A New Candidate for the Treatment of Alzheimer's Disease; Synthesis, Characterization, Investigation of Drug Properties with In Silico Methods

Ömer Dilek; Tolga Acar Yeşil; Tahir Tilki

doi:10.17776/csj.1487341

EN

A New Candidate for the Treatment of Alzheimer's Disease; Synthesis, Characterization, Investigation of Drug Properties with In Silico Methods

Abstract

Brain disorder-caused mortality has emerged as the second of all diseases worldwide in the 21st century. Alzheimer’s Disease (AD) is the most common disease that takes place among brain disorders according to statistics. Therefore, in this study, potential new drug candidate for AD (2-amino-N'-benzylidene-4-(trifluoromethyl)benzohydrazide, ABTH) was synthesized, starting from -CF3 and -NO2 containing carboxylic acid. The structure of ABTH was elucidated using 1H, 13C-APT NMR, FTIR, and Mass analyses. ADMEt properties were calculated and from the ADMEt results, it was observed that the ABTH crossed the Blood-Brain Barrier (BBB), the most important property in evaluating new drug candidate in brain disorders. Molecular Docking studies were conducted using proteins related AD. According to docking studies, 2OI0-ABTH was the highest docking score with the -8.9 kcal/mol. Standard drugs (Donepezil, Galantamine, Rivastigmine) used in AD treatment were also docked with the AD proteins to do meaningful comparison. The molecular docking results showed that the ABTH has higher docking score than standards. Since 2OI0-ABTH complex had the best docking score, it was chosen for the MD simulation studies. From the obtained results, It can be suggested that ABTH promising drug candidate for AD after further investigations were done

Keywords

References

[1] Lu D., Sun Y., Luan Y., He W., Rational design of siRNA-based delivery systems for effective treatment of brain diseases, Pharmaceutical Science Advances, (2) (2024) 100041.
[2] Harder B.G., Blomquist M.R., Wang J., Kim A.J., Woodworth G.F., Winkles J.A., Loftus J.C., Tran N.L., Developments in Blood-Brain Barrier Penetrance and Drug Repurposing for Improved Treatment of Glioblastoma, Frontiers in Oncology, (8) (2018) 462.
[3] Burns A., Robert P., The National Dementia strategy in England, BMJ, (338) (2009) b931–b931.
[4] Brookmeyer R., Johnson E., Ziegler‐Graham K., Arrighi H.M., Forecasting the global burden of Alzheimer’s disease, Alzheimer’s & Dementia, (3) (2007) 186–191.
[5] Hashimoto M., Rockenstein E., Crews L., Masliah E., Role of Protein Aggregation in Mitochondrial Dysfunction and Neurodegeneration in Alzheimer’s and Parkinson’s Diseases, NeuroMolecular Medicine, (4) (2003) 21–36.
[6] Hagmann W.K., The Many Roles for Fluorine in Medicinal Chemistry, Journal of Medicinal Chemistry, (51) (2008) 4359–4369.
[7] Nair A.S., Singh A.K., Kumar A., Kumar S., Sukumaran S., Koyiparambath V.P., Pappachen .LK., Rangarajan T.M., Kim H., Mathew B., FDA-Approved Trifluoromethyl Group-Containing Drugs: A Review of 20 Years, Processes, (10) (2022) 2054.
[8] Tatum L.A., Su X., Aprahamian I., Simple Hydrazone Building Blocks for Complicated Functional Materials, Accounts of Chemical Research, (47) (2014) 2141–2149.

[9] Verma G., Marella A., Shaquiquzzaman M., Akhtar M., Ali M., Alam M., A review exploring biological activities of hydrazones, Journal of Pharmacy and Bioallied Sciences, (6) (2014) 69.
[10] Rollas S., Küçükgüzel S., Biological Activities of Hydrazone Derivatives, Molecules, (12) (2007) 1910–1939.
[11] AlFadly E.D., Elzahhar P.A., Tramarin A., Elkazaz S., Shaltout H., Abu-Serie M.M., Janockova J., Soukup O., Ghareeb D.A., El-Yazbi A.F., Rafeh R.W., Bakkar N.M.Z., Kobeissy F., Iriepa I., Moraleda I., Saudi M.N.S., Bartolini M., Belal A.S.F., Tackling neuroinflammation and cholinergic deficit in Alzheimer’s disease: Multi-target inhibitors of cholinesterases, cyclooxygenase-2 and 15-lipoxygenase, European Journal of Medicinal Chemistry, (167) (2019) 161–186.
[12] DiMasi J.A., Grabowski H.G., Hansen R.W., Innovation in the pharmaceutical industry: New estimates of R&D costs, Journal of Health Economics, (47) (2016) 20–33.
[13] Zhong F., Xing J., Li X., Liu X., Fu Z., Xiong Z., Lu D., Wu X., Zhao J., Tan X., Li F., Luo X., Li Z., Chen K., Zheng M., Jiang H., Artificial intelligence in drug design, Science China Life Sciences, (61) (2018) 1191–1204.
[14] Cui W., Aouidate A., Wang S., Yu Q., Li Y., Yuan S., Discovering Anti-Cancer Drugs via Computational Methods, Frontiers in Pharmacology, (11) (2020) 733.
[15] Ramírez D., Computational Methods Applied to Rational Drug Design, The Open Medicinal Chemistry Journal, (10) (2016) 7–20.
[16] De Vries H.E., Kuiper J., de Boer A.G., Van Berkel T.J., Breimer D.D., The blood-brain barrier in neuroinflammatory diseases. Pharmacological Reviews, (49) (1997) 143–155.
[17] Narayanan R., Gunturi S.B., In silico ADME modelling: prediction models for blood–brain barrier permeation using a systematic variable selection method, Bioorganic & Medicinal Chemistry, (13) (2005) 3017–3028.
[18] Karabacak Atay Ç., Dilek Ö., Tilki T., Dede B., A novel imidazole-based azo molecule: synthesis, characterization, quantum chemical calculations, molecular docking, molecular dynamics simulations and ADMET properties, Journal of Molecular Modeling, (29) (2023) 226.
[19] Karplus M., McCammon J.A., Molecular dynamics simulations of biomolecules, Nature Structural & Molecular Biology, (9) (2002) 646–652.
[20] 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) (2017) 42717.
[21] Banerjee P., Eckert A.O., Schrey A.K., Preissner R., ProTox-II: a webserver for the prediction of toxicity of chemicals, Nucleic Acids Research, (46) (2018) W257–W263.
[22] Hanwell M.D., Curtis D.E., Lonie, Vandermeersch T., Zurek E., Hutchison G.R., Avogadro: an advanced semantic chemical editor, visualization, and analysis platform, Journal of Cheminformatics, (4) (2012) 17.
[23] Trott O., Olson A.J., AutoDock Vina: Improving the speed and accuracy of docking with a new scoring function, efficient optimization, and multithreading, Journal of Computational Chemistry, (31) (2010) 455–461.
[24] Pettersen E.F., Goddard T.D., Huang C.C., Couch G.S., Greenblatt D.M., Meng E.C., Ferrin T.E., UCSF Chimera--A visualization system for exploratory research and analysis, Journal of Computational Chemistry, (25) (2004) 1605–1612.
[25] BIOVIA, 2021 Discovery Studio Visualizer, version 21.1.0.20298. Dassault Systèmes, San Diego, CA.
[26] Berman H.M., The Protein Data Bank, Nucleic Acids Research, (28) (2000) 235–242.
[27] Webb B., Sali A., Comparative Protein Structure Modeling Using MODELLER, Current Protocols in Bioinformatics, (54) (2016) 5.6.1-5.6.37.
[28] Jiménez J., Doerr S., Martínez-Rosell G., Rose A.S., De Fabritiis G., DeepSite: protein-binding site predictor using 3D-convolutional neural networks, Bioinformatics, (33) (2017) 3036–3042.
[29] Martínez-Rosell G., Giorgino T., De Fabritiis G., PlayMolecule ProteinPrepare: A Web Application for Protein Preparation for Molecular Dynamics Simulations, Journal of Chemical Information and Modeling, (57) (2017) 1511–1516.
[30] Galvelis R., Doerr S., Damas J.M., Harvey M.J., De Fabritiis G., A Scalable Molecular Force Field Parameterization Method Based on Density Functional Theory and Quantum-Level Machine Learning, Journal of Chemical Information and Modeling, (59) (2019) 3485–3493.
[31] Doerr S., Harvey M.J., Noé F., De Fabritiis G., HTMD: High-Throughput Molecular Dynamics for Molecular Discovery, Journal of Chemical Theory and Computation, (12) (2016) 1845–1852.
[32] Veber D.F., Johnson S.R., Cheng H.Y., Smith B.R., Ward K.W., Kopple K.D., Molecular Properties That Influence the Oral Bioavailability of Drug Candidates, Journal of Medicinal Chemistry, (45) (2002) 2615–2623.
[33] Savjani K.T., Gajjar A.K., Savjani J.K., Drug Solubility: Importance and Enhancement Techniques, International Scholarly Research Notices Pharmaceutics, (2012) (2012) 1–10.
[34] Gnanaraj C., Sekar M., Fuloria S., Swain S.S., Gan S.H., Chidambaram K., Rani N.N.I.M., Balan T., Stephenie S., Lum P.T., Jeyabalan S., Begum M.Y., Chandramohan V., Thangavelu L., Subramaniyan V., Fuloria N.K, In Silico Molecular Docking Analysis of Karanjin against Alzheimer’s and Parkinson’s Diseases as a Potential Natural Lead Molecule for New Drug Design, Development and Therapy, Molecules, (27) (2022) 2834.
[35] Hardy J., Selkoe D.J., The Amyloid Hypothesis of Alzheimer’s Disease: Progress and Problems on the Road to Therapeutics, Science, (297) (2002) 353–356.
[36] Qıu W., Folsteın M., Insulin, insulin-degrading enzyme and amyloid-β peptide in Alzheimer’s disease: review and hypothesis, Neurobiology of Aging, (27) (2006) 190–198.
[37] Vassar R., Bennett B.D., Babu-Khan S., Kahn S., Mendiaz E.A., Denis P., Teplow D.B., Ross S., Amarante P., Loeloff R., Luo Y., Fisher S., Fuller J., Edenson S., Lile J., Jarosinski M.A., Biere A.L., Curran E., Burgess T., Louis J.-C., Collins F., Treanor J., Rogers G., Citron M., β-Secretase Cleavage of Alzheimer’s Amyloid Precursor Protein by the Transmembrane Aspartic Protease BACE, Science, (286) (1999) 735–741.
[38] Ghosh A.K., Cárdenas E.L., Osswald H.L., The Design, Development, and Evaluation of BACE1 Inhibitors for the Treatment of Alzheimer’s Disease, Alzheimer’s Disease II, (2016) 27–85.
[39] Kremer A., GSK3 and Alzheimer’s disease: facts and fiction…, Frontiers in Molecular Neuroscience, (4) (2011) 17.
[40] Plantone D., Pardini M., Righi D., Manco C., Colombo B.M., N. De Stefano, The Role of TNF-α in Alzheimer’s Disease: A Narrative Review, Cells, 13 (2023) 54.

Details

Primary Language

English

Subjects

Molecular Docking , Organic Chemical Synthesis

Journal Section

Research Article

Authors

Ömer Dilek ^*
0000-0003-1409-782X
Türkiye

Tolga Acar Yeşil
0000-0001-5983-8447
Türkiye

Tahir Tilki
0000-0002-1040-2375
Türkiye

Publication Date

March 25, 2025

Submission Date

May 21, 2024

Acceptance Date

January 30, 2025

Published in Issue

Year 2025 Volume: 46 Number: 1

DOI

https://doi.org/10.17776/csj.1487341

IZ

https://izlik.org/JA85EJ94BG

Cite

RIS / Bibtex

APA

Dilek, Ö., Yeşil, T. A., & Tilki, T. (2025). A New Candidate for the Treatment of Alzheimer’s Disease; Synthesis, Characterization, Investigation of Drug Properties with In Silico Methods. Cumhuriyet Science Journal, 46(1), 62-72. https://doi.org/10.17776/csj.1487341

Öz

A New Candidate for the Treatment of Alzheimer's Disease; Synthesis, Characterization, Investigation of Drug Properties with In Silico Methods

Abstract

Keywords

References

Details

Primary Language

Subjects

Journal Section

Authors

Publication Date

Submission Date

Acceptance Date

Published in Issue

DOI

IZ

Cite