Background and purpose: Synthesis and investigation of pharmacological activity of novel compounds are time and money-consuming. However, computational techniques, docking, and in silico studies have facilitated drug discovery research to design pharmacologically effective compounds.
Experimental approach: In this study, a series of quinazoline derivatives were applied to quantitative structure-activity relationship (QSAR) analysis. A collection of chemometric methods were conducted to provide relations between structural features and cytotoxic activity of a variety of quinazoline derivatives against breast cancer cell line. An in silico-screening was accomplished and new impressive lead compounds were designed to target the epidermal growth factor receptor (EGFR)-active site based on a new structural pattern. Molecular docking was performed to delve into the interactions, free binding energy, and molecular binding mode of the compounds against the EGFR target.
Findings/Results: A comparison between different methods significantly indicated that genetic algorithm-partial least-squares were selected as the best model for quinazoline derivatives. In the current study, constitutional, functional, chemical, resource description framework, 2D autocorrelation, and charge descriptors were considered as significant parameters for the prediction of anticancer activity of quinazoline derivatives. In silico screening was employed to discover new compounds with good potential as anticancer agents and suggested to be synthesized. Also, the binding energy of docking simulation showed desired correlation with QSAR and experimental data.
Conclusion and implications: The results showed good accordance between binding energy and QSAR results. Compounds Q1-Q30 are desired to be synthesized and applied to in vitro evaluation.
Previously reported epidermal growth factor receptor inhibitors bearing quinazoline scaffold.
Among the nitrogen-containing compounds, the quinazoline ring was a very privileged and effective scaffold in pharmaceutical and medicinal chemistry; they have a broad spectrum of biological activities such as anticancer, diuretic, anti-inflammatory, anticonvulsant, antimicrobial, antiviral, antiplasmodial, and antihypertensive effects. Also, among the different quinazoline scaffolds, 2-substituted-4(3H)-quinazolinone has been used as an attractive pharmacophore for drug design purposes. Quinazoline which is substituted at C4, C6, and C7 has been applied as one of the most significant classes of quinazoline-based epidermal growth factor receptor (EGFR) inhibitors. The quinazoline skeleton is also a substructure of natural purine bases, plant alkaloids, and several FDA-approved drugs such as prazosin, alfuzosin, erlotinib, gefitinib.
Partial least squares regression coefficients for the variables used in the genetic algorithm-partial least squares model.
Synthesis and investigation of pharmacological activity of novel compounds, usually take large amounts of expenditures and time. The use of computational techniques, docking, and in silico studies for designing pharmacologically effective compounds has opened a new approach to drug discovery research. Quantitative structure-activity relationships (QSAR) studies, as one of the important subjects in chemometrics, provide the ability to biological activities prediction for the novel or even non-synthesized compounds by medicinal chemists.
Linear QSAR models are mathematical equations that deliver good information for better explaining the mechanisms of action of the compounds, and proving the relationship between chemical structures and pharmacological activities.
Plot of VIP for the descriptors used in genetic algorithm-partial least squares model. VIP, variables important in projection.
The most important phase in constructing QSAR models is the suitable description of the structural and physicochemical properties of chemical structures. These properties called molecular descriptors have a high impact on the pharmacological properties of a compound. Molecular descriptors have been classified into different groups including physiochemical, constitutional, geometrical, topological, and quantum chemical descriptors. Dragon and Hyperchem as two famous computational packages are able to calculate more than 7000 of these parameters.
An important approach for the researchers is establishing a complete SAR of quinazoline derivatives of cytotoxic agents to modify the quinazoline moiety. In this paper, they investigated QSAR studies of some quinazoline derivatives which have been recently reported to exhibit cytotoxic activity against the MCF-7 cell line. Different QSAR models including multiple linear regressions were used to establish the relationship between descriptors and anti-breast cancer activity of compounds. Their QSAR models would be established mathematical equations between pharmacological activities and calculable parameters such as topological, quantum, physicochemical, stereochemical, or electronic parameters. In addition, molecular docking simulation was also done to reach the most favorable conformation and binding modes of all compounds as well as newly designed compounds towards EGFR as possible targets for their anticancer effect.
The main interaction between the active site of epidermal growth factor receptor (PDB ID: 1M17) with compounds 33, 55, 79, and erlotinib.
The molecular docking simulation help us to understand the possible interactions between the ligands and enzyme’s active sites in detail and also helps to design novel potent inhibitors. In 2017, Tu et al. reported docking studies of new synthesized quinazoline bearing aryl semi carbazole. structure I) and showed that tetrahydrofuran substituent was necessary for the anti-cancer activity of these compounds. The in vitro cytotoxic activities of new quinazoline compounds were determined against three human cancer cell lines (A549, MCF-7, and PC-3), and showing that the introduction of quinazoline derivatives bearing 2,3 dihydro-indole or 1,2,3, 4-tetrahydroquinoline moiety could act appropriately to interact with the active site of the EGFR target.
Emami L, Sabet R, Khabnadideh S, Faghih Z, Thayori P. Quinazoline analogues as cytotoxic agents; QSAR, docking, and in silico studies. Res Pharma Sci 2021;16:528-46