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Zenodo
2025
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| Acesso em linha: | https://doi.org/10.5281/zenodo.17015921 |
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| author | Abdur Rahim, Jakia Sultana, Md Shakil Rahman Shoagh, Rifah Tasnia, P. M. Abida Anzum, M. Nasiruddin* |
| author_facet | Abdur Rahim, Jakia Sultana, Md Shakil Rahman Shoagh, Rifah Tasnia, P. M. Abida Anzum, M. Nasiruddin* |
| contents | <p><span lang="EN">Cancer remains a leading cause of mortality globally, necessitating the development of effective therapeutic interventions. This study explores the therapeutic potential of vinblastine and vincristine, two plant-derived vinca alkaloids, through bioinformatics and protein-protein interaction (PPI) network analysis. Using data from Swiss Target Prediction, STRING, and other databases, we identified key genes associated with these compounds and their potential mechanisms of action. The PPI networks of vinblastine and vincristine revealed critical nodes, including PIK3CA, MCHR1, BDKRB1, and CHRM1, which play pivotal roles in tumor progression, angiogenesis, and immune modulation. K-means clustering and MCODE analyses highlighted significant protein clusters influencing cancer-related pathways. ADME and toxicity evaluations via SwissADME and ProTox-II confirmed drug-likeness properties and potential safety profiles, albeit with certain limitations in Lipinski's rule of five. Both compounds demonstrated activity in disrupting microtubule dynamics, inhibiting cancer cell division, and targeting mitotic pathways. These findings underline the importance of vinblastine and vincristine as promising candidates for cancer therapy, with potential implications for the development of novel, targeted therapeutics. Future studies should focus on their molecular mechanisms and clinical applicability to optimize their therapeutic efficacy.</span></p> |
| format | Recurso digital |
| id | zenodo_https___doi_org_10_5281_zenodo_17015921 |
| institution | Zenodo |
| language | |
| publishDate | 2025 |
| publisher | Zenodo |
| record_format | zenodo |
| spellingShingle | Network-Based Analysis and ADMET Profiling of Vinblastine and Vincristine: Insights into Their Therapeutic Potential for Cancer Treatment Abdur Rahim, Jakia Sultana, Md Shakil Rahman Shoagh, Rifah Tasnia, P. M. Abida Anzum, M. Nasiruddin* Vinblastine, Vincristine, Cancer Therapy, Protein-Protein Interaction (PPI) Network, ADMET Profiling <p><span lang="EN">Cancer remains a leading cause of mortality globally, necessitating the development of effective therapeutic interventions. This study explores the therapeutic potential of vinblastine and vincristine, two plant-derived vinca alkaloids, through bioinformatics and protein-protein interaction (PPI) network analysis. Using data from Swiss Target Prediction, STRING, and other databases, we identified key genes associated with these compounds and their potential mechanisms of action. The PPI networks of vinblastine and vincristine revealed critical nodes, including PIK3CA, MCHR1, BDKRB1, and CHRM1, which play pivotal roles in tumor progression, angiogenesis, and immune modulation. K-means clustering and MCODE analyses highlighted significant protein clusters influencing cancer-related pathways. ADME and toxicity evaluations via SwissADME and ProTox-II confirmed drug-likeness properties and potential safety profiles, albeit with certain limitations in Lipinski's rule of five. Both compounds demonstrated activity in disrupting microtubule dynamics, inhibiting cancer cell division, and targeting mitotic pathways. These findings underline the importance of vinblastine and vincristine as promising candidates for cancer therapy, with potential implications for the development of novel, targeted therapeutics. Future studies should focus on their molecular mechanisms and clinical applicability to optimize their therapeutic efficacy.</span></p> |
| title | Network-Based Analysis and ADMET Profiling of Vinblastine and Vincristine: Insights into Their Therapeutic Potential for Cancer Treatment |
| topic | Vinblastine, Vincristine, Cancer Therapy, Protein-Protein Interaction (PPI) Network, ADMET Profiling |
| url | https://doi.org/10.5281/zenodo.17015921 |