1Department of Pharmacy / Ashokrao Mane College of pharmacy, Peth-Vadgaon / Shivaji University 416112, Maharashtra, India.
2Department of Pharmaceutical Chemistry, Assistant Professor / Ashokrao Mane College of pharmacy, Peth-Vadgaon / Shivaji University 416112, Maharashtra, India
A severe type of lung cancer that spreads to other body areas and gets worse over time is called metastatic non-small cell lung cancer NSCLC). Anaplastic lymphoma kinase (ALK) gene mutations are a significant cause of NSCLC. When ALK is altered, it becomes overactive and leads to unchecked cancer cell division. Normally, ALK aids in controlling cell development. One such mutation is G1269A, which modifies the structure of the ALK protein and decreases the efficacy of some medications that target ALK, resulting in drug resistance and the advancement of the disease. It is difficult to find novel medications that can treat the ALK G1269A mutation. Ligand-based drug repurposing, a computer-based method that searches for current medications that might be effective against novel targets, is one helpful tactic. Morgan fingerprint similarity was used in this investigation to choose potential medications. The FDA-approved medications were the focus of this investigation. Structure-based docking is performed using CB-Dock. This study explores the possibility that the antihistaminic drug azelastine may bind to the ALK G1269A mutant protein more effectively than brigatinib. Brigatinib is a targeted cancer medication that inhibits the growth of tumour cells and stops dangerous cancer cells from spreading by inhibiting the activity of the ALK enzyme. Similar to brigatinib, azelastine can bind steadily within the mutant ALK protein's active region, according to computer simulations. Strong contacts, such as hydrogen bonds and hydrophobic interactions, are formed by azelastine with important amino acids surrounding the G1269A mutation site, which aid in stabilising the drug–protein complex. Azelastine may be a promising repurposed medication for treating metastatic non-small cell lung cancer with the ALK G1269A mutation, according to this study. In order to find new therapeutic applications for already available medications, this work presents a novel computational method that combines ligand-based similarity analysis and structure-based docking.
About 85% of all instances of lung cancer worldwide are metastatic non-small cell lung cancer (NSCLC), the most prevalent and deadly kind of the disease.[1] Malignant epithelial cells that start in the lung and move to distant organs like the liver, brain, bone, and adrenal glands are what define it.[2] Due to the lack of early clinical symptoms, the majority of patients with non-small cell lung cancer (NSCLC) are discovered at an advanced or metastatic stage, making it a significant cause of cancer-related mortality.[3] High morbidity and mortality rates are still linked to NSCLC, underscoring the critical need for efficient targeted treatment approaches.[4] Anaplastic lymphoma kinase (ALK), a receptor tyrosine kinase involved in the control of cell proliferation, survival, and metastatic spread, is one of the most important molecular drivers in metastatic non-small cell lung cancer (NSCLC).[5] Chromosome rearrangements, most frequently the EML4–ALK fusion, are the primary cause of constitutive kinase activity and sustained oncogenic signaling in G1269A mutant ALK in NSCLC. [6] When G1269A mutant ALK is activated, downstream signaling pathways such PI3K/AKT, MAPK/ERK, and JAK/STAT are stimulated.[7] These pathways increase tumor growth, improve survival, enable invasion and metastasis, and provide resistance to apoptosis.[8]A specific subset of metastatic NSCLC patients have G1269A mutant ALK rearrangements, which are linked to aggressive disease biology and advanced-stage presentation.[9] However, the identification of the G1269A mutant ALK as a major oncogenic driver has made it a therapeutic target with clinical validation.[10] When compared to traditional chemotherapy, the development of G1269A mutant ALK-targeted tyrosine kinase inhibitors has significantly improved treatment outcomes in metastatic NSCLC, leading to longer progression-free survival, better central nervous system disease control, and an improved overall prognosis.[11] Computed tomography (CT) and positron emission tomography (PET), two modern imaging modalities, usually show large primary tumors with numerous metastatic lesions.[12] Metastatic NSCLC is still mostly incurable, and treatment is frequently palliative, despite advancements in surgery, chemotherapy, radiation, immunotherapy, and targeted medicines.[13] Tumor heterogeneity and genetic evolution often lead to the development of resistance to current treatments.[14] Therefore, the development of disease-modifying medications that target the molecular mechanisms behind tumor growth, metastasis, and therapy resistance is imperative.[15] The main purpose of the FDA-approved anticancer medication brigatinib, a next-generation tyrosine kinase inhibitor, is to prevent abnormal kinase signaling.[16] It has shown substantial anticancer efficacy in advanced lung malignancies and a strong binding affinity toward kinase domains.[17] Brigatinib was chosen as the lead chemical for structure-based drug discovery against G1269A mutant ALK in metastatic NSCLC because of its favorable pharmacokinetic profile and proven clinical safety.[18] We employed a virtual screening technique known as Morgan fingerprint to identify therapy candidates that function similarly to Brigatinib. [19] It is a computer method that examines the structure of molecules to identify hits based on their similarities. This work used CB-Dock, a program that predicts the location of the binding site based on the target's structure, to simulate and measure the mutation. Azelastine emerged from this strategy as a viable option for repurposing. [20] Second-generation histamine H1 receptor antagonists like azelastine are commonly used to treat allergic rhinitis and other allergic diseases. [21] It has a proven track record of clinical use and is well-characterized in terms of its pharmacokinetic profile, safety, and tolerability. [22] Emerging research indicates that azelastine has pleiotropic biological activity beyond its traditional function as an antihistamine, such as modulating inflammatory signaling and calcium homeostasis. [23] Azelastine may have anticancer-relevant effects, including suppression of migration and invasion, activation of apoptosis, and inhibition of tumor cell proliferation, according to recent preclinical research.[24] Interference with signaling pathways, particularly ALK-associated downstream pathways like PI3K/AKT and MAPK, as well as inflammation-driven mechanisms that lead to tumor growth and therapy resistance, is thought to be the cause of these effects.[25] Azelastine is a viable option for therapeutic repurposing in metastatic non-small cell lung cancer (NSCLC) due to its favorable safety profile, oral bioavailability, and ability to modify important signaling networks involved in tumor growth and metastasis.[26] Brigatinib and Azelastin's binding interactions with the protein were assessed in this work using a structure-based drug repurposing method.[27] This work attempts to investigate the viability of Azelastin as a potential alternative or complementary treatment drug targeting G1269A mutant ALK in metastatic NSCLC by comparing its docking performance with that of the well-known lead chemical brigatinib.[28] This study identifies Azelastine as a novel treatment option for metastatic non-small cell lung cancer using a Morgan fingerprint-guided, structure-based medication repurposing approach.[29] The method looks at novel ways to treat a condition that kills NSCLC patients by combining computer chemistry, virtual screening, and docking.[29]
MATERIALS AND METHODS
Ligand-Based Drug Repurposing
The DrugRep platform was used for ligand-based virtual screening. It has an experimental drug library with roughly 5,935 compounds and an FDA-approved medication library with roughly 2,315 compounds. These substances are obtained from DrugBank. [30-31] DrugRep can compare molecules using a variety of techniques, including five universal fingerprinting tools, LigMate, and FitDock.[32] Morgan fingerprints are quick and effective at identifying similar compounds, which is why we selected them.[33] Lead Compound A small-molecule tyrosine kinase inhibitor called brigatinib has been licensed to treat metastatic non-small cell lung cancer (mNSCLC), especially in tumours that have ALK rearrangements. [34] Because of its well-established kinase inhibitory profile, clinical effectiveness, and previous testing in resistant G1269A mutant ALK, brigatinib was chosen as the lead drug. [35] The Morgan Fingerprint Method. Using RDKit, the compounds' structures were transformed into 1,024-bit Morgan fingerprints with a radius of 2. [36] Next, the Tanimoto similarity scores between brigatinib and each of the 87 FDA-approved or investigational substances were calculated. [37] The list was reduced to roughly 20 compounds, which were then examined in greater detail, using a cutoff of ≥ 0.6. [38]
Figure 1: Schematic of ligand-based virtual screening, where input ligands are aligned using various algorithms and searched against diverse drug libraries, with results sorted and visualized.
Docking Studies
CB Dock was used for molecular docking. One tool for blind docking is CB Dock. [39] It uses curvatures to identify cavities in proteins. [40] It creates binding poses and scores them using AutoDock Vina. [41] CB Dock locates potential ligand locations. It forms a box and centre around each location. In every box, it does dock. All stances are then ranked according to how well they bond. [42] The target protein for docking was determined to be the human anaplastic lymphoma kinase (ALK) PDB ID (4ANQ). In metastatic non-small cell lung cancer, it is crucial. [43] Procedure of validation. Two steps comprised the docking process: Binding Site Identification: On the G1269A mutant ALK, CB-Dock identified potential binding sites. [43]
Docking and Scoring: Morgan fingerprint screening was used to locate the compounds. [44]
The binding sites were docked with them. Docking scores and binding postures would be obtained for the compounds. This demonstrated the strength of the ligand-protein bond. [45] Tanimoto similarity coefficients were used to rank the compounds. [46] For docking validation, those above a predetermined threshold (often 0) were chosen. [47] RMSD values and docking scores Docking scores indicate the strength of the binding; a lower value indicates a stronger bond. [48]
RMSD metrics: The accuracy of the docking is assessed using these metrics. [49]
Ligand Binding Energies: The firmness of ligand-protein complexes is assessed using ligand-binding energies. [50] Methods of Statistics Descriptive statistics and correlation analysis were used in the statistical analysis to examine variations in Tanimoto similarity and docking scores. [51] RDKit and Matplotlib were the Python libraries utilised for the analysis and data visualisation. [52]
RESULTS AND DISCUSSION
Results of Ligand-Based Drug Repurposing
Table I: Justification for selecting Brigatinib as the primary ligand for designing new agents targeting metastatic non-small cell lung cancer (mNSCLC):
|
Justification |
Description |
|
Established Clinical Use |
The FDA-approved second-generation tyrosine kinase inhibitor brigatinib is used in clinical settings to treat metastatic non-small cell lung cancer that is positive for ALK. |
|
Mechanism of Action |
Brigatinib suppresses downstream oncogenic signalling pathways such PI3K/AKT, MAPK, and STAT3 by competitively binding to the ATP-binding site of ALK tyrosine kinase. |
|
Potential for Repurposing |
Brigatinib's ability to undergo repurposing outside of ALK-positive mNSCLC is supported by its multi-target kinase inhibition profile, particularly in molecularly heterogeneous tumours. |
|
Existing Pharmacological Data |
In lung cancer models, preclinical and clinical research shows persistent kinase inhibition and a long-lasting therapeutic response. It also has excellent pharmacokinetics and high oral bioavailability. |
|
Analogue Information |
Brigatinib exhibits improved CNS activity and increased effectiveness against ALK resistance mutations, and it shares structural and functional similarities with other ALK inhibitors. |
|
Targeting Comorbidities |
Brigatinib effectively treats intracranial tumours by penetrating the central nervous system, which addresses a significant comorbidity associated to advanced mNSCLC. |
|
Safety Profile |
Brigatinib has a predictable side effect profile and a controlled safety profile. |
|
Opportunity for Novel Therapeutics |
Expanded target indications and precision-based repurposing techniques backed by molecular docking are just a couple of the new therapeutic options made accessible by the multi-kinase inhibitory activity. |
Results of Ligand-Based Screening using the DrugRep platform:
Table II: Binding scores and target interactions of various compounds
|
Rank |
Compound ID |
Name |
Score |
Rank |
Compound ID |
Name |
Score |
|
1 |
DB12267 |
Brigatinib |
1.000 |
11 |
DB11730 |
Ribociclib |
0.294 |
|
2 |
DB12141 |
Gilteritinib |
0.396 |
12 |
DB09073 |
Palbociclib |
0.290 |
|
3 |
DB09063 |
Ceritinib |
0.384 |
13 |
DB09330 |
Osimertinib |
0.290 |
|
4 |
DB11986 |
Entrectinib |
0.309 |
14 |
DB11995 |
Avatrombopag |
0.284 |
|
5 |
DB00619 |
Imatinib |
0.306 |
15 |
DB12500 |
Fedratinib |
0.281 |
|
6 |
DB00408 |
Loxapine |
0.302 |
16 |
DB00276 |
Amsacrine |
0.276 |
|
7 |
DB00363 |
Clozapine |
0.302 |
17 |
DB00590 |
Doxazosin |
0.274 |
|
8 |
DB13256 |
Clothiapine |
0.302 |
18 |
DB00433 |
Prochlorperazine |
0.272 |
|
9 |
DB05294 |
Vandetanib |
0.299 |
19 |
DB06616 |
Bosutinib |
0.271 |
|
10 |
DB00972 |
Azelastine |
0.297 |
20 |
DB11363 |
Alectinib |
0.269 |
Docking studies and Validation process results:
Table III: Validation metrics from PDB-RED
|
Validation Metrics |
Original |
PDB-REDO |
|
Crystallographic Refinement |
||
|
R |
0.1948 |
0.1748 |
|
R-free |
0.2058 |
0.1859 |
|
Bond length RMS Z-score |
0.752 |
0.248 |
|
Bond angle RMS Z-score |
0.771 |
0.605 |
|
Model Quality Raw Scores (percentiles) |
||
|
Ramachandran plot normality |
72 |
82 |
|
Rotamer normality |
86 |
88 |
|
Coarse packing |
N/A |
N/A |
|
Fine packing |
N/A |
N/A |
|
Bump severity |
95 |
95 |
|
Hydrogen bond satisfaction |
N/A |
N/A |
The validation statistics for the original and PDB-REDO models are compared side by side in the table. We see that the PDB-REDO model exhibits enhanced stereochemical and structural quality. Better agreement between the model and experimental data is demonstrated by the crystallographic refinement indicators, which show a decrease in both the R-factor and R-free. Additionally, improved stereochemical geometry is reflected in improved geometric characteristics. With Ramachandran plot normalcy rising from 72 to 82 and rotamer normality marginally improving from 86 to 88, model quality percentile scores provide additional evidence for this improvement. The bump's intensity doesn't change, indicating that both models have comparable levels of steric conflicts. For both structures, parameters including hydrogen bond satisfaction, coarse packing, and fine packing are not accessible (N/A). When compared to the original model, the PDB-REDO-refined structure shows enhanced stereochemical reliability and refinement quality.
Figure 2: Comparative Analysis of Model Quality Metrics: Original vs. PDB-REDO Refinement
Ramachandran plot normality increased from 72 to 82, while rotamer normality slightly improved from 86 to 88, providing additional evidence for the improvement in model quality percentile scores. The bump's intensity doesn't change, indicating that both models have comparable levels of steric conflicts. When compared to the initial model, the PDB-REDO-refined structure generally shows better stereochemical dependability and refinement quality.
Figure 3: Kleywegt-like plot
Figure 3 uses a Kleywegt-like graphic to show the distribution of phi (?) and psi (ψ) dihedral angles for residues inside the protein structure. Red areas in the backdrop colour gradient, which show the largest density of permitted conformational space, represent the most favoured backbone conformations. The original and PDB-REDO model versions' individual residues are superimposed as blue and orange dots, respectively. The degree to which residues fall within preferred regions can be directly evaluated using this visualisation. The comparison demonstrates the PDB-REDO model's enhanced stereochemical quality and refinement accuracy, demonstrating its appropriateness for subsequent molecular docking and drug repurposing research.
Docking results:
Table IV: Binding Affinity Analysis of DrugBank Compounds to Target Pockets: Identification of Potential Drug Candidates
|
Compound Name |
Drug Bank ID |
Best Vina Sore |
Cavity |
Compound Name |
Drug Bank ID |
Best Vina Score |
Cavity |
|
Brigatinib |
DB12267 |
-8.0 |
367 |
Ribociclib |
DB11730 |
-8.9 |
2707 |
|
Gilteritinib |
DB12141 |
-7.8 |
2707 |
Palbociclib |
DB09073 |
-8.8 |
367 |
|
Ceritinib |
DB09063 |
-8.4 |
367 |
Osimertinib |
DB09330 |
-8.5 |
2707 |
|
Entrectinib |
DB11986 |
-10.0 |
2707 |
Avatrombopag |
DB11995 |
-7.8 |
2707 |
|
Imatinib |
DB00619 |
-10.0 |
2707 |
Fedratinib |
DB12500 |
-9.4 |
2707 |
|
Loxapine |
DB00408 |
-10.1 |
2707 |
Amsacrine |
DB00276 |
-10.2 |
2707 |
|
Clozapine |
DB00363 |
-10.3 |
2707 |
Doxazosin |
DB00590 |
-9.8 |
2707 |
|
Clothiapine |
DB13256 |
-9.9 |
2707 |
Prochlorperazine |
DB00433 |
-8.9 |
2707 |
|
Vandetanib |
DB05294 |
-9.5 |
2707 |
Bosutinib |
DB06616 |
-8.9 |
2707 |
|
Azelastine |
DB00972 |
-12.1 |
2707 |
Alectinib |
DB11363 |
-8.7 |
367 |
The docking investigation identified azelastine (DB00972), clozapine (DB00363), and amsacrine (DB00276) as the top candidates with strong binding affinities (Best Vina scores of -12.1, -10.3, and -10.2, respectively throughout Cavity volume (Å3) 2707). Overall, these compounds may be good candidates for additional research in drug repurposing studies due to their excellent binding characteristics across several locations.
Figure 4: Visualization of five binding pockets (Cur Pocket IDs C1–C5) on the target protein
Figure 4 illustrates the structural diversity of potential ligand-binding sites on the target protein by showing five distinct Cur Pocket binding sites with cavity sizes ranging from 2707 ų to 110 ų. This helps identify key sites for docking studies to investigate the binding efficiency of azelastine and its analogs and to enhance their potential for repurposing for metastatic non-small cell lung cancer through improved drug-target interactions.
Figure 5: Molecular Docking of Azelastine with G1269A anaplastic lymphoma kinase (ALK) mutant: Key Binding Interactions for mNSCLC Treatment
The binding mechanism of azelastine within the active region of the G1269A mutant anaplastic lymphoma kinase (ALK), a clinically significant mutation linked to metastatic non-small cell lung cancer, is depicted in Figure 5. ALK mutations cause constitutive kinase activation, which promotes tumor growth and treatment resistance. Strong binding affinity for the mutant ALK active site was shown by the small-molecule antihistamine azelastine, indicating its potential for therapeutic repurposing. Favorable accommodation inside the mutant kinase domain is shown by the compound's occupancy of a crucial area of the ATP-binding pocket that is structurally changed by the G1269A mutation. Hydrogen bonding, hydrophobic interactions, and electrostatic contacts all contribute to the stability of the Azelastine–ALK G1269A complex. In the hinge region, a hydrogen bond is seen between Azelastine's polar functional groups and adjacent residues like GLU or ASP, which helps to anchor the ligand. Stabilization impacted by the G1269A alteration is suggested by more hydrogen bonding interactions with the backbone atoms of residues like GLY or ALA close to the mutation location. The aromatic and aliphatic portions of azelastine are contacted by non-polar residues such as LEU, ILE, VAL, and ALA, which contribute significantly to hydrophobic interactions and ligand retention. The ligand is kept in a stable docking orientation by these interactions. Additionally, Azelastine's protonated amine group clearly interacts electrostatically with negatively charged residues like GLU or ASP to increase binding affinity within the kinase pocket. Azelastine can be well accommodated in the mutant ALK active site due to the binding site's overall spatial and charge complementarity. Azelastine's potential as a repurposed treatment candidate for metastatic non-small cell lung cancer is supported by these interactions, which collectively show that it fits well within the G1269A mutant ALK pocket and may stabilize the protein in a conformation unfavorable for kinase activity.
Molecular Dynamics Simulation Result:
The Azelastine–G1269A Mutant ALK complex's shape change over time in a realistic bodily environment is revealed by the Molecular Dynamics simulation.
Figure 6: Deformability Analysis of Azelastine–G1269A Mutant ALK Complex
According to the deformability plot, the majority of the protein has poor deformability values, and just a few small areas are flexible. Since most of the residues stay below 0.6, the protein structure is generally quite stable and rigid. The graph displays a few prominent peaks around atom indices 100, 200, and 240–250, suggesting localized flexibility in the areas surrounding these indices. These flexible areas are small and probably relate to surface or loop areas rather than the protein's core. There is no unusual movement shown on the graph. The location of the mutation G1269A does not exhibit any unusual movement on the graph. This suggests that the protein's structural stability when linked to azelastine is not negatively impacted by this mutation. Azelastine can function as a stable ligand as a result.
Figure 7. B-Factor Comparison of the Azelastine–G1269A Mutant ALK Complex
The Normal Mode Analysis (red) and the PDB profile exhibit a strong match in the B-factor plot, suggesting that both approaches capture a similar flexibility pattern along the structure. Consistent dynamic behaviour is suggested by the good alignment of peaks and troughs across the atom index. The structural stability of the Azelastine-G1269A mutant ALK complex is supported by this close match.
Figure 8. Dynamic Cross-Correlation Matrix (DCCM) Analysis and Residue Contact Map
The Azelastine–G1269A Mutant ALK complex's residue pairs can be seen moving in tandem or in opposition to one another in the DCCM plot. Residues are traveling together in red spots. Residues migrating in opposing directions are indicated by blue patches. The movement of each residue is displayed on the main diagonal. The way the proteins move together or apart is depicted by this pattern. It displays which portions are more mobile and which are more stable. This can assist in locating the components required to maintain the structure's strength and cling to the ligand. The Azelastine–G1269A Mutant ALK complex's contact map displays the proximity of the residue pairs. Darker areas indicate that the atoms are in close proximity to one another. There is less touch with lighter pieces. These correlations imply consistent communication between essential structural areas that are crucial for ligand binding. Tight packing that maintains the complex's structural stability is confirmed by the contact map, which also emphasizes intensively interacting residues (darker areas).
Pharmacokinetic Profile and Safety Predictions for Azelastine
Table V: ADMET and Physicochemical Profile of Azelastine
|
Category |
Property |
Value / Prediction |
|
Physicochemical |
Molecular Weight (MW) |
381.90 g/mol |
|
TPSA |
38.13 Ų |
|
|
logP |
4.281 (lipophilic) |
|
|
logS |
-4.872 (low solubility) |
|
|
logD |
3.856 |
|
|
H-bond Donors / Acceptors |
0 / 3 |
|
|
Rotatable Bonds |
3 |
|
|
Flexibility |
0.120 |
|
|
Stereocenters |
1 |
|
|
Medicinal Chemistry |
Lipinski / Pfizer / GSK / Golden Triangle |
Accepted / Rejected / Rejected / Accepted |
|
QED |
0.679 (good drug likeness) |
|
|
SAscore |
2.673 (easy to synthesize) |
|
|
Fsp³ |
0.364 |
|
|
NPscore / MCE-18 |
-1.065 / 71.400 |
|
|
PAINS / ALARM NMR / BMS Alerts |
0 / 0 / 0 |
|
|
Absorption |
Caco-2 / MDCK Permeability |
–4.875 / 1.3 × 10?? (High) |
|
P-gp Interaction |
Substrate (++), not an inhibitor |
|
|
HIA / Bioavailability |
High / Positive |
|
|
Distribution |
Plasma Protein Binding (PPB) |
93.953% |
|
Fraction Unbound (Fu) |
4.530% |
|
|
Volume of Distribution (VD) |
2.936 L/kg |
|
|
Metabolism |
CYP Interactions |
Substrate (except CYP2C9) /inhibitor (except CYP1A2, CYP2C9) |
|
Excretion |
Clearance (CL) |
10.442 mL/min/kg |
|
Half-life (T?/?) |
~22 hours |
|
|
Toxicity |
AMES / hERG / H-HT / DILI |
-- / +++ / - / ++ |
|
Eye Irritation |
--- |
|
|
Skin Sensitization Alerts |
+ |
|
|
Tox21 Pathways |
SR-ARE |
+ |
|
Other Pathways |
No significant activation |
|
|
Environmental Tox |
BCF / IGC50 / LC50FM / LC50DM |
1.227 / 4.072 / 4.456 / 4.580 |
Azelastine had favourable pharmacokinetic and physicochemical characteristics. The compound exhibits good membrane permeability due to its low polar surface area (TPSA 38.13 Ų) and moderate molecular weight (381.90 g/mol). Its low water solubility (logS −4.872) and lipophilic character (logP 4.281) are compatible with its comparatively high dispersion potential (logD 3.856). Azelastine exhibits good overall drug-likeness (QED 0.679) and fulfils important drug-likeness filters, such as Lipinski, Pfizer, GSK, and Golden Triangle guidelines. The chemical appears to be simple to synthesis based on the synthetic accessibility score (SAscore 2.673). Despite being projected to be a P-glycoprotein substrate rather than an inhibitor, azelastine exhibits strong Caco-2 and MDCK permeability, indicating efficient intestine absorption. It is anticipated that oral bioavailability and intestinal absorption will be high in humans. Strong plasma protein binding (93.953%) with a low unbound fraction (4.53%) and a comparatively large volume of distribution (2.936 L/kg) was revealed by distribution analysis, indicating widespread tissue penetration. Azelastine functions as a CYP substrate and inhibitor for certain isoforms, according to metabolism projections, indicating potential but controllable drug–drug interaction hazards. The molecule has a comparatively long half-life of around 22 hours and a moderate clearance (10.442 mL/min/kg). According to toxicity prediction, azelastine has no substantial hepatotoxicity or drug-induced liver injury alarms, is non-mutagenic in the AMES test, and has a mild hERG risk. There is little chance of eye discomfort and a mild danger for skin sensitivity. The SR-ARE pathway is activated, according to Tox21 pathway analysis, but other pathways show no discernible activity. Predictions of environmental toxicity indicate a low to moderate risk of water toxicity and bioaccumulation. All things considered, Azelastine's ADMET profile validates its eligibility as a repurposable medication option for more research in metastatic non-small cell lung cancer.
Figure 9: BOILED-Egg model illustrating gastrointestinal absorption (HIA) and blood–brain barrier (BBB) penetration of Azelastine based on WLOGP and TPSA parameters.
The SwissADME BOILED-Egg model was used to assess the pharmacokinetic profile of azelastine. Azelastine is found in the yellow area (yolk) of the BOILED-Egg plot, as the figure illustrates. According to this prediction, there is a high likelihood that the molecule will penetrate the blood–brain barrier (BBB) and show good passive gastrointestinal absorption. Azelastine's low polar surface area and comparatively high lipophilicity, which promote passive diffusion across biological membranes, are reflected in its location inside the yolk. Azelastine does not require specific transporters for absorption, in contrast to peptide-based or metal-chelated substances. Rather, in line with its physicochemical characteristics, passive permeability plays a major role in its pharmacokinetic behavior. Azelastine's capacity to effectively penetrate systemic circulation and reach intracellular targets, such as mutant ALK, is thus supported by the BOILED-Egg prediction. This is significant for its possible repurposing in metastatic non-small cell lung cancer.
CONCLUSION
This study demonstrates how medications that target the anaplastic lymphoma kinase (ALK) present in metastatic non-small cell lung cancer can be detected using a ligand-based virtual screen. First, we selected Brigatinib as a lead chemical because of its proven effectiveness as a tyrosine kinase inhibitor and its strong action against a wide range of G1269A mutant ALK mutations linked to tumour growth and treatment resistance. To identify medications that work well with brigatinib and the G1269A mutant ALK mutation, we examine a wide range of medications. This enables us to identify possible new applications for outdated medications. We discover that azelastine binds to the G1269 mutant ALK with the best match and stability. Because of this, azelastine is a viable option for treating metastatic non-small cell lung cancer (NSCLC). Azelastine interacts with important amino acid residues in the G1269A mutant ALK ATP-binding pocket and catalytic region, according to molecular docking research, indicating that it may disrupt downstream oncogenic signalling cascades and G1269A mutant ALK-mediated phosphorylation. These interactions suggest that azelastine may have previously unknown inhibitory efficacy against ALK-driven signalling in metastatic NSCLC, despite being initially licensed as an antihistaminic medication. The structural stability of the azelastine–G1269A mutant ALK complex under physiological conditions was further confirmed by molecular dynamics simulations, which demonstrated persistent key interactions and sustained conformational stability within the G1269A mutant ALK kinase domain during the simulation period. Azelastine's potential to act as an ALK pathway modulator and its suitability for therapeutic repurposing in G1269A mutant ALK-positive metastatic NSCLC are both supported by the observed stability. This computational method demonstrates the value of molecular modelling and ligand-based virtual screening in finding repurposed medication candidates for metastatic non-small cell lung cancer. Repurposing drugs to target G1269A mutant ALK is a viable approach to address therapy failure and resistance. Nevertheless, additional experimental validation is necessary to validate these results, such as in vivo effectiveness studies and in vitro ALK kinase assays. All things considered, this work highlights how crucial integrative computational tools are to speeding up precision oncology-based drug development and repurposing initiatives.
REFERENCE
Sakshi Patil*, Amisha Jamir Mulla, Tejashree Khamkar, Ligand- and Structure-Based Drug Repurposing by Morgan Fingerprint: Reveals Azelastine as a Candidate Therapy for G1269A Mutant ALK in Metastatic NSCLC, Int. J. Sci. R. Tech., 2026, 3 (1), 338-351. https://doi.org/10.5281/zenodo.18411421
10.5281/zenodo.18411421
