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Designing ESR1 inhibitors for breast cancer: Synthesis and characterization of novel 1,2,3-triazole-salicylaldehyde conjugates via in silico approaches.
*Corresponding author: E-mail address: s.alosaimi@tu.edu.sa (S. Alotaibi)
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Received: ,
Accepted: ,
Abstract
Breast cancer remains a global health priority. Phenolic compounds, like salicylaldehyde and 1,2,3-triazoles, show significant anticancer potential. This study reports the synthesis and characterization (high resolution mass spectrometry-HRMS and nuclear magnetic resonance-NMR) of a novel series of 1,2,3-triazole-salicylaldehyde conjugates. Network pharmacology analysis identified ESR1 as a major target. Density functional theory (DFT) calculations characterized the electronic properties, and Hirschfeld surface analyses evaluated intermolecular interactions. Compound 9d exhibited the most favorable electronic characteristics. Furthermore, all derivatives 9a-d underwent ADMET (absorption, distribution, metabolism, excretion, and toxicity) profiling and molecular docking with ESR1. A 100 ns molecular dynamics simulation confirmed the stability of compound 9d. Overall, all synthesized derivatives showed favorable physicochemical and ADMET properties, with 9d and 9a standing out as promising lead candidates for ESR1 inhibition.
Keywords
1,2,3-triazoles
ESR1
Molecular docking
Network pharmacology
Salicylaldehyde
1. Introduction
Tumor-associated inflammation is a well-established hallmark of cancer, with genetic and epidemiological studies having confirmed its role in disease progression [1]. The infiltration of precancerous and cancerous tissues by immune cells and inflammatory mediators is crucial at all stages of tumor development [2]. Despite advances, cancer remains the second leading cause of death worldwide, largely due to current chemotherapy limitations, including drug resistance and poor selectivity [3]. These challenges underscore the imperative for developing novel chemotherapeutic strategies that are more effective, less toxic, and more selective for cancer cells.
Salicylaldehyde (2-hydroxybenzaldehyde), a key aroma compound in buckwheat, is recognized for its unique hydrogen bonding capabilities and utility in synthesizing fluorescent molecules like coumarin. Its derivatives are particularly appealing for designing bioactive compounds due to their low toxicity at moderate concentrations [4]. 1,2,3-Triazoles, a prominent class of nitrogen-containing heterocycles, are also crucial in medicinal chemistry given their facile synthesis via click chemistry and diverse biological activities, including antiviral, antiepileptic, and antimicrobial effects. Notably, numerous 1,2,3-triazole derivatives have demonstrated significant promise in anticancer applications [5,6]. This combined versatility underscores the potential of conjugating these two scaffolds for novel therapeutic agent development.
There are primarily two categories of hybrids that contain 1,2,3-triazoles (Figure 1): 1. In Type I, the triazole moiety is combined with other anticancer pharmacophores via various linkers; 2. In Type II, the 1,2,3-triazole serves as the linker itself, connecting two distinct anticancer pharmacophores (Figure 1). This review focuses on Type II of triazole hybrids with potential anticancer properties. Our design utilized triazole as the core structure due to its established anticancer effects [7], as well as its ability to inhibit 15-LOX and COX-2 enzymes (Compound 1, Figure 1) [8]. Additionally, sulfonamide-bearing 1,2,3-triazoles have been found to inhibit tumor-associated hCA [9] (Compound 2, Figure 1). Additionally, compound 3 (Figure 1), which includes a salicylaldehyde framework, demonstrated enhanced binding affinity for iron ions [10] and displayed protective properties against oxidative damage [11]. Despite these benefits, compound 3 undergoes rapid hydrolysis in plasma, resulting in a brief in vivo half-life [12]. The functional groups within this structure have been the subject of extensive research due to their relevance in the development of numerous anticancer therapies. Furthermore, in this study, we considered incorporating an aromatic ester, known for its bioactivity, into the targeted compounds (Compound 4) [13]. Due to the promising biological effects of the fragments mentioned below, we will synthesize hybrid compounds incorporating both the salicylaldehyde core and aryl esters linked to a 1,2,3-triazole heterocyclic scaffold.
- Overview of the approach employed in the design of target compounds 9a-d.
The Estrogen Receptor 1 (ESR1 or ERα) is a pivotal therapeutic target in breast cancer due to its critical role in regulating cell growth, differentiation, and metabolism; approximately 70-80% of breast cancers are ER+ and depend on its signaling for proliferation [14]. Endocrine therapies, such as selective estrogen receptor modulators (SERMs), aromatase inhibitors (AIs), and selective estrogen receptor degraders (SERDs), are cornerstones of treatment, but acquired ESR1 mutations (often in the ligand-binding domain) frequently lead to resistance, causing constitutive receptor activation and rendering tumors estrogen-independent in metastatic settings [15]. This highlights an urgent need for novel compounds that can effectively target both wild-type and mutated ESR1. Computational approaches, including in silico design, offer a rational and efficient pathway to identify such new candidates by predicting optimal interactions and favorable ADMET profiles, thereby addressing current therapeutic limitations [16].
Our study presents a novel series of 1,2,3-triazole-salicylaldehyde conjugates designed as ESR1 inhibitors, offering a distinct therapeutic advantage over existing agents by potentially addressing mechanisms of acquired resistance. Current endocrine therapies for ER+ breast cancer, such as AIs and SERMs (e.g., tamoxifen), frequently face limitations due to acquired ESR1 mutations (e.g., Y537S, D538G) in metastatic settings, leading to constitutive receptor activation and rendering tumors estrogen-independent [17,18]. While next-generation oral SERDs (e.g., camizestrant, vepdegestrant) show promise against these mutated forms [19], the landscape of effective therapies against all resistant ESR1 variants is still evolving. Our novel conjugates, integrating both triazole and salicylaldehyde scaffolds, represent a unique chemical space not fully explored in existing ESR1 inhibitors. The in silico studies (docking, MD simulations) conducted in this work specifically target the ESR1 binding site, including mutated variants, to predict potent interactions. This rational design approach aims to develop compounds with potentially improved binding profiles to both wild-type and mutated ESR1, offering a novel structural class to overcome current resistance mechanisms. While initial in silico findings are promising, subsequent in vitro and in vivo evaluations are crucial to fully validate their therapeutic superiority, particularly against resistant ER+ breast cancer models.
2. Materials and Methods
2.1. Synthesis of 1,2,3-triazole 9a-d
Intermediates 7 and 8 have been previously described in [20]. An equimolar amount (14.1 mmol) of 5-azidomethyl-2-hydroxybenzaldehyde (8) and the prop-2-yn-1-yl benzoate derivatives were dissolved in 30 mL of the solvents EtOH:H2O (1:1). Subsequently, a catalytic amount of sodium ascorbate and CuSO4·5H2O were added to the mixture, which was then stirred for 4 h at room temperature.
(1-(3-Formyl-4-hydroxybenzyl)-1H-1,2,3-triazol-4-yl)methyl benzoate (9a). (76%, Solid, 82-88°C). 1H-δ: 11.20 (1H, H-CO), 9.79 (1H, s, OH), 7.75 (2H, d J= 7.65 Hz, HArm), 7.10-7.45 (6H, m, HArm), 7.75 (1H, s, H-C5), 5.29 (2H, s, H2C3’), 4.63 (2H, s, H2C1’). 13C-δ : 195.5 (C4’),167.2 (C2’), 161.5 (CIV), 149.3 (CArm), 146.7 (C4), 138.8 (CHArm), 134.7 (CHArm), 131.7 (CArm), 128.9 (HCArm), 128.2 (CArm), 127.4 (HCArm), 122.7 (C5), 119.6 (CHArm), 53.6 (H2C1’), 51.3 (H2C3’). High resolution mass spectrometry (HRMS), calculated for C18H15N3O4: 338.1096; found: 338.1063.
(1-(3-Formyl-4-hydroxybenzyl)-1H-1,2,3-triazol-4-yl)methyl 4-methylbenzoate (9b). (78%, Solid, 75-77°C). 1H-δ: 11.30 (1H, H-CO), 9.75 (1H, s, OH), 7.67 (2H, d J = 7.49 Hz), 7.10-7.45 (5H,m, HArm), 7.46 (1H, s, H-C5), 5.55 (2H, s, H2C3’), 4.65 (2H, s, H2C1’), 2.45 (3H, s, CH3). 13C-δ : 198.5 (C4’),164.6 (C2’), 162.3 (CIV), 148.1 (C4), 147.6 (CArm), 137.2 (HCArm), 132.5 (H-CArm), 128.2 (CArm), 127.9 (H-CArm), 128.1 (CArm), 124.2 (H-CArm), 124.7 (C5), 118.2 (CHArm), 54.1 (H2C1’), 52.8 (H2C3’), 20.4 (CH3). HRMS, calculated for C19H17N3O4: 352.1253; found: 352.1206.
(1-(3-Formyl-4-hydroxybenzyl)-1H-1,2,3-triazol-4-yl)methyl 4-chlorobenzoate (9c). (81%, Solid, 78-80°C). 1H-δ: 11.35 (1H, H-CO), 9.75 (1H, s, OH), 7.15 (2H, d J = 7.56 Hz), 7.23 (2H, d J = 7.50 Hz), 7.75-8.00 (3H,m, HArm), 7.25 (1H, s, H-C5), 5.38 (2H, s, H2C3’), 4.25 (2H, s, H2C1’). 13C-δ: 198.2 (C4’),168.1 (C2’), 161.4 (CIV), 149.4 (C4), 147.2 (CArm), 139.2 (CHArm), 133.2 (CHArm), 130.2 (CArm), 128.1 (H-CArm), 127.8 (CArm), 125.2 (H-CArm), 121.4 (C5), 119.1 (H-CArm), 54.3 (H2C1’), 52.8 (H2C3’). HRMS, calculated for C18H14ClN3O4: 373.0643, found: 372.9987.
(1-(3-Formyl-4-hydroxybenzyl)-1H-1,2,3-triazol-4-yl)methyl 4-nitrobenzoate (9d). (68%, Solid, 84-86°C). 1H-δ: 10.85 (1H, H-CO), 9.85 (1H, s, OH), 7.53 (2H, d J = 7.63 Hz), 7.72 (2H, d J = 7.65 Hz), 7.00-7.50 (3H,m, HArm), 7.59 (1H, s, H-C5), 5.50 (2H, s, H2C3’), 4.29 (2H, s, H2C1’). 13C-δ: 197.7 (C4’),168.5 (C2’), 165.3 (CIV), 147.5 (C4), 141.5 (CArm), 137.2 (H-CArm), 132.8 (H-CArm), 131.9 (CArm), 128.1 (H-CArm), 127.4 (CArm), 124.8 (H-CArm), 120.2 (C5), 118.3 (H-CArm), 55.4 (H2C1’), 51.7 (H2C3’). HRMS, calculated for C18H14N4O6: 383.0947, found: 383.0985.
2.2. Network pharmacology
Using the SMILES notation of the derivatives, the Swiss Target Prediction database was utilized to predict human target genes [21]. Breast cancer-related genes were screened using DisGeNET, and common targets were identified through Venn diagram analysis. Using DAVID, pathway and functional enrichment analyses were performed on Cellular Component, Molecular Function, and Biological Process, with results significant at p ≤ 0.05. Cytoscape was employed to visualize biomolecular interaction networks, highlighting key targets with high connectivity. The STRING database and CytoHubba plugin were used to construct and analyze protein-protein interaction (PPI) networks, identifying core regulatory genes. Determining a compound’s drug-likeness is a key aspect of drug development, as it reveals whether the compound exhibits properties typical of effective pharmaceuticals [22].
2.3. Physical and chemical characteristics
The physicochemical characteristics of the synthesized compounds were analyzed using Molinspiration software. To assess drug-likeness, the evaluation was based on Lipinski’s Rule of Five, which helps determine the likelihood of favorable oral bioavailability. Compounds that deviate from this rule typically exhibit reduced drug-like properties. According to this rule, compounds are predicted to possess good oral bioavailability when they satisfy specific parameters. Log P values indicate the hydrophobicity of the compounds. Topological polar surface area (TPSA) is an important parameter for predicting drug transport properties. The molecular weight of the ligands is another critical parameter. Hydrogen bonding capacity influences solubility and binding affinity. Rotatable bonds affect the flexibility of the molecule, influencing binding to the target protein [23].
2.4. Bioactivity
The bioactivity scores reflect the compound’s likelihood of interacting with specific drug targets. A score above 0.00 suggests the compound could act as an agonist or activator, while a negative score implies it may have antagonistic or inhibitory effects. Scores between -0.50 and 0.00 indicate moderate activity, suggesting some interaction with the target, though not as strongly as compounds with positive scores. Scores below -0.50 imply the compound is inactive with negligible target binding [24].
2.5. ADME properties
The ADME (Absorption, Distribution, Metabolism, and Excretion) properties of the compounds were predicted in silico using the SwissADME web tool. SMILES notations of the molecules were input into the system, which generated data on key pharmacokinetic parameters, including lipophilicity (Log P), aqueous solubility, gastrointestinal (GI) absorption, blood-brain barrier (BBB) permeability, and metabolic interactions. These predictions were utilized to evaluate the oral bioavailability and overall pharmacokinetic suitability of the compounds as potential drug candidates.
2.6. Toxicity evaluations
Recent advancements in computational techniques have significantly simplified the process of assessing the toxicity of chemical compounds through in silico methods. These approaches can examine various aspects of toxicity, including hepatotoxicity, carcinogenicity, immunotoxicity, and mutagenicity. The toxicity of the synthesized ligands was assessed using the Protox II server, which accepts compounds in SMILES notation for analysis [25].
2.7. Molecular docking
Following network pharmacology analysis, high-degree target proteins were selected for molecular docking using AutoDock 4.2. The ligand was imported into UCSF Chimera via SMILES notation, converted into a 3D structure, and energy minimized to create PDB files. The ESR1 protein (PDB ID: 3OS8) was sourced from the RCSB Protein Data Bank and prepared by removing water molecules and co-crystals and adding polar hydrogens. Docking scores in kcal/mol assessed binding affinity, with PDB files converted to PDBQT format. A grid box was generated by adjusting the macromolecule’s dimensions and saving the parameters as a .gpf file. The docking employed the Lamarckian genetic algorithm, with configurations saved as .dpf files, which were later converted into log files and results stored as .dlg files. Ligand-enzyme interactions were analyzed and visualized in both 2D and 3D using BIOVIA Discovery Studio [26].
2.8. Quantum chemical analysis
Density functional theory (DFT) calculations were performed using Gaussian 09 with the B3LYP functional and a 6-31G(d) basis set. The highest occupied molecular orbital-lowest unoccupied molecular orbital (HOMO-LUMO) energy gap, dipole moment, electronegativity (χ), chemical potential (μ), and hyperpolarizability (β) were computed to assess the electronic properties of the synthesized compounds. Exploring the antiviral potency of γ-FP and PA compounds: Electronic characterization, non-covalent interaction (NCI) analysis, and docking profiling with emphasis on QTAIM aspects. The molecular electrostatic potential (MEP) was mapped to visualize charge distribution. For electron localization function (ELF), and localized orbital locator (LOL) analyses, Multiwfn software was employed to study electron density distribution and charge localization. Multiwfn: A multifunctional wavefunction analyzer. Hirschfeld surface analysis and fragment patch fingerprint plots were generated using Crystal Explorer to examine intermolecular interactions and molecular packing characteristics.
2.9. Molecular dynamics simulation
The protein-ligand complex was subjected to MD simulations using the Desmond module in the Schrödinger suite. Initially, the complex, obtained from docking studies in .pdb format, underwent preparation through the Protein Preparation Wizard. This process involved assigning bond orders, optimizing hydrogen bonding, and applying restrained minimization using the Prime module. The system was then set within an orthorhombic simulation box, ensuring a 10 Å distance from the protein, and was solvated using the SPC water model, with Na+ and Cl- ions added for neutralization. A 100 ns MD simulation was carried out, including a 1 ps relaxation phase. The simulation was executed under NPT conditions, maintaining a temperature of 300 K and a pressure of 1 atm, controlled by the Martyna-Tuckerman-Klein coupling method for pressure and the Nosé-Hoover thermostat for temperature regulation. The OPLS2005 force field was employed, with non-bonded interactions calculated using the r-RESPA integrator. Short-range interactions were updated at every step, while long-range interactions were recalculated every three steps. The particle mesh Ewald method managed long-range electrostatic interactions, using a 9 Å cutoff for Coulombic forces [27]. Post-simulation, the dynamics of the system were analyzed, focusing on metrics such as root mean square deviation (RMSD), root mean square function (RMSF), and the interactions between the protein and ligand.
3. Results and Discussion
3.1. Synthesis
The 1,2,3-triazole-O-benzoyls (9a-d) were synthesized through the multi-step process outlined in Scheme 1. Initially, the starting material, 5-chloromethyl-Salicylaldehyde (7), was obtained by reacting 2-hydroxybenzaldehyde with formaldehyde and HCl, following a previously reported method [20]. The next step involved converting compound 7 into 5-azidomethyl-2-Salicylaldehyde (8) by stirring it with NaN3 in acetone at 25°C, yielding the desired azide (8) with high efficiency. In parallel, propargyl intermediates 9a-d were synthesized through an O-alkylation reaction, where propargyl alcohol and various benzoyl chloride derivatives were reacted in dichloromethane (DCM) with triethylamine (NEt3) as a base, producing prop-2-yn-1-yl-para-X-benzoate derivatives with moderate yields of 64-75%. The final step involved coupling azide (8) with propargyl benzoyls using the CuAAC protocol [28]. This reaction was performed at room temperature in an ethanol-water mixture with sodium ascorbate (reducing agent) and CuSO4·5H2O, resulting in the formation of compounds 9a-d with excellent yields (92-95%). The final products were purified by chromatography (Scheme 1).
- Overview of the synthetic pathway for the target compounds 9a-d.
The confirmation of the newly synthesized hybrids 9a-d was achieved by HRMS, with the expected pseudo-molecular ions at m/z values of 338.1063, 352.1206, 372.9987, and 383.0985 for 9a, 9b, 9c, and 9d, respectively. These values align with the molecular formulas of the monocycloadducts: (9a: C18H15N3O4, 9b: C19H17N3O4, 9c: C18H14ClN3O4, 9d: C18H14N4O6).
A comparative assessment of the nuclear magnetic resonance (NMR) spectra for compound hybrids 9a-d demonstrated a notable similarity, especially in the chemical shift region associated with the triazole ring and the aromatic components. This consistent spectral profile across the series serves as strong evidence for the successful O-alkylation and synthesis of the desired 1,2,3-triazoles (9a-d). To exemplify the distinct spectral variations linked to these reactions, we examined the (1H & 13C)-NMR spectra of hybrid 9a. The appearance of new signals at (1H-δ: 7.75 ppm; 13C-δ: 131.7 ppm) indicates the formation of H-C=N (H-C5) within the newly created triazole ring. Furthermore, the CH2 connecting the triazole structure to the paramethoxythymol unit and the prop-2-yn-1-yl-para-X-benzoate group is evidenced respectively by signals at (H2C3’ δ: 5.26; δ: 53.6 ppm) and (H2C1’ δ: 4.63; δ: 51.3 ppm). Additionally, the aldehyde and alcohol functionalities of salicylaldehyde were identified at (1H-δ: 11.20 ppm; 13C-δ: 195.5 ppm) and (1H-δ: 9.79 ppm), respectively. The aromatic protons resonated between δ 7.00 and 7.65 ppm with a coupling constant generally around 7.55 Hz in the 1H-NMR, while the corresponding aromatic carbons were observed in the 13C-NMR at δ 119 to 138 ppm.
3.2. In-silico study
3.2.1. Network pharmacology
A total of 197 potential target genes for the derivatives 9a-d were predicted through the Swiss Target Prediction database. Moreover, 72 genes linked to breast cancer were retrieved from the DisGeNET database. Venn diagram analysis allowed for the identification of overlapping targets between the breast cancer-related genes and the synthesized 9a-d. This analysis revealed 11 key genes that could be pivotal in breast cancer therapy. Furthermore, four derivatives 9a-d were chosen, along with 11 critical genes and their respective pathways, which were associated with the highest number of breast cancer-related genes. A network diagram was then constructed to visualize the connections between these compounds and their target genes. The identification of multiple targets for each compound implies a potential synergistic effect, suggesting that the interaction of derivatives with various targets may enhance their therapeutic efficacy in treating breast cancer (Figure 2).
- Common targets shared between the disease and the hybrids 9a-d.
3.2.2. Identification of key genes
Through the STRING database, the ten overlapping genes were examined to establish a PPI network, which demonstrates the relationships between different targets linked to the disease’s development and progression. The results from this PPI network analysis were further examined using a network analyzer. This examination highlighted several crucial genes, ranked according to their connectivity degree within the PPI network. The top ten genes have been listed in Table 1 and shown in Figure 3.
| Rank | R1 | R2 | R3 | R4 | R5 | R6 | R7 | R8 | R9 | R10 |
|---|---|---|---|---|---|---|---|---|---|---|
| Gene name | ESR1 | PIK3CA | ERBB2 | AR | EGFR | SRC | CDK4 | MET | FGFR1 | AURKA |
| Degree score | 10 | 9 | 9 | 9 | 9 | 8 | 7 | 7 | 5 | 5 |
- Network diagram of the ten highest-ranked genes.
Among the ranked genes, ESR1 holds the highest position with a score of 10, reflecting its central role in the process. It is followed by PIK3CA, ERBB2, AR, and EGFR, each scoring 9. SRC ranks next with a score of 8, while CDK4 and MET each have a score of 7, and FGFR1 and AURKA score 5, highlighting their significant involvement in breast cancer. This ranking clearly demonstrates the importance of each gene, based on its connectivity score. A high connectivity degree among these genes indicates strong associations, suggesting that these genes are essential targets for breast cancer. The network pharmacology analysis further emphasized the relevance of these genes, with ESR1 standing out for its functional importance in cellular signaling pathways, underscoring its critical role in the disease.
3.2.3. The involvement of the top 10 genes in cancer development
Several key genes involved in cancer were highlighted through network pharmacology analysis, with each gene contributing to tumor growth, progression, and resistance to therapy. The top 10 genes play crucial roles in cancer, particularly breast cancer. ESR1 drives estrogen signaling in hormone receptor-positive tumors, targeted by therapies like SERMs and aromatase inhibitors. PIK3CA mutations activate the PI3K/AKT pathway, promoting cell growth and survival, with PI3K inhibitors (e.g., alpelisib) used in treatment. ERBB2 (HER2) amplification is linked to aggressive breast cancer, targeted by drugs like trastuzumab. AR and EGFR contribute to cancer progression, with hormonal and EGFR inhibitors (e.g., cetuximab) as therapeutic options. SRC promotes metastasis, and its inhibitors (e.g., dasatinib) are being explored. CDK4 regulates the cell cycle and is inhibited by CDK4/6 inhibitors like palbociclib. MET drives metastasis and is targeted by MET inhibitors (e.g., capmatinib). FGFR1 supports angiogenesis and survival, with FGFR inhibitors (e.g., erdafitinib) in use. Finally, AURKA controls mitosis, and AURKA inhibitors (e.g., alisertib) are being developed for high-mitotic cancers.
3.2.4. Network pharmacology analysis of designed benzyl triazole benzoate series derivatives 9a-d targeting breast cancer
The series of benzyl triazole benzoate (BTB) derivatives 9a-d, recognized for their varied pharmacological properties, have attracted interest for their potential application as anticancer agents. This study focuses on products 9a-d, specifically designed to target breast cancer. A network pharmacology approach was utilized to investigate the interactions between these products and breast cancer-related genes. Through this analysis, key genes were identified, highlighting their critical roles in several signaling pathways associated with breast cancer. The ten highest-ranked genes from the network pharmacology analysis are integral to cellular signaling processes and hold substantial relevance in breast cancer research. The KEGG pathway analysis, illustrated in Figure 4, indicates that these genes are involved in multiple cancer-related pathways. Figure 5 summarizes the overview of cellular components, biological processes, and molecular functions. These identified targets have been chosen for further exploration in ADMET and molecular docking studies.
- KEGG pathway analysis.
- Overview of key biological pathways.
3.3. Physicochemical characteristics of the developed ligands
The physicochemical characteristics of the developed ligands, as presented in Table 2, show favorable drug-like properties. The Log P values of the products range from 3.12 to 3.84, indicating moderate lipophilicity, which supports good membrane permeability. The TPSA for all compounds remains consistent at 94.32, except for 9d, which has a higher TPSA of 140.15, suggesting potentially enhanced solubility and permeability for this compound. Molecular weights (MW) range from 337.33 to 382.33 g/mol, all within the acceptable range for oral drugs. The number of hydrogen bond acceptors (HBA) is between six to eight, and hydrogen bond donors (HBD) are consistent at 1 across the compounds, supporting their capacity for molecular interactions. All compounds possess seven or eight rotatable bonds, contributing to molecular flexibility, and none exhibit violations of Lipinski’s Rule of Five, indicating good drug-likeness and bioavailability potential.
| Compound | Log P | TPSA | MW | HBA | HBD | No of rotatable bonds | No of violations |
|---|---|---|---|---|---|---|---|
| 9a | 3.16 | 94.32 | 337.33 | 6 | 1 | 7 | 0 |
| 9b | 3.61 | 94.32 | 351.36 | 6 | 1 | 7 | 0 |
| 9c | 3.84 | 94.32 | 371.78 | 6 | 1 | 7 | 0 |
| 9d | 3.12 | 140.15 | 382.33 | 8 | 1 | 8 | 0 |
3.4. Bioactivity score
The bioactivity scores of the designed ligands, as shown in Table 3, suggest their potential interactions with various biological targets. All compounds show moderate activity as G protein-coupled receptor (GPCR) ligands with scores ranging from -0.14 to -0.27, indicating some affinity for G-protein coupled receptors. Ion channel modulation scores are also moderately low, between -0.17 and -0.24, suggesting limited interaction with ion channels. For kinase inhibition, the ligands exhibit negative values, with 9d showing the lowest score (-0.37), indicating weaker kinase inhibitory potential compared to other compounds. Nuclear receptor ligand activity is also low, with scores between -0.22 and -0.29, reflecting limited potential in this target class. Protease inhibition scores are the lowest across all parameters, especially for 9d (-0.39), suggesting these compounds may not strongly inhibit proteases. Enzyme inhibition scores are slightly better, with 9a showing a positive score (0.01), indicating a potential for enzyme interaction, while other compounds remain below zero. Overall, these scores indicate moderate to low bioactivity across these target classes, with compound 9a showing the most promise as an enzyme inhibitor.
| Product | GPCR ligand | ICM | KI | NRL | PI | EI |
|---|---|---|---|---|---|---|
| 9a | -0.14 | -0.18 | -0.26 | -0.22 | -0.27 | 0.01 |
| 9b | -0.18 | -0.24 | -0.30 | -0.24 | -0.32 | -0.04 |
| 9c | -0.14 | -0.17 | -0.27 | -0.23 | -0.31 | -0.02 |
| 9d | -0.27 | -0.21 | -0.37 | -0.29 | -0.39 | -0.10 |
NRL: Nuclear receptor ligand; KI: Kinase inhibitor; ICM: Ion channel module; EI: Enzyme inhibitor; PI: Protease inhibitor.
3.5. ADME properties
The ADME results of the synthesized compounds 9a-d indicate favorable pharmacokinetic characteristics, particularly supporting their potential for oral administration, and have been tabulated in Table 4. All four compounds exhibited high gastrointestinal (GI) absorption, which is desirable for oral bioavailability, suggesting that the compounds are efficiently absorbed through the intestinal lining. None of the compounds are predicted to cross the BBB, which is advantageous to reduce central nervous system (CNS) related side effects. The compounds are not the substrates for P-glycoprotein, a transporter often associated with drug efflux and multidrug resistance. This may help enhance intracellular drug concentration and reduce the likelihood of efflux-mediated resistance.
| Compound | GI absorption | BBB | P-glyco proetin substrate | CYP1A2 | CYP2C19 | CYP2C9 | CYP2D6 | CYP3A4 | Log Kp (Skin permeation) cm/s |
|---|---|---|---|---|---|---|---|---|---|
| 9a | High | No | No | No | Yes | Yes | No | No | -6.71 |
| 9b | High | No | No | No | Yes | Yes | No | No | -6.53 |
| 9c | High | No | No | Yes | Yes | Yes | No | No | -6.47 |
| 9d | High | No | No | No | Yes | Yes | No | No | -7.11 |
GI adsorption: Gastrointestinal adsorption, BBB: Blood–brain barrier, CYP1A2: Cytochrome P450 1A2, CYP2C9: Cytochrome P450 2C9, CYP2CA9: Cytochrome P450 2CA9, CYP2D6: Cytochrome P450 2D6, CYP3A4: Cytochrome P450 3A4.
In terms of metabolism, all compounds are predicted to inhibit both CYP2C19 and CYP2C9, which are key cytochrome P450 enzymes involved in drug metabolism. Compound 9c additionally inhibits CYP1A2, indicating a broader interaction profile, whereas none of the compounds inhibit CYP2D6 or CYP3A4. This suggests a lower risk of significant drug-drug interactions, especially for compounds 9a, 9b, and 9d that can be analysed during preclinical studies. The log Kp (skin permeation) values, ranging from -6.47 to -7.11 cm/s, indicate moderate to low skin permeability, which is acceptable for oral drugs, although it might limit transdermal applications. These ADME profiles support the compounds’ suitability for oral therapeutic use, with favorable absorption, limited CNS penetration, and manageable metabolic profiles, particularly in the anti-cancer drug development.
3.6. Toxicity results
A summary of toxicity results, including the activity status and probability scores, has been given in Table 5. Compounds 9a-d show inactive carcinogenicity, with high probabilities ranging from 0.59 to 0.67, suggesting a low risk of cancer-related toxicity. Additionally, compounds 9a and 9b demonstrate inactive immunotoxicity (0.80-0.86), indicating minimal potential for immune system-related side effects. Cytotoxicity predictions are favorable for all compounds, with inactive cytotoxicity probabilities ranging from 0.66 to 0.81, reflecting a low likelihood of harming healthy cells. Furthermore, compounds 9a, 9b, and 9c show active BBB permeation (0.52-0.54). Overall, the data highlight the compounds’ favorable safety profile with minimal cytotoxicity and promising therapeutic potential.
| Code | Hepatotoxicity | Carcinogenicity | Immunotoxicity | Mutagenicity | Cytotoxicity | BBB permeation | ||||||
|---|---|---|---|---|---|---|---|---|---|---|---|---|
| Toxi | Probability | Toxi | Probability | Toxi | Probability | Toxi | Probability | Toxi | Probability | Toxi | Probability | |
| 9a | Act | 0.50 | Inac | 0.59 | Inac | 0.80 | Act | 0.50 | Inac | 0.81 | Act | 0.53 |
| 9b | Act | 0.49 | Inac | 0.59 | Inac | 0.86 | Act | 0.51 | Inac | 0.81 | Act | 0.54 |
| 9c | Act | 0.50 | Inac | 0.67 | Act | 0.53 | Inac | 0.55 | Inac | 0.78 | Act | 0.54 |
| 9d | Act | 0.50 | Act | 0.56 | Act | 0.62 | Act | 0.71 | Inac | 0.59 | Inac | 0.52 |
Toxi: Toxicity, Act: Active, Inac: Inactive
3.7. Quantum chemical analysis
The quantum chemical descriptors of compounds 9a-d provide significant insight into their electronic structure, chemical reactivity, and potential applications. The HOMO-LUMO gap (ΔE) serves as an indicator of molecular stability and reactivity, as presented in Table 6. Among the studied compounds, 9d has the smallest ΔE (-0.1259), implying a greater tendency for electronic transitions and higher reactivity. In contrast, compounds 9a-c have relatively similar ΔE values (∼ -0.159), suggesting comparable stability and reactivity. Polarizability (α), which influences molecular interactions with external fields, was estimated for 9a based on its trend with molecular volume. The calculated values indicate that 9d (230.70 a.u) has the highest polarizability, followed by 9b (225.77 a.u) and 9c (224.85 a.u), while 9a has the lowest polarizability (166.20 a.u). This suggests that 9d has a greater ability to distort under an external field, making it more susceptible to electronic interactions. The electrophilicity index (ω), which measures the molecule’s ability to accept electrons, shows that 9d (-0.2794) is the most electrophilic, while 9a (-0.1781), 9b (-0.1714), and 9c (-0.1769) have lower but comparable electrophilic indices. This means that 9d is more likely to participate in reactions involving nucleophiles, making it a strong electron acceptor. Conversely, the nucleophilicity index (N), which indicates electron-donating ability, suggests that 9b (-5.83) is the most nucleophilic, whereas 9d (-3.58) has the lowest nucleophilicity, aligning with its strong electrophilic nature. The chemical potential (μ), a measure of the tendency to lose electrons, follows a similar trend. Additionally, 9d has the lowest μ (-0.1876), confirming that it is the least likely to donate electrons, while 9a-c has slightly higher values, suggesting relatively better electron-donating abilities. The hardness (η) and softness (S) values further reinforce these findings. While, 9d, with the smallest hardness (-0.0629), is the most chemically reactive, 9a-9c have comparable hardness values (∼ -0.079), making them moderately reactive. The softness (S) values further confirm that 9d is more reactive due to its higher softness (-15.89), which allows easier electron exchange.
| Compound | Electronic energy (a.u.) | HOMO (EH) (eV) | LUMO (EL) (eV) | Dipole moment (Debye) | HOMO-LUMO gap (ΔE) (eV) | Electro-negativity (χ) (eV) | Chemical potential (μ) (eV) |
|---|---|---|---|---|---|---|---|
| 9a | -1159.730 | -0.248 | -0.089 | 5.924 | 0.160 | 0.169 | -0.169 |
| 9b | -1199.040 | -0.245 | -0.086 | 5.770 | 0.159 | 0.165 | -0.165 |
| 9c | -1619.310 | -0.248 | -0.088 | 2.935 | 0.159 | 0.168 | -0.168 |
| 9d | -1364.150 | -0.251 | -0.125 | 2.495 | 0.126 | 0.188 | -0.188 |
| Compound | Polarizability A3 | Hardness (η) (J-1) | Softness (S) (J-1) | Hyper-polarizability (β) A5 | Electrophilicity index (ω) (eV) | Nucleophilicity index (N) (eV) | |
| 9a | 166.200 | -0.080 | -12.540 | 984.610 | -0.178 | -5.610 | |
| 9b | 225.770 | -0.080 | -12.580 | 810.270 | -0.171 | -5.830 | |
| 9c | 224.850 | -0.080 | -12.550 | 1096.210 | -0.177 | -5.650 | |
| 9d | 230.700 | -0.063 | -15.890 | 649.280 | -0.279 | -3.580 | |
MEP analysis revealed significant negative potential around oxygen and nitrogen atoms, highlighting their role in hydrogen bonding interactions, as depicted in Figure 6. MEP analysis was conducted to visualize charge distribution within the molecules, which is essential for understanding their interaction with biomolecules. The negative potential regions (red) were localized around the oxygen atoms of the salicylaldehyde moiety, making them suitable sites for hydrogen bonding with amino acid residues in ESR1 and around the triazole ring, indicating possible electrophilic interactions.
- Frontier molecular orbital and MEP for the title compounds.
This charge separation further supports 9d as a highly interactive molecule for ESR1 inhibition. Overall, these descriptors reveal that 9d is the most electrophilic, reactive, and polarizable, making it a potential candidate for catalytic or electronic applications [29]. Unveiling multifunctional inhibitors: holistic spectral, electronic, and molecular characterization, coupled with biological profiling of substituted pyridine derivatives against LD transpeptidase, heme oxygenase, and PPAR gamma [30]. Applications of the Vienna Ab initio simulation package, DFT, and molecular interaction studies for investigating the electrochemical stability and solvation performance of non-aqueous NaMF6 electrolytes for sodium-ion batteries. 9a, 9b, and 9c show intermediate behavior, balancing stability and reactivity, which may make them applicable in diverse chemical environments.
3.8. Topological analysis
To further understand electronic structure, electronic delocalization, and molecular interactions, ELF, Reduced Density Gradient (RDG), and LOL were analysed, as presented in Figures 7-10. ELF analysis showed strong electron localization around oxygen and nitrogen atoms in 9a-d, confirming their ability to form hydrogen bonds with biomolecules. Among all compounds, 9d exhibited the most delocalized electronic density, which enhances its ability to participate in charge transfer interactions. RDG plots revealed strong NCIs, particularly in 9d, suggesting that it can establish stable intermolecular interactions with ESR1. LOL maps confirmed extended delocalization in the triazole core, promoting stability and charge transfer, extensive π-π stacking interactions in 9d, further supporting its strong molecular recognition potential.
- ELF (Electron localization function), RDG (Reduced density gradient), LOL (Localized orbital locator), color hotspot for the compound 9a.
- ELF (Electron localization function), RDG (Reduced density gradient), LOL (Localized orbital locator), color hotspot for the compound 9b.
- ELF (Electron localization function), RDG (Reduced density gradient), LOL (Localized orbital locator), color hotspot for the compound 9c.
- ELF (Electron localization function), RDG (Reduced density gradient), LOL (Localized orbital locator), color hotspot for the compound 9d.
3.9. Hirshfeld surface analysis
Figure 11 revealed prominent H•••H and O•••H interactions, which contributed to molecular stability and packing. The fragment patch fingerprint analysis validated the significance of hydrogen bonding, particularly in 9d, which exhibited extensive donor-acceptor interactions. The fingerprint plots showed strong hydrogen bonding interactions in 9d, with significant H•••H (66%) interactions. Fragment patch analysis revealed that 9d has the highest contribution from π-π stacking interactions, further reinforcing its binding affinity. Fingerprint plots confirmed that O•••H interactions dominate in 9d highest percentage (25.5%), suggesting its strong potential for biomolecular recognition.
- Fingerprint plots for the compounds 9a-d.
3.10. Molecular docking results
The docking analysis revealed that among the designed compounds, compound 9d exhibited the most favorable binding profile, with a docking score of -5.82 kcal/mol, the lowest among the series. It formed six key interactions, including hydrogen bonds with ALA A-312, LYS A-481, LEU A-495, and LEU A-497, a π-anion interaction with ASP A-484, and a π-alkyl interaction with ALA A-491, indicating a stable and potentially inhibitory binding to the ESR1 active site.
Compound 9a showed a docking score of -4.99 kcal/mol, forming five interactions. These included a hydrogen bond with HIS D-476, π-alkyl interactions with ALA B-430 and MET B-437, a π-cation interaction with LYS D-472, and a C-H bond with ARG B-434, reflecting a reasonably stable interaction profile. Compound 9c yielded a docking score of -4.96 kcal/mol, with three interactions, including a hydrogen bond with GLN A-500, π-alkyl interactions with LEU A-497, and Van der Waals interactions with ALA A-491, indicating moderate affinity. Compound 9b had the lowest binding score among the test compounds at -4.90 kcal/mol, forming four interactions: a hydrogen bond with TRP B-526, π-alkyl interactions with TYR B-537, and a π-π stacking interaction with LYS B-531.
The co-crystallized ligand displayed a substantially better docking score of -9.54 kcal/mol, with eleven interactions. These included strong hydrogen bonds with GLU353 and ARG394, along with several alkyl interactions involving LEU346, LEU384, LEU387, LEU391, ALA350, MET343, MET421, and ILE424, and a π-π T-shaped interaction with PHE404. This indicates a higher binding affinity and a more extensive interaction as a standard for comparison.
These findings suggest that the compound 9d stands out for its stronger interactions and better docking score among the series, suggesting its potential as a promising ESR1 inhibitor for further development. 2/3D interactions of the designed compounds have been shown in Figure 12 and Table 7.
- 2/3-D structural interactions of the compounds.
| Product | Docking score (Kcal mol−1) | Interacting residues count. | Residue type and the form of interaction |
|---|---|---|---|
| 9a | -4.99 | 5 | HIS D-476 (H-bond); ALA B-430, MET B-437(Pi-Alkyl); LYS D-472 (Pi-Cation); ARG B-434 (C-H bond) |
| 9b | -4.90 | 4 |
TRP B-526 (H-bond); TYR B-537(Pi-Alkyl); LYS B-531 (Pi-Pi stacked) |
| 9c | -4.96 | 3 |
GLN A-500 (H-bond); LEU A-497 (Pi-Alkyl); ALA A-491 (Vanderwal) |
| 9d | -5.82 | 6 | ALA A-312, LYS A-481, LEU A-495, LEU A -497 (H-bond); ASP A-484 (Pi-Anion); ALA A-491(Pi- Alkyl) |
| Co-crystal | -9.54 | 11 | GLU353, ARG394 (H-bond), LEU391, LEU384, LEU387, LEU346, ALA350, MET343, MET421, ILE424 (Alkyl), PHE404 (pi-pi T shaped) |
3.11. MD simulation results
A 100 ns MD simulation was performed to evaluate the stability of the interactions between the ESR1 protein and compound 9d. The RMSD and RMSF of the ESR1-9d complex were assessed, as shown in Figures 13 and 14. In the RMSD plot, the Cα atom deviations in ESR1, along with 9d fitted to ESR1, are illustrated. ESR1’s RMSD gradually increased, peaking at 7 Å around 18 ns, followed by stabilization at an average of 4.8 Å. For compound 9d, RMSD rose in a stepwise manner, reaching a maximum of 18.1 Å at 38 ns, and then stabilized at an average of 14.9 Å for the remainder of the simulation. This increase in RMSD indicates structural changes in the protein and ligand relative to the reference structure at 0 ns. The stabilized RMSD curve suggests minimal structural disruption and long-lasting protein-ligand interactions.
- The RMSD plot of the ESR1-9d complex.
- The RMSF plot of the ESR1-9d complex. (RMSF: Root mean square fluctuation).
The RMSF plot from the MD simulation highlights residue-level positional fluctuations throughout the simulation. Notably, HIS377 and HIS474 exhibited the highest fluctuations. Most residues with significant RMSF values did not interact directly with the ligand, 9d, and thus did not influence the stability of the interactions between 9d and ESR1.
The intermolecular interactions between the protein ESR1 and ligand 9d were tracked over a 100 ns MD simulation, with the results shown in Figures 15 and 16. Figure 15(a) displays the total number of interactions, starting with six interactions at the beginning, fluctuating throughout the simulation, and averaging 4.7 bonds. Figure 15(b) summarizes residue-level contacts, with ASN532, SER527, and ALA350 being the key residues maintaining consistent interactions with the ligand. Figure 16 depicts the interaction fraction for each residue, revealing that hydrogen bonds involving SER527, SER536, and ASN532, along with hydrophobic contacts from VAL533 and LEU539, and water bridges formed by ASN532, VAL534, and ALA350, were particularly stable. The 2D representation highlights long-lasting interactions, notably the hydrogen bond with SER527 and a water bridge with ASN532, which persisted for over 30% of the simulation. These stable interactions suggest that the ESR1-9d complex is highly stable, indicating the potential of compound 9d for further therapeutic exploration against ESR1.
- Intermolecular interactions between ESR1 and 9d. (a) Total number of interactions observed over time and (b) Contacts made by the residues over the time.
- Histogram of interaction fraction observed for various interactions made by residues in ESR1 with 9d during MD simulation.
4. Conclusions
Our study successfully reports the synthesis and comprehensive characterization of a novel series of 1,2,3-triazole-salicylaldehyde conjugates 9a-d, with their structures unequivocally confirmed by HRMS and 1H/13C NMR spectroscopy. Through network pharmacology analysis, ESR1 was identified as a pivotal therapeutic target for these compounds in breast cancer. Subsequent DFT calculations revealed that compound 9d possesses the most favorable electronic characteristics, suggesting its potential as an ESR1-targeted anticancer agent. In silico investigations, including molecular docking, indicated that 9d (p-nitro) and 9a exhibited the most promising binding affinities to ESR1, with energies of -5.82 and -4.99 kcal/mol, respectively, driven by robust interactions with key residues such as LEU A-497 and HIS D-476. Furthermore, a 100 ns molecular dynamics (MD) simulation affirmed the conformational stability of compound 9d within the ESR1 binding site, thus validating its potential as an effective candidate for targeted breast cancer therapy.
Acknowledgment
The authors would like to acknowledge Deanship of Graduate Studies and Scientific Research, Taif University for funding this work.
CRediT authorship contribution statement
Saad Alotaibi and Mohammed Alotaibi: Conceptualization, Supervision, Validation, Data curation, Software, Methodology, Visualization, Investigation, Writing, Original draft preparation, Writing-Reviewing and Editing.
Declaration of competing interest
The authors declare that they have no competing interests.
Declaration of generative AI and AI-assisted technologies in the writing process
The authors confirm that there was no use of artificial intelligence (AI)-assisted technology for assisting in the writing or editing of the manuscript and no images were manipulated using AI.
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