Suad A. Alghamdi, Mohammed Alissa, Muhammad Suleman
{"title":"释放Alisertib治疗乳腺癌的潜力:利用网络药理学和基因表达网络深入探索分子靶点","authors":"Suad A. Alghamdi, Mohammed Alissa, Muhammad Suleman","doi":"10.1007/s12247-025-10004-9","DOIUrl":null,"url":null,"abstract":"<div><h3>Purpose</h3><p>Breast cancer is a prevalent and serious disease marked by uncontrolled cell growth. Alisertib, a small-molecule inhibitor, shows potential in cancer treatment by blocking cell proliferation. This study uses a network pharmacology approach to identify Alisertib's potential targets in breast cancer.</p><h3>Methods</h3><p>We used network pharmacology, molecular dynamic simulation and binding free energies approaches to identify the potential molecular target of Alisertib in Breast Cancer.</p><h3>Results</h3><p>SwissTarget, SuperPred, and CLCpred identified 100, 721, and 327 potential targets for Alisertib, respectively. From DisGeNet, 6,941 markers associated with breast cancer were identified, among which 29 proteins overlapped as both disease-associated genes and drug targets. Furthermore, the CytoHubba identified 10 hub genes from the PPI network of 29 common targets, with FGFR2, FGFR4, and MAPK7 ranked best based on their degree score. Moreover, the docking analysis revealed a docking scores of -8.854 kcal/mol, -7.373 kcal/mol and -7.262 kcal/mol for FGFR2, FGFR4, and MAPK7-Alisertib complexes respectively. The stable interaction of identified targets and Alisertib was further validated by the 200 ns molecular dynamics simulation. Binding free energy calculations using MM/GBSA yielded values of -61.0977 kcal/mol for the FGFR2-alisertib complex, -52.0032 kcal/mol for FGFR4-alisertib, and -47.9903 kcal/mol for MAPK7-alisertib. These results suggest that Alisertib exhibits stronger binding affinity for FGFR2, FGFR4 and MAPK7 compared to the control.</p><h3>Conclusion</h3><p>These findings suggest that Alisertib has a strong binding affinity and favorable pharmacological interactions with FGFR4, FGFR2, and MAPK7, highlighting its potential as a targeted therapeutic for breast cancer. Consequently, Alisertib warrants further investigation in preclinical and clinical settings to evaluate its efficacy in treating malignant neoplasm of the breast.</p></div>","PeriodicalId":656,"journal":{"name":"Journal of Pharmaceutical Innovation","volume":"20 3","pages":""},"PeriodicalIF":2.7000,"publicationDate":"2025-06-13","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":"0","resultStr":"{\"title\":\"Unlocking the Therapeutic Potential of Alisertib in Breast Cancer: An In-Depth Exploration of Molecular Targets Using Network Pharmacology and Gene Expression Network\",\"authors\":\"Suad A. Alghamdi, Mohammed Alissa, Muhammad Suleman\",\"doi\":\"10.1007/s12247-025-10004-9\",\"DOIUrl\":null,\"url\":null,\"abstract\":\"<div><h3>Purpose</h3><p>Breast cancer is a prevalent and serious disease marked by uncontrolled cell growth. Alisertib, a small-molecule inhibitor, shows potential in cancer treatment by blocking cell proliferation. This study uses a network pharmacology approach to identify Alisertib's potential targets in breast cancer.</p><h3>Methods</h3><p>We used network pharmacology, molecular dynamic simulation and binding free energies approaches to identify the potential molecular target of Alisertib in Breast Cancer.</p><h3>Results</h3><p>SwissTarget, SuperPred, and CLCpred identified 100, 721, and 327 potential targets for Alisertib, respectively. From DisGeNet, 6,941 markers associated with breast cancer were identified, among which 29 proteins overlapped as both disease-associated genes and drug targets. Furthermore, the CytoHubba identified 10 hub genes from the PPI network of 29 common targets, with FGFR2, FGFR4, and MAPK7 ranked best based on their degree score. Moreover, the docking analysis revealed a docking scores of -8.854 kcal/mol, -7.373 kcal/mol and -7.262 kcal/mol for FGFR2, FGFR4, and MAPK7-Alisertib complexes respectively. The stable interaction of identified targets and Alisertib was further validated by the 200 ns molecular dynamics simulation. Binding free energy calculations using MM/GBSA yielded values of -61.0977 kcal/mol for the FGFR2-alisertib complex, -52.0032 kcal/mol for FGFR4-alisertib, and -47.9903 kcal/mol for MAPK7-alisertib. These results suggest that Alisertib exhibits stronger binding affinity for FGFR2, FGFR4 and MAPK7 compared to the control.</p><h3>Conclusion</h3><p>These findings suggest that Alisertib has a strong binding affinity and favorable pharmacological interactions with FGFR4, FGFR2, and MAPK7, highlighting its potential as a targeted therapeutic for breast cancer. Consequently, Alisertib warrants further investigation in preclinical and clinical settings to evaluate its efficacy in treating malignant neoplasm of the breast.</p></div>\",\"PeriodicalId\":656,\"journal\":{\"name\":\"Journal of Pharmaceutical Innovation\",\"volume\":\"20 3\",\"pages\":\"\"},\"PeriodicalIF\":2.7000,\"publicationDate\":\"2025-06-13\",\"publicationTypes\":\"Journal Article\",\"fieldsOfStudy\":null,\"isOpenAccess\":false,\"openAccessPdf\":\"\",\"citationCount\":\"0\",\"resultStr\":null,\"platform\":\"Semanticscholar\",\"paperid\":null,\"PeriodicalName\":\"Journal of Pharmaceutical Innovation\",\"FirstCategoryId\":\"3\",\"ListUrlMain\":\"https://link.springer.com/article/10.1007/s12247-025-10004-9\",\"RegionNum\":4,\"RegionCategory\":\"医学\",\"ArticlePicture\":[],\"TitleCN\":null,\"AbstractTextCN\":null,\"PMCID\":null,\"EPubDate\":\"\",\"PubModel\":\"\",\"JCR\":\"Q2\",\"JCRName\":\"PHARMACOLOGY & PHARMACY\",\"Score\":null,\"Total\":0}","platform":"Semanticscholar","paperid":null,"PeriodicalName":"Journal of Pharmaceutical Innovation","FirstCategoryId":"3","ListUrlMain":"https://link.springer.com/article/10.1007/s12247-025-10004-9","RegionNum":4,"RegionCategory":"医学","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":null,"EPubDate":"","PubModel":"","JCR":"Q2","JCRName":"PHARMACOLOGY & PHARMACY","Score":null,"Total":0}
Unlocking the Therapeutic Potential of Alisertib in Breast Cancer: An In-Depth Exploration of Molecular Targets Using Network Pharmacology and Gene Expression Network
Purpose
Breast cancer is a prevalent and serious disease marked by uncontrolled cell growth. Alisertib, a small-molecule inhibitor, shows potential in cancer treatment by blocking cell proliferation. This study uses a network pharmacology approach to identify Alisertib's potential targets in breast cancer.
Methods
We used network pharmacology, molecular dynamic simulation and binding free energies approaches to identify the potential molecular target of Alisertib in Breast Cancer.
Results
SwissTarget, SuperPred, and CLCpred identified 100, 721, and 327 potential targets for Alisertib, respectively. From DisGeNet, 6,941 markers associated with breast cancer were identified, among which 29 proteins overlapped as both disease-associated genes and drug targets. Furthermore, the CytoHubba identified 10 hub genes from the PPI network of 29 common targets, with FGFR2, FGFR4, and MAPK7 ranked best based on their degree score. Moreover, the docking analysis revealed a docking scores of -8.854 kcal/mol, -7.373 kcal/mol and -7.262 kcal/mol for FGFR2, FGFR4, and MAPK7-Alisertib complexes respectively. The stable interaction of identified targets and Alisertib was further validated by the 200 ns molecular dynamics simulation. Binding free energy calculations using MM/GBSA yielded values of -61.0977 kcal/mol for the FGFR2-alisertib complex, -52.0032 kcal/mol for FGFR4-alisertib, and -47.9903 kcal/mol for MAPK7-alisertib. These results suggest that Alisertib exhibits stronger binding affinity for FGFR2, FGFR4 and MAPK7 compared to the control.
Conclusion
These findings suggest that Alisertib has a strong binding affinity and favorable pharmacological interactions with FGFR4, FGFR2, and MAPK7, highlighting its potential as a targeted therapeutic for breast cancer. Consequently, Alisertib warrants further investigation in preclinical and clinical settings to evaluate its efficacy in treating malignant neoplasm of the breast.
期刊介绍:
The Journal of Pharmaceutical Innovation (JPI), is an international, multidisciplinary peer-reviewed scientific journal dedicated to publishing high quality papers emphasizing innovative research and applied technologies within the pharmaceutical and biotechnology industries. JPI''s goal is to be the premier communication vehicle for the critical body of knowledge that is needed for scientific evolution and technical innovation, from R&D to market. Topics will fall under the following categories:
Materials science,
Product design,
Process design, optimization, automation and control,
Facilities; Information management,
Regulatory policy and strategy,
Supply chain developments ,
Education and professional development,
Journal of Pharmaceutical Innovation publishes four issues a year.