IN-SILICO STRATEGIES IN ALZHEIMER’S DRUG DISCOVERY: MOLECULAR DOCKING, ADMET, AND PHARMACOLOGICAL PROFILING OF NOVEL HYDRAZIDE DERIVATIVES

Abstract

Background:
Alzheimer’s disease (AD) is a progressive neurodegenerative disorder marked by memory loss, cognitive decline, and behavioral changes, primarily affecting the elderly population. It is histopathologically characterized by the accumulation of β-amyloid plaques and neurofibrillary tangles composed of hyperphosphorylated tau proteins. Despite extensive research, therapeutic options for AD remain limited, often only alleviating symptoms without halting disease progression. (1) The urgent need for disease-modifying agents has prompted researchers to explore novel compounds through computational approaches. Among emerging pharmacophores, hydrazide derivatives have garnered attention due to their wide-ranging biological activities, including neuroprotective and anti-oxidative properties. Leveraging in-silico techniques such as molecular docking and ADMET (Absorption, Distribution, Metabolism, Excretion, and Toxicity) profiling enables cost-effective and efficient screening of these compounds for potential anti-Alzheimer’s activity.

Objective: This study aims to evaluate the therapeutic potential of newly designed hydrazide derivatives as inhibitors of key enzymes involved in Alzheimer’s pathology, particularly acetylcholinesterase (AChE) and butyrylcholinesterase (BChE). The research focuses on identifying promising lead candidates by assessing their binding affinities, drug-likeness, and pharmacokinetic properties using in-silico methodologies.

Methods: A library of novel hydrazide derivatives was designed based on structure-activity relationship (SAR) insights. Molecular docking simulations were conducted using AutoDock Vina to predict the binding affinities and interaction profiles of the compounds with AChE and BChE. The crystal structures of the target enzymes were obtained from the Protein Data Bank (PDB), and protein-ligand interactions were visualized using tools like Discovery Studio and PyMOL. ADMET profiling was performed using SwissADME and pkCSM to evaluate the drug-likeness, oral bioavailability, and safety parameters of the ligands. Additionally, Lipinski’s Rule of Five and Veber’s rules were applied to determine pharmacokinetic feasibility.

Results: Several hydrazide derivatives demonstrated strong binding affinity towards AChE and BChE, with docking scores indicating favorable interactions within the active site residues, including π-π stacking, hydrogen bonding, and hydrophobic contacts. The most promising candidates exhibited binding energies comparable to or better than standard inhibitors such as donepezil and rivastigmine. ADMET analysis revealed that the top hits possessed high gastrointestinal absorption, low blood-brain barrier permeability (selectively beneficial in reducing peripheral side effects), and minimal predicted hepatotoxicity or cardiotoxicity. Furthermore, the majority of the compounds adhered to Lipinski’s and Veber’s rules, suggesting good oral bioavailability and drug-likeness.

Conclusions: The in-silico evaluation of hydrazide derivatives presents a promising avenue for the development of novel anti-Alzheimer’s agents. The integration of molecular docking and ADMET profiling enabled the identification of potential leads with favorable interaction profiles and acceptable pharmacokinetic properties. While these findings provide a solid foundation, experimental validation through in-vitro and in-vivo studies is essential to confirm the therapeutic efficacy and safety of the shortlisted compounds. This computational strategy not only accelerates the early stages of drug discovery but also reduces the reliance on time-consuming and costly laboratory procedures.