133-1.
17. 17. Jang C, Yadav DK, Subedi L, Venkatesan R, Venkanna A, Afzal S, Lee E, Yoo J, Ji E, Kim SY, Kim MH. Identification of novel acetylcholinesterase inhibitors designed by pharmacop- hore-based virtual screening, molecular docking and bioassay. Sci Rep. 2018;8(1):14921. doi: 10.1038/s41598-018-33354-6.
18. 18. Korabecny J, Dolezal R, Cabelova P, Horova A, Hruba E, Ricny J, Sedlacek L, Nepovimova E, Spilovska K, Andrs M, Musilek K, Opletalova V, Sepsova V, Ripova D, Kuca K. 7-MEOTA-donepezil like compounds as cholinesterase inhibitors: Synthesis, pharmacological evaluation, molecular modeling and QSAR studies. Eur J Med Chem. 2014;82:426-38. doi: 10.1016/j.ejmech. 2014.05.066.
19. 19. Brunetti L, Leuci R, Carrieri A, Catto M, Occhineri S, Vinci G, Gambacorta L, Baltrukevich H, Chaves S, Laghezza A, Altomare CD, Tortorella P, Santos MA, Loiodice F, Piemontese L. Structure-based design of novel donepezil-like hybrids for a multi-target approach to the therapy of Alzheimer's disease. Eur J Med Chem. 2022;237:114358. doi: 10.1016/j.ejmech. 2022.114358.
20. 20. Ismaili L, Refouvelet B, Benchekroun M, Brogi S, Brindisi M, Gemma S, Campiani G, Filipic S, Agbaba D, Esteban G, Unzeta M, Nikolic K, Butini S, Marco-Contelles J. Multitarget compounds bearing tacrine- and donepezil-like structural and functional motifs for the potential treatment of Alzheimer's disease. Prog Neurobiol. 2017;151:4-34. doi: 10.1016/j.pneurobio. 2015.12.003.
21. 21. Akhoon BA, Choudhary S, Tiwari H, Kumar A, Barik MR, Rathor L, Pandey R, Nargotra A. Discovery of a New Donepezil- like Acetylcholinesterase Inhibitor for Targeting Alzheimer's Disease: Computational Studies with Biological Validation. J Chem Inf Model. 2020;60(10):4717-4729. doi: 10.1021/acs. jcim.0c00496.
22. 22. Costanzo P, Cariati L, Desiderio D, Sgammato R, Lamberti A, Arcone R, Salerno R, Nardi M, Masullo M, Oliverio M. Design, Synthesis, and Evaluation of Donepezil-Like Compounds as AChE and BACE-1 Inhibitors. ACS Med Chem Lett. 2016;7(5):470-5. doi: 10.1021/acsmedchemlett.5b00483.
23. 23. Miles JA, Ross BP. Recent Advances in Virtual Screening for Cholinesterase Inhibitors. ACS Chem Neurosci. 2021 Jan 6;12(1):30-41. doi: 10.1021/acschemneuro.0c00627.
24. 24. Accelrys Discovery Studio, Accelrys, San Diego, CA
25. 25. Rampogu S, Baek A, Son M, Park C, Yoon S, Parate S, Lee KW. Discovery of Lonafarnib-Like Compounds: Pharmacophore Modeling and Molecular Dynamics Studies. ACS Omega. 2020;5(4):1773-1781. doi: 10.1021/acsomega.9b02263.
26. 26. Jiang CS, Ge YX, Cheng ZQ, Song JL, Wang YY, Zhu K, Zhang H. Discovery of new multifunctional selective acetylcholinesterase inhibitors: structure-based virtual screening and biological evaluation. J Comput Aided Mol Des. 2019;33(5):521-530. doi: 10.1007/s10822-019-00202-2.
27. 27. Schrodinger, LLC. Protein preparation. New York, NY, USA: Schrodinger LLC.
28. 28. 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):681-91. doi: 10.1007/s10822-007- 9133-z.
29. 29. Jorgensen WL, Tirado-Rives J. The OPLS [optimized potentials for liquid simulations] potential functions for proteins, energy minimizations for crystals of cyclic peptides and crambin. J Am Chem Soc. 1988;110(6):1657-66. doi: 10.1021/ja00214a001.
30. 30. Sastry GM, Adzhigirey M, Day T, Annabhimoju R, Sherman W. Protein and ligand preparation: parameters, protocols, and influence on virtual screening enrichments. J Comput Aided Mol Des. 2013;27(3):221-34. doi: 10.1007/s10822-013-9644-8.
31. 31. Tripathi SK, Muttineni R, Singh SK. Extra precision docking, free energy calculation and molecular dynamics simulation studies of CDK2 inhibitors. J Theor Biol. 2013;334:87-100. doi: 10.1016/j.jtbi.2013.05.014.
32. 32. Ibrar A, Khan A, Ali M, Sarwar R, Mehsud S, Farooq U, Halimi SMA, Khan I, Al-Harrasi A. Combined in Vitro and in Silico Studies for the Anticholinesterase Activity and Pharmacokinetics of Coumarinyl Thiazoles and Oxadiazoles. Front Chem. 2018;6:61. doi: 10.3389/fchem.2018.00061.
33. 33. Wu B, Zhang Z, Dou G, Lv X, Ge J, Wang H, Xie H, Zhu D. Novel natural inhibitors targeting B-RAF(V600E) by computational study. Bioengineered. 2021;12(1):2970-2983. doi: 10.1080/21655979.2021.1943113.
34. 34. Sari S., Yilmaz M. Acetylcholinesterase inhibition, molecular docking and ADME prediction studies of new dihydrofuran-piperazine hybrid compounds. Med Chem Res. 2021;30:2114– 2126. doi: 10.1007/s00044- 021-02788-5.
35. 35. Wiesner J, Kriz Z, Kuca K, Jun D, Koca J. Acetylcholinesterases-- the structural similarities and differences. J Enzyme Inhib Med Chem. 2007;22(4):417-24. doi:10.1080/14756360701421294.
36. 36. Bourne Y, Taylor P, Radić Z, Marchot P. Structural insights into ligand interactions at the acetylcholinesterase peripheral anionic site. EMBO J. 2003;22(1):1-12. doi: 10.1093/emboj/cdg005.
37. 37. Khalid S, Zahid MA, Ali H, Kim YS, Khan S. Biaryl scaffold-focused virtual screening for anti-aggregatory and neuroprotective effects in Alzheimer's disease. BMC Neurosci. 2018;19(1):74. doi: 10.1186/s12868-018-0472-6.
38. 38. Roy S, Kumar A, Baig MH, Masařík M, Provazník I. Virtual screening, ADMET profiling, molecular docking and dynamics approaches to search for potent selective natural molecules based inhibitors against metallothionein-III to study Alzheimer’s disease. Methods San Diego Calif. 2015;83:105–10.
39. 39. Iqbal D, Khan MS, Waiz M, Rehman MT, Alaidarous M, Jamal A, Alothaim AS, AlAjmi MF, Alshehri BM, Banawas S, Alsaweed M, Madkhali Y, Algarni A, Alsagaby SA, Alturaiki W. Exploring the Binding Pattern of Geraniol with Acetylcholinesterase through In Silico Docking, Molecular Dynamics Simulation, and In Vitro Enzyme Inhibition Kinetics Studies. Cells. 2021 Dec 14;10(12):3533. doi: 10.3390/cells10123533.
40. 40. Ambure P, Kar S, Roy K. Pharmacophore mapping-based virtual screening followed by molecular docking studies in search of potential acetylcholinesterase inhibitors as anti-Alzheimer's agents. Biosystems. 2014;116:10-20. doi: 10.1016/j.biosystems. 2013.12.002.
41. 41. Gupta S, Fallarero A, Järvinen P, Karlsson D, Johnson MS, Vuorela PM, Mohan CG. Discovery of dual binding site acetylcholinesterase inhibitors identified by pharmacophore modeling and sequential virtual screening techniques. Bioorg Med Chem Lett. 2011;21(4):1105-12. doi: 10.1016/j.bmcl.2010.12.131.
42. 42. Bezoari MD, Boothe G. Determination of potential multi-target inhibitors of alzheimer’s disease in silico. J Undergraduate Chem Res. 2019;18(4):21.
43. 43. Ambure P, Bhat J, Puzyn T, Roy K. Identifying natural compounds as multi-target-directed ligands against Alzheimer's disease: an in silico approach. J Biomol Struct Dyn. 019;37(5):1282- 1306. doi: 10.1080/07391102.2018.1456975.
44. 44. David B, Schneider P, Schäfer P, Pietruszka J, Gohlke H. Discovery of new acetylcholinesterase inhibitors for Alzheimer's disease: virtual screening and in vitro characterisation. J Enzyme Inhib Med Chem. 2021;36(1):491-496. doi: 10.1080/14756366.2021.1876685.
45. 45. Silva MA, Kiametis AS, Treptow W. Donepezil Inhibits Acetylcholinesterase via Multiple Binding Modes at Room Temperature. J Chem Inf Model. 2020;60(7):3463-3471. doi: 10.1021/ acs.jcim.9b01073.
46. 46. Barak D, Kronman C, Ordentlich A, Ariel N, Bromberg A, Marcus D, Lazar A, Velan B, Shafferman A. Acetylcholinesterase peripheral anionic site degeneracy conferred by amino acid arrays sharing a common core. J Biol Chem. 1994;269(9):6296- 305.
47. 47. Chen Y, Lin H, Zhu J, Gu K, Li Q, He S, Lu X, Tan R, Pei Y, Wu L, Bian Y, Sun H. Design, synthesis, in vitro and in vivo evaluation of tacrine–cinnamic acid hybrids as multi-target acetyl- and butyrylcholinesterase inhibitors against Alzheimer's disease. RSC Adv. 2017;7,33851-33867.
48. 48. Pourshojaei Y, Abiri A, Eskandari K, Haghighijoo Z, Edraki N, Asadipour A. Phenoxyethyl Piperidine/Morpholine Derivatives as PAS and CAS Inhibitors of Cholinesterases: Insights for Future Drug Design. Sci Rep. 2019 Dec 27;9(1):19855. doi: 10.1038/s41598-019-56463-2.
49. 49. Duarte Y, Gutierrez M, Álvarez R, Alzate-Morales JH, Soto- Delgado J. Experimental and Theoretical Approaches in the Study of Phenanthroline-Tetrahydroquinolines for Alzheimer's Disease. ChemistryOpen. 2019;8(5):627-636. doi: 10.1002/ open.201900073.