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Journal of Drug Delivery and Therapeutics

Open Access to Pharmaceutical and Medical Research

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Open Access Full Text Article                                                                            Review Article

Beyond Current Therapies: In Silico Drug Repurposing as a Strategy to Overcome Tyrosine Kinase Inhibitor Resistance in NSCLC

Sanae Baghrous *, Ikram Ghicha, Fatiha Bousselham, Roussaint Doussou-Yovo, Hasnaa Bazhar, Youness Kadil, Imane Rahmoune, Houda Filali 

Laboratory of Pharmacology-Toxicology, Faculty of Medicine and Pharmacy of Casablanca, Hassan II University of Casablanca, Morocco. Laboratory of Scientific and Clinical Research in Cancer Pathologies, CEDoc UH2

 

 

INTRODUCTION

Drug repurposing (known as repositioning) refers to a spectrum of methodologies focused on identifying novel therapeutic applications of pharmaceuticals that have already received regulatory approval or are under investigation in experimental stages. This approach leverages existing pharmacological data to explore alternative clinical uses beyond intended indications. There are several benefits to this approach, rather than creating a completely novel drug for a certain need ¹. The repurposing of drugs has become essential due to the global COVID-19 pandemic. The fast spread of diseases and the associated infection risks have made new treatments urgently needed. Advances in computational methods, along with accessible biomedical datasets like gene expression signatures, pharmaceutical databases, and online health communities, have driven the development of computational drug repositioning techniques. These methods mainly use data mining, machine learning, and network analyses^2,3. Both experimental and computational techniques are used in the repurposing process, which aims to identify therapeutic targets not only for cancer but also for other diseases⁴. 

Lung cancer remains the leading cause of cancer-related deaths, with non-small-cell lung cancer (NSCLC) accounting for over 80% of cases worldwide^5,6.

Treatment of NSCLC via targeted kinase inhibition focuses on pathways involved in receptor-tyrosine-kinase-mediated inhibition of cell proliferation. Anaplastic Lymphoma Kinase (ALK), Epidermal Growth Factor Receptor (EGFR), Mesenchymal-Epithelial Transition factor (MET), and ROS Proto-Oncogene 1 (ROS1) are pharmaceutical targets for cell lung cancer treatment that are linked to the presence of oncogenic driver mutations in NSCLC^7,8.

Although significant progress has been made with TKIs, their effectiveness is constantly undermined by the development of resistance⁹. Given this ongoing clinical challenge, this paper makes the case for and describes the strategic use of In Silico drug repurposing as a key approach to proactively identify and develop effective treatments that overcome TKI resistance in NSCLC. We explain how advanced computational methods, including the combination of genomic and phenomic data with artificial intelligence, can greatly speed up the discovery of new uses for existing drugs. Moreover, this work highlights the urgent need for future research to turn promising In Silico findings into robust clinical solutions, addressing the substantial unmet need for approved repurposed therapies in TKI-resistant NSCLC.

OVERVIEW OF TYROSINE KINASE INHIBITORS IN LUNG CANCER

Role of Tyrosine Kinases in Lung Cancer Pathogenesis

Receptor tyrosine kinases (RTKs) are transmembrane proteins with extracellular ligand-binding domains that phosphorylate tyrosine residues, triggering signaling pathways controlling cell proliferation, differentiation, and death. In lung cancers, various RTKs have been identified, where they act as proto-oncogenes contributing to malignant transformation, cell proliferation, motility, and survival ^10-13. 

 The kinase remains inactive as a monomer until ligand binding induces dimerization and autophosphorylation, initiating a phosphorylation cascade [Figure 1, A]^10,14,15,22.

In NSCLC, RTKs like EGFR, ALK, ROS1, and MET often become overexpressed or mutated, leading to uncontrolled cell proliferation. Tyrosine kinase inhibitors (TKIs) are used to target these aberrant signals, offering more effective treatments than chemotherapy^16,17.

Key mechanisms of RTK activation include:

• Gain-of-function mutations: Mutations in RTKs bypass normal regulation, driving tumor growth ^18,15,22. [Figure 1, C]
• Genomic amplification: Increased copies of RTK genes like EGFR and MET enhance signaling, contributing to cancer progression^19,20,22. [Figure 1, D]
• Chromosomal rearrangements: Fusions, such as EML4-ALK in NSCLC, lead to continuous activation of RTKs, promoting tumorigenesis^21,22. [Figure 1, B]

 

Figure 1: Mechanisms of Receptor Tyrosine Kinase Activation: Normal Physiological Processes (A) and Pathogenic Alterations (B, C, and D) [Created by BioRender.com]

 

 

FDA-APPROVED TKIs FOR NSCLC

Several therapeutic options are available for NSCLC, including surgery, radiation therapy, chemotherapy, targeted therapies, and immunotherapy, either alone or in combination²³. The Food and Drug Administration has authorized 39 drugs for the treatment of NSCLC, of which 17 are tyrosine kinase inhibitors²⁴ [Table 1]. 

Starting with Del19/L858R mutations and moving on to target other variants, such as T790M, EGFR-TKIs have evolved in response to specific EGFR mutations, primarily targeting the allosteric and ATP-binding sites of EGFR to produce reversible or irreversible effects²⁵. The first EGFR-TKI used to address the Del19/L858R mutation was gefitinib, which was approved in 2003(26). Mobocertinib is the newest EGFR-TKI approved for NSCLC that targets the EGFR exon 20 insertion mutation²⁷. In contrast, osimertinib is a third-generation drug that targets the T790M mutation in cancer patients with EGFR sensitive or resistant tumors²⁸.

The discovery of ALK-TKIs was made possible by molecular characterization of non-small cell lung cancer, particularly by identifying the EML4-ALK fusion gene²⁹. In 2011, the FDA approved crizotinib as a first-generation ALK inhibitor for the treatment of non-small cell lung cancer with ALK rearrangement³⁰. There have been several ALK-TKIs developed after crizotinib, including ceritinib, alectinib, brigatinib, and lorlatinib³¹. Although crizotinib is effective in targeting ALK, ROS1, and MET, resistance can develop with extended use, mainly because it cannot cross the blood-brain barrier³². Improved brain penetration is a benefit of second-generation TKIs, such as ceritinib, alectinib, brigatinib, and lorlatinib, which solve the problem of the blood-brain barrier³¹. Nevertheless, it is important to note that resistance to TKIs continues to be a serious challenge, and this problem persists even with third-generation inhibitors³³. 

 

 

Table 1: FDA-approved drugs as TKI in NSCLC

Drug	Category	Approval for NSCLC	Ref
Gefitinib	EGFR TKI	2003-2015 (first line)	26,34
Erlotinib	EGFR TKI	2004	26
Afatinib	EGFR TKI	2013	35
Osimertinib	EGFR TKI	2015	36
Alectinib	ALK Inhibitor	2015	37
Ceritinib	ALK Inhibitor	2014	38
Dabrafenib	BRAF Inhibitor	2017	39
Trametinib	MEK1/2 Inhibitor (EGFR mutation)	2017	39
Brigatinib	ALK Inhibitor	2017	27
Lorlatinib	ALK Inhibitor	2018	33
Dacomitinib	EGFR TKI (2nd Gen)	2018	40
Entrectinib	ALK, ROS1, TRK Inhibitor	2019	41
Capmatinib	MET Inhibitor	2020	42
Selpercatinib	RET Inhibitor	2020	43
Pralsetinib	RET Inhibitor	2020	44
Tepotinib	MET Inhibitor	2021	42
Mobocertinib	EGFR TKI (3rd Gen)	2021	27
Repotrectinib	ROS1 TKI	2023	45

 

 

Despite the existence of effective therapies for NSCLC, substantial death rates are still observed as a result of resistance to both on-target and off-target TKIs^46,25. This persistent resistance remains a significant challenge; therefore, researchers need to explore new approaches to treat cancer⁹. 

MECHANISMS OF RESISTANCE TO TKI IN NSCLC

Understanding the diverse mechanisms by which NSCLC cells develop resistance to TKIs is fundamental, as these insights directly inform the design of effective counter-strategies.

Resistance to TKI therapies can be classified into primary (intrinsic) resistance and secondary (acquired) resistance. Primary resistance occurs when the treatment does not show a response from the beginning, typically indicated by disease progression within the first few months of starting TKI treatment. In contrast, secondary resistance develops over time, often following an initial phase of a positive therapeutic response ³⁰.

Resistance to TKIs in NSCLC primarily arises from various molecular alterations that bypass the inhibitory effects of these drugs. These mechanisms can be broadly categorized into on-target mutations that directly affect drug binding and off-target activations of alternative signaling pathways. For instance, common on-target mutations, such as the EGFR T790M mutation, modify the ATP-binding pocket of the kinase, decreasing the affinity of first and second-generation TKIs^28,47. Similarly, specific mutations in ALK and ROS1 can create steric hindrance or change protein conformation, obstructing effective drug binding. Other resistance mechanisms involve the activation of alternative receptor tyrosine kinases or downstream signaling components, which can circumvent the inhibited pathway [Table 2]. Understanding these basic molecular events is essential for developing strategies, including drug repurposing, to address TKI resistance ^48–50

 

 

Table 2:  Common mutations associated with TKI resistance

RTK	Mutations	TKIs	Prevalence (%)	Ref
EGFR	T790M	Gefitinib	50–60%	47
	G796S/C797S L792X	Osimertinib	24.7% 10.8%	51-53
ALK	G1202R	Crizotinib	2%	49
		Alectinib	21–29%
		Ceritinib
		Brigatinib
	I1171T	Crizotinib  Alectinib	2% 12%
	L1196M/Q	Crizotinib	7%
ROS1	G2032R	Crizotinib	38%	50     48
		Lorlatinib	32%
	D2033N	Crizotinib	2.4%
	L2026M	Crizotinib	8%

 

 

IN SILICO STRATEGIES FOR DRUG REPURPOSING

In light of the complexities and costs associated with de novo drug discovery, In Silico drug repurposing represents a highly strategic and accelerated pathway to overcome TKI resistance. This approach leverages sophisticated computational methods to identify new therapeutic applications for existing, FDA-approved drugs, offering a significantly advantageous risk-benefit profile and mitigating development risks. 

Computational approach 

Despite substantial progress in the treatment of non-small cell lung cancer using therapies such as tyrosine kinase inhibitors, immunotherapy, and drug combinations, achieving a complete cure and enhancing overall survival rates, particularly in metastatic NSCLC, remains challenging. Drug resistance is a significant barrier that results in treatment failure, disease recurrence, and disease progression. Examples include resistance to Erlotinib and Crizotinib caused by mutations in targets such as EGFR and ROS1, respectively^54,55. Given these challenges, In Silico drug repurposing emerges as a highly promising strategy, offering a more advantageous risk-benefit ratio and significantly mitigating development risks and costs compared to de novo drug discovery⁵⁶.

  The drug repositioning approach involves computational and experimental strategies to identify novel therapeutic uses of existing drugs. New targets, dose regimens, and treatment techniques can be identified with the help of knowledge of pharmacological mechanisms, disease genomes, and molecular pathways⁵⁷. 

Computational drug repositioning uses two main strategies to identify the novel therapeutic applications of known drugs. The target-based and drug-based approaches⁵⁸. 

Within these overarching strategies, various In Silico methodologies are utilized, including network analyses, genomics and phenomics data integration, and increasingly, machine learning techniques ^58–60.

Genome and Phenome approach 

Computational techniques and network analysis have proven essential in discovering biomarker genes for the early identification and treatment of NSCLC. Researchers have employed co-expression network analysis to identify genes that may function as biomarkers while simultaneously assessing the effectiveness of current pharmacological interventions for NSCLC treatment ^61,62. Genetic data, such as genome-wide association studies (GWAS) and gene/protein networks, are used in network-based drug repurposing to identify novel applications for existing drugs. This approach enhances the identification of "druggable" genes near disease-linked targets, even if the targets themselves cannot be directly targeted⁶³.

The phenome, representing all phenotypic traits, provides a unique method for drug repositioning by connecting a drug's biological activities and adverse effects to disease phenotypes. This methodology relies on the concept that similarities between the symptoms of a disease and the adverse effects of a drug may suggest that the two conditions share the same biological pathways, thereby making the drug a potential treatment for the disease. This approach employs phenotypic data to identify novel therapeutic applications of current medications ⁵⁹. The integration of phenotypic data with other biological information, such as genomic data, can improve drug repositioning efforts and reveal new therapeutic applications for existing drugs. To anticipate novel drug-drug interactions and disease associations, computational models have been devised that integrate phenotypic similarities with genotype-disease relationships or drug-drug interaction data, even for diseases with unknown biological mechanisms. These methods use data from multiple sources to identify unknown connections and enhance the treatment options^64-66.

Machine Learning Based Methods

Machine learning is a potent tool in pharmaceutical research, providing solutions to numerous challenges in drug development. It can optimize clinical trials, predict drug-target interactions, expedite drug discovery, and reduce costs by analyzing large datasets and identifying patterns. ML serves as a critical catalyst for innovation in the pharmaceutical sector, with applications that involve the identification of prospective drug candidates and the customization of treatments⁶⁷. ML algorithms can be applied in both target-based and drug-based repurposing strategies. For instance, in target-based approaches, ML can predict binding affinities between drugs and novel targets or identify cryptic protein pockets for binding. In drug-based approaches, ML can be used to analyze chemical similarities or shared biological activities across vast drug datasets to suggest new indications. A preliminary study explored the use of machine learning to predict how cancer cells respond to treatments based on their genomic and chemical profiles and suggested the possibility of drug repurposing^16,68-70.

DRUG REPURPOSING AS A STRATEGY TO OVERCOME TKI RESISTANCE 

Studies indicate that the development of new cancer drugs faces a significantly higher failure rate after initial clinical trials (Phase 1), with only 3.4% progressing further. This contrasts with the development of drugs for cardiovascular, infectious, and autoimmune diseases, which demonstrate substantially higher success rates in the same early stages ⁷¹. Therefore, repurposing existing drugs could help accelerate the discovery of cancer treatments more quickly and potentially reduce overall cancer mortality, especially non-small cell lung cancer⁷².

Repurposing drugs for NSCLC

While the concept of drug repurposing offers significant promise for overcoming TKI resistance, it is critical to acknowledge the current landscape: to date, no repurposed drug has received regulatory approval specifically for addressing TKI resistance in NSCLC. This stark reality underscores the urgency for more targeted research and serves as the primary reason for the limited availability of extensive clinical data on approved repurposed agents in this specific context. Despite this, ongoing clinical trials and compelling preclinical studies provide strong proof-of-concept for the viability of this strategy. Table 3 presents a summary of ongoing clinical trials exploring the use of repurposed drugs, demonstrating the active pursuit of these therapies. Furthermore, preclinical studies highlight promising candidates and mechanisms by which repurposed drugs can bypass resistance [Table 4].

 

 

 

 

Table 3: Ongoing clinical trials exploring the use of repurposed drugs for treating NSCLC.

Trial ID	Phase	Planned End Date	Treatment Approach	Target Patient Group	Enrollment	Overall Objective
NCT03546829	Phase 1	December 2027	Vancomycin with focused radiation therapy	Early-stage NSCLC	40	Evaluate immune response and treatment effectiveness
NCT04980716	Phase 3	July 2026	Cardiovascular-related drugs (e.g., perindopril, statins, beta-blockers)	NSCLC patients with cardiovascular comorbidities	524	Assess overall survival and heart-related side effects
NCT05636592	Phase 1	December 2027	Statins plus PD-1/PD-L1 inhibitors	General NSCLC cases	250	Investigate tumor response and survival outcomes
NCT05445791	Phase 3	July 2025	Metformin	NSCLC patients with EGFR mutations	312	Focus on progression-free survival and treatment response rate
NCT05096663	Phase 2/3	December 2027	Combination of B12, Dexamethasone, various chemotherapies, and Ramucirumab	Advanced or recurrent NSCLC (Stage III/IV)	478	Study overall survival and progression-free survival

 

Table 4: A summary of drugs being evaluated in preclinical models for potential use in treating NSCLC.

Drug	First Indication	Targeted Cell Lines	Animal Studies	Mechanism(s) of Action	Outcomes/Effects	Ref
Atorvastatin	Treat hypercholesterolemia	NSCLC A-549 cells	-	Inhibits HMG-CoA reductase, modulates Akt/mTOR pathways, activates MAPK signaling, and triggers autophagy and ferroptosis	Suppresses tumor growth, induces apoptosis, enhances cancer treatment effectiveness	73-75
Celecoxib	A nonsteroidal anti-inflammatory drug employed in the treatment of osteoarthritis and dysmenorrhea	SGC-7901 cancer cells	-	Blocks COX-2, disrupts the PDK1/Akt signaling pathway, increases PPAR-γ and p53 expression	Triggers cell death, reduces tumor growth, halts cell cycle progression	76-78
Itraconazole	An antifungal agent utilized for the treatment of both systemic and superficial fungal infections	NSCLC xenograft models (LX-14, LX-7)	-	Inhibits endothelial cell migration, proliferation, and angiogenesis	Slows tumor growth, prolongs survival without tumor progression	79,80
Lovastatin	Employed to reduce elevated cholesterol and prevent cardiovascular disease	A549 lung cancer cells	-	Suppresses EGFR/Akt signaling, boosts AMPK activity, causes cells to exit the cell cycle and enter apoptosis	Inhibits cancer cell proliferation, promotes apoptosis, and interferes with cell survival pathways	81
Mebendazole	Broad-spectrum anthelmintic prescribed for intestinal parasitic infections	A549, H1299, H460, WI38	-	Inhibits microtubule formation, reduces cell motility, modulates p53 and STAT3 signaling	Triggers mitotic arrest, induces apoptosis, reduces cancer colony formation	82
Pitavastatin	A statin used to manage dyslipidemia and reduce cardiovascular risk	EGFR TKI-resistant NSCLC cell lines (A549, Calu6)	-	Inhibits EGFR/K-RAS, promotes apoptosis, disrupts tumor growth and angiogenesis	Reduces tumor growth, induces cell death	83
Simvastatin	Prescribed to reduce serum cholesterol and prevent atherosclerotic cardiovascular disease	Bm7 (R248W) p53 mutant cells, A459 cancer cells	Balb/C nude mice	Activates caspase-dependent apoptosis, promotes mutant p53 degradation, disrupts lipid raft formation	Decreases tumor cell motility, induces apoptosis, inhibits cell growth	84

 

 

CHALLENGES AND FUTURE DIRECTIONS 

Despite these advances, computational drug repositioning models face significant hurdles in development and reliability. Simulating biological systems using theoretical methods is made more difficult by missing, biased, or inaccurate data. Experimental factors (e.g., patient, age and environment) can affect gene expression signatures, resulting in inconsistent and biased findings. Changes in gene expression in some pharmacological targets may not be significant, leading to misleading statistical outcomes. Without high-resolution structural data for drug targets, drug-target interactions are challenging to identify⁸⁵. However, several obstacles limit the full potential of machine learning in drug repositioning. These include overfitting, unreliable biological data, difficulty in interpreting models, and the need for comprehensive clinical validation. There is a lack of high-quality consistent datasets, which restricts the credibility of ML models. To overcome these challenges, researchers developing more sophisticated methods to improve molecular structure representation and predictive accuracy, including deep learning models. Transparent insights into model predictions can also be obtained using interpretable ML approaches. Moreover, systematic and large-scale drug repositioning, as well as improved prediction accuracy, can be achieved by integrating various data sources, including chemical, omics, and clinical data. Despite these challenges, machine learning holds strong potential to revolutionize drug repositioning, especially as models improve in interpretability, robustness, and clinical validation⁶⁷.

CONCLUSION

Tyrosine kinase inhibitor (TKI) resistance in non-small cell lung cancer (NSCLC) remains a persistent challenge, necessitating innovative and accelerated therapeutic strategies. This paper advocates for In Silico drug repurposing as a potent and essential approach to overcome this critical barrier. By systematically leveraging computational methodologies, integrating genomic and phenotypic datasets, and deploying advanced machine learning, we can significantly accelerate the identification of novel indications for existing drugs. Despite promising preclinical and ongoing clinical efforts, the absence of approved repurposed drugs for TKI resistance in NSCLC underscores a significant unmet clinical need. Emphasizing repurposing offers a cost-effective solution, providing broader treatment access, especially for patients with limited income. Ultimately, In Silico drug repurposing is a strategic, efficient, and cost-effective pathway to expand the therapeutic arsenal against TKI-resistant NSCLC, representing a curcial imperative for improving patient outcomes and reshaping future treatment.

Funding: The authors did not receive financial support and sponsorship from individuals or organizations/institutions. 

Authors’ contributions: Sanae BAGHROUS, Ikram GHICHA, Fatiha BOUSELHAM, Vigniako Roussaint DOSSOU-YOVO, and Hasnaa BAZHAR contributed to the study's conception and initial drafting. Sanae BAGHROUS wrote the first draft, with all authors providing feedback on earlier versions. Supervision was carried out by Youness KADIL, Imane RAHMOUNE, and Houda FILALI. All authors have reviewed and approved the final version of the manuscript.

Acknowledgements: Not applicable. 

Conflicts of Interest: The authors declare no conflicts of interest

Authors should disclose any personal or financial relationships that could be viewed as potential conflicts of interest in relation to the publication on manuscript file just before references section.

Ethics approval and consent to participate: Not applicable. 

REFERENCES: 

1. Pushpakom S, Iorio F, Eyers PA, Escott KJ, Hopper S, Wells A, et al. Drug repurposing: progress, challenges and recommendations. Nat Rev Drug Discov. 2019 Jan;18(1):41-58. https://doi.org/10.1038/nrd.2018.168

2. Li J, Zheng S, Chen B, Butte AJ, Swamidass SJ, Lu Z. A survey of current trends in computational drug repositioning. Brief Bioinform. 2016 Jan;17(1):2-12. https://doi.org/10.1093/bib/bbv020

3. Harris M, Bhatti Y, Buckley J, Sharma D. Fast and frugal innovations in response to the COVID-19 pandemic. Nat Med. 2020 Jun;26(6):814-7. https://doi.org/10.1038/s41591-020-0889-1

4. Dalwadi SM, Hunt A, Bonnen MD, Ghebre YT. Computational approaches for drug repurposing in oncology: untapped opportunity for high value innovation. Front Oncol. 2023 May 18;13:1198284. https://doi.org/10.3389/fonc.2023.1198284

5. Marmor HN, Zorn JT, Deppen SA, Massion PP, Grogan EL. Biomarkers in lung cancer screening: a narrative review. Curr Chall Thorac Surg. 2023 Feb;5:5-5. https://doi.org/10.21037/ccts-20-171

6. Subramanian J, Regenbogen T, Nagaraj G, Lane A, Devarakonda S, Zhou G, et al. Review of Ongoing Clinical Trials in Non-Small-Cell Lung Cancer: A Status Report for 2012 from the ClinicalTrials.gov Web Site. Journal of Thoracic Oncology. 2013 Jul;8(7):860-5. https://doi.org/10.1097/JTO.0b013e318287c562

7. Chevallier M, Borgeaud M, Addeo A, Friedlaender A. Oncogenic driver mutations in non-small cell lung cancer: Past, present and future. WJCO. 2021 Apr 24;12(4):217-37. https://doi.org/10.5306/wjco.v12.i4.217

8. Broekman F. Tyrosine kinase inhibitors: Multi-targeted or single-targeted? WJCO. 2011;2(2):80. https://doi.org/10.5306/wjco.v2.i2.80

9. Gainor JF, Shaw AT. Emerging Paradigms in the Development of Resistance to Tyrosine Kinase Inhibitors in Lung Cancer. JCO. 2013 Nov 1;31(31):3987-96. https://doi.org/10.1200/JCO.2012.45.2029

10. Taruneshwar Jha K, Shome A, Chahat, Chawla PA. Recent advances in nitrogen-containing heterocyclic compounds as receptor tyrosine kinase inhibitors for the treatment of cancer: Biological activity and structural activity relationship. Bioorganic Chemistry. 2023 Sep;138:106680. https://doi.org/10.1016/j.bioorg.2023.106680

11. Ansari J, Palmer DH, Rea DW, Hussain SA. Role of Tyrosine Kinase Inhibitors in Lung Cancer. ACAMC. 2009 Jun 1;9(5):569-75. https://doi.org/10.2174/187152009788451879

12. Pisick E, Jagadeesh S, Salgia R. Receptor Tyrosine Kinases and Inhibitors in Lung Cancer. The Scientific World JOURNAL. 2004;4:589-604. https://doi.org/10.1100/tsw.2004.117

13. Pawson T. Regulation and targets of receptor tyrosine kinases. European Journal of Cancer. 2002 Sep;38:S3-10. https://doi.org/10.1016/S0959-8049(02)80597-4

14. Tan AC, Vyse S, Huang PH. Exploiting receptor tyrosine kinase co-activation for cancer therapy. Drug Discovery Today. 2017 Jan;22(1):72-84. https://doi.org/10.1016/j.drudis.2016.07.010

15. Du Z, Lovly CM. Mechanisms of receptor tyrosine kinase activation in cancer. Mol Cancer. 2018 Dec;17(1):58. https://doi.org/10.1186/s12943-018-0782-4

16. Sawyers C. Rational therapeutic intervention in cancer: kinases as drug targets. Current Opinion in Genetics & Development. 2002 Feb 1;12(1):111-5. https://doi.org/10.1016/S0959-437X(01)00273-8

17. Paul MK, Mukhopadhyay AK. Tyrosine kinase - Role and significance in Cancer. Int J Med Sci. 2004;101-15. https://doi.org/10.7150/ijms.1.101

18. Cancer Genome Landscapes | Science [Internet]. [cited 2024 Dec 16]. Available from: https://www.science.org/doi/abs/10.1126/science.1235122 

19. Carraway KL, Sweeney C. EGF receptor activation by heterologous mechanisms. Cancer Cell. 2002 Jun 1;1(5):405-6. https://doi.org/10.1016/S1535-6108(02)00076-4

20. Selvaggi G, Novello S, Torri V, Leonardo E, De Giuli P, Borasio P, et al. Epidermal growth factor receptor overexpression correlates with a poor prognosis in completely resected non-small-cell lung cancer. Annals of Oncology. 2004 Jan;15(1):28-32. https://doi.org/10.1093/annonc/mdh011

21. Rothenstein JM, Chooback N. ALK Inhibitors, Resistance Development, Clinical Trials. Current Oncology. 2018 Jun 1;25(11):59-67. https://doi.org/10.3747/co.25.3760

22. Zhao S, Li J, Xia Q, Liu K, Dong Z. New perspectives for targeting therapy in ALK-positive human cancers. Oncogene. 2023 Jun 9;42(24):1959-69. https://doi.org/10.1038/s41388-023-02712-8

23. Thai AA, Solomon BJ, Sequist LV, Gainor JF, Heist RS. Lung cancer. The Lancet. 2021 Aug;398(10299):535-54. https://doi.org/10.1016/S0140-6736(21)00312-3

24. Drugs Approved for Lung Cancer - NCI [Internet]. [cited 2024 Dec 23]. Available from: https://www.cancer.gov/about-cancer/treatment/drugs/lung

25. Singh S, Sadhukhan S, Sonawane A. 20 years since the approval of first EGFR-TKI, gefitinib: Insight and foresight. Biochimica et Biophysica Acta (BBA) - Reviews on Cancer. 2023 Nov;1878(6):188967. https://doi.org/10.1016/j.bbcan.2023.188967

26. Cohen MH, Williams GA, Sridhara R, Chen G, McGuinn WD, Morse D, et al. United States Food and Drug Administration Drug Approval Summary: Gefitinib (ZD1839; Iressa) Tablets.

27. Markham A. Mobocertinib: First Approval. Drugs. 2021 Nov 1;81(17):2069-74. https://doi.org/10.1007/s40265-021-01632-9

28. Gomatou G, Syrigos N, Kotteas E. Osimertinib Resistance: Molecular Mechanisms and Emerging Treatment Options. Cancers. 2023 Jan 30;15(3):841. https://doi.org/10.3390/cancers15030841

29. Soda M, Choi YL, Enomoto M, Takada S, Yamashita Y, Ishikawa S, et al. Identification of the transforming EML4-ALK fusion gene in non-small-cell lung cancer. Nature. 2007 Aug;448(7153):561-6. https://doi.org/10.1038/nature05945

30. Lin JJ, Riely GJ, Shaw AT. Targeting ALK: Precision Medicine Takes on Drug Resistance. Cancer Discovery. 2017 Feb 1;7(2):137-55. https://doi.org/10.1158/2159-8290.CD-16-1123

31. Testa U, Castelli G, Pelosi E. Alk-rearranged lung adenocarcinoma: From molecular genetics to therapeutic targeting. Tumori. 2024 Apr;110(2):88-95. https://doi.org/10.1177/03008916231202149

32. Costa DB, Kobayashi S, Pandya SS, Yeo WL, Shen Z, Tan W, et al. CSF Concentration of the Anaplastic Lymphoma Kinase Inhibitor Crizotinib. JCO. 2011 May 20;29(15):e443-5. https://doi.org/10.1200/JCO.2010.34.1313

33. Syed YY. Lorlatinib: First Global Approval. Drugs. 2019 Jan;79(1):93-8. https://doi.org/10.1007/s40265-018-1041-0

34. 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 Mar 15;22(6):1307-12. https://doi.org/10.1158/1078-0432.CCR-15-2266

35. Dungo RT, Keating GM. Afatinib: First Global Approval. Drugs. 2013 Sep 1;73(13):1503-15. https://doi.org/10.1007/s40265-013-0111-6

36. Greig SL. Osimertinib: First Global Approval. Drugs. 2016 Feb 1;76(2):263-73. https://doi.org/10.1007/s40265-015-0533-4

37. Larkins E, Blumenthal GM, Chen H, He K, Agarwal R, Gieser G, et al. FDA Approval: Alectinib for the Treatment of Metastatic, ALK-Positive Non-Small Cell Lung Cancer Following Crizotinib. Clinical Cancer Research. 2016 Nov 1;22(21):5171-6. https://doi.org/10.1158/1078-0432.CCR-16-1293

38. Ceritinib: First Global Approval | Drugs [Internet]. [cited 2024 Dec 24]. Available from: https://link.springer.com/article/10.1007/s40265-014-0251-3

39. Odogwu L, Mathieu L, Blumenthal G, Larkins E, Goldberg KB, Griffin N, et al. FDA Approval Summary: Dabrafenib and Trametinib for the Treatment of Metastatic Non-Small Cell Lung Cancers Harboring BRAF V600E Mutations. The Oncologist. 2018 Jun 1;23(6):740-5. https://doi.org/10.1634/theoncologist.2017-0642

40. Shirley M. Dacomitinib: First Global Approval. Drugs. 2018 Dec 1;78(18):1947-53. https://doi.org/10.1007/s40265-018-1028-x

41. Marcus L, Donoghue M, Aungst S, Myers CE, Helms WS, Shen G, et al. FDA Approval Summary: Entrectinib for the Treatment of NTRK gene Fusion Solid Tumors. Clinical Cancer Research. 2021 Feb 15;27(4):928-32. https://doi.org/10.1158/1078-0432.CCR-20-2771

42. Mathieu LN, Larkins E, Akinboro O, Roy P, Amatya AK, Fiero MH, et al. FDA Approval Summary: Capmatinib and Tepotinib for the Treatment of Metastatic NSCLC Harboring MET Exon 14 Skipping Mutations or Alterations. Clinical Cancer Research. 2022 Jan 15;28(2):249-54. https://doi.org/10.1158/1078-0432.CCR-21-1566

43. Bradford D, Larkins E, Mushti SL, Rodriguez L, Skinner AM, Helms WS, et al. FDA Approval Summary: Selpercatinib for the Treatment of Lung and Thyroid Cancers with RET Gene Mutations or Fusions. Clinical Cancer Research. 2021 Apr 15;27(8):2130-5. https://doi.org/10.1158/1078-0432.CCR-20-3558

44. Griesinger F, Curigliano G, Thomas M, Subbiah V, Baik CS, Tan DSW, et al. Safety and efficacy of pralsetinib in RET fusion-positive non-small-cell lung cancer including as first-line therapy: update from the ARROW trial. Annals of Oncology. 2022 Nov;33(11):1168-78. https://doi.org/10.1016/j.annonc.2022.08.002

45. Cho BC, Camidge DR, Lin JJ, Kim SW, Solomon B, Dziadziuszko R, et al. OA03.06 Repotrectinib in Patients with ROS1 Fusion-positive (ROS1+) NSCLC: Update from the Pivotal Phase 1/2 TRIDENT-1 Trial. Journal of Thoracic Oncology. 2023 Nov;18(11):S50-1. https://doi.org/10.1016/j.jtho.2023.09.035

46. De Mello RA, Neves NM, Tadokoro H, Amaral GA, Castelo-Branco P, Zia VADA. New Target Therapies in Advanced Non-Small Cell Lung Cancer: A Review of the Literature and Future Perspectives. JCM. 2020 Nov 3;9(11):3543. https://doi.org/10.3390/jcm9113543

47. Kobayashi S, Boggon TJ, Dayaram T, Jänne PA, Kocher O, Meyerson M, et al. EGFR mutation and resistance of non-small-cell lung cancer to gefitinib. N Engl J Med. 2005 Feb 24;352(8):786-92. https://doi.org/10.1056/NEJMoa044238

48. McCoach CE, Le AT, Gowan K, Jones K, Schubert L, Doak A, et al. Resistance Mechanisms to Targeted Therapies in ROS1+ and ALK+ Non-small Cell Lung Cancer. Clin Cancer Res. 2018 Jul 15;24(14):3334-47. https://doi.org/10.1158/1078-0432.CCR-17-2452

49. Gainor JF, Dardaei L, Yoda S, Friboulet L, Leshchiner I, Katayama R, et al. Molecular Mechanisms of Resistance to First- and Second-Generation ALK Inhibitors in ALK -Rearranged Lung Cancer. Cancer Discovery. 2016 Oct 1;6(10):1118-33. https://doi.org/10.1158/2159-8290.CD-16-0596

50. Lin JJ, Choudhury NJ, Yoda S, Zhu VW, Johnson TW, Sakhtemani R, et al. Spectrum of Mechanisms of Resistance to Crizotinib and Lorlatinib in ROS1 Fusion-Positive Lung Cancer. Clin Cancer Res. 2021 May 15;27(10):2899-909. https://doi.org/10.1158/1078-0432.CCR-21-0032

51. Ou SHI, Cui J, Schrock AB, Goldberg ME, Zhu VW, Albacker L, et al. Emergence of novel and dominant acquired EGFR solvent-front mutations at Gly796 (G796S/R) together with C797S/R and L792F/H mutations in one EGFR (L858R/T790M) NSCLC patient who progressed on osimertinib. Lung Cancer. 2017 Jun;108:228-31. https://doi.org/10.1016/j.lungcan.2017.04.003

52. Klempner SJ, Mehta P, Schrock AB, Ali SM, Ou SHI. Cis-oriented solvent-front EGFR G796S mutation in tissue and ctDNA in a patient progressing on osimertinib: a case report and review of the literature. Lung Cancer (Auckl). 2017;8:241-7. https://doi.org/10.2147/LCTT.S147129

53. Yang Z, Yang N, Ou Q, Xiang Y, Jiang T, Wu X, et al. Investigating Novel Resistance Mechanisms to Third-Generation EGFR Tyrosine Kinase Inhibitor Osimertinib in Non-Small Cell Lung Cancer Patients. Clinical Cancer Research. 2018 Jul 1;24(13):3097-107. https://doi.org/10.1158/1078-0432.CCR-17-2310

54. D'Angelo A, Sobhani N, Chapman R, Bagby S, Bortoletti C, Traversini M, et al. Focus on ROS1-Positive Non-Small Cell Lung Cancer (NSCLC): Crizotinib, Resistance Mechanisms and the Newer Generation of Targeted Therapies. Cancers. 2020 Nov 6;12(11):3293. https://doi.org/10.3390/cancers12113293

55. Tripathi V, Khare A, Shukla D, Bharadwaj S, Kirtipal N, Ranjan V. Genomic and computational-aided integrative drug repositioning strategy for EGFR and ROS1 mutated NSCLC. International Immunopharmacology. 2024 Sep;139:112682. https://doi.org/10.1016/j.intimp.2024.112682

56. Ashburn TT, Thor KB. Drug repositioning: identifying and developing new uses for existing drugs. Nat Rev Drug Discov. 2004 Aug;3(8):673-83. https://doi.org/10.1038/nrd1468

57. Zarei P, Ghasemi F. The Application of Artificial Intelligence and Drug Repositioning for the Identification of Fibroblast Growth Factor Receptor Inhibitors: A Review. Advanced Biomedical Research [Internet]. 2024 Jan [cited 2025 Mar 8];13. Available from: https://journals.lww.com/10.4103/abr.abr_170_23  https://doi.org/10.4103/abr.abr_170_23

58. Würth R, Thellung S, Bajetto A, Mazzanti M, Florio T, Barbieri F. Drug-repositioning opportunities for cancer therapy: novel molecular targets for known compounds. Drug Discovery Today. 2016 Jan;21(1):190-9. https://doi.org/10.1016/j.drudis.2015.09.017

59. Dudley JT, Deshpande T, Butte AJ. Exploiting drug-disease relationships for computational drug repositioning. Briefings in Bioinformatics. 2011 Jul 1;12(4):303-11. https://doi.org/10.1093/bib/bbr013

60. Parisi D, Adasme MF, Sveshnikova A, Bolz SN, Moreau Y, Schroeder M. Drug repositioning or target repositioning: A structural perspective of drug-target-indication relationship for available repurposed drugs. Computational and Structural Biotechnology Journal. 2020;18:1043-55. https://doi.org/10.1016/j.csbj.2020.04.004

61. Gao X, Cai Y, Wang Z, He W, Cao S, Xu R, et al. Estrogen receptors promote NSCLC progression by modulating the membrane receptor signaling network: a systems biology perspective. J Transl Med. 2019 Dec;17(1):308. https://doi.org/10.1186/s12967-019-2056-3

62. MotieGhader H, Tabrizi-Nezhadi P, Deldar Abad Paskeh M, Baradaran B, Mokhtarzadeh A, Hashemi M, et al. Drug repositioning in non-small cell lung cancer (NSCLC) using gene co-expression and drug-gene interaction networks analysis. Sci Rep. 2022 Jun 8;12(1):9417. https://doi.org/10.1038/s41598-022-13719-8

63. Doumat G, Daher D, Zerdan MB, Nasra N, Bahmad HF, Recine M, et al. Drug Repurposing in Non-Small Cell Lung Carcinoma: Old Solutions for New Problems. Current Oncology. 2023 Jan 5;30(1):704-19. https://doi.org/10.3390/curroncol30010055

64. LINKING PHARMGKB TO PHENOTYPE STUDIES AND ANIMAL MODELS OF DISEASE FOR DRUG REPURPOSING [Internet]. [cited 2025 Mar 11]. Available from: https://www.worldscientific.com/doi/epdf/10.1142/9789814366496_0038

65. Gottlieb A, Stein GY, Oron Y, Ruppin E, Sharan R. INDI: a computational framework for inferring drug interactions and their associated recommendations. Molecular Systems Biology. 2012 Jan;8(1):592. https://doi.org/10.1038/msb.2012.26

66. Sridhar D, Fakhraei S, Getoor L. A probabilistic approach for collective similarity-based drug-drug interaction prediction. Bioinformatics. 2016 Oct 15;32(20):3175-82. https://doi.org/10.1093/bioinformatics/btw342

67. Cai L, Chu J, Xu J, Meng Y, Lu C, Tang X, et al. Machine learning for drug repositioning: Recent advances and challenges. Current Research in Chemical Biology. 2023;3:100042. https://doi.org/10.1016/j.crchbi.2023.100042

68. Menden MP, Iorio F, Garnett M, McDermott U, Benes CH, Ballester PJ, et al. Machine Learning Prediction of Cancer Cell Sensitivity to Drugs Based on Genomic and Chemical Properties. Raghava GPS, editor. PLoS ONE. 2013 Apr 30;8(4):e61318. https://doi.org/10.1371/journal.pone.0061318

69. Cousins HC, Nayar G, Altman RB. Computational Approaches to Drug Repurposing: Methods, Challenges, and Opportunities. Annual Review of Biomedical Data Science. 2024 Aug 23;7(1):15-29. https://doi.org/10.1146/annurev-biodatasci-110123-025333

70. Urbina F, Puhl AC, Ekins S. Recent Advances in Drug Repurposing Using Machine Learning. Curr Opin Chem Biol. 2021 Dec;65:74-84. https://doi.org/10.1016/j.cbpa.2021.06.001

71. Wong CH, Siah KW, Lo AW. Estimation of clinical trial success rates and related parameters. Biostatistics. 2019 Apr 1;20(2):273-86. https://doi.org/10.1093/biostatistics/kxx069

72. Fan T, Zhang M, Yang J, Zhu Z, Cao W, Dong C. Therapeutic cancer vaccines: advancements, challenges and prospects. Sig Transduct Target Ther. 2023 Dec 13;8(1):450. https://doi.org/10.1038/s41392-023-01674-3

73. Amin F, Fathi F, Reiner Ž, Banach M, Sahebkar A. The role of statins in lung cancer. Arch Med Sci [Internet]. 2021 Mar 18 [cited 2025 Apr 23]; Available from: https://www.archivesofmedicalscience.com/The-role-of-statins-in-lung-cancer,123225,0,2.html  https://doi.org/10.5114/aoms/123225

74. Effect of Statins on Lung Cancer Molecular Pathways: A Possible Therapeutic Role [Internet]. [cited 2025 Apr 23].  https://doi.org/10.3390/ph15050589

75. Hosseinimehr SJ, Ghasemi F, Flahatgar F, Rahmanian N, Ghasemi A, Asgarian-Omran H. Atorvastatin Sensitizes Breast and Lung Cancer Cells to Ionizing Radiation. Iran J Pharm Res. 2020;19(2):80-8.

76. Chen L, He Y, Huang H, Liao H, Wei W. Selective COX-2 inhibitor celecoxib combined with EGFR-TKI ZD1839 on non-small cell lung cancer cell lines: in vitro toxicity and mechanism study. Med Oncol. 2008 Jun 1;25(2):161-71. https://doi.org/10.1007/s12032-007-9015-1

77. Liu X, Yue P, Zhou Z, Khuri FR, Sun SY. Death Receptor Regulation and Celecoxib-Induced Apoptosis in Human Lung Cancer Cells. JNCI: Journal of the National Cancer Institute. 2004 Dec 1;96(23):1769-80. https://doi.org/10.1093/jnci/djh322

78. Haynes A, Shaik MS, Chatterjee A, Singh M. Formulation and Evaluation of Aerosolized Celecoxib for the Treatment of Lung Cancer. Pharm Res. 2005 Mar 1;22(3):427-39. https://doi.org/10.1007/s11095-004-1881-z

79. Aftab BT, Dobromilskaya I, Liu JO, Rudin CM. Itraconazole Inhibits Angiogenesis and Tumor Growth in Non-Small Cell Lung Cancer. Cancer Research. 2011 Oct 30;71(21):6764-72. https://doi.org/10.1158/0008-5472.CAN-11-0691

80. The effect of itraconazole on the clinical outcomes of patients with advanced non-small cell lung cancer receiving platinum-based chemotherapy: a randomized controlled study | Medical Oncology [Internet]. [cited 2025 Apr 25]. https://link.springer.com/article/10.1007/s12032-021-01475-0 

81. Sanli T, Liu C, Rashid A, Hopmans SN, Tsiani E, Schultz C, et al. Lovastatin Sensitizes Lung Cancer Cells to Ionizing Radiation: Modulation of Molecular Pathways of Radioresistance and Tumor Suppression. Journal of Thoracic Oncology. 2011 Mar 1;6(3):439-50. https://doi.org/10.1097/JTO.0b013e3182049d8b

82. Liang Z, Chen Q, Pan L, She X, Chen T. Mebendazole induces apoptosis and inhibits migration via the reactive oxygen species-mediated STAT3 signaling downregulation in non-small cell lung cancer. J Thorac Dis. 2024 Feb 29;16(2):1412-23. https://doi.org/10.21037/jtd-23-1978

83. Ciaramella V, Sasso FC, Di Liello R, Corte CMD, Barra G, Viscardi G, et al. Activity and molecular targets of pioglitazone via blockade of proliferation, invasiveness and bioenergetics in human NSCLC. J Exp Clin Cancer Res. 2019 Apr 26;38(1):178. https://doi.org/10.1186/s13046-019-1176-1

84. Duarte D, Vale N. Antidepressant Drug Sertraline against Human Cancer Cells. Biomolecules. 2022 Oct;12(10):1513. https://doi.org/10.3390/biom12101513

85. Jarada TN, Rokne JG, Alhajj R. A review of computational drug repositioning: strategies, approaches, opportunities, challenges, and directions. J Cheminform. 2020 Dec;12(1):46. https://doi.org/10.1186/s13321-020-00450-7