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Macitentan as a repurposed MCL-1 inhibitor in triple-negative breast cancer: Insights from molecular docking, MM-GBSA, and MD simulations
*Corresponding author: E-mail address: ahmedadelashour@yahoo.com (A Ashour)
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Received: ,
Accepted: ,
Abstract
Triple-negative breast cancer (TNBC) remains a significant clinical challenge due to its aggressive nature and lack of targeted therapies. Overexpression of myeloid cell leukemia-1 (MCL-1) and polo-like kinase 1 (PLK1) contributes to tumor progression and therapeutic resistance, making them making them attractive therapeutic targets. This study explores the repurposing potential of FDA-approved endothelin receptor antagonists (ERAs); Macitentan, Ambrisentan, and Bosentan against MCL-1 and PLK1 using a comprehensive computational approach. Molecular docking, molecular mechanics-generalized born surface area (MM-GBSA) binding free energy calculations and molecular dynamics (MD) simulations analyses were employed to evaluate binding affinity and complex stability. Macitentan demonstrated the strongest binding to both targets, particularly MCL-1, with favorable docking (–9.023 kcal/mol) and MM-GBSA (–48.53 kcal/mol) scores, while Ambrisentan showed moderate affinity. Benchmarking with co-crystallized ligands confirmed the superior binding of R78 to PLK1, shifting the study’s focus to MCL-1. MD simulations confirmed the stability of Macitentan and Ambrisentan complexes with MCL-1, maintaining the root mean square deviation (RMSD) < 3.5 Å with minor drift around 4 Å and root mean square fluctuation (RMSF) < 2.5 Å. These findings support further experimental validation of Macitentan as a repurposed MCL-1 targeted therapy for TNBC.
Keywords
Cancer
Endothelin receptor antagonists
MCL-1
PLK1
Molecular dynamics
TNBC
1. Introduction
Breast cancer (BC) is the second cause of death in women where in 2020 around 685,000 deaths of over 2.3 million new cases has been estimated (Łukasiewicz et al., 2021; Michaeli et al., 2024; Oliveira et al., 2024). BC is classified, based on the expression of hormone receptors, into four categories which are estrogen receptor-positive (ER+), progesterone receptor positive (PR+), human epidermal growth factor receptor two positive (HER2+), and triple-negative breast cancer (TNBC) (Bafna et al., 2020; Liao et al., 2021).
TNBC is a heterogeneous and the most aggressive type of BC which lacks of expression of any of the hormone receptors and has incidence rate of 15-20% of BC diagnosed premenopausal women at age less than 40 years (Akash et al., 2023a; Damaskos et al., 2021; Spini et al., 2020). TNBC has been associated with high mortality rates and an overall estimated recurrence rate of 75% (Salama and Khairy, 2021; Yin et al., 2020). Due to the complicated genetic phenotype, high malignancy and the higher rates of recurrence of TNBC, endocrine therapy is associated with poor outcomes and resistance in this type (Bhattacharya et al., 2023; Lin et al., 2023; Masci et al., 2023). Accordingly, treatment options for TNBC are too limited, nevertheless chemotherapy based therapy is the current treatment strategy used for TNBC which includes anticancer drugs from various classes that inhibits cell growth such as DNA intercalating agents (anthracyclines), DNA topoisomerase II blockers, alkylating agents (cyclophosphamide), antimetabolites (5-flourouracil) and anti-microtubules (taxanes) (Ávalos-Moreno et al., 2020; Larsson et al., 2024; Won and Spruck, 2020). Chemotherapy could be placed before surgery to reduce tumor size and reduce development of new ones, hence helps in better delimitation of surgical areas (Yang et al., 2022; Yeh et al., 2021).
Although, TNBC when compared to other BC types; is considered an aggressive type of BC that is associated with high recurrence rate, metastasis and irresponsiveness to hormonal therapy, hence mandates deep understanding of the oncogenic process that can aid in finding new targets for new therapeutic lines (Shoaib et al., 2023; Yang et al., 2022; Yeh et al., 2021). Accordingly, TNBC necessitates the urgent demand for investigation of new therapeutic molecules. Among several pathways of TNBC oncogenic pathways; B-cell lymphoma 2 (Bcl2) like protein 1 (also known as myeloid leukemia cell differentiation protein, MCL-1) is one of a family of proteins of Bcl-2 which plays significant role in cancer cells oncogenesis through inhibition of cellular apoptosis thereby increasing cancer cells survival (Perri et al., 2018; Pratelli et al., 2023; Winder and Campbell, 2022). MCL-1 and polo-like kinase 1 (PLK-1) proteins are proposed as new targets in the pathophysiology of TNBC (Dong and Alahari, 2024; Nieto-Jimenez et al., 2020). PLK 1 has been found to play a major role in the oncogenesis through many steps of mitosis of cancer cells including signaling transduction, transcription activation where its overexpression associated with poor cancer survival (Ueda et al., 2019; Zanini et al., 2024; Zhao et al., 2021). Moreover, Endothelin receptor stimulation leads to angiogenesis in many cancer types as they are potent mitogens with the ability of inducing cellular proliferation. They are overexpressed in breast tumors compared with non-neoplastic tissue where endothelin receptors A (ETa) and B (ETb) subtypes are associated with more invasion and metastasis (Bagnato et al., 2011; Son et al., 2021).
The advances in biomedical sciences linked with the knowledge of protein structure and the updated protein libraries have revealed a great impact to the improvement in drug discovery and development using computational techniques (Almihyawi et al., 2022; Iwaloye et al., 2023). In this concern, computational aided drug design (CADD) has been playing active roles in drug discovery and development processes via several approaches and tools (like molecular docking, dynamic simulation, density functional theory and other tools) that helped not only in saving time and cost that associated with the traditional drug discovery and development but also lower the chances of failure during clinical or pre-clinical trials (Akash et al., 2023b; Ávalos-Moreno et al., 2020; Brogi et al., 2020; Ramírez and Caballero, 2018; Tripathi et al., 2019). Drug repurposing is a CADD tool which search for new uses for the investigated and the approved drugs, thereby this strategy will accelerate the process of drug discovery and development (Ávalos-Moreno et al., 2020; Shivani et al., 2024). It is also noteworthy to state that there are many drugs for BC have been discovered by drug repurposing (Kiriiri et al., 2020; Zhou et al., 2023). Endothelin receptor antagonists like ambrisentan, bosentan and macitentan were approved for pulmonary arterial hypertension treatment and the have showed promising in vitro anticancer activity, thus, the inhibition of this protein is beneficial in treating cancer (Correale et al., 2018; Kappes et al., 2020a; Son et al., 2021).
The conventional anticancer drugs show unspecific distribution in the body where low target exposure leading to therapeutic insufficiencies, failure, resistance, and emergence of toxicities (Khoshbayan et al., 2020; Sibuyi et al., 2021). Accordingly, the use of more targeted drug delivery system including nanoparticle drug delivery system would help overcoming these issues while increasing the bioavailability and efficacy (James et al., 2024; Sánchez-Coronilla et al., 2020; Tariq et al., 2019).
In this study, drug repurposing of endothelin receptor antagonists (ERAs); Macitentan, Ambresentan and Bosentan as dual inhibitors for PLK1 and MCL-1 for TNBC was investigated using molecular docking, molecular dynamics.
2. Materials and Methods
Maestro v 12.8 of Schrodinger suite was utilized for in silico studies. Molecular dynamics (MD) simulations were conducted using academic Desmond v 6.5 by D. E. Shaw Research.
2.1 Protein preparation
The 3D structure of PLK1 (PDB:2RKU) and MCL-1 (PDB: 6UDV) were downloaded from the RSCB protein data bank (https://www.rcsb.org/). Using protein preparation wizard panel (PPW) in Maestro; the obtained proteins were pre-processed in order to check their structures, verify the assignment of bonds, binding orders, add missing hydrogen atoms, complete missing side chains, and remove crystallographic water beyond 5 Å form the ligands. Moreover, proteins were minimized using the optimized potential for liquid simulations (OPLS3e) force field (Alzain, 2022).
2.2 Ligand preparation
The Endothelin receptor antagonists’ structures were downloaded from CHEMSPIDER database (http://www.chemspider.com/) (Fig. 1). LigPrep tool of Schrodinger was used to prepare the ligands with the focus create 3D structures with correct chirality, ionization states, stereochemistry and tautomer. Charge neutralization to obtain the biological relevant pH (pH 7), desalting was carried out using Epik v4.7, which depends on Hammett and Taft methodologies. Additionally, the polar residues’ protonation statuses were adjusted using PROPKA where maximum 32 stereoisomer per compound was produced (Omer et al., 2022). further, the degree of ionization was evaluated at pH 7 and ligands were subjected to energy minimization using OPLS4 force field (Makki et al., 2023).
2.3 Receptor grid generation and docking
The binding site was first determined in the place of the pre-existing ligand using the receptor grid generation module of the Schrödinger suite, which generate a grid box around the pre-existing ligand. Accordingly, the receptor grids box and dimensions for both MCL-1 (6UDV) and PLK1 (2RKU) were centered on the co-crystallized ligands where, respectively, was (X: 52.48, Y: -40.99, Z: -37.35) and (X: -47.75, Y: 12.13, Z: 16.28) with the same dimensions 10x10x10 Å for each of them. Then cross-docking of prepared protein and prepared ligands was performed by using cross-docking module on Schrodinger suite in XP precision (Ali et al., 2023).
2.4 MM-GBSA binding free energy calculations
The XP docking used in the previous step gives the top-scoring ligands but is also incapable to calculate the binding free energy of the ligand-protein complex. Thus, Prime’s MM-GBSA tool of Maestro was used to predict the binding free energy. The docking pose viewer (pv) file obtained from the XP mode of Glide was used for the calculation (Omer et al., 2022).
2.5 Molecular dynamics (MD) simulation
The docking procedure considers the receptor as a rigid structure. However, it’s dynamic in the human body (the drugs conformation) change as the receptor atoms and particle in moving state (Salmaso and Moro, 2018). Thus, combining the fast and inexpensive docking with accurate MD will improve the screening process and development of new hits. Herein, complexes of MCL-1 with the short-listed drug were carried forward to MD simulation for 50 ns using the Academic Desmond software to get insight into the ligand-binding stability.
The MD system was immersed in TIP3P water molecules in an orthorhombic box with a size of 10 Å × 10 Å × 10 Å that was then neutralized by adding 0.15 M NaCl in a buffer. Additionally, NPT (isothermal-isobaric) ensemble was used at a temperature of 300 K and pressure of 1.01325 bars which were further maintained by the Nose–Hoover thermostat and the Martyna Tobias-Klein algorithms, respectively (Alzain et al., 2023). Furthermore, during the MD simulation, a total of 1,000 frames at every 50 ps recording interval for 50 ns were collected. The root mean square deviation (RMSD) and root mean square fluctuation (RMSF) along the trajectories were used to analyze the complexes stability (Kumar et al., 2020).
3. Results
3.1 Molecular docking (MD)
Herein, silico techniques were used to investigate ERAs as potential agents in the treatment of TNBC. Three approved ERAs; Macitentan, Ambrisentan and Bosentan were docked against PLK1 and MCL-1 protein along with their co-crystalized ligands Q51 and R78 (BI 2536), respectively to explore binding affinity and interactions of the tested ligands (Table 1).
| Ligand | Docking score (Kcal/mol) | MM-GBSA dG bind (kcal/mol) |
|---|---|---|
| MCL-1 protein | ||
| Macitentan | -9.023 | -48.53 |
| Ambrisentan | -7.751 | -8.91 |
| Bosentan | -0.460 | +10.69 |
| Q51 | -4.452 | -7.66 |
| PLK 1 protein | ||
| Macitentan | -5.366 | -45.64 |
| Ambrisentan | -6.695 | -13.63 |
| Bosentan | -5.937 | -43.57 |
| R78 (BI 2536) | -12.633 | -92.47 |
The docking scores obtained for Macitentan, Ambrisentan, Bosentan, and Q51 with the MCL-1 protein were -9.023, -7.751, -0.460 and -4.452 kcal/mol respectively. The MD scores expressed in Kcal/mol reflect the quality of the ligand’s interactions with the protein. A lower (more negative) energy means more stable interactions. Obtained results suggest that Macitentan and Ambrisentan had strong interactions, followed by Bosentan, while the weakest interaction was observed with the co-crystallized ligand.
The interactions of MCL-1 protein with these ligands were illustrated in Fig. 2 and Table 2. Macitentan displayed three hydrogen bonds with water molecules, bridged hydrogen bond with THR 266, pi-pi interactions with PHE 270 and pi- cation bond with ALA 227. While Ambrisentan established one pi-pi interaction with PHE 228. In contrast, Bosentan showed negligible interaction with MCL-1 protein. The co-crystallized ligand Q51 formed four hydrogen bonds with water molecules and a bridging hydrogen bond with THR266.
| Ligand | Pi-Pi interaction | H bonds | Pi-cation | Others |
|---|---|---|---|---|
| Mcl 1 protein | ||||
| Macitentan | PHE 270 | Water, THR 266 | ------------ | ALA 227 halogen bond with Br |
| Ambrisentan | PHE 228 | -------- | ||
| Bosentan | ----- | ---------- | ||
| Q51 | -------- | Water, THR 266 | ------- | ------- |
| PLK 1 protein | ||||
| Macitentan | ------- | Water, ARG 136, LYS 82 | ------- | ARG 136 halogen bond with Br |
| Ambrisentan | PHE 183 | ARG 136 | Salt bridge with ARG 136 | |
| Bosentan | ------ | ARG 136, CYS 133 | ------- | |
| R78 (BI 2536) | ------- | LYS 82, ASP 194, CYS 133, water, LEU 59, PHE 58 | ------- | ------- |
In PLK 1 protein the docking scores were as follows: Macitentan scored –5.366 Kcal/mol, Ambrisentan –6.695 Kcal/mol, Bosentan –5.937 Kcal/mol, and the PLK1 co-crystallized ligand R78 (BI 2536) showed the highest binding affinity with a score of –12.633 Kcal/mol (Table 1). ARG136 emerged as a key residue in the interactions of ligands with PLK1 (Fig. 3). Macitentan’s binding involved a network of direct and water-mediated hydrogen bonds with water, ARG136 and LYS82, complemented by a π–cation interaction between ARG136 and the bromine atom. Similarly, Ambrisentan interacted with ARG136 through both a salt bridge and a π–cation interaction. In the case of Bosentan, ARG136 facilitated binding through two hydrogen bonds, in conjunction with CYS133. In contrast to other ligands, the interaction of the co-crystallized ligand was mediated by hydrogen bonds involving LYS82, ASP194, CYS133, LEU59 and PHE58 water molecule within PLK1 binding pocket.
3.2 MM-GBSA binding free energy
The Prime MM-GBSA binding free energy calculations were used to estimate the binding energies for the interactions between ligand and the proteins. In MCL-1, Macitentan displayed the best binding energy (-48.53 kcal/mol) followed by Ambrisentan (-8.91 kcal/mol) and Q51 ligand (-7.66 kcal/mol) and Bosentan (+10.69 kcal/mol). Meanwhile, in PLK 1 protein the co- crystallized ligand R78 exhibited the best binding energy (-92.47 kcal/mol) followed by Macitentan (-45.64 kcal/mol), Bosentan (-43.57 kcal/mol) and finally Ambrisentan (-13.63 kcal/mol) (Table 1).
Macitentan and Ambrisentan exhibited strong binding affinity for the MCL-1 protein, as evidenced by both docking scores and MM-GBSA binding energy calculations. This alignment in results reinforced their selection for further computational studies. In contrast, R78 consistently demonstrated high affinity for PLK1, whereas Macitentan, Ambrisentan, and Bosentan had significantly lower affinities, suggesting limited potential for their repurposing against PLK1.It is noteworthy to state that Q51 and R78 have been used as benchmarking for Mcl-1 and PLK-1, respectively, where they were redocked to each target to validate the docking and MM-GBSA results of the three repurposed drugs on each target. Accordingly, such comparison has confirmed the reliability of the workflow.
Furthermore, the energy decomposition of the MM-GBSA analysis revealed that Macitentan’s high affinity (-48.53 kcal/mol) is primarily driven by exceptional van der Waals interactions (-50.98 kcal/mol) and strong electrostatic stabilization (-40.67 kcal/mol). This quantitative breakdown clarified that Macitentan not only fits more closely fitting into the lipophilic groove of MCL-1 than the co-crystallized ligand Q 51 but also establishes a more energetically favorable polar network.
3.3 Molecular dynamics (MD) simulation
Macitentan and Ambrisentan MCL-1 protein complexes were carried forward for molecular dynamic simulation. The RMSD was analyzed in order to validate the stability of the drug-receptor complex while the RMSF was analyzed to estimate the drug-receptor complex interaction variation from the mean position during the simulation.
The RMSD plot analysis of Macitentan and Ambrisentan -MCL-1 protein complex revealed that the protein was stabilized around 3.5 Å within the first 10 ns of the simulation, then minor drift to 4 Å was observed around 35 ns to the end of the simulation. Moreover, Macitentan and Ambrisentan RMSD plots were equilibrated after 12 ns around 3.5 Å, then minor fluctuation was observed between 25 ns to 35 ns after which equilibrium was attained around 3 Å to the end of the simulation (Fig. 4).
The results of RMSF were represented in Fig. 5 and revealed that Macitentan and Ambrisentan complex showed low fluctuations (0.8 to 2.5 Å) throughout the simulation except for the spike that is observed with N-terminal residues.
The interaction profile of the ligands with the MCL-1 protein was examined to identify key residues contributing to the stability of the complexes during molecular dynamics simulations. For Macitentan, the residue ARG263 was crucial, forming both direct hydrogen (50%), while LYS234 contributed through hydrophobic interactions (20%). In contrast, for Ambrisentan LYS234 emerged as the primary interacting residue, engaging in hydrogen bonding for 80% of the simulation time, along with ionic interactions (8%) and water bridges (12%). Additionally, MET231 contributed 20% through hydrophobic interactions, further stabilizing the complex (Fig. 6). Additionally, the stability of the key residue-ligand contacts was quantified by calculating their occupancy percentages throughout the 50 ns MD simulation. For Macitentan, while the binding mode evolved from docking, ARG263 emerged as a critical anchor with 50% occupancy, supplemented by hydrophobic interactions with LYS234 (20%). For Ambrisentan, the hydrogen bond with LYS234 proved exceptionally stable with an 80% occupancy
4. Discussion
TNBC is characterized by its aggressive nature and the lack of effective targeted therapies (McArthur, 2018; Xiong et al., 2024). A significant feature of TNBC cells is the overexpression of MCL-1 and PLK1, both of which play crucial roles in cell survival, proliferation, and resistance to treatment (Duan et al., 2024; Goodwin et al., 2015; Lee et al., 2017; Maire et al., 2013; Siraj et al., 2023; Ueda et al., 2019). Inhibiting MCL1 has been shown to induce apoptosis and suppress proliferation in TNBC models (Duan et al., 2024; Goodwin et al., 2015). Remarkably, high levels of MCL-1 are present in approximately 83% of TNBC cell lines and are linked to resistance against chemotherapeutics, such as cisplatin and mitogen-activated extracellular signal-regulated kinase (MEK) inhibitors, with MCL-1 inhibition capable of restoring drug sensitivity (Lee et al., 2017). Similarly, targeting PLK1 has significant effects, leading to G2/M cell cycle arrest, polyploidy, and apoptosis (Maire et al., 2013; Siraj et al., 2023; Ueda et al., 2019). Given their critical roles in TNBC progression and resistance, both MCL-1 and PLK1 stand out as promising targets for the development of inhibitors. Focusing on these proteins could provide new therapeutic strategies to enhance treatment efficacy and overcome resistance in TNBC patients.
In this context, FDA-approved ERAs have shown promising anti-tumor activity in preclinical models and are currently under clinical investigation as novel cancer therapies. Beyond their traditional role in blocking endothelin receptors, ERAs are more and more recognized for their potential to modulate other oncogenic pathways (Kappes et al., 2020b, 2020a; Son et al., 2021). Given their known safety profiles and emerging evidence of anticancer activity, exploring ERAs as repurposed agents targeting novel molecular pathways, such as MCL-1 and PLK1 in TNBC, represents a compelling opportunity for TNBC therapeutic development.
In the current study, we conducted silico drug repurposing for three ERAs: Macitentan, Ambrisentan, and Bosentan targeting MCL-1 and PLK1. Utilizing silico tools not only speeds up the identification of potential therapeutic candidates but also improves the accuracy of target interactions, thereby streamlining the drug development process for TNBC treatments.
The workflow included several key computational techniques: The MD, MM-GBSA binding free energy calculations, and MD simulations. The MD was employed to predict the binding affinity, prioritize compounds based on their docking scores, and identify potential interactions within the binding pocket. Then binding free energy calculations were performed to refine the predictions and provide a more accurate estimation of the binding affinities of the shortlisted compounds. To further validate the interactions and explore the dynamic behavior of the compounds, MD simulations were performed. The docking and MM-GBSA binding energy analysis revealed significant differences in the binding affinities of ERAs toward MCL-1 and PLK1 proteins. Among them, Macitentan demonstrated the strongest binding to both targets, with docking scores of –9.023 and –5.366 kcal/mol for MCL-1 and PLK1, respectively, and highly favorable MM-GBSA binding energies of –48.53 and –45.64 kcal/mol. These results suggest stable and energetically favorable interactions. Ambrisentan showed moderate binding affinity, particularly to PLK1 (docking score –6.695 kcal/mol), but its MM-GBSA energy (–13.63 kcal/mol) was considerably less favorable than Macitentan’s. Bosentan, on the other hand, exhibited poor binding to Mcl-1 with a near-zero docking score (–0.460 kcal/mol) and a positive MM-GBSA energy (+10.69 kcal/mol), indicating an unstable interaction. Whereas, its MM-GBSA energy for PLK1 was (-43.57 kcal/mol), but further confirming weak binding the co-crystallized ligand R78. The co-crystallized ligands, Q51 for MCL-1 and R78 (BI 2536) for PLK1, served as benchmarks in our analysis. R78 exhibited the most favorable binding profile, achieving a docking score of -12.633 kcal/mol and an MM-GBSA value of -92.47 kcal/mol. In contrast, Macitentan, Ambrisentan, and Bosentan exhibited significantly weaker interactions with PLK1, suggesting limited potential for their repurposing against this target. Consequently, we decided to focus on MCL-1 for the next steps in our study. MD simulations were performed on Macitentan and Ambrisentan, which were identified as the most stable ERAs based on docking and MM-GBSA results. The outputs of the MD simulations included RMSD and RMSF metrics, which serve as markers of structural stability over time. The low RMSD and RMSF values for both drugs indicated good stability throughout most of the simulation period.
The 2D interactions of the MCL-1 ligands post-docking identified PHE 270, PHE 228, THR 266, and ALA 227 as key residues involved in ligand binding. During MD simulations, the interaction profiles evolved. For Macitentan, ARG263 was the primary residue mediating the interaction, while Ambrisentan primarily stabilized the complex through LYS234.
Recent advancements in drug delivery systems utilizing nanotechnology for cancer treatment have revealed promising results, particularly in achieving precise tumor targeting, enhancing pharmacokinetic properties, reducing toxicity to normal cells, and minimizing side effects compared to conventional chemotherapeutics.
5. Conclusions
Molecular docking, MM-GBSA binding free energy, and MD were the in-silico techniques used in this study to analyze the possibility of using endothelin receptor antagonists; Macitentan, Ambrisentan and Bosentan as anticancer drugs in the treatment of TNBC. According to this study, Macitentan and Ambrisentan showed good affinity against MCl-1 protein which confirmed by the high docking score, MM-GBSA binding free energy results, stable MD simulations. Further in vitro and in vivo studies should be conducted.
Acknowledgment
The authors extend their appreciation to Prince Sattam bin Abdulaziz University for funding this research work through the project number (PSAU/2025/01/38777)
CRediT authorship contribution statement
Abdalla Fadlalla: Methodology, software, data interpretation, writing original draft; Alaa A. Makki, Walaa Ibraheem, Mohamed Abdelrhman, Abdulrahim A. Alzain: Supervision, project administration, conceptualization, methodology, software, data interpretation, writing original draft, review & editing; Ahmed Ashour, Wadah Osman, Mohammed Hamed Alqarni, Ahmed I. Foudah, Mohamed A. El-Sakhawy: Software, project administration, writing, review & editing. All authors have read and agreed to the published version of the manuscript.
Declaration of competing interest
The authors declare that they have no known competing financial interests or personal relationships that could have appeared to influence the work reported in this paper.
Data availability
Results will be provided upon an adequate inquiry.
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.
References
- A drug design strategy based on molecular docking and molecular dynamics simulations applied to development of inhibitor against triple-negative breast cancer by scutellarein derivatives. PLoS One. 2023;18:e0283271. https://doi.org/10.1371/journal.pone.0283271
- [Google Scholar]
- Revolutionizing anti-cancer drug discovery against breast cancer and lung cancer by modification of natural genistein: An advanced computational and drug design approach. Front Oncol. 2023;13:1228865. https://doi.org/10.3389/fonc.2023.1228865
- [Google Scholar]
- Design of novel phosphatidylinositol 3-kinase inhibitors for Non-Hodgkin’s lymphoma: Molecular docking, molecular dynamics, and density functional theory studies on gold nanoparticles. Molecules. 2023;28:2289. https://doi.org/10.3390/molecules28052289
- [Google Scholar]
- Integrated computer-aided drug design and biophysical simulation approaches to determine natural anti-bacterial compounds for Acinetobacter baumannii. Sci Rep. 2022;12:6590. https://doi.org/10.1038/s41598-022-10364-z
- [Google Scholar]
- Insights from computational studies on the potential of natural compounds as inhibitors against SARS-CoV-2 spike omicron variant. SAR QSAR Environ Res. 2022;33:953-968. https://doi.org/10.1080/1062936X.2022.2152486
- [Google Scholar]
- Insights into the inhibition of mycolic acid synthesis by cytosporone e derivatives for tuberculosis treatment via an in silico multi-target approach. Chem Africa. 2023;6:1811-1831. https://doi.org/10.1007/s42250-023-00605-7
- [Google Scholar]
- Drug repurposing for triple-negative breast cancer. J Pers Med. 2020;10:200. https://doi.org/10.3390/jpm10040200
- [Google Scholar]
- Computer-aided ligand discovery for estrogen receptor alpha. Int J Mol Sci. 2020;21:4193. https://doi.org/10.3390/ijms21124193
- [Google Scholar]
- Role of the endothelin axis and its antagonists in the treatment of cancer. Br J Pharmacol. 2011;163:220-233. https://doi.org/10.1111/j.1476-5381.2011.01217.x
- [Google Scholar]
- Cell-of-origin targeted drug repurposing for triple-negative and inflammatory breast carcinoma with HDAC and HSP90 inhibitors combined with niclosamide. Cancers (Basel). 2023;15:332. https://doi.org/10.3390/cancers15020332
- [Google Scholar]
- Editorial: In silico methods for drug design and discovery. Front Chem. 2020;8:612. https://doi.org/10.3389/fchem.2020.00612
- [Google Scholar]
- Endothelin-receptor antagonists in the management of pulmonary arterial hypertension: Where do we stand? Vasc Health Risk Manag. 2018;14:253-264. https://doi.org/10.2147/VHRM.S133921
- [Google Scholar]
- Investigational drug treatments for triple-negative breast cancer. J Pers Med. 2021;11:652. https://doi.org/10.3390/jpm11070652
- [Google Scholar]
- Small molecule Mcl-1 inhibitor for triple negative breast cancer therapy. Front Cell Dev Biol. 2024;12:1408107. https://doi.org/10.3389/fcell.2024.1408107
- [Google Scholar]
- Novel markers of MCL1 inhibitor sensitivity in triple-negative breast cancer cells. J Biol Chem. 2024;300:107375. https://doi.org/10.1016/j.jbc.2024.107375
- [Google Scholar]
- Myeloid cell leukemia-1 is an important apoptotic survival factor in triple-negative breast cancer. Cell Death Differ. 2015;22:2098-2106. https://doi.org/10.1038/cdd.2015.73
- [Google Scholar]
- Computer-aided drug design in anti-cancer drug discovery: What have we learnt and what is the way forward? J Pharmacol Exp Ther. 2023;41:101332. https://doi.org/10.1016/j.imu.2023.101332
- [Google Scholar]
- Small molecule therapeutics in the pipeline targeting for triple-negative breast cancer: origin, challenges, opportunities, and mechanisms of action. Int J Mol Sci. 2024;25:6285. https://doi.org/10.3390/ijms25116285
- [Google Scholar]
- Ambrisentan, an endothelin receptor type A-selective antagonist, inhibits cancer cell migration, invasion, and metastasis. Sci Rep. 2020;10:15931. https://doi.org/10.1038/s41598-020-72960-1
- [Google Scholar]
- DFT-QTAIM study of gold nanoparticles and cyclic peptide as effective drug nanocarriers. Orbital: Electron J Chem. 2020;12 https://doi.org/10.17807/orbital.v12i3.1519
- [Google Scholar]
- Exploring different approaches to improve the success of drug discovery and development projects: A review. Futur J Pharm Sci. 2020;6 https://doi.org/10.1186/s43094-020-00047-9
- [Google Scholar]
- Drug repurposing against SARS-CoV-2 receptor binding domain using ensemble-based virtual screening and molecular dynamics simulations. Computers in Biology and Medicine. 2020;135:1-11. http://doi: 10.1016/j.compbiomed.2021
- [Google Scholar]
- Repurposing proteasome inhibitors for improved treatment of triple-negative breast cancer. Cell Death Discov. 2024;10:57. https://doi.org/10.1038/s41420-024-01819-5
- [Google Scholar]
- MYC and MCL1 Cooperatively promote chemotherapy-resistant breast cancer stem cells via regulation of mitochondrial oxidative phosphorylation. Cell Metab. 2017;26:633-647. https://doi.org/10.1016/j.cmet.2017.09.009
- [Google Scholar]
- Small-molecule drug discovery in triple negative breast cancer: Current situation and future directions. J Med Chem. 2021;64:2382-2418. https://doi.org/10.1021/acs.jmedchem.0c01180
- [Google Scholar]
- Identifying candidate drugs based on transcriptional landscape associated with triple-negative breast cancer. J Holistic Pharm. 2023;4:318-324. https://doi.org/10.1016/j.jhip.2023.12.001
- [Google Scholar]
- Breast cancer—Epidemiology, risk factors, classification, prognostic markers, and current treatment strategies—an updated review. Cancers. 2021;13:4287. https://doi.org/10.3390/cancers13174287
- [Google Scholar]
- Polo-like kinase 1: A potential therapeutic option in combination with conventional chemotherapy for the management of patients with triple-negative breast cancer. Cancer Res. 2013;73:813-823. https://doi.org/10.1158/0008-5472.CAN-12-2633
- [Google Scholar]
- Cytosporone E analogues as BRD4 inhibitors for cancer treatment: Molecular docking and molecular dynamic investigations. J Biomol Struct Dyn. 2023;41:12643-12653. https://doi.org/10.1080/07391102.2023.2167122
- [Google Scholar]
- Recent advances in drug discovery for triple-negative breast cancer treatment. Molecules. 2023;28:7513. https://doi.org/10.3390/molecules28227513
- [Google Scholar]
- Innovations and challenges in the treatment of metastatic triple-negative breast cancer. J Oncol Pract. 2018;14:290-291. https://doi.org/10.1200/JOP.18.00224
- [Google Scholar]
- Breast cancer drugs: FDA approval, development time, efficacy, clinical benefits, innovation, trials, endpoints, quality of life, value, and price. Breast Cancer. 2024;31:1144-1155. https://doi.org/10.1007/s12282-024-01634-x
- [Google Scholar]
- Inhibition of the mitotic kinase PLK1 overcomes therapeutic resistance to BET inhibitors in triple negative breast cancer. Cancer Lett. 2020;491:50-59. https://doi.org/10.1016/j.canlet.2020.06.020
- [Google Scholar]
- Advances in breast cancer drug discovery: A review of therapeutic strategies and studies involving photosensitizers, caged xanthones and thiosemicarbazones derivatives. J Braz Chem Soc 2024 https://doi.org/10.21577/0103-5053.20230128
- [Google Scholar]
- Drug repurposing for SARS-CoV-2 main protease: Molecular docking and molecular dynamics investigations. Biochemistry and Biophysics Reports. 2022;29:101225. https://10.1016/j.bbrep.2022.101225
- [Google Scholar]
- Drug repurposing for SARS-CoV-2 main protease: Molecular docking and molecular dynamics investigations. Biochem Biophys Rep. 2022;29:101225. https://doi.org/10.1016/j.bbrep.2022.101225
- [Google Scholar]
- Therapeutic potential of Bcl-xL/Mcl-1 synthetic inhibitor JY-1-106 and retinoids for human triple-negative breast cancer treatment. Oncol Lett. 2018;15:7231-7236. https://doi.org/10.3892/ol.2018.8258
- [Google Scholar]
- MCL1 inhibition overcomes the aggressiveness features of triple-negative breast cancer MDA-MB-231 cells. Int. J Mol Sci. 2023;24:11149. https://doi.org/10.3390/ijms241311149
- [Google Scholar]
- Is It Reliable to take the molecular docking top scoring position as the best solution without considering available structural data? Molecules. 2018;23:1038. https://doi.org/10.3390/molecules23051038
- [Google Scholar]
- Polo-like kinase 1(PLK1) immunohistochemical expression in triple negative breast carcinoma: A probable therapeutic target. Asian Pac J Cancer Prev. 2021;22:3921-3925. https://doi.org/10.31557/APJCP.2021.22.12.3921
- [Google Scholar]
- Bridging molecular docking to molecular dynamics in exploring ligand-protein recognition process: An overview. Front Pharmacol. 2018;9:923. https://doi.org/10.3389/fphar.2018.00923
- [Google Scholar]
- Theoretical study on the interactions between ibrutinib and gold nanoparticles for being used as drug delivery in the chronic lymphocytic leukemia. J Mol Liq. 2020;316:113878. https://doi.org/10.1016/j.molliq.2020.113878
- [Google Scholar]
- Drug repurposing: A retrospective revolution in breast cancer medicine. Discov Med. 2024;1 https://doi.org/10.1007/s44337-024-00012-y
- [Google Scholar]
- Exploring the potential of approved drugs for triple-negative breast cancer treatment by targeting casein kinase 2: Insights from computational studies. PLoS One. 2023;18:e0289887. https://doi.org/10.1371/journal.pone.0289887
- [Google Scholar]
- Multifunctional gold nanoparticles for improved diagnostic and therapeutic applications: A review. Nanoscale Res Lett. 2021;16:174. https://doi.org/10.1186/s11671-021-03632-w
- [Google Scholar]
- PLK1 and PARP positively correlate in middle eastern breast cancer and their combined inhibition overcomes PARP inhibitor resistance in triple negative breast cancer. Front Oncol. 2024;13:1286585. https://doi.org/10.3389/fonc.2023.1286585
- [Google Scholar]
- Repurposing macitentan with nanoparticle modulates tumor microenvironment to potentiate immune checkpoint blockade. Biomaterials. 2021;276:121058. https://doi.org/10.1016/j.biomaterials.2021.121058
- [Google Scholar]
- Repurposing of drugs for triple negative breast cancer: An overview. Ecancermedicalscience. 2020;14:1071. https://doi.org/10.3332/ecancer.2020.1071
- [Google Scholar]
- DFT study of the therapeutic potential of phosphorene as a new drug-delivery system to treat cancer. RSC Adv. 2019;9:24325-24332. https://doi.org/10.1039/c9ra02778e
- [Google Scholar]
- Computational design of multi-target drugs against breast cancer. In: methods in pharmacology and toxicology, multi-target drug design using chem-bioinformatic approaches Methods in pharmacology and toxicology, multi-target drug design using chem-bioinformatic approaches. New York, NY: Springer New York; p. :443-458. https://doi.org/10.1007/7653_2018_24
- [Google Scholar]
- Therapeutic potential of PLK1 inhibition in triple-negative breast cancer. Lab Invest. 2019;99:1275-1286. https://doi.org/10.1038/s41374-019-0247-4
- [Google Scholar]
- MCL-1 is a clinically targetable vulnerability in breast cancer. Cell Cycle. 2022;21:1439-1455. https://doi.org/10.1080/15384101.2022.2054096
- [Google Scholar]
- Triple-negative breast cancer therapy: Current and future perspectives (Review) Int J Oncol. 2020;57:1245-1261. https://doi.org/10.3892/ijo.2020.5135
- [Google Scholar]
- Advancements and challenges in triple-negative breast cancer: A comprehensive review of therapeutic and diagnostic strategies. Front Oncol. 2024;14:1405491. https://doi.org/10.3389/fonc.2024.1405491
- [Google Scholar]
- Therapeutic progress and challenges for triple negative breast cancer: Targeted therapy and immunotherapy. Mol Biomed. 2022;3:8. https://doi.org/10.1186/s43556-022-00071-6
- [Google Scholar]
- Systems medicine design for triple-negative breast cancer and non-triple-negative breast cancer based on systems identification and carcinogenic mechanisms. IJMS. 2021;22:3083. https://doi.org/10.3390/ijms22063083
- [Google Scholar]
- Triple-negative breast cancer molecular subtyping and treatment progress. Breast Cancer Res. 2020;22:61. https://doi.org/10.1186/s13058-020-01296-5
- [Google Scholar]
- Dual TTK/PLK1 inhibition has potent anticancer activity in TNBC as monotherapy and in combination. Front Oncol. 2024;14:1447807. https://doi.org/10.3389/fonc.2024.1447807
- [Google Scholar]
- Deciphering the performance of polo-like kinase 1 in triple-negative breast cancer progression according to the centromere protein U-phosphorylation pathway. Am J Cancer Res. 2021;11:2142-2158.
- [Google Scholar]
- Informatics on drug repurposing for breast cancer. Drug Des Devel Ther. 2023;17:1933-1943. https://doi.org/10.2147/DDDT.S417563
- [Google Scholar]