Samuel O Olubode, Mutolib O Bankole, Precious A Akinnusi, Olayinka S Adanlawo, Kehinde I Ojubola, Daniel O Nwankwo, Onome E Edjebah, Ayomide O Adebesin, Abigail O Ayodele
{"title":"Molecular Modeling Studies of Natural Inhibitors of Androgen Signaling in Prostate Cancer.","authors":"Samuel O Olubode, Mutolib O Bankole, Precious A Akinnusi, Olayinka S Adanlawo, Kehinde I Ojubola, Daniel O Nwankwo, Onome E Edjebah, Ayomide O Adebesin, Abigail O Ayodele","doi":"10.1177/11769351221118556","DOIUrl":null,"url":null,"abstract":"<p><p>Prostate cancer is the second most common disease in men and the sixth leading cause of death from cancer globally, with 20 million men expected to be affected by 2024 thus considered as chronic illness which requires immediate attention. As an androgen-dependent illness that relies on the androgen receptor for development and progression, inhibition of the androgen receptor can lead to a therapeutic solution, hence serving as a vital therapeutic target. This study focused on the computational analysis of the inhibitory potentials of Vitis vinifera, a reported plant with anti-cancer properties, against androgen receptor employing molecular docking, ADMET studies, Binding energy study, pharmacophore modeling, and molecular dynamics simulation approaches. After the investigation, it was determined that 5 compounds: cis-piceid, cis-astrigin, gallocatechin, phlorizin, and trans-polydatin, might be possible androgen receptor inhibitors since they had higher docking scores and ADMET qualities than compared standards, with cis-piceid being the best-predicted inhibitor.</p>","PeriodicalId":35418,"journal":{"name":"Cancer Informatics","volume":" ","pages":"11769351221118556"},"PeriodicalIF":2.4000,"publicationDate":"2022-08-13","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"https://ftp.ncbi.nlm.nih.gov/pub/pmc/oa_pdf/1d/9a/10.1177_11769351221118556.PMC9379963.pdf","citationCount":"7","resultStr":null,"platform":"Semanticscholar","paperid":null,"PeriodicalName":"Cancer Informatics","FirstCategoryId":"1085","ListUrlMain":"https://doi.org/10.1177/11769351221118556","RegionNum":0,"RegionCategory":null,"ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":null,"EPubDate":"2022/1/1 0:00:00","PubModel":"eCollection","JCR":"Q2","JCRName":"MATHEMATICAL & COMPUTATIONAL BIOLOGY","Score":null,"Total":0}
引用次数: 7
Abstract
Prostate cancer is the second most common disease in men and the sixth leading cause of death from cancer globally, with 20 million men expected to be affected by 2024 thus considered as chronic illness which requires immediate attention. As an androgen-dependent illness that relies on the androgen receptor for development and progression, inhibition of the androgen receptor can lead to a therapeutic solution, hence serving as a vital therapeutic target. This study focused on the computational analysis of the inhibitory potentials of Vitis vinifera, a reported plant with anti-cancer properties, against androgen receptor employing molecular docking, ADMET studies, Binding energy study, pharmacophore modeling, and molecular dynamics simulation approaches. After the investigation, it was determined that 5 compounds: cis-piceid, cis-astrigin, gallocatechin, phlorizin, and trans-polydatin, might be possible androgen receptor inhibitors since they had higher docking scores and ADMET qualities than compared standards, with cis-piceid being the best-predicted inhibitor.
期刊介绍:
The field of cancer research relies on advances in many other disciplines, including omics technology, mass spectrometry, radio imaging, computer science, and biostatistics. Cancer Informatics provides open access to peer-reviewed high-quality manuscripts reporting bioinformatics analysis of molecular genetics and/or clinical data pertaining to cancer, emphasizing the use of machine learning, artificial intelligence, statistical algorithms, advanced imaging techniques, data visualization, and high-throughput technologies. As the leading journal dedicated exclusively to the report of the use of computational methods in cancer research and practice, Cancer Informatics leverages methodological improvements in systems biology, genomics, proteomics, metabolomics, and molecular biochemistry into the fields of cancer detection, treatment, classification, risk-prediction, prevention, outcome, and modeling.