INTRODUCTION:

Deaths due to lung cancer (LC) are common worldwide, and it accounts for one-third of cancer-related deaths in the USA, surpassing the combined death rates from pancreatic, colon, breast, and prostate cancers.¹

LC leads to a mortality of 8.1% among all cancers. Non-small cell lung cancer (NSCLC) accounts for 80-85% of cancer-related deaths among cancer². Globally, 85% of the patients of NSCLC are in the advanced stage, and currently, there is no cure³. The current drug therapy for NSCLC has failed to provide adequate therapeutic relief due to the occurrence of a mutation or delayed response to the medicine⁴. Therefore, there is a need to fast-track the drug discovery process by utilizing technology like computational chemistry or in silico drug discovery research⁵. Researchers consistently uncover new mutations that contribute to resistance to targeted medicines. Targeted cell signalling pathway is emerging, and a potential target of NSCLC is focal adhesion kinase (FAK)⁶. Studies have shown that in the majority of NSCLC cases, FAK is overexpressed and phosphorylated.

The FAK consists of three main parts: the N-terminal, the kinase domain, and the C-terminal. The N-terminal features the ezrin-radixin-moesin (FERM) domain, which is structured into three lobes (F1, F2, and F3) arranged in a cloverleaf-like design. The C-terminal includes the focal adhesion targeting (FAT) domain, which contains a proline-rich region. The F1 lobe exhibits a structure similar to ubiquitin, with five strands of beta-pleated sheets shielded by alpha-helices. The F2 lobe plays a crucial role in regulating the function of FAK. It contains the KAKTLRK sequence, which is significant for the activation of FAK's binding with growth factors, thereby promoting cell stimulation and adhesion. The F3 region of FAK does not permit binding with acidic phospholipid or phosphorylated tyrosine peptides due to its structural characteristics. The FERM domain is essential for autoinhibition, as it can bind to the C-lobe of the kinase domain in a trans-conformation, interacting with the F2 lobe of the FERM domain. Consequently, the FERM domain of FAK causes inhibition at various levels, including the suppression of autophosphorylation⁷. In NSCLC conditions, there will be an alteration in the expression of FAK, leading to uncontrolled cell proliferation. The downregulation pathway is activated due to this, which leads to uncontrolled cell proliferation⁸.

This study aims to identify potential NSCLC agents by targeting FAK. In silico methods were used to identify the potential target by methods like targeted drug development procedures. Protein-ligand optimization, and docking screening were performed. The confirmatory analysis of FAK protein interaction with potential molecules was determined using molecular dynamic (MD) simulation analysis, where the interaction is analysed thoroughly in a simulated biological system. We have selected a few synthetic compounds which are reported in recently published data from the chemistry department of MIT, Manipal⁹. We have selected natural flavonoids and docked them to determine their binding and inhibiting ability through FAK. The best molecules to target FAK receptors in NSCLC conditions were identified.

MATERIALS AND METHOD:

Computational Simulations: All computational procedures related to drug repurposing and its validation were performed using the Maestro interface available in the Schrödinger suite (www.schrodinger.com).

Ligand Preparation: A set of approximately 2800 drug compounds approved by the US FDA was sourced from the DrugBank database (https://go.drugbank.com/) for the repurposing workflow. These compounds were processed and converted into a suitable 3D molecular format using LigPrep. The Epik module was employed to assign ionization states corresponding to physiological pH (7.4). Subsequently, energy minimization of the ligand structures was achieved using the OPLS3e force field¹⁰.

Protein Preparation: FAK crystal structures, identified by the PDB IDs 4KAO, 3B23, 4K9Y, and 4GU6, were retrieved from the Protein Data Bank ¹¹. Structural preprocessing and optimization were executed using the Protein Preparation Wizard (PPW) in Schrödinger®, ensuring correct bond assignments, addition of hydrogens, and appropriate protonation states ¹².

Molecular Docking and Prime MM-GBSA: Structure-based virtual screening was carried out using the Glide tool. A receptor grid was generated automatically using the standard protocol of the Grid Generation module. The Extra Precision (XP) docking algorithm was used to screen all 2800 compounds, after which the top ten candidates with the best docking scores were shortlisted. For each ligand, the most favorable binding pose was retained ¹³. To further refine the results, Prime MM-GBSA analysis was conducted to compute binding free energies and assess ligand strain, assuming a rigid receptor and flexible ligands ¹⁴.

Molecular Dynamics Simulation: The most promising candidate molecules were subjected to MD simulations using the Desmond package ¹⁵. Each system was simulated for 50 nanoseconds, during which 1000 trajectory frames were captured. Post-simulation, interaction dynamics and structural stability were analyzed extensively using trajectory analysis tools and interaction mapping ¹⁶.

RESULTS:

Protein selection: The study aimed to inhibit the ATP binding site of FAK in the NSCLC condition. The four proteins, namely 4KAO, 3B23, 4K9Y and 4GU6, were downloaded from PDB. All the proteins selected showed a complete structure. Table 1 shows all the selected crystal structures which were shortlisted for the study.

Table 1: represents the selected proteins from PDB of homo sapiens.

PDB ID	Resolution	Reference
4KAO	2.39	17
3BZ3	2.20	18
4K9Y	2.00	17
4GU6	1.95	19

Results of Molecular Docking:

The XP analysis of all four selected proteins based on the resolution was optimized under 3Å manually using the Glide module of Schrodinger. The interaction of the protein was checked for CYS502 and ASP564 affinity, which are ATP site interactions in FAK in the NSCLC condition. The glide score (G score) of each protein when binding to various FAK ligands with ATP site affinity was studied (Table 2).

Table 2: Represents the ligand-protein interaction score

Protein	Ligand	G score
4GU6	3BZ3	-9.454
	4KAO	-6.483
	4K9Y	-6.113
	4GU6	-9.454
3BZ3	3BZB	-11.972
	4GU6	-10.000
	4K9Y	-2.267
	4KAO	-1.521
4K9Y	4KAO	-12.781
	4K9Y	-10.036
	3BZ3	-8.607
	4GU6	-7.441
4KAO	4KAO	-12.015
	4K9Y	-9.450
	3BZ3	-7.534
	4GU6	-5.980

Identification of Druggable site in the protein:

In 4GU6, there were 3 druggable sites, two allosteric inhibitory sites and one active ATP site and 4k9Y was found to have 4 druggable sites, one of which lies in the ATP active site and the other three lie in the allosteric site of the protein. The druggable site is the site in a protein which can be used to predict ligand binding (20). Site 1 in protein 4GU6 and 4K9Y was selected, which is the Y397 region of FAK with at highest phosphorylation ability (21).

Site Scoring:

Site score analysis was used to determine the best binding site of the ligands with the protein. The site score of the interaction showed the potent druggable site in the protein (Table 3). The site score of the 4K9Y protein was observed to be better than that of the protein 4GU6. So, we proceeded with 4K9Y protein for docking of coumarins and natural compounds at site 1 of the protein.

Table 3: Ligand Interactions with the Proteins

Protein	Site	Å	Ligand	Site score
4K9Y	1	18	4K9Y	-13.442
			4KAO	-13.352
			3BZ3	-8.850
			4GU6	-5.330
4GU6	1	18	3BZ3	-9.601
			4GU6	-9.044
			4KAO	-6.021
			4K9Y	-1.309

Results of Prime MM-GBSA:

MM GBSA was done to identify the interaction affinity of the protein with the ligand (Table 4). The results of the MM-GBSA dG binding score confirmed that ligand binding energy to protein 4K9Y is greater than that of 4GU6 because of the higher percentage binding score. Therefore, the protein and ligand of 4K9Y are selected for further screening analysis.

Table 4: MM-GBSA dG binding score of Ligands

Protein	Site	Å	Ligand	MM-GBSA dG bind (%)
4K9Y	1	18	4K9Y	110.78
			4KAO	101.94
			3BZ3	62.82
			4GU6	48.70
4GU6	1	18	3BZ3	68.04
			4GU6	70.40
			4KAO	53.85
			4K9Y	51.93

Screening methodologies:

Around 35 synthetic coumarin structures were identified, which had the potential to act as an inhibitor of FAK activation in case of NSCLC (10). Natural phenolic derivatives were identified and explored further for NSCLC by targeting the FAK pathway (22). XP screening analysis was selected due to its high precision in the results, and the docking score (D score) were analyzed to find out the best coumarin compounds with FAK ATP site affinity against NSCLC (Table 5).

Table 5: D score of ligands

Protein	Coumarin Ligand	D score
4K9Y	7SP3d	-10.711
	6SB3c	-9.468
	6SB5d	-9.374
	7SP4b	-9.038
	6SP3d	-9.035
	6SB5c	-8.994
	6SB5a	-8.915
	7SP5a	-8.880
	6SP4b	-8.795
	7SP3h	-8.767
	6SB3e	-8.674
	6SB3d	-8.650
	6SP3h	-8.641
	6SP4a	-8.628
	7SP4a	-8.612
	Diosmin	-12.199
	Epigallocatechin	-9.982
	Curcumin	-8.215
	Caffeic acid	-6.063

MD simulation analysis:

MD simulation of 6SB5C:

Figure 1: MD simulation of coumarin compound 6SB5C. a. Root-mean square deviation graph, b. bond formation diagram at various amino acid sites and c. is the protein ligand interaction diagram.

The RMSD value tells us about the interaction between the protein and the ligand at every time interval. The interaction of the protein with the ligand was found to be good and continuous throughout the trajectory of 1000 frames (Figure 1a). Cysteine (CYS_502) form an H-bond and a water bridge. Aspartate (ASP_564) and Lysine (LYS_454) form H-bond and water bridge. Phenylalanine (PHE_565, PHE_478) forms a hydrophobic contact. (Figure 1b). The ligand was forming H-bonds with CYS_502, LYS_454, and ASP_564 and pi-pi stack hydrophobic interaction with amino acid HIS_544 and PHE_478 (Figure 1c). A detailed MD simulation file has been uploaded for the readers for any further reference in supplementary file 1.

MD simulation of 7SP3d:

Figure 2: MD simulation of coumarin compound 7SP3d. a. Root-mean square deviation graph, b. bond formation diagram at various amino acid sites and c. is the protein ligand interaction diagram.

The graph (Figure 2a) shows the binding stability of 4K9Y protein with coumarin compound (7SP3d). There was a continuous point of contact and the protein-ligand show best stability. Hydrogen bonds, hydrophobic, Ionic and water bridges are 4 contacts to which ligand and protein are binds. Cysteine (CYS_502) form H-bond and water bridge. Aspartate (Asp_564) and Lysine (LYS_454) form H-bond and water bridge. Methionine (MET_499) forms hydrophobic contact. The interaction fraction is above 1.6 (Figure 2b). The ligand was forming H-bond with CYS_502 LYS_454 and ASP_564 plays important role in terms of drug design as it affects the absorption of drug, metabolism and specificity of drug and pi-pi stack hydrophobic interaction with amino acid HIS_544 (Figure 2c). Detailed MD simulation file has been uploaded for the readers for any further reference in supplementary file 2.

MD simulation of Diosmin:

Figure 3: MD simulation of coumarin compound Diosmin. a. Root-mean square deviation graph, b. bond formation diagram at various amino acid sites and c. is the protein ligand interaction diagram.

This graph shows the binding stability of the 4K9Y protein with a natural polyphenolic compound (Diosmin). As the RMSD value is 2.25Å, it means the ligand shows good interaction with the protein (Figure 3a). Cysteine (CYS502) form an H-bond and a water bridge. Aspartate (Asp564), Threonine (THR503) and Lysine (LYS454) form H-bond and water bridge. Glutamate (GLU471) forms the water bridge. Methionine (MET_499) forms a hydrophobic contact. The interaction fraction is above 1.6 (Figure 3b). The ligand was forming an H-bond with CYS_502, LYS_454, and THR_503, which plays an important role in terms of drug design as it affects the absorption of the drug, metabolism and specificity of the drug (Figure 3c). The detailed MD simulation file has been uploaded in supplementary file 3.

DISCUSSION:

LC is the second leading alarming health problem and is fatal for both males and females globally ²³. The receptors like FAK, KRAS, MEK, EGFR, HER2, when overexpressed, might lead to a mutation condition in LC²⁴. The main reason for the failing NSCLC therapy is due to the mutation in the tyrosine kinase domain, leading to the development of resistance towards various pharmacotherapies²⁵. The most druggable site of FAK, i.e. the ATP site of the receptor, was targeted to increase the chances of a therapeutic outcome in the study²⁶.

Natural polyphenolic compounds are well known for chemopreventive response and potential xenohormetic response, which together initiate the antioxidant defense system in cancerous cells ²⁷. Natural polyphenols are known for anti-metastatic activity, such as DNA interaction, anti-metastasis and angiogenesis actions. These abilities can be traced back to their effect on cell cycle inhibition ability, activity against down-regulatory pathways in cells and apoptosis initiation ²⁷. Diosmin is a polyphenolic flavonoid used as an anti-inflammatory and antioxidant agent ²⁸. Diosmin is also reported to be used as an anti-cancer agent due to its ability to antagonize the Dalton ascitic lymphomas (DAL) in solid tumor conditions and has a potent cytokine inhibitory effect ²⁹. All these factors contribute to the ability of diosmin to act as an antagonist of Fak in the case of NSCLC therapy identification.

These compounds, namely 7SP3d, 6SB5c and diosmin, have the ability to inhibit the over-expression of FAK in NSCLC conditions due to their ability to stop ROS in cancerous cells. These compounds can be further explored for pre-clinical analysis to find out it’s potential.

REFERENCES:

1. Leiter A, Veluswamy RR, Wisnivesky JP. The global burden of lung cancer: current status and future trends. Nat Rev Clin Oncol [Internet]. 2023; 20(9): 624–39. Available from: https://doi.org/10.1038/s41571-023-00798-3

2. Zappa C, Mousa SA. Non-small cell lung cancer: current treatment and future advances. Transl lung cancer Res. 2016; Jun; 5(3): 288–300.

3. Araghi M, Mannani R, Heidarnejad maleki A, Hamidi A, Rostami S, Safa SH, et al. Recent advances in non-small cell lung cancer targeted therapy; an update review. Cancer Cell Int [Internet]. 2023; 23(1): 162. Available from: https://doi.org/10.1186/s12935-023-02990-y

4. Maity S, Pai KSR, Nayak Y. Advances in targeting EGFR allosteric site as anti-NSCLC therapy to overcome the drug resistance. Pharmacol Reports [Internet]. 2020; 72(4): 799–813. Available from: https://doi.org/10.1007/s43440-020-00131-0

5. Stillman NR, Kovacevic M, Balaz I, Hauert S. In silico modelling of cancer nanomedicine, across scales and transport barriers. npj Comput Mater [Internet]. 2020; 6(1): 92. Available from: https://doi.org/10.1038/s41524-020-00366-8

6. Yuan M, Huang L-L, Chen J-H, Wu J, Xu Q. The emerging treatment landscape of targeted therapy in non-small-cell lung cancer. Signal Transduct Target Ther [Internet]. 2019; 4(1): 61. Available from: https://doi.org/10.1038/s41392-019-0099-9

7. Yen-Pon E, Li B, Acebrón-Garcia-De-Eulate M, Tomkiewicz-Raulet C, Dawson J, Lietha D, et al. Structure-Based Design, Synthesis, and Characterization of the First Irreversible Inhibitor of Focal Adhesion Kinase. ACS Chem Biol. 2018; 13(8): 2067–73.

8. Aakriti J, Vithalkar MP, Maity S, Baby K, Nagareddy PR, Nayak Y. Focal adhesion kinase (FAK): emerging target for drug-resistant malignant tumors. Mol Biol Rep. 2025; Feb 20; 52(1): 248. Available from: https://doi.org/10.1007/s11033-025-10296-7.

9. Shivaprasad Shetty M, Anil Kumar HS, Anil Kumar N V. Synthesis and α-Amylase Inhibition Studies of Some Coumarin Derivatives. Pharm Chem J. 2018; 52(6): 526–30.

10. Baby K, Maity S, Mehta CH, Suresh A, Nayak UY, Nayak Y. Targeting SARS-CoV-2 Main Protease: A Computational Drug Repurposing Study. Arch Med Res. 2021; 52(1): 38–47.

11. Heinrich T, Seenisamy J, Emmanuvel L, Kulkarni SS, Bomke J, Rohdich F, et al. Fragment-based discovery of new highly substituted 1H-pyrrolo[2,3-b]- and 3H-imidazolo[4,5-b]-pyridines as focal adhesion kinase inhibitors. J Med Chem. 2013; 56(3): 1160–70.

12. Satarker S, Maity S, Mudgal J, Nampoothiri M. In silico screening of neurokinin receptor antagonists as a therapeutic strategy for neuroinflammation in Alzheimer’s disease. Mol Divers. 2022; 26(1): 443–66. Available from: https://doi.org/10.1007/s11030-021-10276-6

13. Vibhavari RJA, Rao V, Cheruku SP, Kumar BH, Maity S, Nandakumar K, et al. Enhancing temozolomide antiglioma response by inhibiting O6-methylguanine-DNA methyltransferase with selected phytochemicals: in silico and in vitro approach. 3 Biotech. 2023; 13(12): 1–19. Available from: https://doi.org/10.1007/s13205-023-03821-7

14. Greenidge PA, Kramer C, Mozziconacci JC, Wolf RM. MM/GBSA binding energy prediction on the PDBbind data set: Successes, failures, and directions for further improvement. J Chem Inf Model. 2013; 53(1): 201–9.

15. Brueckner AC, Shields B, Kirubakaran P, Suponya A, Panda M, Posy SL, et al. MDFit: automated molecular simulations workflow enables high throughput assessment of ligands-protein dynamics. J Comput Aided Mol Des. 2024; 38(1): 24. Available from: https://doi.org/10.1007/s10822-024-00564-2

16. Baby K, Maity S, Mehta C, Suresh A, Nayak UY, Nayak Y. In silico drug repurposing of penicillins to target main protease mpro of SARS-CoV-2. Pharm Sci. 2020; 26: S52–62.

17. Gradler U, Bomke J, Musil D, Dresing V, Lehmann M, Holzemann G, et al. Bioorganic and Medicinal Chemistry Letters Fragment-based discovery of focal adhesion kinase inhibitors. Bioorg Med Chem Lett. 2013; 23(19): 5401–9.

18. Roberts WG, Ung E, Whalen P, Cooper B, Hulford C, Autry C, et al. Antitumor activity and pharmacology of a selective focal adhesion kinase inhibitor, PF-562, 271. Cancer Res. 2008; 68(6): 1935–44.

19. Heinrich T, Seenisamy J, Emmanuvel L, Kulkarni SS, Bomke J, Rohdich F, et al. Fragment-based discovery of new highly substituted 1H-pyrrolo[2,3-b]- and 3H-imidazolo[4,5-b]-pyridines as focal adhesion kinase inhibitors. J Med Chem. 2013; 56(3): 1160–70. Available from: https://doi.org/10.3390/cancers13184705.

20. Alzyoud L, Bryce RA, Al Sorkhy M, Atatreh N, Ghattas MA. Structure-based assessment and druggability classification of protein–protein interaction sites. Sci Rep. 2022; 12(1): 7975. Available from: https://doi.org/10.1038/s41598-022-12105-8

21. Al-Ghabkari A, Qasrawi DO, Alshehri M, Narendran A. Focal adhesion kinase (FAK) phosphorylation is a key regulator of embryonal rhabdomyosarcoma (ERMS) cell viability and migration. J Cancer Res Clin Oncol. 2019; Jun; 145(6): 1461–9.

22. Martinez-perez C, Ward C, Cook G, Mullen P, Mcphail D, Harrison DJ, et al. Novel flavonoids as anti-cancer agents : mechanisms of action and promise for their potential application in breast cancer. 2014; 1017–23.

23. Rodak O, Peris-Díaz MD, Olbromski M, Podhorska-Okołów M, Dzięgiel P. Current Landscape of Non-Small Cell Lung Cancer: Epidemiology, Histological Classification, Targeted Therapies, and Immunotherapy. Cancers (Basel). 2021; 13(18).

24. Reck M, Carbone DP, Garassino M, Barlesi F. Targeting KRAS in non-small-cell lung cancer: recent progress and new approaches. Ann Oncol. 2021; 1; 32(9): 1101–10. Available from: https://doi.org/10.1016/j.annonc.2021.06.001

25. Leonetti A, Sharma S, Minari R, Perego P, Giovannetti E, Tiseo M. Resistance mechanisms to osimertinib in EGFR-mutated non-small cell lung cancer. Br J Cancer. 2019; 121(9): 725–37. Available from: http://dx.doi.org/10.1038/s41416-019-0573-8

26. Pang X-J, Liu X-J, Liu Y, Liu W-B, Li Y-R, Yu G-X, et al. Drug Discovery Targeting Focal Adhesion Kinase (FAK) as a Promising Cancer Therapy. Molecules. 2021; 26(14):4250. Available from: https://doi.org/10.3390/molecules26144250

27. Maheshwari N, Sharma MC. Anticancer Properties of Some Selected Plant Phenolic Compounds: Future Leads for Therapeutic Development. J Herb Med . 2023; 42: 100801. Available from: https://doi.org/10.1016/j.hermed.2023.100801

28. Gerges SH, Wahdan SA, Elsherbiny DA, El-Demerdash E. Pharmacology of Diosmin, a Citrus Flavone Glycoside: An Updated Review. Eur J Drug Metab Pharmacokinet. 2022; 47(1): 1–18.

29. Yao X, Gu X, Jin S, Shi K, Gao X, Wang Q, et al. Anticancer and Anti-inflammatory Effect of Diosmin against Dalton Ascitic Lymphoma Induced Leukemia. J Oleo Sci. 2021; 70(5): 665–73.

Received on 25.10.2024 Revised on 15.02.2025

Accepted on 19.05.2025 Published on 01.12.2025

Available online from December 06, 2025

Research J. Pharmacy and Technology. 2025;18(12):6016-6020.

DOI: 10.52711/0974-360X.2025.00869

This work is licensed under a Creative Commons Attribution-NonCommercial-ShareAlike 4.0 International License. Creative Commons License.