INTRODUCTION:

Lung cancer remains the leading cause of cancer-related deaths globally. The development of molecular targeted therapy has significantly transformed the treatment landscape for non-small-cell lung cancer (NSCLC), the most common type of lung cancer. Notably, gefitinib, the first molecular targeted therapy, has successfully doubled the survival time for NSCLC patients Metastasis in the brain, bone, liver, adrenal gland, and respiratory system are the most common cases of NSCLC¹. The two main histological subtypes of NSCLC are adenocarcinoma (ADC; ~50%) and squamous cell carcinoma (SCC; ~40%).

Typically, ADCs develop in the more distal airways, while SCCs originate in the more proximal airways and are more closely linked to smoking and chronic inflammation compared to ADCs².

Chemotherapy and radiation therapy are less effective for NSCLC than SCLC^3-5. A patient treated with chemotherapy combined with targeted therapy can improve survival rate in metastatic advanced NSCLC^6-8. Adenocarcinoma mainly occurs due to specific mutations of the epidermal growth factor receptor (EGFR) tyrosine kinase domain^9-11. Downstream regulation of mitogen-activated protein kinases (MAPK) and phosphatidylinositol 3-kinases (PI3K) signaling pathways also cause non-small cell lung adenocarcinoma. Among these factors, EGFR mutation is predominant and over-expressed in adenocarcinoma patients.

EGFR is from the family of ERBB proteins^12,13. ERBB receptors are a group of receptor tyrosine kinase i.e. ErbB1 (EGFR or Her1), ErbB2 (Her2), ErbB3 (Her3), ErbB4 (Her4)^14-16. They are involved in growth and survival like important cellular functions. EGFR is an ERbB1 type of receptor^17-19.It leads to inappropriate activation of anti-apoptotic Ras signaling cascade which further leads to uncontrolled cell proliferation^20,21. Gefitinib or erlotinib are the first-line drugs for the treatment of patients with previously untreated²², EGFR mutation-positive advanced NSCLC²³.

In 2015, the US FDA approved gefitinib for the first-line treatment of NSCLC²⁴. Gefitinib functions by targeting epidermal growth factor receptor (EGFR) signaling through its adenosine triphosphate binding sites. It also inhibits the tyrosine phosphorylation of EGFRs, thereby blocking downstream signaling pathways and preventing cancer cell proliferation. Currently, gefitinib is employed as a first-line treatment for non-small-cell lung cancer (NSCLC)^25,26. Treatment with Gefitinib and erlotinib is more advantageous over antibody treatment due to more penetrability in the tumor cells²⁷. They are also used as second and third-line treatments following chemotherapies. From multiple clinical trials, it has been reported that the combination treatment of EGFR inhibitors with docetaxel and pemetrexed, caused significant improvements in progression-free survival and overall survival²⁸. In NSCLC other tyrosine kinase VEGF also remains over expressed. So, multi-tyrosine kinase inhibitors can also solve the drug resistance problem^29,30.

MATERIALS AND METHODS:

The Pharmacophore-based Hit compound finding and Lead optimization of the Hit compound were performed using Discovery Studio (D.S.) 4.1 software³¹. The 2D structures of the compounds were drawn by ChemBioDraw Ultra 12.0³². The ADMET properties and toxicity study of the hit and reference compounds were calculated by using SwissADME³³ and Discovery Studio (D.S.) 4.1 software²¹. The 3D crystal structure of Gefitinib EGFR-1 protein complex was downloaded from the Protein Data Bank (PDB: 4WKQ)³⁴.

Receptor-Ligand pharmacophore generation protocol:

The NSCLC drug Gefitinib (Standard) is a potent inhibitor of EGFR-1. A 3D-crystal structure EGFR-1 Gefitinib complex (PDB CODE: 4WKQ) was selected for Receptor-Ligand Pharmacophore generation. The Receptor-Ligand Pharmacophore Generation protocol generates several selective Pharmacophores based on interaction between a ligand and a protein³⁵. The receptor-ligand interaction was translated into a pharmacophore by selecting chemical features like hydrogen bond acceptor (HBA), hydrogen bond donor (HBD), Hydrophobic³⁶, Positive Ionizable (PI)³⁶, and Ring Aromatic (RA). From the complex, a set of selective candidate pharmacophore model was developed. The Pharmacophore with the highest selectivity score and more chemical feature was chosen by the process of Genetic Function Approximation (GFA) model³⁷. Best Pharmacophore (Pharmacophore-1) was selected as ‘Feature Mapping protocol’ that accounted possible pharmacophore feature maps for EGFR-1 Gefitinib complex.

3D Ligands database constructing protocol:

The ligand database was created by ‘Build 3D database protocol’ using Maybridge_Hit Discover, and Mini Maybridge database^38,39 integrated in DS. The algorithm ‘BEST’ was used to generate the top conformational arrangement of ligands. Ligands with RO5⁴⁰violations were removed from the ligand database.

Virtual Screening Protocol using Drug-like compounds from few databases:

The best pharmacophore model has been utilized for virtual screening. Maybridge_Hit Discover and Mini Maybridge^38,39ligand database was screened to identify and explore the most fitted ligand with the desired conformation of the given 3D query pharmacophoric spaces (Pharmacophore-1). Screen library protocol strategy was followed to screen the ligand library. It maps multiple ligands to a single Pharmacophore with a group of features. This process selects Hit compounds with high fit values according to how perfectly the ligand gets aligned and fitted with the Pharmacophore-1⁴¹. The highest the fit score represents a best match.

Lead optimization protocol:

Lead optimization is performed for modification of the chemical structure of promising compounds and scaffolds in order to improve Pharmacological activity, target selectivity, bioavailability, pharmacokinetic properties, and reduce toxicity, with the aim of new drug development. This process is most important to improve interaction with receptors and to generate novel compounds with different scaffolds by replacing one or more fragments in a lead structure. The diverse structures often offer different choices in terms of chemical accessibility and prospects for lead optimization⁴².

Aligning the generated Hits to the 3D query Pharmacophore:

The final lead-optimized Hit compounds were aligned with the Pharmacophore-1.The alignments of the compounds from different angles were visualized to justify their selection.

Molecular docking and calculating binding energy:

Dock Ligands (LibDock)⁴³ is a high throughput docking protocol that uses protein site features referred to as HotSpots. It detects two types of sites, polar and non-polar. A polar site is preferred by a polar ligand atom (for example a hydrogen bond donor or acceptor) and a non-polar site preferred by a non-polar atom (for example a carbon atom). Catalyst conformation generation tool was used to generate different conformation for the ligands⁴⁴. Further, docked poses were energy minimized by CHARMm energy minimization algorithm⁴⁵. The rigid minimized ligand poses are placed into the active site for scoring of the ligand poses. Docking function Piecewise Linear Potential (PLP) associate well with ligand conformation and target receptors binding affinities⁴⁶. High PLP value is the indicator of strong binding affinity. Binding energy is an energy calculation between a receptor and a ligand. The equation used for binding energy calculation is Energy_Binding = Energy_Complex - Energy_Ligand - Energy_Receptor⁴⁷.

RESULTS AND DISCUSSION:

Pharmacophore generation:

The Pharmacophoric Feature generation was performed by selecting 16 features; Hydrogen bond acceptor: 4, Hydrogen bond donor: 1, Hydrophobic: 4, Positive Ionizable: 1, and Ring aromatic: 6. Receptor Ligand pharmacophore was initiated with the 3D crystal structure of EGFR-1 complexed with Gefitinib. Protocol based six maximum features was used to generate output Pharmacophore and minimum feature distance was selected to be 2.5Å. The best 10 pharmacophores were selected from 28873 generated candidates. The Pharmacophore-1 contains features like HBA, Hydrophobic, and positive ionizable group and their inter-feature distances are illustrated in Figure 1. From the top 10 pharmacophores, Pharmacophore-1 was selected due to high selectivity score (9.1381) and highest number of features (5) i.e. AHHHP.

(a) 3D Quarry Pharmacophore

(b) Pharmacophoric distance

Figure 1. (a) Pharmacophore-1 features and (b) distance between pharmacophoric features.

Virtual Screening:

LibDock was used to screen few ligand databases with a large pool of ligands against the input pharmacophoric features to select desired drug-like ligands with similar features as in Gafitinib. Maybridge_Hit Discover and Mini Maybridge comprised of a total of 51,663 compounds were used to create 3D ligand databases by using D.S.⁴⁸. The Screen Library protocol ⁴⁹ was used to select a greater number of Hits with high fit values. From the protocol, 6 best fit compounds were selected. The ADMET (Absorption, Distribution, Metabolism, Excretion, and Toxicity) related properties of these 6 compounds, were computed by the ADMET Descriptors Protocol (D.S.). Finally, 4 compounds are filtered from the screening experiment. From the four compounds, the compound with highest fit value (3.93) after pharmacophoric interaction with 3D Query Pharmacophore was considered as Hit compound (Hit-1). Among the 4 compound, Hit-1 only has lead like structure. Hit-1 was selected as a best hit compound for its highest fit score, acceptable ADMET properties and, lead like natures. Further docking study of Hit-1 was done with EGFR-1 receptor to compare the fit score with the standard. The compound Hit-1 LibDock score was 92.8916 which was less than the standard compound gefitinib (112.64). Hence lead optimization of compound Hit- 1 was attempted to improve the interaction with the receptor and to get more bioactive and less toxic compound like standard.

Lead optimization:

To improve Fit score and interactions with EGFR-1 receptor, lead optimization of Hit-1 was carried out. After lead optimization 97 novel optimized ligands were generated. Among them two optimized ligands (Hit-2 and Hit-3) achieved high fit score, and better binding affinity towards EGFR-1 as compared to standard. 2D structure of Hit-2 and Hit-3 were drawn by ChemBioDraw Ultra 12.0 (Figure 2). The descriptors of these two Hit compounds were computed in the ADMET Descriptors Protocol (D.S.). Their ADMET properties of these compounds are in acceptable range (Table 1). Lead optimized ligands, all the interaction (i.e. Fragment hydrogen bond, bumps and pi interactions) are analyzed and docking parameters are compared with the standard. Comparison of interaction in lead optimized point is shown in (supplementary table-S1) and (Figure. 3).

Figure 2. 2D Structure of Gefitinib, Hit-2 and Hit-3.

Figure 3. From Hit-1, Lead Optimized compounds Hit-2 and Hit-3. Red dotted oval denotes bio-isosteric replacement; Green dotted ovals denote lead optimized points.

Alignment of the Hit-2 and Hit-3 Compounds with 3D Query Pharmacophore:

The molecules Hit-2 and Hit-3 were properly aligned with Pharmacophore-1 (Figure 4). The compound Gefitinib was also screened on the same Pharmacophore-1 to observe how well it can fit with that Pharmacophore. The fit score of Gefitinib, Hit-2 and Hit-3 compounds were 4.75388, 4.83269 and 4.76094 respectively.

Figure 4. Gefitinib, Hit-1, Hit-2, and Hit-3 pharmacophoric interaction with 3D Query Pharmacophore.

Molecular Docking:

Docking is a computational technique widely used to simulate how a small molecule (drug) interacts to a complementary site of protein and predicts molecular interaction at the active site. It also calculates free energy of the complex. The two lead optimized Hit compounds (Hit-2 and Hit-3) and the gefitinib were docked in the active site of EGFR-1. The concept was to compute and compare the docking parameters (LibDock score, Binding energy) and the docked pose (-PLP1, and –PLP2 values) of the two optimized Hit compounds with the gefitinib are given in Table 2.

Table 2. Molecular docking parameter of Hit-1, Hit-2, Hit-3 and Reference with EGFR-1.

Compound	Lib Dock score	Binding energy	-PLP1	-PLP2	Conventional Hydrogen bond
HIT-1	92.8916	-83.1152	76.45	72.29	2
HIT-2	115.78	-93.8844	99.33	98.65	3
HIT-3	115.086	-72.3878	103.73	91.84	1
Gefitinib	112.64	-95.65	101.96	90.79	1

Interaction of Hit-2 with EGFR-1:

Hit-2 forms three conventional hydrogen bonds with SER720, MET793, and THR854 residue of EGFR-1 (PDB ID: 4WKQ). It forms some typical carbon hydrogen bond interaction with GLY719 (three), GLN791, MET793, EU788, ALA743, GLY724, and SER720. It also creates hydrophobic π-alkyl interaction with VAL726, ALA743 (two), CYS775, MET793, and LEU844 (two) (Figure 5).

Figure 5. 2D and 3D interaction of Hit-2 with EGFR-1.

Interaction of Hit-3 with EGFR-1:

Hit-3 forms one conventional hydrogen bond with MET793 residue of EGFR-1 (PDB ID: 4WKQ). It also forms, some typical carbon hydrogen bond interaction with MET793, ASP855, ASN842 (two), ALA743 (two), and LEU788. It also creates different hydrophobic interaction like pi-sigma interaction with GLY796, alkyl-alkyl interaction with VAL726, ALA743 and, LYS745, Pi-alkyl interaction with LEU718, LEU844 (two) (Figure 6).

Figure 6. 2D and 3D interaction of Hit-3 with EGFR-1.

Hit-2 and Hit-3 Binding study with VEGFR-2 receptor:

The lead optimized hit compounds (Hit-2 and Hit-3) binding study also has been done with VEGFR-2 to check the multi targeted potency which may overcome drug resistance and EGFR mutation related problems. VEGFR-2 is an angiogenic receptor. Like EGFR, it is also highly expressed in NSCLC. From the study it was found that compounds also have excellent docking results with VEGFR-2 (Table-3) (Figure 7 & Figure 8).

Figure 7. 3D interaction of Hit-2 with VEGFR-2.

Figure 8. 3D interaction of Hit-3 with VEGFR-2.

Table 3. Molecular docking parameter of Hit-1, Hit-2, Hit-3 and Reference with VEGFR-2.

Compound	Lib Dock score	Binding energy	-PLP1	-PLP2	Conventional Hydrogen bond
HIT-2	133.669	-71.3102	118.69	115.56	3 (LYS868, ASP1046, GLY1015)
HIT-3	135.789	-149.406	115.72	99.96	0
Gefitinib	129.873	-146.652	115.96	107.7	1(ASP1046)

Synthetic Accessibility of Hit-2 and Hit-3:

SwissADME was used to calculate synthetic accessibility score⁴¹. It was found that Hit-2 and Hit-3 score were 3.30 and 3.29, respectively, which is similar to gefitinib score of 3.26. The synthetic accessibility score in a scale of 1-10, depicts the ease of synthesizing an organic compound. Lower value designates easy to synthesize a compound, and higher value designates difficult to synthesize.

By using the Feature Mapping tools of D.S all the Pharmacophore maps for the Gefitinib-EGFR complex have been computed and used them for the final Pharmacophore feature map generation. According to the scoring function the best Pharmacophore Feature Map has best selectivity score was chosen as Pharmacophore-1. To increase the reliability of the Pharmacophore feature map, Gefitinib was screened on the Map where fit score and alignment with Pharmacophore were checked visually. 51,663 drug like diverse compounds were screened through the 3D query pharmacophore of Gefitinib-EGFR complex. For best fit value Compound Methyl 2-((6,7-dimethoxyquinazolin-4-yl) thio) acetate was chosen as Hit-1. It is also tested for ADMET prediction which is in the optimum range (Table 1). Hit-1 has lead like properties, but compared to gefitinib, Hit-1’s fit score, Binding energy, Lib Dock scores are low. To improve the pharmacophoric features, molecular docking, and ADMET parameters of Hit-1 lead optimization were performed. So, in Hit-1 lead optimization was done in ‘6’ position by following the structure of the gefitinib. From the 97 optimized lead, two Hit compounds scoring functions are analogous or higher than the gefitinib. ‘6’ position’s methoxy group was optimized and replaced by 3-(piperazin-1-yl) propan-1-oxy group and 3-morpholinopropan-1-oxy to get the compound methyl 2-((7-methoxy-6-(3-(piperazin-1-yl) propoxy) quinazolin-4-yl) thio) acetate (Hit-2) and with methyl 2-((7-methoxy-6-(3-morpholinopropoxy) quinazolin-4-yl) thio) acetate (Hit-3) correspondingly (Figure 2). Comparative evaluation of compounds Hit-2 and Hit-3 with standard revealed that fit score of Hit-2 (4.83269) and Hit-3 (4.76094) is better than standard’s fit score (4.75388). Molecular docking comparison among Hit-2, Hit-3, and Gefitinib with EGFR-1 revealed that Hit-2 and Hit-3 Lib dock score (115.78 and 115.078 consecutively) and conventional hydrogen bonding (3 and 1) was better than or equal to gefitinib (112.64 and 1). Compounds Hit-2 and Hit-3 binding study with VEGFR-2 confirmed that they may be potential inhibitors of angiogenesis in NSCLC. So, they may act as multi tyrosine kinase inhibitors. ADMET prediction of the final Hit compounds (Hit-2 and Hit-3) was performed to assess any potential risk of toxicity. Both the Hit compounds, cleared the different predictive models like, predication of drug-drug interaction by cytochrome P450 2D6 (CYP2D6) model, prediction of potential organ toxicity by hepatotoxicity model, predication model for Human Intestinal Absorption (HIA) of orally administered drugs, Aqueous solubility prediction model to predict Aqueous solubility of each compound in the water at 25°C, and to predict compound binding with carrier proteins in the blood, plasma protein binding model. The compounds were also non mutagen according to TopKat prediction model. The ADMET solubility of Hit compounds was also in optimal label. This screening process lowering the chance of last stage elimination of compound from the drug discovery process.

CONCLUSION:

NSCLC is a very common lung cancer which needs targeted treatment to restrict metastasis. EGFR-TKR inhibitors are selective choice for NSCLC. In 2015 US FDA approved EGFR inhibitor Gefitinib as first line treatment for NSCLC. So, pharmacophoric feature of Gefitinib-EGFR complex has the potential to identify novel compounds targeting EGFR. In the present study comparative Molecular Docking, binding affinity prediction, ligand based interactions at active site of the EGFR receptor with Hit-2 and Hit-3 and gefitinib has been performed. Multi targeted effect in silico docking study has been performed with other NSCLC related tyrosine kinase receptor i.e. VEGFR-2. From the entire study it has been concluded that compounds Hit-2 and Hit-3 Fit score, Lib Dock score, Binding affinity, conventional hydrogen bond interactions are higher or equal to gefitinib. Their ADMET properties are also in an acceptable range. Further, due to low Synthetic accessibility score of Hit-2 and Hit-3 compounds will be chemically synthesized very easily.

FEATURE ASPECT:

2-((7-methoxy-6-(3-(piperazin-1-yl) propoxy) quinazolin-4-yl) thio) acetate (Hit-2) and compound methyl 2-((7-methoxy-6-(3-morpholinopropoxy) quinazolin-4-yl) thio) acetate (Hit-3) must be synthesis. They will be biologically evaluated through in-vitro enzyme inhibition assay and proliferation assay on NSCLC cell line (HCC827). EGFR and VEGFR-2 inhibition mechanism also will be established through western blotting on HCC827 cell line. For advance study in-vivo anti cancer evaluation of active compounds will be done on Xenograft model of human NSCLC to observe inhibition of EGFR and VEGFR-2 through IHC score.

ABBREVIATION:

EGFR: Endothelial growth factor receptor; VEGFR: Vascular Endothelial growth factor receptor; NSCLC: Non-small cell lung cancer; SCLC: Small cell lung cancer; MAPK: Mitogen-activated protein kinases; PI3K: Phosphatidylinositol 3-kinases; ERBB: Erythroblastic leukemia viral oncogene homologue; HER: Human Endothelial Growth factor receptor; ADMET: Absorption Distribution Metabolism Excretion Toxicity; HBA: Hydrogen bond acceptor: HBD: Hydrogen bond donor; RO5: Rule of Five; PLP: Piecewise Linear Potential; IHC: Immunohistochemistry.

ACKNOWLEDGMENT:

We owe thanks to Dr. S. Sen, a pioneer in this research realm, for his eternal inspiration, which encouraged one of the authors to design and accomplish the experiments to the recent trends in drug discovery. We are equally indebted to the college authority for their moral support.

COMPLIANCE WITH ETHICAL STANDARDS:

This article does not contain any studies involving human participants performed by any of the authors and does not contain any studies involving animals performed by any of the authors.

SUPPLEMENTARY INFORMATION:

The detailed EGFR-1 interaction with lead optimized point of Hit-2 and Hit-3 are supplemented in the Table S1.

REFERENCES:

1. Nagano T, Tachihara M, Nishimura Y. Molecular Mechanisms and Targeted Therapies Including Immunotherapy for Non-Small Cell Lung Cancer. Current Cancer Drug Targets. 2019; 19(8): 595-630. doi:10.2174/1568009619666181210114559

2. Chen Z, Fillmore CM, Hammerman PS, Kim CF, Wong KK. Non-small-cell lung cancers: a heterogeneous set of diseases. Nature Reviews Cancer. 2014; 14(8): 535-546. doi:10.1038/nrc3775

3. Board PC. BRCA1 and BRCA2: Cancer Risks and Management (PDQ®). InPDQ Cancer Information Summaries [Internet] 2023. National Cancer Institute (US).

4. Nag MK, Patel S, Panik R, Shrivastava S, Daharwal SJ, Singh MR, Singh D. Lung cancer targeting: a review. Research Journal of Pharmacy and Technology. 2013; 6(11): 1302-1306.

5. Sari S, Andayani TM, Endarti D, Widayati K. Health-related quality of life in non-small cell lung cancer (NSCLC) patients with mutation of epidermal growth factor receptor (EGFR) in Indonesia. Research Journal of Pharmacy and Technology. 2020; 13(1): 443-447.

6. Shukla Maitri Jayeshkumar, Patel Yashvi, Bhavin Bhimani, Ghanshyam Patel, Jaimin Patel, Upendra Patel. Nebulization Therapy for Lung Cancer. Research Journal of Pharmacy and Technology. 2019; 12(2): 920-934. doi: 10.5958/0974-360X.2019.00157.4

7. Sindhu PS, Ramamurthy B. Lung Cancer Detection using Image Processing Technique. Research Journal of Pharmacy and Technology. 2018; 11(5): 2045-2049. doi: 10.5958/0974-360X.2018.00379.7

8. Yao Y, Fareed R, Zafar A, Saleem K, Huang T, Duan Y, Rehman MU. State-of-the-art combination treatment strategies for advanced stage non–small cell lung cancer. Frontiers in Oncology. 2022; 12: 958505. https://doi.org/10.3389/fonc.2022.958505

9. Sari S, Andayani TM, Endarti D, Widayati K. Cost-effectiveness analysis of afatinib versus gefitinib in non-small cell lung cancer (NSCLC) with epidermal growth factor receptor (EGFR) mutation in Indonesia: observational studies with retrospectives. Research Journal of Pharmacy and Technology. 2022; 15(4): 1598-1602.doi: 10.52711/0974-360X.2022.00267

10. Liu TC, Jin X, Wang Y, Wang K. Role of epidermal growth factor receptor in lung cancer and targeted therapies. American Journal of Cancer Research. 2017; 7(2): 187.

11. Carcereny E, Morán T, Capdevila L, Cros S, Vilà L, de los Llanos Gil M, Remón J, Rosell R. The epidermal growth factor receptor (EGRF) in lung cancer. Translational Respiratory Medicine. 2015; 3: 1-8. https://doi.org/10.1186/s40247-015-0013-z

12. Marmor MD, Skaria KB, Yarden Y. Signal transduction and oncogenesis by ErbB/HER receptors. International Journal of Radiation Oncology Biology Physics. 2004; 58(3): 903-913. https://doi.org/10.1016/j.ijrobp.2003.06.002

13. Padmapriya A, Preetha S, Selvaraj J, Sridevi G. Effect of Carica papaya seed extract on IL -6 and TNF-α in human lung cancer cell lines - an In vitro study. Research Journal of Pharmacy and Technology. 2022; 15(12): 5478-5482. doi: 10.52711/0974-360X.2022.00924

14. Morabito A, Carillio G, Daniele G, Piccirillo MC, Montanino A, Costanzo R, Sandomenico C, Giordano P, Normanno N, Perrone F, Rocco G. Treatment of small cell lung cancer. Critical Reviews in Oncology/Hematology. 2014;91(3):257-270.

15. Arteaga CL, Engelman JA. ERBB receptors: from oncogene discovery to basic science to mechanism-based cancer therapeutics. Cancer Cell. 2014; 25(3): 282-303.

16. Muniyan R. Prediction of alternate drugs for Crizotinib resistant mutated-ALK inhibitors in lung cancer treatment: An In silico approach. Research Journal of Pharmacy and Technology. 2020; 13(8): 3643- 3647. doi: 10.5958/0974-360X.2020.00644.7

17. Wieduwilt MJ, Moasser MM. The epidermal growth factor receptor family: biology driving targeted therapeutics. Cell Mol Life Sci. 2008; 65(10): 1566-1584. doi: 10.1007/s00018-008-7440-8.

18. Le Dinh Chac, Bui Bao Thinh, Nguyen Thi Yen. Anti-cancer activity of dry extract of Anoectochilus setaceus Blume against BT474 breast cancer cell line and A549 lung cancer cell line. Research Journal of Pharmacy and Technology. 2021; 14(2): 730-734. doi: 10.5958/0974-360X.2021.00127.X

19. Gayathri K, Vaidhehi V. An Automatic Identification of Lung Cancer from different types of Medical Images. Research Journal of Pharmacy and Technology. 2019; 12(5): 2109-2115. doi: 10.5958/0974-360X.2019.00350.0

20. Gou HF, Li X, Qiu M, Cheng K, Li LH, Dong H, Chen Y, Tang Y, Gao F, Zhao F, Men HT, Ge J, Su JM, Xu F, Bi F, Gao JJ, Liu JY. Epidermal growth factor receptor (EGFR)-RAS signaling pathway in penile squamous cell carcinoma. PLoS One. 2013; 8(4): e62175. doi: 10.1371/journal.pone.0062175.

21. Devgun M, Singh L, Sharma S. Structure prediction and In-silico designing of drugs against plant homeodomain finger protein 14 for suppression of malignant transformation and tumorigenicity of non small cell lung cancer. Research Journal of Pharmacy and Technology. 2022; 15(10): 4621-4626. doi: 10.52711/0974-360X.2022.00775

22. Oxnard GR, Miller VA. Use of erlotinib or gefitinib as initial therapy in advanced NSCLC. Oncology (Williston Park). 2010; 24(5): 392-399.

23. Kuykendall A, Chiappori A. Advanced EGFR mutation-positive non-small-cell lung cancer: case report, literature review, and treatment recommendations. Cancer Control. 2014; 21(1): 67-73. doi: 10.1177/107327481402100110.

24. Kazandjian D, Blumenthal GM, Yuan W, He K, Keegan P, Pazdur R. FDA Approval of Gefitinib for the Treatment of Patients with Metastatic EGFR Mutation-Positive Non-Small Cell Lung Cancer. Clinical Cancer Research. 2016; 15; 22(6): 1307-1312. doi: 10.1158/1078-0432.

25. Hanna N, Lilenbaum R, Ansari R, Lynch T, Govindan R, Jänne PA, Bonomi P. Phase II trial of cetuximab in patients with previously treated non–small-cell lung cancer. Journal of Clinical Oncology. 2006; 24(33): 5253-5258. doi: 10.1200/JCO.2006.08.2263.

26. Robert F, Blumenschein G, Herbst RS, Fossella FV, Tseng J, Saleh MN, Needle M. Phase I/IIa study of cetuximab with gemcitabine plus carboplatin in patients with chemotherapy-naive advanced non-small-cell lung cancer. Journal of Clinical Oncology. 2005; 23(36): 9089-9096. doi: 10.1200/JCO.2004.00.1438.

27. Langer CJ. Emerging role of epidermal growth factor receptor inhibition in therapy for advanced malignancy: focus on NSCLC. International Journal of Radiation Oncology Biology Physics. 2004; 58(3): 991-1002.

28. de Marinis F, Pereira JR, Fossella F, Perry MC, Reck M, Salzberg M, Jassem J, Peterson P, Liepa AM, Moore P, Gralla RJ. Lung Cancer Symptom Scale outcomes in relation to standard efficacy measures: an analysis of the phase III study of pemetrexed versus docetaxel in advanced non-small cell lung cancer. Journal of Thoracic Oncology. 2008; 3(1): 30-36. doi: 10.1097/JTO.0b013e31815e8b48.

29. Kobayashi S, Boggon TJ, Dayaram T, Jänne PA, Kocher O, Meyerson M, Johnson BE, Eck MJ, Tenen DG, Halmos B. EGFR mutation and resistance of non-small-cell lung cancer to gefitinib. New England Journal of Medicine. 2005; 352(8): 786-792. doi: 10.1056/NEJMoa044238.

30. Kobayashi S, Ji H, Yuza Y, Meyerson M, Wong KK, Tenen DG, Halmos B. An alternative inhibitor overcomes resistance caused by a mutation of the epidermal growth factor receptor. Cancer Research. 2005; 65(16): 7096-7101. doi: 10.1158/0008-5472.

31. Dassault Systèmes BIOVIA, Discovery Studio, 4.1, San Diego: Dassault Systèmes, 2017.

32. Chemical Structure Drawing Standard, Cambridge Soft Corporation, USA, 2010.

33. Daina A, Michielin O, Zoete V. SwissADME: a free web tool to evaluate pharmacokinetics, drug-likeness and medicinal chemistry friendliness of small molecules. Scientific Reports. 2017; 7: 42717. doi: 10.1038/srep42717.

34. RCSB Protein Data Bank, “RCSB PDB: Homepage,” Rcsb.org. [Online]. https://www.rcsb.org/

35. Arooj M, Sakkiah S, Kim S, Arulalapperumal V, Lee KW. A combination of receptor-based pharmacophore modeling & QM techniques for identification of human chymase inhibitors. PLoS One. 2013; 8(4): e63030. doi: 10.1371/journal.pone.0063030.

36. Wolber G, Langer T. LigandScout: 3-D pharmacophores derived from protein-bound ligands and their use as virtual screening filters. Journal of Chemical Information and Modeling. 2005; 45(1): 160-169. doi: 10.1021/ci049885e.

37. Jin B, Hopfinger AJ. A proposed common spatial pharmacophore and the corresponding active conformations of some TxA2 receptor antagonists. Journal of Chemical Information and Computer Sciences 1994; 34(4): 1014-1021. doi: 10.1021/ci00020a041.

38. Yang J, Wang D, Jia C, Wang M, Hao G, Yang G. Freely Accessible Chemical Database Resources of Compounds for In Silico Drug Discovery. Current Medicinal Chemistry. 2019; 26(42): 7581-7597. doi: 10.2174/0929867325666180508100436.

39. http://www.maybridge.com

40. Lipinski CA. Lead- and drug-like compounds: the rule-of-five revolution. Drug Discovery Today Technology. 2004; 1(4): 337-341. doi: 10.1016/j.ddtec.2004.11.007.

41. Pal S, Kumar V, Kundu B, Bhattacharya D, Preethy N, Reddy MP, Talukdar A. Ligand-based Pharmacophore Modeling, Virtual Screening and Molecular Docking Studies for Discovery of Potential Topoisomerase I Inhibitors. Computational and Structural Biotechnology Journal. 2019;17:291-310. doi: 10.1016/j.csbj.2019.02.006.

42. Joseph-McCarthy D, Baber JC, Feyfant E, Thompson DC, Humblet C. Lead optimization via high-throughput molecular docking. Current Opinion in Drug Discovery & Development. 2007; 10(3): 264-274.

43. Diller DJ, Merz Jr KM. High throughput docking for library design and library prioritization. Proteins: Structure, Function, and Bioinformatics. 2001; 43(2): 113-124.

44. Kristam R, Gillet VJ, Lewis RA, Thorner D. Comparison of conformational analysis techniques to generate pharmacophore hypotheses using catalyst. Journal of Chemical Information and Modeling. 2005; 45(2): 461-476. doi: 10.1021/ci049731z.

45. Brooks BR, Brooks CL 3rd, Mackerell AD Jr, Nilsson L, Petrella RJ, Roux B, Won Y, Archontis G, Bartels C, Boresch S, Caflisch A, Caves L, Cui Q, Dinner AR, Feig M, Fischer S, Gao J, Hodoscek M, Im W, Kuczera K, Lazaridis T, Ma J, Ovchinnikov V, Paci E, Pastor RW, Post CB, Pu JZ, Schaefer M, Tidor B, Venable RM, Woodcock HL, Wu X, Yang W, York DM, Karplus M. CHARMM: the biomolecular simulation program. Journal of Computational Chemistry. 2009; 30(10): 1545-1614. doi: 10.1002/jcc.21287.

46. Atkovska K, Samsonov SA, Paszkowski-Rogacz M, Pisabarro MT. Multipose binding in molecular docking. International Journal of Molecular Sciences. 2014; 15(2): 2622-2645. doi: 10.3390/ijms15022622.

47. Cavasotto CN. Binding free energy calculation using quantum mechanics aimed for drug lead optimization. Quantum Mechanics in Drug Discovery. 2020:257-268.

48. Sarker K, Ghosh A, Saha A, Mishra S, Sen S. Pharmacophore Based Design of Probable FGFR-1 Inhibitors from the 3D Crystal Structure of Infigratinib-A Drug Used in the Treatment of Cholangiocarcinomas. Letters in Drug Design & Discovery. 2022; 19(4): 314-322.

49. Zhang G, Guo S, Cui H, Qi J. Virtual Screening of Small Molecular Inhibitors against DprE1. Molecules. 2018; 23(3): 524. doi: 10.3390/molecules23030524.

Received on 26.10.2024 Revised on 14.02.2025

Accepted on 12.04.2025 Published on 10.02.2026

Available online from February 16, 2026

Research J. Pharmacy and Technology. 2026;19(2):772-779.

DOI: 10.52711/0974-360X.2026.00111

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

SL No.	Compound code	M.W	Fit Score	cLogP	HBA	HBD	Synthetic Accessibility
1	Hit-1	294	3.9362	2	6	0	2.64
2	Hit-2	406	4.83269	2.27	3	10	3.30
3	Hit-3	407	4.76094	2.68	1	7	3.29
4	Gefitinib	446	4.75388	3.86	1	7	3.26