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ANALYSIS OF CURCUMIN AND PIPERINE FOR BREAST CANCER PREVENTION AND TREATMENT VIA SIMULATION AND ARTIFICIAL INTELLIGENCE METHODS - Peeref

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id="pdf-container-fallback" style="display: none;"> <a href="https://peeref-open.s3.amazonaws.com/storage/uploads/posters/929e06c0-876a-4cc7-9bba-77a5da455cc4.pdf"> <img src="https://peeref-open.s3.amazonaws.com/storage/uploads/posters/929e06c0-876a-4cc7-9bba-77a5da455cc4.jpg" alt="ANALYSIS OF CURCUMIN AND PIPERINE FOR BREAST CANCER PREVENTION AND TREATMENT VIA SIMULATION AND ARTIFICIAL INTELLIGENCE METHODS"> </a> </div> <div class="d-flex justify-content-between"> <div class="mx-3 p-3 text-muted font-weight-bold"> <span>25 次浏览</span> <span class="mx-2">&#183;</span> <span>6 次下载</span> <span class="mx-2">&#183;</span> <span>☆☆☆☆☆ 0.0</span> </div> <div id="btn-view-full-screen" class="my-2 text-right" style="display: none;"> <button class='btn btn-link'>全屏</button> </div> </div> <div class="m-3 p-3"> <h3 class="mt-0"> <span class="oa-icon ml-0 mr-1"> <img src="https://peeref-open.s3.amazonaws.com/images/open-access-symbol.svg" alt="" title="Open Access"> </span> ANALYSIS OF CURCUMIN AND PIPERINE FOR BREAST CANCER PREVENTION AND TREATMENT VIA SIMULATION AND ARTIFICIAL INTELLIGENCE METHODS </h3> <div class="d-flex justify-content-between flex-wrap flex-md-nowrap"> <div class="mb-4 mb-md-0"> <p class="font-weight-bold"> 发表日期 February 05, 2025 (DOI: <a href="https://doi.org/10.54985/peeref.2502p1908939" target="_blank">https://doi.org/10.54985/peeref.2502p1908939</a>) </p> <div class="bg-info text-white px-3 d-inline-block"> <small>未经同行评议</small> </div> </div> </div> <hr> <dl> <dt> <h4>作者</h4> </dt> <dd> <div class="mb-3"> Ashwin Sivakumar<sup>1</sup> , Rishi Senthil Kumar<sup>1</sup> , Rithika N<sup>2</sup> , Sheena Christabel Pravin<sup>3</sup> , Reena Monica P<sup>4</sup> </div> <div class="mb-3"> <ol class="pl-4"> <li>B .Tech Electronics and computer engineering,Vellore institute of technology chennai</li> <li>B .Tech Electronics and communication engineering,Vellore institute of technology chennai</li> <li>Assistant professor( Senior Grade),Vellore institute of technology chennai</li> <li>Associate Dean academics Vellore institute of technology chennai</li> </ol> </div> </dd> </dl> <dl> <dt> <h4>会议/活动</h4> </dt> <dd> National Conference for Undergraduate Research in Biosciences, NCURB 2024, December 2024 (IISER Tirupati, India) </dd> </dl> <dl> <dt> <h4>海报摘要</h4> </dt> <dd>This study highlights the combined therapeutic potential of Curcumin and Piperine, leveraging machine learning-driven insights for drug synergy prediction and optimization in cancer treatment.Utilized deep learning model to predict the drug synergy of these compounds based on Bliss’s Additivity Scores. Molecular docking studies assess their binding affinities with TRPV1 and STAT3 proteins,the key targets in cancer pathways and dose-response simulations provide insights into their efficacy. The ADME (Absorption, Distribution, Metabolism, and Excretion) analysis evaluates drug-likeness, bioavailability, and toxicity to determine their pharmacokinetic suitability. Results indicate that Piperine exhibits stronger binding affinity and lower IC50 values compared to Curcumin, suggesting its higher potential in targeting cancer pathways. However pharmacokinetic limitations such as low permeability and rapid clearance affect their effectiveness.</dd> </dl> <dl> <dt> <h4>关键词</h4> </dt> <dd>Drug synergy prediction, Neutraceuticals, Cancer treatment, Deep learning</dd> </dl> <dl> <dt> <h4>研究领域</h4> </dt> <dd>Bioengineering, Pharmacy and Pharmacology</dd> </dl> <dl> <dt> <h4>参考文献</h4> </dt> <dd> <ol class="pl-4"> <li>Aggarwal, B. B., Sundaram, C., Malani, N., Ichikawa, H. (2007). CURCUMIN: THE INDIAN SOLID GOLD. In B. B. Aggarwal, Y.-J. Surh, S. Shishodia (Eds.), The Molecular Targets and Therapeutic Uses of Curcumin in Health and Disease (pp. 1–75). Boston, MA: Springer US.</li> <li>Gupta, S. C., Prasad, S., Kim, J. H., Patchva, S., Webb, L. J., Priyadarsini, I. K., Aggarwal, B. B. (2011). Multitargeting by curcumin as revealed by molecular interaction studies. Natural Product Reports, 28(12), 1937–1955</li> <li>Bulat Zagidullin, Jehad Aldahdooh, Shuyu Zheng, Wenyu Wang, Yinyin Wang, Joseph Saad, Alina Malyutina, Mohieddin Jafari, Ziaurrehman Tanoli, Alberto Pessia, and Jing Tang. DrugComb: an integrative cancer drug combination data portal. Nucleic Acids Research, 47(W1): W43-W51, 2019. https://doi.org/10. 1093/nar/gkz337</li> <li>S. Dallakyan and A. J. Olson, “Small-molecule library screening by docking with PyRx, ” Methods in M o l e c u l a r B i o l o g y , v o l . 1 2 6 3 , p p . 2 4 3 – 2 5 0 , 2 0 1 5 . A v a i l a b l e : https://api.semanticscholar.org/CorpusID:23500642 [22] H. Wang, “Prediction of protein–ligand binding affinity via deep learning models, ” Briefings in Bioinformatics, vol. 25, no. 2, pp. bbae081, Mar. 2024. doi: 10.1093/ bib/bbae081. Available: https://doi.org/10.1093/bib/bbae081</li> <li>N. A. Khazanov and H. A. Carlson, “Exploring the Composition of Protein-Ligand Binding Sites on a Large Scale, ” PLOS Computational Biology, vol. 9, no. 11, pp. 1–14, Nov. 2013. doi: 10.1371/journal.pcbi.1003321. Available: https: //doi.org/10.1371/journal.pcbi.1003321</li> <li>P. Schmidtke, V. Le Guilloux, J. Maupetit, and P. Tuff´ery, “fpocket: online tools for protein ensemble pocket detection and tracking, ” Nucleic Acids Research, vol. 38, pp. 582–589, May 2010. doi: 10.1093/nar/gkq383. Available: https://doi. org/10.1093/nar/gkq383</li> </ol> </dd> </dl> <dl> <dt> <h4>基金</h4> </dt> <dd> <span class="text-muted">暂无数据</span> </dd> </dl> <dl> <dt> <h4>补充材料</h4> </dt> <dd> <span class="text-muted">暂无数据</span> </dd> </dl> <dl> <dt data-toggle="collapse" data-target="#collapseAdditional" style="cursor: pointer;"> <h4> 附加信息 <i class="ivu-icon ivu-icon-md-arrow-dropdown"></i> </h4> </dt> <dd class="collapse" id="collapseAdditional"> <dl> <dt>利益冲突</dt> <dd class="text-secondary"> No competing interests were disclosed. </dd> </dl> <dl> <dt>数据可用性声明</dt> <dd class="text-secondary"> Data sharing not applicable to this poster as no datasets were generated or analyzed during the current study. </dd> </dl> <dl> <dt>知识共享许可协议</dt> <dd class="text-secondary"> Copyright © 2025 Sivakumar et al. 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