An Evaluation of the Pharmacokinetics and Virtual Screening of Highly Effective Hybrid Compounds for Alzheimer's Disease
Year: 2024
Type: article
Abstract: Alzheimer’s disease remains challenging due to complex etiology and limited therapies. Hybrid multitarget drugs show promise. This study applies computer-aided drug design to assess 25 hybrids against key AD proteins. Virtual screening, molecular docking, simulations, and pharmacokinetic analyses identified compounds with high binding affinities, supporting further development and clinical translation. The top-performing compounds were further analysed using LIGPLOT+ V 2.2.7 for 2D interaction mapping and PyMol V 2.5 for 3D visualisation of ligand-receptor interactions. Additionally, pharmacokinetic properties, including ADMET (Absorption, Distribution, Metabolism, Excretion, and Toxicity) profiles, were predicted to assess the therapeutic potential of these compounds as anti-AD agents. Out of the 25 compounds screened, ten exhibited significant interactions with the protein targets, showing high binding affinities based on their docking scores. Notable compounds, such as compound 3 (-37.41 kcal/mol), compound 19 (-35.68 kcal/mol), and compound 20 (-36.88 kcal/mol), demonstrated superior binding energy compared to the reference compound, which had a docking score of -26.30 kcal/mol. The amino acids ASN349, PHE168, and SER67 were identified as key residues involved in hydrogen and hydrophobic bonding with the ligands. These interactions are critical as they play roles in neurotransmitter production, including dopamine and norepinephrine, which are implicated in treating and managing A.D. symptoms. Pharmacokinetic and ADMET-ox predictions further supported the therapeutic potential of all the screened compounds, suggesting favourable drug-like properties and minimal toxicity. While in-silico results are encouraging, further in-vitro and in-vivo validation is needed. These findings lay a solid foundation for developing multitargeted therapies for Alzheimer’s disease.
Authors: Ibrahim A. Abdulganiyyu, Abduljelil Ajala, Maimuna Shehu Rufai, Balogun Joshua Babalola, Balogun Sadia Ufeli, Lawan Hassan Adamu, Nazifi Abbas, Ibrahim Sani Ahmad, Jamil Dauda Usman, Kabiru M. Aujara, Abbas Sambo
Language: English
Subfield: Computational Theory and Mathematics
Field: Computer Science
Domain: Physical Sciences
Open Access status: diamond
OpenAlex ID: W4412676715