Please use this identifier to cite or link to this item: http://localhost:8080/xmlui/handle/123456789/7405
Title: In silico analysis of noscapine compounds as anti-tumor agents targeting the tubulin receptor
Authors: Nulamuga, Benson
Uzairu, Adamu
Babalola, Ibrahim T.
Ibrahim, Muhammad T.
Umar, Abdullahi B.
Keywords: Anti-tumor
Bioisosterism
Docking
Model
Pharmacokinetics
Tubulin
Issue Date: 2023
Publisher: Journal of Taibah University Medical Sciences
Series/Report no.: Original Article;32-44
Abstract: Objective: This research aims to develop a mathematical model that relates the structural features of noscapine with anti-tumor activity, to explains the mode of binding between noscapine compounds and the target receptor tubulin by docking analysis. By considering the results of docking analysis and predictions of pharmacokinetic properties/drug likeness, we designed novel noscapine compounds as anti-tumor agents against pancreatic cancer. Methods: We used an in silico quantitative structure eactivity relationship (QSAR) approach, molecular docking analysis and online tools for pharmacokinetics and drug likeness prediction to develop novel compounds. Results: A QSAR model with good validations parameters and quality of fit (R2 ¼ 0.9731, Q2 CV ¼ 0.9434, R2 adj ¼ 0.9647 and R2 test set ¼ 0.8343) was built utilizing 70% of the dataset as a training set and the remaining 30% as an external validation to ascertain its predictive capability. Three novel compounds were designed: D3, D4 and D6 with binding scores of 11.2, 10.2 and 10.6 kcal/mol, respectively, exhibiting high affinity towards the tubulin receptor than the template (parent compound) and the co-crystallized ligand (E*) with a binding score of 9.2 kcal/mol. Conclusion: The QSAR approach and molecular docking analysis is an important approach for modern drug discovery. Pharmacokinetics studies of the selected novel compounds revealed good drug properties and can be used as candidate compounds for the development of anti-tumor agents for pancreatic cancer.
URI: http://localhost:8080/xmlui/handle/123456789/7405
ISSN: 1658-3612
Appears in Collections:Vol 18 No 1 (2023)

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