Computational discovery of lead compounds targeting G1202R-based ALK triple mutations conferring lorlatinib resistance in EML4-ALK variant 3 non-small cell lung cancer

1. INTRODUCTION

Lung cancer continues to be the foremost cause of mortality that affects both genders, with approximately 2.5 million diagnoses and 1.8 million deaths worldwide. It is the most commonly diagnosed cancer in men, while it is the second most prevalent in women [1]. It is subdivided into small cell lung cancer (SCLC) and non-small cell lung cancer (NSCLC) [2]. Out of these two, NSCLC is the most prevalent lung cancer, which accounts for 85%, while SCLC accounts for 15% [3]. The cause of lung cancer varies, but the primary risk factor is considered to be smoking, which accounts for 80% of lung cancers [4]. Non-smokers also develop lung cancer due to various other reasons, including genetics, exposure to certain chemical substances or cancer-causing agents (arsenic, chromium, nickel, asbestos, and so on) [5,6]. The most frequent driver mutations identified in NSCLC are the epidermal growth factor receptor (EGFR), Kirsten rat sarcoma virus (KRAS), and ALK genes [7]. Chromosomal rearrangements of ALK are found in 3%–5% of all NSCLC cases and produce oncogenic fusion proteins [8]. The most common observed genomic alteration is the EML4-ALK gene fusion. The ALK rearrangement in NSCLC was first identified in 2007 in a cohort of NSCLC patients in Japan. Due to chromosomal inversion within chromosome 2, the EML4 (echinoderm microtubule-associated protein-like 4) gene fuses with ALK, resulting in the EML4-ALK protein [9,10]. In ALK fusions, the C-terminal is encoded by ALK, and the N-terminal is usually encoded by the partner gene (often EML4) [10,11]. This fusion of EML4-ALK results in oncogenic signaling and promotes cell growth, proliferation, and survival [12]. The EML4 activates ALK, which in turn activates various downstream signaling pathways, including the RAS/RAF/MEK/ERK, PI3K/AKT/mTOR, and JAK/STAT pathways [13,14]. Numerous variants of EML4-ALK have been identified, with the ALK kinase domain being a consistent feature across all variants. These variants arise due to truncation at different points within the EML4 region and result in 15 distinct types, including V1 (33%), V2 (10%), V3a/b (29%), V4, V5a/b, and V6. Among these, variants 1 and 3 are the most common in NSCLC patients, with variant 3 linked to high tumor aggressiveness and limited efficacy against TKIs [10,15,16].

Various tyrosine kinase inhibitors (TKIs) have been used to inhibit the kinase activity of ALK. These include crizotinib (first-generation), ceritinib, brigatinib, alectinib (second-generation), and lorlatinib (third-generation), which are approved by the FDA for the treatment of ALK+ NSCLC [17,18]. Even though these ALK-TKIs have been effective in treating advanced ALK+ NSCLC patients, resistance inevitably develops within a few years due to resistance mechanisms, including ALK-dependent (on-target) and ALK-independent (off-target) mechanisms [19,20]. ALK-dependent resistance emerges from on-target mechanisms, including single point mutations or compound (double or triple) mutations, which impair TKIs’ binding to the ALK pocket [21]. ALK-independent or “off-target” mechanisms triggered by EGFR activation [22], KIT activation [23], HER activation [24], SRC activation [25], MET amplification [26], MAPK pathway [27], JAK/STAT pathway [28], and PI3K/AKT pathway [29]. Crizotinib, a tyrosine kinase inhibitor, was the first approved inhibitor for ALK, as it inhibits the ALK kinase function, and was approved by the FDA in 2011. Subsequently, it also received approval for use as a first-line treatment (1L) for ALK+ NSCLC; however, patients developed resistance within a year or more [30]. The patients developed resistant mutations such as G1269A, C1156Y, and L1196M, accounting for 30% during crizotinib therapy [31,32]. To combat this resistance, second-generation drugs such as ceritinib, brigatinib, and alectinib were developed [33]. These drugs showed better clinical activity than crizotinib and effectively targeted G1269A and L1196M mutations [20]. Though the second-generation TKIs were effective, resistance inevitably developed within a few years due to acquired mutations, predominantly the G1202R mutation, which accounts for 50% of on-target resistance. This mutation was identified after the administration of all second-generation TKIs and was found to be the predominant resistance mutation [31,32,34]. Many studies have reported that patients subsequently develop resistance against all ALK-TKIs [35].

Lorlatinib, an ATP-competitive, brain-penetrant third-generation TKI, is approved for use in patients who have developed resistance to earlier TKIs [36,37]. The solvent-front mutation (G1202R) is found at the front of the ATP-binding region, which reduces the binding of TKIs and is responsible for the resistance against 2G TKIs that are strongly inhibited by lorlatinib [38]. Despite effective activity against resistance mutations, subsequent treatment with 1G and 2G TKIs, followed by lorlatinib, limited its efficacy and resulted in acquired compound mutations [37,39,40]. Mutations such as L1196M, G1202R, D1203N, F1174C/L, and I1171N were identified in the patients after lorlatinib treatment [39]. These mutations are detected as either single or compound mutations and confer resistance to lorlatinib as well as to other ALK-TKIs [37,41]. The 5-year results from the phase III CROWN trial revealed that using lorlatinib as the first-line therapy improved the median progression-free survival (PFS) over crizotinib in patients with advanced ALK+ NSCLC [42]. However, circulating tumor DNA analysis from the same trial revealed that the patients who received lorlatinib as the first-line therapy did not harbor ALK mutations. This indicates ALK-independent resistance, mediated by bypass signaling of various pathways, including EGFR, MAPK, JAK/STAT, or MET, which are not targeted by current ALK-TKIs [43-45]. Even though the recent CROWN trial demonstrated a higher PFS with lorlatinib (1L), targeting and inhibiting bypass signaling pathways remains challenging. When lorlatinib is used sequentially, compound mutations emerge due to drug pressure, conferring a higher level of resistance to TKI therapies [46]. Fourth-generation (4G) ALK-TKIs, such as TPX-0131 and NVL-655, have been developed to inhibit compound mutations that are resistant to lorlatinib [47]. TPX-0131, a brain-penetrant molecule, was explicitly designed and intended to fit into the ATP-binding site and to overcome solvent-front mutations (G1202R) and gatekeeper mutations (L1196M). Although it demonstrated significant activity against all resistance mutations in preclinical studies, its clinical study was terminated [48]. NVL-655, another 4G TKI that binds to the ATP-binding pocket and is active against compound mutations, as well as other less common single resistance mutations [49]. These drugs have shown great potential in combating resistance mutations in preclinical studies; however, safety evaluations and other measures are crucial for validating the drug [50]. All-generation ALK-TKIs have shown positive outcomes in ALK+ NSCLC patients. Nevertheless, on-target mutations can arise post-treatment and pose a significant challenge in the form of single and compound mutations (double or triple). Lorlatinib exhibited high efficiency in treating ALK patients; however, it has now become resistant, creating an unmet clinical need to develop next-generation inhibitors to combat the lorlatinib-resistant compound mutations, particularly double and triple mutations [31,51].

Previously, several in silico studies employing molecular dynamics (MD) simulations and binding free energy calculations were reported to provide insights into the mechanism of drug resistance conferred by ALK single and double mutants against different TKIs. These computational studies investigated several single and double mutations such as C1156Y [52], I1171N/S/T[53], F1174C/L/V [52,54–56], L1196M [57], L1198F [58,59], G1202R [58,60,61], G1269A [57], S1206C [52], C1156Y/F1198F [52], I1171N/F1174I [62], G1202R/L1196M, G1202R/L1198F and I1171N/L1198F [63], and E1210L/S1206C [52]. Collectively, these studies revealed how these ALK mutations in the ALK binding pocket affect the TKI binding and confer resistance to various TKIs, including crizotinib, ceritinib, alectinib, brigatinib, and lorlatinib. In the era of personalized medicine, therapeutic approaches in cancer have evolved toward tailoring treatments based on specific biomarkers, genetic alterations, and molecular subtypes, leading to improved clinical outcomes [64]. However, to date, there has not been any prior in silico study focused on ALK triple mutations aimed at identifying inhibitors against them. This lack of in silico investigation represents a significant gap that needs to be addressed, as it can play a critical role in the early stages of the drug discovery process and contribute to advancements in personalized medicine. To address this gap, the present work focuses on the two triple mutations (G1202R+L1204V+G1269A and G1202R+S1206F+G1269A) that were detected in EML4-ALK V3 patients using next-generation sequencing [37,40]. These two patients acquired these compound triple mutations after receiving the sequential treatment with 1G, 2G, and lorlatinib. In this work, we aim to screen these triple mutations and further identify potential lead compounds using various computational pipelines, including virtual screening, ADMET predictions, molecular docking, and MD simulations. The graphical abstract of the study’s workflow is shown in Figure 1.

Figure 1. The graphical abstract of the study’s workflow. [Click here to view]

2. MATERIALS AND METHODS

3. RESULTS

3.4. Molecular docking

Lorlatinib, TPX-0131, CID 118003002, and CID 145224731 were docked with WT-ALK and triple mutations (G1202R+L1204V+G1269A and G1202R+S1206F+G1269A) using the default parameters of AutoDock Tools in three repeats. WT-CID 118003002 exhibited the highest average binding energy of –7.97 kcal/mol, and the interacting residues were LEU1122, VAL1180, MET1199, GLY1202, ARG1253, ASN1254, LEU1256, and GLY1269. Following this, WT-Lorlatinib had an average binding energy of –7.68 kcal/mol, with interacting residues including LEU1122, VAL1130, ALA1148, LYS1150, GLU1197, MET1199, LEU1256, and GLY1269. WT-TPX-0131 had an average binding energy of –7.44 kcal/mol, and its interacting residues were LEU1122, VAL1130, ALA1148, LYS1150, LEU1196, GLU1197, MET1199, ARG1253, ASN1254, LEU1256, ALA1269, and ASP1270. WT-CID 145224731 exhibited an average binding energy of –6.85 kcal/mol, and its interacting residues were VAL1130, ALA1148, LYS1150, LEU1196, GLU1197, MET1199, LEU1256, and ALA1269. In triple mutation 1, TM1-CID 118003002 exhibited the highest average binding energy of –8.02 kcal/mol, and its interacting residues were LEU1122, ALA1126, VAL1130, ALA1148, GLY1197, MET1199, ARG1202, LEU1256, and ALA1269. TM1-Lorlatinib had an average binding energy of –7.69 kcal/mol, and its interacting residues were LEU1122, ALA1126, VAL1130, ALA1148, LYS1150, GLU1167, MET1199, LEU1256, ALA1269, and ASP1270. TM1-TPX-0131 had an average binding energy of –7.40 kcal/mol, and its interacting residues were LEU1122, VAL1130, ALA1148, LEU1196, MET1199, ARG1202, ASN1254, LEU1256, and ALA1269. TM1-CID 145224731 had an average binding energy of –7.26 kcal/mol, and its interacting residues were LEU1122, VAL1130, ALA1148, LYS1150, LEU1196, MET1199, ARG1202, LEU1256, ALA1269, and ASP1270. Similarly, in triple mutation 2, TM2-CID 145224731 exhibited the highest average binding energy of –8.49 kcal/mol, and its interacting residues were PHE1127, VAL1130, LYS1150, GLY1167, LEU1196, ARG1202, ARG1253, LEU1256, and ASP1270. TM2-TPX-0131 had an average binding energy of –8.43 kcal/mol, and its interacting residues were LEU1122, VAL1130, ALA1148, LYS1150, GLU1167, LEU1196, GLY1197, MET1199, ARG1202, ARG1253, LEU1256, ALA1269, and ASP1270. TM2-CID 118003002 had an average binding energy of –7.18 kcal/mol, and its interacting residues were LEU1122, VAL1130, ALA1148, LYS1150, LEU1196, GLY1197, MET1199, ARG1202, LEU1256, and ALA1269. TM2-Lorlatinib had an average binding energy of –7.13 kcal/mol, and its interacting residues were LEU1122, VAL1130, ALA1148, LYS1150, LEU1196, GLY1197, MET1199, ARG1202, LEU1256, and ALA1269. The average binding affinities of the protein-ligand complexes are reported in Table 1, and their 2D interactions were visualized using the Discovery Studio (Figs. 2–4).

Figure 2. Two-dimensional structural representation of the molecular interactions of Wild-Type ALK (WT-ALK) Protein with the ligands. A) WT with Lorlatinib, B) WT with CID 118003002, C) WT with TPX-0131, and D) WT with CID 145224731. [Click here to view]

Figure 3. Two-dimensional structural representation of the molecular interactions of ALK Triple Mutation 1 (TM1 - G1202R+L1204V+G1269A) with the ligands. A) TM1 with Lorlatinib, B) TM1 with CID 118003002, C) TM1 with TPX-0131, and D) TM1 with CID 145224731. [Click here to view]

Figure 4. Two-dimensional structural representation of the molecular interactions of ALK Triple Mutation 2 (TM2 - G1202R+S1206F+G1269A) with the ligands. A) TM2 with Lorlatinib, B) TM2 with CID 118003002, C) TM2 with TPX-0131, and D) TM2 with CID 145224731. [Click here to view]

Table 1. Binding affinities, hydrogen-bond interactions, and molecular dynamics parameters (RMSD, RMSF, Rg, SASA)
of WT-ALK and ALK triple mutations with their ligands.

Mutations	Ligands (PubChem ID)	Binding Affinity (kcal/mol)	H-Bonds	RMSD	RMSF	Rg	SASA
WT-ALK	Apo	–	–	0.294 ± 0.067	0.125 ± 0.101	2.047 ± 0.013	158.661 ± 2.640
	Lorlatinib (71731823)	–7.68	1 (GLY A:1197)	0.220 ± 0.037	0.101 ± 0.059	2.034 ± 0.012	154.636 ± 2.492
	CID 118003002	–7.97	2 (MET A:1199, GLY A:1269)	0.220 ± 0.037	0.098 ± 0.071	2.032 ± 0.009	154.560 ± 2.309
	TPX-0131 (156024486)	–7.44	2 (LYS A:1150, MET A:1199)	0.225 ± 0.026	0.103 ± 0.064	2.045 ± 0.013	155.128 ± 2.558
	CID 145224731	–6.85	1 (MET A:1199)	0.320 ± 0.057	0.130 ± 0.083	2.049 ± 0.014	159.281 ± 3.127
TM1	Apo	–	–	0.290 ± 0.033	0.116 ± 0.079	2.053 ± 0.016	156.667 ± 3.093
	Lorlatinib (71731823)	–7.69	2 (GLU A:1167, ASP A:1270)	0.260 ± 0.031	0.109 ± 0.063	2.045 ± 0.015	153.826 ± 3.039
	CID 118003002	–8.02	3 (LEU A:1122, ARG A:1202)	0.216 ± 0.025	0.108 ± 0.070	2.021 ± 0.018	153.818 ± 3.703
	TPX-0131 (156024486)	–7.40	1 (MET A:1199)	0.278 ± 0.041	0.116 ± 0.066	2.052 ± 0.016	155.067 ± 2.895
	CID 145224731	–7.26	1 (MET A:1199)	0.321 ± 0.037	0.118 ± 0.075	2.059 ± 0.012	157.443 ± 2.478
TM2	Apo	–	–	0.305 ± 0.044	0.127 ± 0.066	2.071 ± 0.015	158.309 ± 3.211
	Lorlatinib (71731823)	–7.13	1 (GLY A:1197)	0.294 ± 0.034	0.122 ± 0.076	2.068 ± 0.013	157.521 ± 2.567
	CID 118003002	–7.18	1 (GLY A:1197)	0.260 ± 0.017	0.112 ± 0.063	2.064 ± 0.014	157.273 ± 2.466
	TPX-0131 (156024486)	–8.43	1 (MET A:1199)	0.256 ± 0.020	0.110 ± 0.059	2.062 ± 0.011	157.074 ± 2.606
	CID 145224731	–8.49	2 (LYS A:1150, ARG A:1253)	0.251 ± 0.023	0.108 ± 0.062	2.061 ± 0.015	156.490 ± 2.616

3.5. Molecular dynamics simulation

MD simulations were performed for WT-ALK and ALK with triple mutations (G1202R+L1204V+G1269A and G1202R+S1206F+G1269A) to evaluate their structural stability, compactness, and intermolecular hydrogen bonding. A total of 15 MD simulation runs were conducted for 100 ns, including 3 Apo’s and 12 complexes.

For the Wild-Type ALK protein complexes with ligands, the RMSD profiles reveal that the conformational stability of the WT-Apo protein exhibits relatively higher deviations, stabilizing around ~0.35 nm after approximately 30 ns. WT in complex with CID 145224731 exhibited the maximum RMSD (~0.42 nm), demonstrating increased structural deviation. Conversely, WT with Lorlatinib and CID 118003002 exhibited lower RMSD values, averaging ~0.23 nm and 0.22 nm, respectively, indicating enhanced structural stability. WT-TPX-0131 complex maintained intermediate stability (~0.26 nm), suggesting that the CID 118003002 complex confers better structural conservation compared to other complexes (Fig. 5A). For the RMSF analysis of the Wild-Type protein complexes with the ligands, it was revealed that most complexes possess similar overall fluctuation profiles, with distinct peaks between residues 1140–1160 and 1280–1300. These regions likely correspond to loop or unstructured segments. Notably, the TPX-0131 complex exhibited the highest local flexibility near residue 1290 (~0.75 nm), whereas the Apo and CID 145224731 complexes showed more moderate fluctuations across these residues. WT-CID 118003002 had reduced fluctuations, implying tighter ligand-induced residue stabilization in key regions (Fig. 5B). From the Rg plots, we observed that all the systems maintained consistent Rg values between ~2.00 and 2.10 nm, suggesting that the overall protein compactness was preserved. WT with CID 118003002 showed the lowest and most consistent Rg (~2.02 nm), indicating a compact and stable structure. In contrast, the Apo form and CID 145224731 displayed greater Rg fluctuations, suggesting increased structural flexibility or partial unfolding. Lorlatinib and TPX-0131 complexes exhibited intermediate behavior, consistent with moderate structural tightening (Fig. 5C). From the intermolecular hydrogen bonding analysis, the CID 118003002 demonstrated sustained formation of 3–4 hydrogen bonds from 70 ns onwards, indicating strong and stable interactions. Lorlatinib and TPX-0131 exhibited periodic hydrogen bonding with peaks around 2–3 bonds. In contrast, CID 145224731 demonstrated minimal interactions (Fig. 5D). These findings indicate stronger and more stable binding of CID 118003002, correlating with the lower RMSD and Rg values. From the SASA plot, the WT with CID 118003002 and lorlatinib showed the lowest average SASA values (~152–155 nm²), reflecting reduced surface exposure likely due to ligand-induced folding. The WT-Apo protein and CID 145224731 complex had slightly elevated SASA (~160–165 nm²), indicative of more solvent-accessible and potentially flexible structures. The TPX-0131 complex exhibited variable SASA values, but generally trended lower, consistent with its compactness observed in the Rg analysis (Fig. 5E).

Figure 5. Comparative molecular dynamics simulation analyses of Wild-Type ALK (WT-ALK) in Apo form and in complex with various ligands over the simulation period of 100 ns. (A) Root Mean Square Deviation (RMSD) plots showing backbone stability, where lower and converged RMSD values indicate greater structural stability of the complexes. (B) Root Mean Square Fluctuation (RMSF) profiles of WT-ALK highlighting residue-level flexibility, with lower fluctuations reflecting reduced local flexibility upon ligand binding. (C) Radius of gyration (Rg) analysis depicting overall protein compactness, where minor fluctuations in Rg values suggest higher structural integrity. (D) Hydrogen bond interaction patterns between WT-ALK and the ligands, highlighting the stability and persistence of binding interactions during the simulation. (E) Solvent-Accessible Surface Area (SASA) plots illustrating changes in protein surface exposure upon ligand binding in WT-ALK, with reduced SASA values indicating enhanced protein compactness. Color scheme: WT-Apo (orange), WT-Lorlatinib (magenta), WT-CID 118003002 (indigo), WT-TPX-0131 (green), WT-CID 145224731 (blue). [Click here to view]

For the ALK with TM1 protein, in the presence of ligands, the TM1 Apo protein and the TM1-CID 145224731 complex exhibited the highest RMSD values (~0.35–0.38 nm), indicating greater conformational deviations during simulation. The TM1-Lorlatinib and TM1-TPX-0131 complexes exhibited more stable trajectories, with RMSD values stabilizing around 0.25–0.28 nm. Notably, the TM1-CID 118003002 complex demonstrated the lowest average RMSD (~0.22 nm), indicating strong conformational stability upon binding (Fig. 6A). From the RMSF analysis, significant peaks in flexibility were observed around residues ~1145 and ~1290. Among these, the Apo TM1 protein showed the highest fluctuation at residue 1145 (~0.75 nm), suggesting increased mobility in this region. In contrast, the CID 118003002 and TPX-0131 complexes showed reduced fluctuations, indicating enhanced rigidity upon ligand binding. Lorlatinib and CID 145224731 bound forms maintained intermediate fluctuation (Fig. 6B). From the radius of gyration, the TM1-CID 118003002 complex maintained the most compact structure with Rg values consistently below ~2.05 nm. The lorlatinib-bound TM1 and Apo form displayed slightly higher values (~2.05–2.07 nm). At the same time, TPX-0131 and CID 145224731 showed increased Rg fluctuations (~2.10 nm), indicating less structural compactness and possible expansion (Fig. 6C). These findings align with the RMSD results and support enhanced structural integrity of the CID 118003002 complex. From the analysis of the number of intramolecular hydrogen bond formations, the lorlatinib complex exhibited the highest number of hydrogen bonds (3–5) in the early simulation period (0–30 ns), while CID 118003002 displayed consistent hydrogen bonding throughout, especially after 60 ns (3–4 bonds), suggesting sustained and stable interaction. The CID 145224731 complex and TPX-0131 showed fewer and less persistent H-bonds, typically 1–2 bonds (Fig. 6D). From the SASA plots, the TM1-CID 118003002 complex and the TM1-Lorlatinib complex exhibited the lowest SASA values (~150–155 nm²), indicating tighter packing and reduced solvent exposure. In contrast, the Apo form and TPX-0131 complex had slightly elevated SASA values (~160–165 nm²), indicating a more open structure. CID 145224731 showed fluctuating exposure levels, suggesting intermediate surface rearrangements (Fig. 6E).

Figure 6. Molecular dynamics (MD) simulation analysis of the TM1 (G1202R+L1204V+G1269A) in both Apo form and ligand-bound states over a 100 ns simulation. (A) Root Mean Square Deviation (RMSD): The backbone RMSD profiles of TM1 in Apo and ligand-bound states. A stable convergence of RMSD values within the simulation period indicates that the complexes have reached structural equilibrium. (B) Root Mean Square Fluctuation (RMSF): Per-residue fluctuations of TM1, showing the effect of ligand binding on local flexibility compared to the Apo structure, where lower fluctuations indicate less flexibility. (C) Radius of Gyration (Rg): Rg plots demonstrating the compactness and overall stability of TM1 in the Apo and ligand-bound states, where low Rg values correspond to better overall structural integrity of the protein-ligand complex. (D) Hydrogen bond interaction patterns between TM1 and the ligands, where consistent hydrogen bonds suggest persistent and stable binding during the simulation. (E) Solvent Accessible Surface Area (SASA): SASA plots comparing the TM1-Apo and ligand-bound states, indicating changes in solvent exposure and structural conformation upon ligand binding. Lower SASA values represent enhanced compactness and reduced solvent exposure upon ligand binding. Color scheme: TM1-Apo (orange), TM1-Lorlatinib (magenta), TM1-CID 118003002 (indigo), TM1-TPX-0131 (green), and TM1-CID 145224731 (blue). [Click here to view]

For the ALK with TM2 protein, in the presence of ligands, the Apo TM2 protein and TM2-Lorlatinib complex exhibited the highest RMSD values, peaking at ~0.4 nm and ~0.42 nm, respectively, particularly in the early phase (0–30 ns), indicating reduced stability. In contrast, TM2-CID 145224731, TM2-TPX-0131, and TM2-CID 118003002 exhibited more stable profiles, with RMSD values consistently ranging from 0.25 to 0.3 nm. The CID 145224731 complex had the lowest average RMSD (~0.24 nm), suggesting enhanced structural retention (Fig. 7A). From the RMSF analysis, the increased fluctuations were observed at residue regions ~1145 and ~1290. TM2-Lorlatinib and Apo TM2 protein showed the highest local flexibility, particularly near residue 1145 (~0.45–0.70 nm). TM2-CID 145224731 and TPX-0131 complexes demonstrated lower fluctuations across most regions, indicating stronger structural rigidity and reduced local motions. TM2-CID 118003002 showed moderate flexibility with fluctuations ~0.4 nm throughout (Fig. 7B). From the Rg plots, the Apo TM2 protein exhibited the highest Rg values (~2.10 nm), indicating a more expanded structure. TM2-Lorlatinib and TM2-CID 118003002 complexes showed intermediate Rg values (~2.05–2.08 nm). TM2-TPX-0131 and TM2-CID 145224731 exhibited tighter packing, with average Rg values of ~2.03–2.05 nm, indicating increased conformational compactness upon ligand binding (Fig. 7C). From the intermolecular hydrogen bonds analysis, TM2-CID 145224731 demonstrated the most consistent hydrogen bonding (1–3 bonds) over the entire simulation. TM2-CID 118003002 also maintained steady interactions, particularly after 40 ns. TM2-Lorlatinib exhibited early spikes in hydrogen bond formation, but its binding was less consistent later. TM2-TPX-0131 exhibited intermittent bonding throughout the simulation (Fig. 7D). Finally, the SASA analysis revealed that the Apo TM2 consistently had the highest SASA (~165 nm²), indicating a more solvent-exposed and less compact structure. TM2-lorlatinib and TM2–TPX-0131 had intermediate exposure, while TM2-CID 118003002 (indigo) and TM2-CID 145224731 (blue) exhibited the lowest SASA values (~150–155 nm²), reinforcing the observation that they have more compact and less exposed conformations (Fig. 7E). The average values of RMSD, RMSF, Rg, and SASA for both the WT-ALK and ALK triple mutations are provided in Table 1. The details of the online tools and software used in this study, along with their web links and dates accessed, are given in Table 2.

Figure 7. Molecular dynamics (MD) simulation analysis of the TM2 (G1202R+S1206F+G1269A) in both Apo form and ligand-bound states over a 100 ns simulation. A) Root Mean Square Deviation (RMSD): The backbone RMSD profiles of TM2 in Apo and ligand-bound states. A stable convergence of RMSD values within the simulation period indicates that the complexes have reached structural equilibrium. B) Root Mean Square Fluctuation (RMSF): Per-residue fluctuations of TM2, showing the effect of ligand binding on local flexibility compared to the Apo structure, where lower fluctuations indicate less flexibility. C) Radius of Gyration (Rg): Rg plots demonstrating the compactness and overall stability of TM2 in the Apo and ligand-bound states, where low Rg values correspond to better overall structural integrity of the protein-ligand complex. D) Hydrogen bond interaction patterns between TM2 and the ligands where consistent hydrogen bonds suggest persistent and stable binding during the simulation. E) Solvent Accessible Surface Area (SASA): SASA plots comparing the TM2-Apo and ligand-bound states, indicating changes in solvent exposure and structural conformation upon ligand binding. Lower SASA values represent enhanced compactness and reduced solvent exposure upon ligand binding. Color scheme: TM2-Apo (orange), TM2-Lorlatinib (magenta), TM2-CID 118003002 (indigo), TM2-TPX-0131 (green), and TM2-CID 145224731 (blue). [Click here to view]

Table 2. Tools, software, and web resources used in this study with their corresponding web addresses and retrieval dates.

Tools/software	Weblinks	Date of last accessed	References
UniProt	https://www.uniprot.org/	Accessed on 25 March 2025	[65]
SWISS-MODEL	https://swissmodel.expasy.org/	Accessed on 25 March 2025	[64]
PredictSNP	https://loschmidt.chemi.muni.cz/predictsnp1/	Accessed on 25 March 2025	[68]
SNPs&GO	https://snps-and-go.biocomp.unibo.it/snps-and-go/	Accessed on 25 March 2025	[69]
Meta-SNP	https://snps.biofold.org/meta-snp/	Accessed on 25 March 2025	[70]
Polyphen-2	https://genetics.bwh.harvard.edu/pph2/	Accessed on 25 March 2025	[71]
iStable	http://predictor.nchu.edu.tw/iStable/	Accessed on 25 March 2025	[72]
DynaMut	https://biosig.lab.uq.edu.au/dynamut/	Accessed on 25 March 2025	[73]
DynaMut2	https://biosig.lab.uq.edu.au/dynamut2/	Accessed on 25 March 2025	[74]
DUET	https://biosig.lab.uq.edu.au/duet/	Accessed on 25 March 2025	[75]
DDMut	https://biosig.lab.uq.edu.au/ddmut/	Accessed on 25 March 2025	[76]
mCSM	https://biosig.lab.uq.edu.au/mcsm/	Accessed on 25 March 2025	[77]
CUPSAT	https://cupsat.brenda-enzymes.org/	Accessed on 25 March 2025	[78]
PoPMuSiC	https://soft.dezyme.com/query/create/pop	Accessed on 25 March 2025	[79]
ConSurf	https://consurf.tau.ac.il/consurf_index.php	Accessed on 3 April 2025	[80]
PubChem	https://pubchem.ncbi.nlm.nih.gov/	Accessed on 29 March 2025	[81]
PyRx version 0.8	https://pyrx.sourceforge.io/	Accessed on 29 March 2025	[82]
SwissADME	https://www.swissadme.ch/	Accessed on 7 April 2025	[81]
pkCSM	https://biosig.lab.uq.edu.au/pkcsm/	Accessed on 7 April 2025	[82]
ADMETlab 3.0	https://admetlab3.scbdd.com/	Accessed on 7 April 2025	[83]
ProTox 3.0	https://tox.charite.de/protox3/	Accessed on 7 April 2025	[84]
AutoDock Tools v1.5.6	https://ccsb.scripps.edu/mgltools/downloads/	Accessed on 14 April 2025	[85]
Discovery Studio v24.1.0.23298	https://discover.3ds.com/discovery-studio-visualizer-download	Accessed on 17 April 2025	[86]
GROMACS version 2025	https://www.gromacs.org/	Accessed on 19 April 2025	[87]

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4. DISCUSSION

About 85% of lung cancer cases correspond to NSCLC. In recent years, the PFS and overall survival (OS) rates have improved due to the therapeutic development of targeted kinase inhibitors and early diagnosis [38]. The use of ALK-TKIs has dramatically improved the outcomes of NSCLC patients. However, different fusion variants of EML4-ALK exhibit other characteristics, which affect the inhibition of EML4-ALK when various TKIs are used [35]. EML4-ALK variant 3 shows reduced sensitivity to ALK-TKIs compared to other variants [92]. Patients with variant 3 exhibited enhanced metastasis and poor overall survival compared to those with different variants (V1 or V2) [93]. EML4-ALK variant 3 was found to be the second most frequent variant, after variant 1, in a cohort of 129 patients. The resistance mutations were primarily observed in variant 3 compared to variant 1 [94].

The ALK-TKIs which are approved for use in EML4-ALK+ NSCLC, include first-, second-, and third-generation drugs [95]. Although crizotinib is used as the first-line (1L) therapy for ALK+ NSCLC, resistance mutations develop within the kinase domain, with the most frequent mutations being L1196M and G1269A. Acquired secondary mutations that confer resistance to crizotinib are I1151T, C1156Y, L1152P/R, I1171T/N/S, F1174V, G1202R, S1206C/Y, and E1210K [96]. Brain metastases occur in a significant proportion of ALK+ NSCLC patients, with approximately 20% of cases at the time of diagnosis and 40%–50% developing brain metastases during disease progression. High incidences of CNS metastases are reported in ALK+ NSCLC patients following ALKi therapy, often emerging within a few years of crizotinib treatment [19,97]. Crizotinib exhibits poor CNS penetration and is ineffective in treating brain metastases in patients [98]. The second-generation TKIs are considered the gold standard treatment for metastatic ALK+ NSCLC, as these TKIs have shown better clinical outcomes compared to the first-generation TKI, crizotinib [99]. Subsequent second-generation TKIs, including lorlatinib, have demonstrated higher blood–brain barrier permeability and more substantial inhibitory effects [18,100]. However, resistance to second-generation inhibitors arises due to secondary mutations, predominantly solvent-front mutations, which occur in approximately 33%–37% of patients who relapse [31,48]. Other mutations that confer resistance to these inhibitors are G1269A, L1198F, D1203N, E1210K, and L1196M [30]. These mutations induce resistance by altering the protein conformation and/or the ATP binding site, leading to steric hindrance during TKI binding. This results in reduced binding efficiency of TKIs. This is referred to as ALK-dependent or “on-target” resistance [31].

Most of the compound mutations were acquired following lorlatinib treatment when the solvent-front mutation (G1202R) exists as a primary mutation [101]. The G1202R mutation lies in the solvent front of the ATP pocket. The substitution of arginine for glycine alters the binding site, thereby disrupting the binding of ALK inhibitors [38]. Several studies confirmed that this resistance mutation is much more commonly found in the EML4-ALK variant 3 rather than variant 1 [39,94,102,103]. Using Ba/F3 cell lines, Yoda and his colleagues identified several compound mutations, including G1202R+G1269A, G1202R+L1198F, and G1202R+L1196, that emerged from G1202R, along with other non-G1202R-based compound mutations. Based on MD simulation results, they found that G1202R destabilized the P-loop of the ALK protein and disrupted the interaction between the ALK and lorlatinib. The P-loop tends to widen after the substitution of arginine, causing the pyrazole moiety of lorlatinib to move up. This led to the weakening of van der Waals and CH-π interactions (noncovalent interactions), which induced additional strain on the ligand [37]. G1269A is located at the ATP-binding pocket and has a direct contact with crizotinib. The substitution of alanine in place of glycine causes a steric clash between the alanine’s methyl group and crizotinib’s dichlorofluorophenyl ring, leading to crizotinib resistance [104]. S1206F is a rare mutation observed in ALK with other substitutions like cysteine and tyrosine in place of phenylalanine. The S1206F mutation was found to be sensitive to ensartinib in vitro (IC₅₀ < 50nM) [105]. S1206C showed resistance to brigatinib but remains sensitive to certinib, alectinib, and lorlatinib. In contrast, S1206Y confers resistance to crizotinib, alectinib, and brigatinib but is sensitive to certinib and lorlatinib. Along with other mutations such as G1202R, G12069A, I1171T/N/S, and E1210K, this S1206Y mutation alters the αC-helix conformation or causes steric hindrance [99]. L1204V is another rare secondary ALK mutation observed only after sequential therapy and identified in patients resistant to lorlatinib [37]. Compound mutations have been identified in patients who relapse after the sequential use of 1G, 2G and 3G TKIs [39]. Approximately 35% of patients will develop resistance to lorlatinib when they are treated with ALK-TKIs sequentially [37]. To date, only limited progress has been made in addressing compound resistance mutations [106]. Several recent computational studies have investigated ALK-targeted drug discovery. A survey by Faya Castillo et al. [107] employed pharmacophore modeling, molecular docking, and MD simulations, proposing two FDA-approved drugs, mitoxantrone and abacavir, as potential repositioned drugs for ALK. Similarly, a hierarchical virtual screening method identified two possible candidates, F6524-1593 and F2815-0802, based on pharmacophore modeling to inhibit ALK using a similar pipeline [108]. In addition, a machine learning model integrated with MD simulations has been employed to identify ALK inhibitors from a natural compound library within the ZINC20 database. Two scaffolds, ZINC3870414 and ZINC8214398, were identified as promising candidates for optimization to inhibit ALK [109]. While these studies provide valuable insights into general ALK inhibition targeting WT-ALK, they did not address resistant mutations. In contrast, our analysis focuses on identifying potential lead compounds targeting clinically reported lorlatinib-resistant triple mutations (G1202R+L1204V+G1269A and G1202R+S1206F+G1269A). We use computational approaches, including virtual screening, molecular docking, ADMET prediction, and molecular dynamics simulations, which are well established and have shown promising results in several previous reports [110–113]. These two triple mutations were detected in patients who had acquired the G1202R mutation (pre-lorlatinib) and developed compound mutations post-lorlatinib treatment [37,40]. This is the first study to investigate ALK triple mutations using computational approaches to identify potential lead compounds that can overcome lorlatinib resistance.

In this study, a combination of computational tools was employed to predict pathogenicity and stability. The use of multiple in silico tools helps improve the accuracy and reliability of predictions [69,114]. Pathogenic mutations cause misfolding events and decrease the stability of the mutated protein [115]. The mutations G1202R and S1206F were found to be highly deleterious, while the mutations L1204V and G1269A were predicted to be both deleterious and neutral by various pathogenicity prediction tools. Protein stability plays a pivotal role in maintaining the proper structure and functional activity of a protein, as changes in its stability can lead to incorrect folding, degradation, or abnormal aggregation [116,117]. The tools, such as DUET, SDM, mCSM, and PopMuSiC, predicted that the mutations G1202R, L1204V, and G1269A destabilize the ALK protein structure, thereby altering its stability and drug binding affinity to TKIs. The evolutionary conservation of a residue in a protein sequence is crucial in determining whether a mutation has any adverse effect on the host [118]. The ConSurf analysis predicted that G1202 and L1204 were highly conserved, which are expected to be more destructive than in variable regions [119]. In addition, these residues were functionally and structurally critical. S1206 and G1269 were predicted as variable and conserved residues.

To identify novel compounds targeting the triple mutations (G1202R+L1204V+G1269A and G1202R+S1206F+G1269A), we performed virtual screening using the Autodock Vina module in PyRx. A total of 308 (lorlatinib similar compounds) and 305 (TPX-0131 similar compounds) were screened against the triple mutations. Following this, ADMET properties were predicted using SwissADME and ProTox 3.0. ADMET analysis revealed that the compounds CID 118003002 and CID 145224731 had favorable properties and were considered for docking. These two compounds possess high gastrointestinal absorption and satisfy drug-likeness filters, including Lipinski’s rule of five and Ghose, Veber, and Eagan. In addition, we used pkCSM and ADMETlab 3.0 to predict and evaluate the pharmacokinetic properties of the identified hits along with lorlatinib and TPX-0131. Analysis of toxicity using ProTox 3.0 revealed that the two compounds can penetrate the BBB and exhibit no cytotoxicity, carcinogenicity, immunotoxicity, or nephrotoxicity. The predicted LD50s of CID 118003002 and CID 145224731 were 200 mg/kg and 800 mg/kg, respectively.

Molecular docking analysis was performed with lorlatinib, TPX-0131, CID 118003002, and CID 145224731 in conjunction with the ALK triple mutations. The docking results revealed that CID 118003002 exhibited a higher binding energy of –8.02 kcal/mol with the triple mutation 1 and established two H-bond interactions with LEU1122 and ARG1202 (mutant residue) (Fig. 3B). While the reference ligands, lorlatinib and TPX-0131, exhibited binding affinities of –7.69 kcal/mol and –7.40 kcal/mol, respectively. The hydrophobic interactions between the nonpolar regions of the ligands and the ALK protein play a crucial role in stabilizing the complex and enhancing the binding specificity. Both hydrogen bonds and hydrophobic interactions are essential for designing potent lead candidates [120]. The compound CID 118003002 formed multiple hydrophobic interactions with ALA1126, VAL1130, ALA1148, GLU1197, MET1199, LEU1256, and ALA1269. Similarly, these hydrophobic interactions were also observed in the reference compounds (Fig. 3A and B). In the case of triple mutation 2, the compound CID 145224731 exhibited the highest binding energy of –8.43 kcal/mol compared to lorlatinib (–7.13 kcal/mol) and TPX-0131 (–8.43 kcal/mol). The compound established two H-bond interactions with LYS1150 and ARG1253. It formed hydrophobic interactions with PHE1127, VAL1130, LEU1196, ARG1202, and LEU1256 (Fig. 3D). Lorlatinib and TPX-0131 were docked to the WT-ALK, which exhibited higher docking scores of –8.9 and –9.5 kcal/mol, respectively. However, in the F1174C/L/V mutants, both compounds showed reduced binding affinities. This decrease can be due to the substitutions of different amino acids at position 1174, which altered the binding pocket of ALK [54]. Docking analysis of both WT-ALK and triple mutants indicated the impact of mutation on ligand binding efficiency [121]. Notably, the residues G1202R, L1204V, S1206F, and G1269A, located at the binding site of ALK, have direct contact with the inhibitors during binding. The presence of triple mutations (G1202R+L1204V+G1269A and G1202R+S1206F+G1269A) can alter the binding pocket, compromising the binding of approved TKIs, which were confirmed through docking analysis. Interestingly, in our study, the compounds, CID 118003002 and CID 145224731, demonstrated higher binding affinities with ALK triple mutations than WT-ALK. We have employed the molecular dynamics simulations to evaluate the structural stability and ligand interactions of Wild-Type ALK and two triple mutant variants (TM1 and TM2) in complex with the existing and identified potential lead compounds. The crucial parameters, including RMSD, RMSF, Rg, SASA, and the change in the number of intermolecular hydrogen bonds, were analyzed to evaluate the dynamic behavior of each ALK-inhibitor complex system. Among the identified compounds, CID 118003002 consistently demonstrated better performance with both WT and TM1 proteins throughout the simulation period. This compound exhibited the lowest RMSD and Rg values, indicating high conformational stability and compactness with both WT-ALK and ALK with TM1. In addition, reduced RMSF and persistent hydrogen bonding between the WT-ALK and ALK with TM1 complexes suggest strong and stable ligand-induced structural rigidity (Figs. 5 and 6). These results indicate that the compound CID 118003002 is a promising lead compound for overcoming resistance associated with the TM1 mutation. On the other hand, the compound with CID 145224731 showed better potential with the TM2, by possessing consistent hydrogen bonding, lower RMSD, and compact Rg values (Fig. 7). Overall, these results highlight CID 118003002 as a potential lead for ALK-WT and ALK with the TM1 mutation, and CID 145224731 as a promising lead for targeting the ALK with the TM2 mutation, providing a possible direction for the development of mutation-specific ALK inhibitors.

Notably, the compound CID 118003002, which exhibited stronger interactions with WT-ALK and TM1, has been previously reported to inhibit ALK and c-MET kinases with potential applications in cancer therapy [122]. In contrast, the other compound, CID 145224731, has no prior reported information against ALK, representing a novel scaffold for further investigation. The identified lead compounds can serve as a foundation for further rational drug design and optimization as scaffolds, which may help in the development of tailored therapeutic strategies for ALK+ NSCLC. Although the identified potential lead compounds demonstrated favorable pharmacokinetic properties and lower predicted toxicity, the findings are solely based on in silico analysis, which is one of the study’s limitations. While molecular docking and MD simulations provided comprehensive insights, advanced methods such as free energy perturbations and quantum mechanics/molecular mechanics approaches could offer more accurate and detailed predictions of ligand binding to ALK triple mutations. Applying these advanced methods in future studies will enhance the protein-ligand dynamics and help in understanding the electronic and structural characteristics of the ALK active site upon compound mutations. This will also improve the overall accuracy and reliability of results compared to classical simulations. In addition, further in vitro and in vivo studies are necessary to confirm their pharmacokinetic characteristics, toxicity, and efficacy of these two compounds against the reported ALK triple mutations (G1202R+L1204V+G1269A and G1202R+S1206F+G1269A).

5. CONCLUSION

Various computational approaches were employed in this study to identify potential lead compounds targeting lorlatinib-resistant ALK triple mutations (G1202R+L1204V+G1269A and G1202R+S1206F+G1269A). The pathogenicity, stability changes, and the conservation nature of the mutant position were studied. The existing drugs, lorlatinib and TPX-0131, were used as the reference to obtain similar compounds. The compounds with CID 118003002 and 145224731 were obtained as similar compounds with better interaction and ADMET properties. Molecular docking analysis revealed that CID 118003002 had strong interactions with WT-ALK and TM1, with binding scores of –7.97 kcal/mol and –8.02 kcal/mol, respectively, whereas CID 145224731 showed a binding score of –8.49 kcal/mol with TM2. Both compounds were subsequently subjected to 100 ns molecular dynamics simulations to evaluate their stability and interaction profiles with the ALK triple mutants. Ultimately, the compound CID 118003002 showed greater stability and compactness with ALK-WT and TM1, along with the formation of consistent hydrogen bonds at the end of the simulation, indicating better interactions. In contrast, the CID 145224731 compound showed better simulation results with TM2, exhibiting a higher degree of structural stability and rigidity. Both these compounds served as promising lead candidates for overcoming the lorlatinib-resistant ALK triple mutations. However, further experimental validations are required to confirm the efficacy of CID 118003002 and CID 145224731 against the ALK triple mutations.

6. ACKNOWLEDGMENTS

The authors acknowledge Meenakshi Academy of Higher Education and Research, Chennai, for infrastructure support.

7. AUTHOR CONTRIBUTIONS

All authors made substantial contributions to conception and design, acquisition of data, or analysis and interpretation of data; took part in drafting the article or revising it critically for important intellectual content; agreed to submit to the current journal; gave final approval of the version to be published; and agree to be accountable for all aspects of the work. All the authors are eligible to be an author as per the International Committee of Medical Journal Editors (ICMJE) requirements/guidelines.

8. FINANCIAL SUPPORT

There is no funding to report.

9. CONFLICT OF INTEREST

The authors report no financial or any other conflicts of interest in this work.

10. ETHICAL APPROVAL

This study does not involve experiments on animals or human subjects.

11. DATA AVAILABILITY

All data generated and analyzed are included in this research article.

12. PUBLISHER’S NOTE

All claims expressed in this article are solely those of the authors and do not necessarily represent those of the publisher, the editors and the reviewers. This journal remains neutral with regard to jurisdictional claims in published institutional affiliation.

13. USE OF ARTIFICIAL INTELLIGENCE (AI)-ASSISTED TECHNOLOGY

The authors declare that they have not used artificial intelligence (AI)-tools for writing and editing of the manuscript, and no images were manipulated using AI.

14. SUPPLEMENTARY MATERIAL

The supplementary material can be accessed at the Link here: https://japsonline.com/admin/php/uploadss/4769_pdf.pdf

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