Year: 2025
Type: article
Abstract: Objective: This study aims to evaluate the drug similarity of biologically active compounds from Nigella Damascena using in silico methods. The primary goal is to assess their potential for pharmaceutical development. Theoretical Framework: The use of plant-derived compounds in medicine is gaining prominence due to their efficacy, safety, and accessibility. Bioinformatics and computational modeling significantly accelerate drug discovery and reduce research costs. This study focuses on in silico prediction of pharmacological activity and toxicity of six Nigella Damascena alkaloids using SwissADME and ProTox-II software. Method: Computational modeling was performed to analyze the physicochemical properties, pharmacokinetics, drug-likeness, and toxicity of the selected alkaloids. SwissADME was used to evaluate Lipinski’s rule compliance, bioavailability, and blood-brain barrier penetration, while ProTox-II provided toxicity assessments. Results and Discussion: All studied alkaloids exhibited high drug similarity, meeting Lipinski’s criteria and demonstrating significant bioavailability. The compounds showed strong blood-brain barrier penetration, indicating potential applications in central nervous system-targeted drugs. However, some compounds exhibited neurotoxicity and hepatotoxicity risks, requiring further in vitro and in vivo studies. Research Implications: These findings highlight the potential of Nigella Damascena alkaloids for pharmaceutical applications. The rapid screening provided by in silico methods allows efficient selection of promising candidates for further experimental validation. Originality/Value: This research contributes to bioinformatics-based drug discovery, demonstrating the utility of in silico techniques in identifying novel plant-derived therapeutic compounds.
Authors: Yuliia Kolb, Svyatoslav Polovkovych
Institution: Lviv Polytechnic National University
Language: English
Subfield: Computational Theory and Mathematics
Field: Computer Science
Domain: Physical Sciences
Open Access status: hybrid
OpenAlex ID: W4410340275