ArticleJournal of Taibah University Medical Sciences2023
Pharmacokinetic profiling of quinazoline-4(3H)-one analogs as EGFR inhibitors: 3D-QSAR modeling, molecular docking studies and the design of therapeutic agents.
Article in Journal of Taibah University Medical Sciences, 2023. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 5 papers.
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Who cites it
5 citing papers in PubMed, 19 citations in OpenAlex.
- Computer-aided discovery of novel SmDHODH inhibitors for schistosomiasis therapy: Ligand-based drug design, molecular docking, molecular dynamic simulations, drug-likeness, and ADMET studies.PLoS neglected tropical diseases · 2024Article
- Ligand based-design of potential schistosomiasis inhibitors through QSAR, homology modeling, molecular dynamics, pharmacokinetics, and DFT studies.Journal of Taibah University Medical Sciences · 2024Article
- 3D-QSAR and Molecular Dynamics Study of Isoxazole Derivatives to Identify the Structural Requirements for Farnesoid X Receptor (FXR) Agonists.Molecules (Basel, Switzerland) · 2024Article
- Virtual screening and molecular dynamics studies of novel small molecules targetingIn silico pharmacology · 2024Article
- Ligand-based drug design of quinazolin-4(3H)-ones as breast cancer inhibitors using QSAR modeling, molecular docking, and pharmacological profiling.Journal of the Egyptian National Cancer Institute · 2023Article
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5 authors at 1 institution in 1 country.
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Abstract
Objectives: Breast tumor is ranked as the most common tumor type identified among women globally with over 1.7 million cases annually, representing 11.9% of the total number of cancer cases. Approved anti-breast tumor drugs exhibit several side effects and some patients develop resistance during the early treatment stage. This study aimed to use an in-silico approach to identify and design potential therapeutic agents. Methods: Robust 3D-QSAR models were developed using quinazoline-4(3H)-one analogs as EGFR inhibitors. The best model was then selected based on statistical parameters and was subsequently used to design more potent therapeutic agents. Molecular docking simulation was executed using the data set and the designed compounds to identify lead compounds which were further screened by pharmacokinetic profiling by applying SwissADME and pkCSM software. Results: Internal validations of the best CoMFA and CoMSIA models (R Conclusions: Pharmacological studies identified compounds 20, 24 26 and the designed compounds 2, 3, 5 as feasible drug candidates. However, these theoretical findings should now be validated experimentally.
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