DOCKING – BASED VIRTUAL SCREENING OF LIPINSKI COMPLIANT 2 -ARYLQUINA-ZOLIN – 4 – ONE DERIVATIVES: A MOMENTUM TO THE DISCOVERY OF NOVEL EGFR INHIBITORS

DOCKING - BASED VIRTUAL SCREENING OF LIPINSKI COMPLIANT 2 -ARYLQUINA-ZOLIN - 4 - ONE DERIVATIVES: A MOMENTUM TO THE DISCOVERY OF NOVEL EGFR INHIBITORS

P. K. Arora ^{* 1} S. Kumar ² and S. K. Bansal ³

Faculty of Pharmacy ¹, IFTM University, School of Pharmaceutical Sciences ², Moradabad - 244001, Uttar Pradesh, India.

Ram-Eesh Institute of Vocational and Technical Education, Greater Noida - 201310, Uttar Pradesh, India.

ABSTRACT: The epidermal growth factor receptor (EGFR), which is a potential anticancer drug target, is over-expressed in non-small-cell lung cancer (NSCLC). The present study is an attempt to explore the human EGFR (protein data bank code: 1M17) inhibition potential of Lipinski compliant compounds possessing 2-arylquinazoli-4-one scaffold with chalcone structural motif; by docking analysis, using Auto Dock 4.0 and Discovery Studio Visualizer. Docking experiments were validated by docking the reported co-crystallized erlotinib confermer at the active site of a target protein. The root means square deviation (RMSD) calculated for the docked co-crystallized confermer by using UCSF chimera was 0.989Ao. Five compounds C21, C42, C47, C10, and C46, were found as the most potent in-silico EGFR inhibitors and their free energy of binding (BE) came in the range of -45.56 kJ/mol to -41.25 kJ/mol. Absorption and toxicity predictions of the compounds were done using ad met SAR, an online prediction tool. The BE of the reference compound afatinib was found to be -32.72 kJ/mol. The understanding of protein-legend interactions would give accurate guidance for the rapid development of low molecular weight EGFR inhibitors.

Molecular modeling, Anticancer agents, Computational chemistry, Drug design, Docking, EGFR inhibitors

INTRODUCTION: Docking-based drug design by using the structure of target protein remains one of the most rational and speedy approaches in drug discovery paradigms. The knowledge about the amino acid residues interacting with the specific groups of the chemical entity leads to proposals for the synthesis of the new highly potent chemical entities ¹. In recent years, virtual screening by computational methods has become an essential part of drug discovery projects ². During the past decade, profound research has initiated a new era of cancer treatment involving drugs with novel molecular targets.

all types of cancer, lung cancer is the most commonly diagnosed cancer, there have been huge efforts for treatment advancements over the last 20 years, but still, poor prognosis persists for patients with advanced non-small cell lung cancer (NSCLC) ³. One of the main reasons behind the poor prognosis of NSCLC is EGFR overexpression ⁴.

Clinical trials demonstrated that erlotinib and afatinib have greater efficacy regarding proliferation free survival (PFS) and response rate than conventional chemotherapy (viz; cisplatin, carboplatin, etc.), and as a result, these drugs were approved by the US Food and Drug Administration (FDA) ^{5, 6}. As compared to conventional chemotherapy (viz; cisplatin, carboplatin, etc.), the toxicities associated with low molecular weight EGFR tyrosine kinase inhibitors are more tolerable (viz; rash and diarrhea and rarely interstitial lung disease) ⁷.

The comparative clinical studies between erlotinib and afatinib reported the superiority of afatinib over erlotinib regarding proliferation-free survival and overall survival in the advanced NSCLC patients, but the incidence of treatment-related diarrhea and stomatitis was greater with afatinib than that of erlotinib ^{8, 9}. With the objective of searching new better low molecular weight EGFR tyrosine kinase inhibitors with superior potency, 47 Lipinski compliant 2-arylquinazolin-4-one incur-porated chalcones were designed, their absorption capability through the human intestine and blood-brain barrier (BBB), and their toxicity (mutagen city and carcinogenicity) and LD₅₀, were predicted using ad met SAR, an online prediction tool. The compounds were docked to evaluate their EGFR inhibition potential in terms of free binding energy (BE) and Inhibition Constant (KI). The results of these studies will give a momentum to discover novel low molecular weight EGFR tyrosine kinase inhibitors with distinguished virtues over the existing ones.

MATERIALS AND METHOD:

Sources and Softwares: In the present docking study, X-ray diffraction 3D crystal structure of EGFR from protein data bank (https:// www.rcsb.org/, PDB ID: 1M17) was used as a target protein ¹⁰. The open-source software tools used were Discovery Studio Visualizer 2017 R2 (Dassault Systèmes BIOVIA, https://www.3 dsbiovia.com), Marvin Sketch version 18.23 (Chemaxon Ltd; http://www.Chemaxon.com), Auto Dock 4.0 MGL tools (The Scripps Research Institute, Molecular Graphics Laboratory, 10550 North Torrey Pines Road, CA, 92037), UCSF chimera (https://www.cgl.ucsf.edu/chimera). The online web tools used were swiss ADME (http://www.swissadme.ch), and ad met SAR (http://lmmd.ecust.edu.cn/admetsar1). All the docking experiments were done on a 1.7 GHz Intel (R) core i5 system with 3.8 GB of RAM and a Red Hat Enterprise Linux 6.6 operating system.

Data Set of Ligands: A data set of ligands was prepared and screened for the Lipinski drug-likeness by using the Swiss ADME online server. Among them, 47 ligands complied the Lipinski rule of five; hence they constituted the data set ^{11, 12}.

Absorption and Toxicity Prediction: Absorption capability of 47 ligands through the human intestine and blood-brain barrier (BBB), and their toxicity (mutagenicity and carcinogenicity) and LD₅₀, were predicted using ad met SAR, an online prediction tool.

Protein Preparation: It consists of several steps; firstly, the crystal structure of Human epidermal growth factor receptor (EGFR) PDB code 1M17 complexed with erlotinib was downloaded in .pdb format from protein data bank (PDB; http://www.rcsb.org/pdb) as shown in Fig. 1A and loaded to Discovery Studio Visualizer for the removal of water molecules, non bonded atoms and co-crystallized erlotinib. The refined 3D crystal structure Fig. 1B of 1 m17 protein was made ready for auto grid computing and docking experiments using Auto Dock 4.0 MGL tools by adding polar hydrogen, merging non-polar hydrogen, and adding Kollman charges than was saved in. pdbqt format ^{13, 14}.

FIG. 1: (A) EGFR (1M17) COMPLEXED WITH ERLOTINIB (B) EGFR (1M17)

Ligand preparation: MarvinSketch version 18.23 was used to draw the 2D structures and then subsequently to 3D structures; of all the 47 ligands, explicit hydrogen was added, the geometry of ligands were then cleaned, and energy minimization of the ligands was done in an MMFF94 force field by gradient optimization function of Marvin Sketch. The ligand 3D structures were saved in. pdb format. The ligands were prepared as per the protocol mentioned in autodock tutorial (http://autodock.scripps.edu). The ligand structure (.pdb file) was opened in Auto Dock Tools. All atoms of the ligand were assigned AD4 type, polar hydrogen was added, and non-polar hydrogen was merged, Gasteiger charges were added, then ligand structure was saved in .pdbqt format for autogrid computing and docking experiments ^{13, 14}.

Validation of Docking Experiment: The co-crystallized conformer of erlotinib was separated from the EGFR crystal structure using Discovery Studio Visualizer. The separated co-crystallized conformer was prepared as per the protocol of ligand preparation and then was re-docked with the prepared target protein 1M17 under different grid parameters and docking parameters to get the docked pose having minimum RMSD value with respect to the reported erlotinib co-crystallized conformer. The set of grid and docking parameters that had given the least RMSD value were selected for performing docking experiments. The RMSD values were calculated by using UCSF chimera.

Molecular Docking Studies: The validated grid parameters and docking parameters were employed for autogrid computing and docking studies. In preparing the grid parameter file (.gpf), 3D grid box was placed at the centre of the target protein (PDB: 1M17); along x, y, and z-axis of the 3D grid box 100 points were selected, a grid spacing of 0.375 A ° (roughly a quarter of the length of a carbon-carbon single bond) was used. By using .gpf file of the ligand understudy, Autogrid 4.0 was run to generate. glg files having grid maps of interaction energies of various atom types present in the ligand. In preparing the docking parameter file (.dpf), 50 independent runs (each run was comprised of an initial population of 150 individuals), with step sizes of 0.2 A° for translations and 5 A° for orientations and torsions, a maximum number of 2,500,000 energy evaluations, the maximum number of generations of 27,000, an elitism value (number of top individuals that automatically survive) of 1 and a number of active torsion of 9 was used. By using .dpf file of the ligand, Autodock4.0 was run to generate .dlg file.

Docking Analysis, Visualization of Docked Pose and Interactions: Of the three different search algorithms offered by Autodock 4.0, the Lamarckian genetic algorithm (LGA) based on the optimization algorithm was used. The free energy of binding (BE) and inhibition constant (KI) of ligands were obtained from the. dlg files of the respective ligands. The complex between the best-fit ligand conformer and the target protein (1M17), was opened in Discovery Studio Visualizer to observe predicted binding pose of the ligand and its interactions with the target macromolecule (1M17).

RESULTS AND DISCUSSION: There are a number of currently available drugs that are based on quinazolinone scaffold (viz; raltitrexed for large intestine cancer treatment, methaqualone has sedative effects), in-vitro EGFR inhibition assay on certain quinazolinone and quinazoline derivatives have been carried out, and some of them are reported as potent EGFR inhibitors ^15-17. The immense therapeutic potential of the quinazolinone scaffold motivated the present research to choose quinazolinone as a scaffold in designing novel EGFR inhibitors.

FIG. 2: (A) AFATINIB (B) 2-ARYLQUINAZOLIN-4-ONE DERIVATIVES

It is reported that afatinib interacts with the target EGFR by Michael addition reaction ¹⁸. The presence of carbon-carbon α-β unsaturated carbonyl site in afatinib Fig. 2A makes it a good substrate for Michael addition reaction, keeping this fact in mind quinazolinone derivatives were designed to have carbon-carbon α-β unsaturated carbonyl site Fig. 2B and to follow the Lipinski rule of drug-likeness. There are many reports which justify the incorporation of chalcone structural motif, for the development of new anticancer drugs ^{19, 20}. Recently, 1, 4 - dihydroindeno [1, 2 - c] pyrazole chalcones, 1, 3, 4 - oxadiazole / chalcone hybrids and thienoquinoline-2-carboxamide chalcone derivatives have been reported as significant EGFR inhibitors ^21-23. In the present work, before performing docking experiments, 47 Lipinski compliant ligands were analyzed for human intestinal absorption (HIA), blood-brain barrier absorption (BBBA), toxicity potential (mutagenicity and carcinogenicity), and their lethal dose (LD₅₀), using ad met SAR.  The results are listed in Table 1. The absorption through BBB and the human intestine is expressed in terms of probability. All the ligands in the data set showed good HIA and BBB absorption. None of the compounds were carcinogenic, and except C41, all the other compounds were non-mutagenic.

TABLE 1: ABSORPTION, TOXICITY AND LD₅₀ ANALYSIS OF 2-ARYLQUINAZOLIN-4-ONE INCORPORATED CHALCONES

Docking Validation: The reported co-crystallized confermer of erlotinib with target EGFR (1M17) was redocked and the minimum RMSD obtained between the co-crystallized confermer and its redocked pose was 0.989A^o as shown in Fig. 3, which implies that the used grid parameters and docking parameters had successfully generated the docking pose which is very close to the reported co-crystallized confermer ¹⁰.

FIG. 3: (A) CO-CRYSTALLIZED ERLOTINIB CONFERMER (B) REDOCKED POSE ERLOTINIB CO-CRYSTALLIZED CONFERMER (C) SUPERIMPOSED CO-CRYSTALLIZED ERLOTINIB CONFERMER AND ITS REDOCKED POSE

Molecular Docking Results: All the docked compounds binded at the reported binding pocket of EGFR (1M17) Fig. 4 that ensures their ligand efficiency and the accuracy of the docking experiments. The docked ligands are ranked on the basis of free binding energy (BE) and inhibition constant (KI). The interactions were observed by using Discovery Studio Visualizer. The docking analysis is tabulated in Table 2. Afatinib Fig. 5 forms conventional hydrogen bonds, with GLN767 and ASP831, Pi-Sigma interactions with LEU694 and LEU820, Pi-Alkyl interactions with ALA719 and VAL702 & Alkyl interactions with LYS721, MET742, and LEU768.

FIG. 4: AFATINIB AND FIVE MOST POTENT LIGANDS IN THE BINDING POCKET OF EGFR (A) AFATINIB (B) C21 (C) C42 (D) C47 (E) C10 (F) C46

TABLE 2: MOLECULAR DOCKING ANALYSIS

FIG. 5: DOCKING INTERACTIONS BETWEEN AFATINIB AND EGFR (1M17). (A) 3D- INTERACTIONS (B) 2D- INTERACTIONS

C21 Fig. 6 forms two conventional hydrogen bonds with MET769; Pi-Cation interaction with LYS721, Pi-Sulphur interaction with MET742, Pi-Sigma interactions with LEU694, and Pi-Alkyl interactions with VAL702, ALA719, and LEU820. C42 Fig. 7 forms a hydrogen bond with MET769; Pi-Cation interaction with LYS721, Pi-Sigma interaction with LEU694, and Pi-Alkyl interactions with LEU694, VAL702, LYS704, ALA719, LYS721, MET742, LEU768, PRO770, and LEU820. C47 Fig. 8 forms hydrogen bonds with LYS721 and MET769, Pi-Anion interaction with ASP831, Pi-Sulphur interaction with CYS773, Pi-Sigma interactions with LEU820 and GLY772, and Pi-Alkyl interactions with VAL702, ALA719, LYS721, MET742, LEU764, and LEU820.

FIG. 6: DOCKING INTERACTIONS BETWEEN C21 AND EGFR (1M17). 3D- INTERACTIONS (B) 2D- INTERACTIONS

FIG. 7: DOCKING INTERACTIONS BETWEEN C42 AND EGFR (1M17). (A) 3D- INTERACTIONS (B) 2D- INTERACTIONS

FIG. 8: DOCKING INTERACTIONS BETWEEN C47 AND EGFR (1M17). (A) 3D- INTERACTIONS (B) 2D- INTERACTIONS

FIG. 9: DOCKING INTERACTIONS BETWEEN C10 AND EGFR (1M17). (A) 3D- INTERACTIONS (B) 2D- INTERACTIONS

C10 Fig. 9 forms a hydrogen bond with ASP831, Pi-Anion interaction with ASP831, Pi-Sulphur interactions with MET742 and CYS773, Pi-Sigma interaction with LEU820 and Pi-Alkyl interactions with LEU694, VAL702, and LEU820.

C46 Fig. 10 forms a hydrogen bond with ASP831, Pi-Cation interaction with LYS704, Pi-Anion interaction with ASP831, Pi-Sulphur interaction with MET742, Alkyl interaction with ARG817, and Pi-Alkyl interactions with LEU694, VAL702, LYS721, LEU768, PRO770, CYS773, and LEU820. C26 Fig. 11 forms a hydrogen bond with LYS721, Pi-Cation interaction with ARG817, Pi-Anion interaction with ASP831, Pi-Sigma interaction with ARG817, Alkyl interaction with LEU694, and Pi-Alkyl interactions with VAL702, ALA719, LYS721, MET742, LEU764, and LEU820. C24 Fig. 12 forms a hydrogen bond with ASP831, Pi-sulphur interactions with MET742 and CYS773, Pi-Sigma interaction with LEU820, Alkyl interactions with ARG817 and CYS773 and Pi-Alkyl interactions with LEU694, VAL702, LYS721, CYS773, and LEU820. C44 Fig. 13 forms a hydrogen bond with ASP831, Pi-Cation interactions with LYS704, Pi-Anion interaction with ASP831, Pi-Sulphur interaction with MET742 and, Pi-Alkyl interactions with LEU694, VAL702, LYS721, LEU768, PRO770 and LEU820.

FIG. 10: DOCKING INTERACTIONS BETWEEN C46 AND EGFR (1M17). (A) 3D- INTERACTIONS (B) 2D- INTERACTION

FIG. 11: DOCKING INTERACTIONS BETWEEN C26 AND EGFR (1M17). (A) 3D- INTERACTIONS (B) 2D- INTERACTION

FIG. 12: DOCKING INTERACTIONS BETWEEN C24 AND EGFR (1M17). (A) 3D- INTERACTIONS (B) 2D- INTERACTIONS

C2 Fig. 14 forms a hydrogen bond and Pi-Anion interaction with ASP831, Pi-Sulphur interaction with MET742, Alkyl interactions with LYS704 and LEU768 and Pi-Alkyl interactions with LEU694, VAL702, LYS 704, LYS721, LEU768, PRO770, and LEU820. C23 Fig. 15 forms a hydrogen bond and Pi-Anion interaction with ASP831 and Pi Alkyl interactions with LEU694, VAL702, LYS 704, LYS721, LEU768, PRO770, and LEU820.

FIG. 13: DOCKING INTERACTIONS BETWEEN C44 AND EGFR (1M17). (A) 3D- INTERACTIONS (B) 2D- INTERACTIONS

FIG. 14: DOCKING INTERACTIONS BETWEEN C2 AND EGFR (1M17). (A) 3D- INTERACTIONS (B) 2D- INTERACTION

FIG. 15: DOCKING INTERACTIONS BETWEEN C23 AND EGFR (1M17). (A) 3D- INTERACTIONS (B) 2D- INTERACTIONS

CONCLUSION: The docking analysis reveals that the designed 2-arylquinazolin-4-one incorporated chalcones, are satisfactory scaffolds for EGFR (1M17) inhibition. It was observed that the amino acid residues ASP831, CYS773, LYS721, and MET 769 were important for H-bonding interactions. LYS721, LYS704, and ARG817 were important for pi-cation interactions. For pi-anion interaction, ASP831 was important, and for pi-sulphur interactions, MET742 and CYS773 were important. The amino acid residues LEU820, VAL702, MET742, ALA719, PRO770, LYS704, LEU768, LEU764, and LEU694 were important for pi-alkyl interactions. Hence, the study gives molecular insight into the binding process of the designed quinazolinones with the target EGFR protein (1M17). Lower binding energies, Lipinski compliance, good human intestinal as well as blood-brain barrier absorption, non-carcinogenicity, and non-mutagenicity of the designed ligands prompt the research to move on for the synthesis of in-silico potent EGFR inhibitors and carrying out the in-vitro assays to confirm their potency. Hence, the present research gives momentum to the discovery of low molecular weight novel EGFR inhibitors to treat advanced non-small-cell lung cancer (NSCLC) with a comparatively better prognosis.

ACKNOWLEDGMENTS: Nil

CONFLICTS OF INTEREST: The authors declare that they have no conflicts of interest.

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    How to cite this article:

    Arora PK Kumar S and Bansal SK: Docking - based virtual screening of lipinski compliant 2 -arylquina-zolin - 4 - one derivatives: a momentum to the discovery of novel egfr inhibitors. Int J Pharm Sci & Res 2021; 12(3): 1699-12. doi: 10.13040/IJPSR.0975-8232. 12(3).1699-12.

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