INTRODUCTION: Alzheimer's Disease (AD) is a progressive neurodegenerative disorder affecting the central nervous system. While the precise etiology remains uncertain, multiple abnormalities have been associated with its progression, including reduced levels of the neurotransmitter acetylcholine (ACh), ?-amyloid (A?) aggregation, hyperphosphorylated tau protein deposition, oxidative stress, and activation of monoamine oxidase (MAO)¹. Current therapeutic approaches primarily revolve around palliative treatments through acetylcholinesterase (AChE) inhibitors such as donepezil, rivastigmine, and galantamine. However, it has been observed over time that these drugs exhibit limited efficacy in clinical practice. Consequently, pursuing novel AChE inhibitors remains an area of intense interest in disease modification². The interaction and reversible or irreversible inhibition of these sites are pivotal in determining the activity of an AChE inhibitor³. Therefore, the development of new AChE inhibitors necessitates the application of rational planning strategies. Structure-Based Drug Design (SBDD) and Ligand-Based Drug Design (LBDD) approaches emerge as promising avenues to streamline the search for novel bioactive compounds, reducing the time and financial resources invested in drug development.
AIMS: This study presents the outcomes of a molecular modeling protocol aimed at discovering potential AChE inhibitors. Our primary objective was identifying candidate compounds with anti-AD properties and subsequently synthesizing the selected compound.
METHODS: The in silico investigation employed a LBDD approach, which involved the structural minimization of active and inactive compounds retrieved from the literature using Avogadro software. The minimized structures were then utilized to create a pharmacophoric model for active compounds (constructive) and inactive compounds (subtractive) using Pharmagist. Subsequently, PyMol software was employed to superimpose these models and identify common points, which were excluded to generate a more robust model. A search for potentially active compounds was conducted on the ZINCPharmer server, with a Root Mean Square Deviation (RMSD) threshold of up to 0.2, a molar mass range of 250 to 500 g/mol, and 4 to 5 rotatable bonds. Compounds obtained from this server underwent a SBDD approach using molecular docking. Initially, the protein structure was retrieved from the Protein Data Bank, with a resolution of 2.35 Å and containing the drug donepezil as a ligand. A re-docking analysis was conducted to select the algorithm for subsequent studies, with GOLD® software for docking and PyMOL for RMSD calculation. The compound that demonstrated the most favorable results in this study underwent meticulously planned structural modifications to enhance interactions and produce analogs with superior activity. Key characteristics were retained, such as the fused triazole heterocycle and the mercaptoacetamide group. Adjustments were made to the aromatic rings to assess the potential for improved interactions with adjacent residues. These newly designed derivatives were evaluated using the SwissADME server to evaluate their physicochemical properties, ensuring compliance with Lipinski's rules and the absence of PAINS alerts. Finally, these newly designed analogs were analyzed through molecular docking, employing the same parameters for compounds obtained from the ZINC database. The synthesis of intermediates involved the condensation of arylhydrazine and the requisite aldehydes, followed by iodine-mediated oxidative cyclization. Mercapto-N-phenylacetamide intermediates were obtained from the condensation of mercaptoacetic acid with the required amines. Finally, the ultimate compounds were synthesized through nucleophilic aromatic substitution. 1H and 13C NMR characterized all intermediates and final compounds.
RESULTS: The set of active compounds displayed predicted IC50 values ranging from 0.0009 to 3.24 ?M, while inactive compounds exhibited values greater than 10 ?M, providing a substantial distinction between the two groups. The selected model for the active compounds consisted of five components, which achieved the highest score of 29.698. The ZINCPharm platform yielded thirteen compounds aligned closely with the standard drug donepezil. The score of each analyzed compound was considered to select the compound for further modifications. Compound 44 emerged as a promising candidate, with a score of 99.1238 compared to the standard drug donepezil, which scored 107.687 in the same assay. Visual examination of the complexes demonstrated that this compound exhibited interactions akin to the drug, particularly in the CAS region of the enzyme. After structural modifications, the derivatives filtered through SwissADME were narrowed down to twenty-six molecules. Molecular docking studies of these analogs revealed that the score of seven of them exceeded that of compound 44. Moreover, three analogs attained scores higher than the reference drug donepezil (score: 107.68). As an illustration, the interactions observed in derivative 84 were analyzed and compared to compounds 44 and donepezil to confirm interactions with the key residues for AChE inhibitory activity. Following the in silico study, all intermediates and three final compounds were successfully synthesized and characterized using 1H and 13C NMR.
CONCLUSIONS: The in silico investigation yielded promising analogs with superior AChE inhibition results compared to the reference drug, donepezil. The synthetic route employed was adequate for synthesizing all intermediates and three final compounds. As a result, this work holds promise in offering potential lead compounds for treating Alzheimer's Disease.
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Comissão Organizadora
Francisco Mendonça Junior
Pascal Marchand
Teresinha Gonçalves da Silva
Isabelle Orliac-Garnier
Gerd Bruno da Rocha
Comissão Científica
Ricardo Olimpio de Moura
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