INTRODUCTION:

Cancer, one of the familiar causes of death, has caught the attention of researchers across the globe. In recent years, lung cancer is most prominently predicted to take the lives of 1.8 million people every year, whereas 0.5 million women die from breast cancer.Standard cancer treatment methods include chemotherapy and anticancer drug usage; however, these approaches have serious drawbacks, like limited therapeutic windows, drug resistance, and toxicities, necessities the designing and production of novel anticancer agents^1,2. With the recent introduction of molecular biomarkers to typical diagnostic panels, the detection of cancer is undergoing an important change. The molecular changes comprise those influencing DNA, RNA, microRNAs (miRNAs), and proteins³. Breast cancer can be treated when detected in the early stage. The modern method of detecting breast cancer includes the use of extracellular fluids⁴, COX-2 Inhibitors⁵, and microbubble ultrasound⁶ along with already available methods.

There has been a remarkable growth in the use of nanotechnology for the treatment of breast cancer in recent years^7,8. As a result, prior to actual synthesis, it is crucial to design novel substituted coumarin derivatives that can be used as anticancer agents⁹. The molecular docking method may be used to understand the behavior of medicinal compounds at protein target binding sites as well as to provide insight into vital biochemical processes which is very important in drug discovery¹⁰. This assists one to grasp the atomic relationship between the molecule and the protein. Among the various heterocyclic compounds, coumarin derivatives¹¹ are a form of natural plant metabolite that seems to have anticoagulant¹², anti-inflammatory¹³, antimicrobial¹⁴, aromatase antagonist¹⁵, antifungal and antibacterial¹⁶ etc. characters. Integrating the coumarin moiety with various anticancer pharmacophores is a promising method for decreasing side effects, reducing drug resistance, and potentially delivering a valuable therapeutic intervention for cancer treatment. In this context, we focus on the computational development of novel coumarin nucleoside compounds exhibiting pharmacophore properties of EGFR inhibitors. In this research work, molecular docking analysis and pharmacokinetic properties were evaluated, which aid in the formulation of medications by assisting with chemical absorption, distribution, biotransformation, excretion, and toxicity. MD simulation studies, drug-likeness and target prediction were used to assess the newly created coumarin nucleoside scaffolds as potent antagonists to the receptor for epidermal growth factor.

MATERIALS AND METHODS:

To study the binding mode of the title compounds into the active site of Epidermal Growth Factor Receptor, Software Autodock 4.2.6, UCSF Chimera 1.15, Biovia discovery studio visualizer, ACD/ChemSketch, Open Babel GUI 3.1.1, for molecule docking an open chemical toolbox, pkCSM, a free web tool for ADMET prediction, Target prediction was done using Swiss Target Prediction a free software and MD simulation studies were performed.

Ligand Preparation:

A series of twelve recently developed coumarin nucleoside compounds were developed using the ChemSketch tool. All coumarin nucleoside derivatives were transformed by using open Babel GUI 3.1.1, and the energy of all the selected compounds was reduced with the help of UCSF-Chimera software 1.15 by applying 10 steps of conjugate gradient and steepest descent 100 steps. After identification of the aromaticity requirements (d" 7.5), root and number of torsions, all the energy lowered structures were transferred into PDBQT file format.

Receptor Preparation:

A three-dimensional X-ray structure of Epidermal Growth Factor Receptor with 4- anilinoquinazoline inhibitor erlotinib was retrieved from the Protein Data Bank (PDB) (PDB Code 1M17). Ions, water molecules and ligand were eliminated from the protein structure and Kollman charges and polar hydrogen were added. After applying AutoDock Tool 4 charges the PDB format files of proteins were converted into file format. Grid parameter (.gpf) files were created by adjusting the grid box dimension to (40X40X40) for (0.375 spacing) protein 1M17 with grid parameters of X= 22.013690, Y=0.252828 and Z=52.794034. A grid log file (.glg) was generated, and ADT was used to generate a docking log file (.dlg) using the input file format (.dpf). The docking studies were carried out using the lamarkain method (genetic search) with 10 docking runs for each grid to examine the largest conformational space for the ligand. Both the maximum number of evaluations and the maximum number of generations were set to (2500000 and 27000 respectively). Finally, the best-fit complexes were manually analyzed using the AutoDock Tool. The discovery studio visualizes and ranks docked receptor and ligand interactions based on binding energy.

Prediction of Drug Likeness and ADMET Properties:

The compounds were examined by application of Lipinski’s Rule of Five, which declares of the molecule should not exceed more than 500 Daltons, that it can comprise a maximum of hydrogen bonds (05), hydrogen bond acceptors (10), rotatable bonds (10) and should be the number should not exceed more than above numbers. The tPSA characteristic was associated to the transportation of molecules passively across the blood brain barrier and membranes. Apart from compounds 1,4,7 (tPSA more than 140), all studied compounds meet the Gastrointestinal absorption criteria. All the molecules had WLOGP values less than 5 (which indicates whether a chemical is harmful or not). These chemicals were moderately easy to synthesize (less than 5 on the scale). This indicates that these molecules will be easy to produce in the laboratory and will be active, drug-like, and have oral bioavailability. The compounds showed excellent intestine absorption percent and it was above 60%. The BBB permeability (logBB) values were more than -2.5. Moreover, the Central Nervous System permeability (Log PS) values of all compounds were less than -3 except for compound 9,10 which shows that apart from compound 9,10 all the compounds can penetrate the CNS. It was noted that all the compounds were not toxic to the skin.

RESULTS:

Molecular Docking Studies of Designed Compounds:

Negative binding energy was shown by all newly designed Coumarin nucleoside inhibitors (Table 1). The general structure of coumarin nucleoside is shown (Figure 1).

Molecular Docking of Newly Designed EGRF inhibitors with 1M17:

The Protein Preparation Wizard was used to create the protein structures then water molecules were removed (none of which were found to be maintained in the interaction with the protein). Missing hydrogens were then added to corelate with pH 7 while considering the appropriate ionization states for the acidic and basic amino acids. To relieve steric disagreements among the residues created by the addition of hydrogen atoms, the protonation state and correct charge were assigned to the resultant structure, and energy minimization with a root mean square deviation (RMSD) of 0.30 was done using the OPLS-2005 force field. Finally, the energy of the enzyme structure was reduced using the OPLS-2005 force field to ease steric conflicts among the residues caused by the insertion of hydrogen atoms until the RMSD constraint of 0.3 was attained, and this structure was used for further docking research. In 3D Maestro's construction panel, molecules meant to be docked were built and adjusted using the LigPrep module. The compounds were subjected to the OPLS-2005 force field, which comprises hydrogen addition, bond length and angle modification, proportionalities of chiralities, ionization states, tautomers, stereo chemical and ring conformations and partial charge assignment. Lastly, the ligand molecules energy was reduced until they met an RMSD criterion of 0.01. The active docking site of DprE1 was found using Glide's Receptor Grid Generator panel. A grid box of 40X40X40 dimensions was established on the centroid of the native ligand inside the crystal complex, allowing researchers to investigate a greater portion of the enzyme structure. In regard to the biological target, the standard coordinate acts as a co-crystallized ligand that functions as an inhibitor molecule. The optimized ligand structures were docked to the specified active site using an increased precision scoring technique (i.e., GlideXP) to score the docking poses and establish their binding affinities to the EGFR enzyme. The Maestro's Poseview programme was used to examine the docking poses output data file and evaluate the interaction with the residues of the active site.

General Structure:

Figure 1: General structure of coumarin nucleoside scaffold

Table for Substituted Derivatives:

When the general structure of coumarin nucleoside was drawn different groups were substituted on R1, R2 and R3 position. Also, different nucleosides were substituted (1,9-dihydro-6H-purin-6-one and 6-chloro-9H-purine) and thus 12 compounds with good bioavailability were selected and docking was done and different properties of these compounds like the drug likeliness, ADMET properties, Target prediction, Toxicity were studied.

Interactions types of newly designed derivatives:

Table 1: Molecular docking interaction of newly designed coumarin nucleoside derivatives as EGFR inhibitors with 1M17

Compound Code	Binding Energy	Residues Involved	H- Bond Distance (Å)	Hydrophobic and other interaction
1	-6.711	Glu 738 Met 769 Asp 831 Asp 831	2.60 2.04 2.76 2.80	Leu 694 Lew 820 Lew 820
2	-7.091	Glu 738	1.99	Lew 694 Lew 694 Met 769 Leu 820
3	-6.459	Glu 738 Met 769 Asp 831	2.58 2.07 2.25	Leu 694 Leu 820 Leu 820
4	-7.018	Met 769 Asp 831	2.00 2.32	Leu 694 Val 702 Ala 719 Lys 721 Lys 721
5	-6.513	Thr 766 Met 769 Asp 831	2.56 1.99 2.94	Leu 694 Leu 694 Leu 820 Leu 820
6	-6.012	Met 769 Thr 766	2.04 2.37	Leu 694 Leu 694
7	-6.412	Leu 694 Glu 738	2.75 1.78	Leu 694 Leu 694 Met 769 Leu 820
8	-6.495	Glu 738 Thr 766 Pro 770	2.73 2.40 1.95	Leu 694
9	-6.499	Met 769 Cys 773 Asp 776	1.89 3.02 1.74	Leu 820 Lys 721
10	-6.03	--	--	Leu 694 Leu 820 Leu 694
11	-6.992	Thr 766 Met 769	2.24 2.03	Leu 820 Leu 694 Leu 820 Leu 694
12	-6.722	Gly 772	1.16	Leu 694 Val 702 Ala 719 Met 769
AQ4	-8.614	Met 769 Cys 773	2.03 1.99	Leu 694 Lys 721 Leu 764 Lys 721

2D and 3D Structure

Figure 2: 2D & structures of compound 2

Figure 3: 2D&3D structures of compound 4

In coumarin nucleoside derivative 2 (Figure. 2) the hydroxy group of nucleophile shows hydrogen bonding with Glu738 (H-bond distance 1.99Å). In coumarin nucleoside derivative 4 (Figure.3) the carbonyl oxygen of coumarin ring reveals hydrogen bonding with Met769 (H-bond distance 2.00Å) while the nitrogen from the nucleophile ring shows hydrogen bonding with Asp831 (H-bond distance 2.32Å).

Drug Likeliness Characterises:

When the 12 derived compounds of coumarin were drawn with the help of ChemDraw 20.0 software they were converted into. SMILES format and they were analysed using software SwissADME and pkCSM and their results were noted (Table 2).

Prediction of Drug Likeness and ADMET Properties:

The compounds were subjected to Lipinski's Rule of Five, which says that the molecular mass of the compound should not be more than 500 Daltons, that it should have a maximum of 5 hydrogen bonds, 10 rotatable bonds,10 hydrogen bond acceptors and that the log P value should not be more than 5. The tPSA property has been connected to passive molecular transport across membranes and the blood-brain barrier. Except for compound 1,4,7 (tPSA greater than 140), all compounds investigated fulfil the GI absorption criterion. WLOGP values for all of the drugs were less than 5. (which indicates whether a chemical is harmful or not). On the scale, these compounds are rather straightforward used to synthesise. This implies that these compounds will be simple to make in the lab and will be drug-like and orally available. The compounds have a high intestinal absorption percentage of more than 60%. The results of BBB permeability (logBB) were more than -2.5. Moreover, except for compounds 9,10, all of the compounds had CNS permeability (Log PS) values greater than -3, implying that all of the compounds may permeate the brain. (Table 3).

Table 2: Drug-likeness properties of newly designed coumarin nucleoside derivatives

Compound Code	Molecular Weight (g/mol)	WLOGP	HBD	HBA	RO5	RB	tPSA (Å)	SA
1	353.29	0.45	4	7	0	4	153.9	2.90
2	371.73	1.40	3	6	0	4	133.74	2.90
3	416.19	1.51	3	6	0	4	133.74	2.95
4	369.33	0.07	4	6	1	5	163.24	3.82
5	399.79	2.01	3	6	0	4	133.74	2.01
6	405.29	2.91	3	9	0	5	133.74	2.91
7	353.30	0.33	4	6	0	4	159.76	2.99
8	373.75	1.94	3	7	0	4	130.09	3.11
9	390.18	2.76	2	6	0	4	113.77	2.82
10	434.63	2.87	2	6	0	4	113.77	2.85
11	418.23	3.37	2	6	0	4	113.77	3.05
12	368.77	2.90	3	4	0	4	114.01	2.86

(MW: Molecular weight, HBD: Hydrogen bond donor, HBA: Hydrogen bond acceptor, RO5: Rule of five, RB: Rotatable bonds, tPSA: Total polar surface area, SA: Synthetic Accessibility)

Table 3: ADMET properties of newly designed coumarin nucleoside derivatives

Compound Code

ABS int ABS(%)

Dist-LogBB

Dist-LogPS

2D6

3A4

1A2

2C19

2C9

2D6

3A4

Excreation

AMES Toxicity

60.14

-1.756

-4.284

0.774

68.435

-1.778

-3.129

0.759

68.171

-1.798

-3.129

0.717

69.062

-1.398

-3.055

0.562

68.596

-1.79

-4.01

0.734

62.583

-2.314

-3.184

0.531

60.109

-1.666

-3.441

0.894

75.585

-1.827

-3.855

0.719

82.428

-1.825

-2.747

0.665

80.943

-1.682

-2.745

0.611

82.589

-1.841

-3.555

0.656

74.124

-1.694

-3.031

0.799

Target Prediction:

Compound targeted studies were carried out on the basis of similarity to familiar therapies in order to determine their targets, study the molecular mechanisms underlying a certain phenotypic or bioactivity, and rationalise any adverse effects. Based on Common Name, Target, ChEMBL-ID, Uniprot ID, Target Class, Probability, and Known actives in 2D/3D, the top 50 results of the closely related receptors were shown in a pie chart. (Figure 6 and Figure 7).

Figure 6: Target prediction of compound 2

Figure 7: Target prediction of compound 4

Binding Free Energy:

1M17, comp2, and AQ4 were determined using the generalized born surface area technique mmgsba.py and the VSGB solvation model, along with the OPLS5 force field and one-step sample size, the MM-GBSA was measured in the final fifty frames of the simulation trajectory by aggregating the separate energy modules of covalent, Columbic, Vander-Waals forces, hydrogen bond, self-contact, solvation of ligand and protein and lipophilic using the notion of additivity.

We may find out Δ G bind by using the following numbers in the following equation:

Δ G bind = Δ GMM + Δ GSolv - Δ GSA

Molecular Dynamics Stimulation (MDS):

The stability and convergence of 1M17 Apo (the sole protein), 1M17 comp2, and 1M17 AQ4 were studied using molecular dynamics and simulation (MD). The RMSD of the C-backbone of 1M17 Apo was 3.1, while the RMSD of comp2 bound protein was 3.0. The RMSD of 1M17 AQ4 was 3.1. (Figure 8A). All the RMSD values are within acceptable range which should be below or near to 3 Å. Satisfactory convergence and stable conformation is the indication of the stable RMSD plot during simulation. Due to higher affinity of the ligand it can be proposed that comp2 and AQ4 bound to 1M17 are quite stable in complex. The plot for root mean square fluctuations (RMSF) showed large spikes of fluctuation displayed in the plot (RMSF) for 1M17 Apo-protein are observed at 10-16, 30-45, 48-60, 60-80, 82- 110 and 152-180 residues due to higher flexibility of the residues. 1M17 with comp2 also exhibited fluctuations at 20-54, 125-146, 150, 175-182 and 230-240 residue positions (Figure 8B). While 1M17 with AQ4 exhibited fewer significant fluctuations except at 10-16, 30-45, 48-60, 60-80, 82-110 and 152-180 residues. Most of the residues were less fluctuating indicating amino acid conformations were too rigid during the simulation time. Fewer fluctuations indicate less active protein since more flexibility allows the protein to become functionally active. Therefore, the protein structure is too much rigid during simulation study in ligand bound conformations observed from RMSF plots. The measure of compactness of the protein is the Radius of gyration (Rg). Here in this study, 1M17-Apo Cα- backbone displayed increment and followed by lowering of radius of gyration (Rg) from 18.0 to 17.8 Å (Figure 8C) on the other hand, lowering and stable pattern also observed for AQ4 bound 1M17 from 17.8 to 17.9 Å (Figure 8 C). Initial lowering and up rise of the peak was observed for comp2 bound to 1M17 from 17.8 and 18.2 Å (Figure 8 C). Stable and lowering of gyration (Rg) shows with AQ4 single hydrogen bond was observed on an average (Figure 8 D). After the Rg analysis, patterns were found in both the ligand bound and unbound states. It clearly shows that unbound form of comp2 and AQ4 to 1M17 protein, there was a large surface area accessible to solvent in all cases (Figure 8 E-F). When compared to the unbound state, the value decreased in the bound state with ligand (Figure 8 E-F). The overall analysis of Rg reveals that the interaction of the ligands forces the corresponding proteins to become more compact.

Figure 8 (A)

Figure 8(B)

Figure 8 (C)

Figure 8(D)

Figure 8 (E)

Figure 8(F)

Figure 8. MD simulation analysis of 100 ns trajectories of (A) Cα backbone RMSD of 1M17_Apo (red), 1M17_comp2 (green) and IM17-AQ4 (black) (B) RMSF of Cα backbone of 1M17_Apo (red), 1M17_comp2 (green) and IM17-AQ4 (black). (C) Cα backbone radius of gyration (Rg) of 1M17_Apo (red), 1M17_comp2 (green) and IM17-AQ4 (black). (D) Formation of hydrogen bonds in 1M17_comp2 (green) and IM17-AQ4 (black). Solvent accessible surface area of (E) 1M17_comp2, (F) IM17-AQ4.

Molecular mechanics Generalized Born Surface Area (MM-GBSA) Calculations:

The binding free energies were estimated using the MD simulation trajectory for each complex 1M17 comp2 and 1M17 AQ4. The results (Table 4) show that the biggest contribution to the Δ Gbind in stability of the simulated complexes was attributable to Δ G bind Colomb, Δ Gbind Lipo, where as Δ Gbind Covalent and Δ Gbind SolvGB contributed to the instability of the comparable complexes. The 1M17 AQ4 larger binding (Table 4). Our findings demonstrated that 1M17 AQ4 has a high affinity for protein binding, as well as efficiency in binding to the chosen protein and the ability to create stable protein-ligand complexes.

Table 4. Binding free energy components for the 1M17_comp2 and 1M17_AQ4 calculated by MM-GBSA.

Energies (kcal/mol)	1M17_comp2	1M17_AQ4
Δ G bind	-49.58±2.73	-69.83±3.5
Δ G bind Lipo	-22.99±1.01	-19.83±2.3
Δ G bindvd W	-44.29±1.18	-59.66±2.29
Δ G bind Coulomb	-11.31±2.11	-2.14±1.01
Δ G bind H bond	-1.47±0.24	-0.45±0.07
Δ G bind Solv GB	22.74±2.18	21.22±1.4
Δ G bind Covalent	2.13±0.86	3.41±0.86

CONCLUSION:

Coumarin nucleoside derivatives were molecular docked onto the binding location of the EGFR in order to better comprehend and demonstrate how the named chemicals could generate anticancer action. The receptors of growth factors play a significant role in the control of epithelial cell development and differentiation. The HER/erbB family of receptors include the orphan HER2/neu (erb-2), the neuregulin/neuregulin receptors HER3(erb-3) and HER4(erb-4). As our understanding of the pathophysiological mechanisms underlying breast cancer has evolved, so has the finding of biomolecular markers. The cell surface receptor is the only receptor with which EGFR family members interact directly. The EGFR is activated by ligands like PI3 (Phosphoinositide 3-kinases), Ras- Raf-MAPK (mitogen activated protein kinase), JNK (c-Jun NH2-terminal kinase) and PLC (phospholipase C) are the major EGFR signalling pathways. Loss of cell polarity is one of the primary features observed when most EGFR family members are active, leading breast epithelial cells to scatter and invade. The dysregulation of EGFR pathways has been discovered to be related to the processes of metastasis and angiogenesis. It has also been discovered that this is the cause of poor prognosis in a lot of human malignancies. One method of EGFR overexpression is EGFR gene amplification, which has been associated with oligodendrogliomas, glioblastomas, lung cancer, gastric cancer, and breast cancer. In 0.8-14% of breast cancer tumours, the EGFR gene was amplified. However, gene amplification has been related to metaplastic breast cancer, a form of TNBC (Triple-Negative Breast Cancer), in 25% of individuals. This identifies EGFR as a critical target for the research of possible anticancer drugs. The docking simulation results revealed that the title compounds (1-12) could fit snugly into the active site of EGFR by forming several binding and non-bonded contacts, occupying coordinates that were extremely near to those of the native ligand with varied degrees of affinity. Their docking values varied from -7.02 to -7.87, whereas erlotinib had a docking score of -8.845, indicating a high affinity for the target. All of the derivatives had a similar binding structure and were retained inside the active pocket by a combination of bound and non-bonded (steric and electrostatic) interactions with the active site residues. Compounds 2 and 4 were investigated for target prediction. Desmond Schrödinger LLC performed 100 nanoseconds time scale simulation investigations on the 1M17 protein (1M17 Apo), 1M17 comp2 and 1M17 AQ4.

ACKNOWLEDGMENT:

The authors are thankful to the School of Health Sciences and Technology, Department of Pharmaceutical Sciences, Dr. Vishwanath Karad MIT World Peace University, Pune for providing the necessary facilities to carry out this research work.

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Received on 09.04.2023 Modified on 13.07.2023

Research J. Pharm. and Tech 2024; 17(3):1008-1014.

DOI: 10.52711/0974-360X.2024.00156