In Silico ADMET and Drug-Likeness Evaluation of Withaferin A from Withania somnifera for Potential Application in Alzheimer's Disease

Background: Alzheimer’s disease is a progressive neurodegenerative disorder characterized by cognitive decline, memory impairment, and neuronal loss. Despite extensive research, effective disease-modifying therapies remain limited. Natural compounds have emerged as promising candidates due to their multi-targeted pharmacological properties. Objective: The present study aimed to evaluate the pharmacokinetic (ADMET) properties and drug-likeness profile of Withaferin A, a bioactive compound derived from Withania somnifera, using in silico approaches for its potential application in Alzheimer’s disease. Methods: The chemical structure of Withaferin A was retrieved from the PubChem database in SMILES format and analyzed using the DeepPK platform. Various pharmacokinetic parameters, including absorption, distribution, metabolism, excretion, and toxicity, were predicted using deep learning-based computational models. Key parameters such as intestinal absorption, blood–brain barrier permeability, cytochrome P450 interactions, clearance, and toxicity endpoints were evaluated. Results: Withaferin A demonstrated favorable absorption characteristics, including high intestinal absorption and predicted oral bioavailability. The compound exhibited moderate distribution with significant blood–brain barrier penetration potential. Metabolic analysis indicated minimal interaction with major cytochrome P450 enzymes, except CYP3A4. The excretion profile suggested moderate clearance and a short half-life. However, toxicity predictions revealed concerns such as AMES mutagenicity and hERG inhibition, indicating potential safety risks. Conclusion: Withaferin A exhibits promising pharmacokinetic properties and drug-likeness characteristics, supporting its potential as a candidate for further investigation in Alzheimer’s disease. However, its toxicity profile necessitates additional experimental validation. This study highlights the importance of in silico ADMET analysis in early-stage drug discovery.