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Ethnomedicine Claim Directed in Silico Prediction of Anticancer Activity
BACKGROUND: The merits of ethnomedicine-led approach to identify and prioritize anticancer medicinal plants have been challenged as cancer is more likely to be poorly understood in traditional medicine practices. Nonetheless, it is also believed that useful data can be generated by combining ethnobo...
Autores principales: | , , , |
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Formato: | Online Artículo Texto |
Lenguaje: | English |
Publicado: |
Research and Publications Office of Jimma University
2018
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Materias: | |
Acceso en línea: | https://www.ncbi.nlm.nih.gov/pmc/articles/PMC5866292/ https://www.ncbi.nlm.nih.gov/pubmed/29622910 |
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author | Girma, Biniyam Mulisa, Eshetu Tessema, Shibru Amelo, Wote |
author_facet | Girma, Biniyam Mulisa, Eshetu Tessema, Shibru Amelo, Wote |
author_sort | Girma, Biniyam |
collection | PubMed |
description | BACKGROUND: The merits of ethnomedicine-led approach to identify and prioritize anticancer medicinal plants have been challenged as cancer is more likely to be poorly understood in traditional medicine practices. Nonetheless, it is also believed that useful data can be generated by combining ethnobotanical findings with available scientific studies. Thus, this study combined an ethnobtanical study with ligand based in silico screening to identify relevant medical plants and predict their anticancer potential based on their phytoconstiutents reported in scientific literatures. METHODS: First, relevant medicinal plants were identified through an ethnobotanical survey. A list of phytochemicals was prepared based on literature review of articles which reported on the natural products of identified medicinal plants. Then, their phytochemicals were subjected to in silico evaluation, which included a hybrid score similarity measure, rule of five, Ghose-Viswanadhan-Wendoloski (GVW)-indices and structural features criteria, to predict their anticancer activity and drugability. RESULTS: A total of 18 medicinal plants and 265 phytoconstituents were identified. The natural product pool constituted 109(41.13%) terpenoids, 67(25.28%) phenolics, 29(10.94%) simple and functionalized hydrocarbons, 26(9.81%) alkaloids, 25(9.43%) glycosides and 9(3.40%) compounds belonging to different phytochemical classes. The similarity measure using CDRUG identified 34(12.73%) phytochemicals with high (p-Value < 0.05) and 35(13.21%) with moderate possibility (p-Value < 0.1) of anticancer activity. In fact, three of the predicted compounds had the same structure with known anticancer compounds (HSCORE=1). The 80% GVW-indices based antineoplastic drugabilityranges were all mate by 25 of the predicted compounds. Predicted compounds were also shown to have ring structures and functional groups deemed important for anticancer activity. CONCLUSIONS: Given the findings, there is a promising anticancer activity by the traditionally used medicinal plants and a potential for the predicted phytochemicals to be pursued as possible hits or me-too drugs. |
format | Online Article Text |
id | pubmed-5866292 |
institution | National Center for Biotechnology Information |
language | English |
publishDate | 2018 |
publisher | Research and Publications Office of Jimma University |
record_format | MEDLINE/PubMed |
spelling | pubmed-58662922018-04-05 Ethnomedicine Claim Directed in Silico Prediction of Anticancer Activity Girma, Biniyam Mulisa, Eshetu Tessema, Shibru Amelo, Wote Ethiop J Health Sci Original Article BACKGROUND: The merits of ethnomedicine-led approach to identify and prioritize anticancer medicinal plants have been challenged as cancer is more likely to be poorly understood in traditional medicine practices. Nonetheless, it is also believed that useful data can be generated by combining ethnobotanical findings with available scientific studies. Thus, this study combined an ethnobtanical study with ligand based in silico screening to identify relevant medical plants and predict their anticancer potential based on their phytoconstiutents reported in scientific literatures. METHODS: First, relevant medicinal plants were identified through an ethnobotanical survey. A list of phytochemicals was prepared based on literature review of articles which reported on the natural products of identified medicinal plants. Then, their phytochemicals were subjected to in silico evaluation, which included a hybrid score similarity measure, rule of five, Ghose-Viswanadhan-Wendoloski (GVW)-indices and structural features criteria, to predict their anticancer activity and drugability. RESULTS: A total of 18 medicinal plants and 265 phytoconstituents were identified. The natural product pool constituted 109(41.13%) terpenoids, 67(25.28%) phenolics, 29(10.94%) simple and functionalized hydrocarbons, 26(9.81%) alkaloids, 25(9.43%) glycosides and 9(3.40%) compounds belonging to different phytochemical classes. The similarity measure using CDRUG identified 34(12.73%) phytochemicals with high (p-Value < 0.05) and 35(13.21%) with moderate possibility (p-Value < 0.1) of anticancer activity. In fact, three of the predicted compounds had the same structure with known anticancer compounds (HSCORE=1). The 80% GVW-indices based antineoplastic drugabilityranges were all mate by 25 of the predicted compounds. Predicted compounds were also shown to have ring structures and functional groups deemed important for anticancer activity. CONCLUSIONS: Given the findings, there is a promising anticancer activity by the traditionally used medicinal plants and a potential for the predicted phytochemicals to be pursued as possible hits or me-too drugs. Research and Publications Office of Jimma University 2018-01 /pmc/articles/PMC5866292/ /pubmed/29622910 Text en © 2018 Biniyam Girma, et al. This is an open access article distributed under the terms of the Creative Commons Attribution License, which permits unrestricted use, distribution, and reproduction in any medium, provided the original author and source are credited. |
spellingShingle | Original Article Girma, Biniyam Mulisa, Eshetu Tessema, Shibru Amelo, Wote Ethnomedicine Claim Directed in Silico Prediction of Anticancer Activity |
title | Ethnomedicine Claim Directed in Silico Prediction of Anticancer Activity |
title_full | Ethnomedicine Claim Directed in Silico Prediction of Anticancer Activity |
title_fullStr | Ethnomedicine Claim Directed in Silico Prediction of Anticancer Activity |
title_full_unstemmed | Ethnomedicine Claim Directed in Silico Prediction of Anticancer Activity |
title_short | Ethnomedicine Claim Directed in Silico Prediction of Anticancer Activity |
title_sort | ethnomedicine claim directed in silico prediction of anticancer activity |
topic | Original Article |
url | https://www.ncbi.nlm.nih.gov/pmc/articles/PMC5866292/ https://www.ncbi.nlm.nih.gov/pubmed/29622910 |
work_keys_str_mv | AT girmabiniyam ethnomedicineclaimdirectedinsilicopredictionofanticanceractivity AT mulisaeshetu ethnomedicineclaimdirectedinsilicopredictionofanticanceractivity AT tessemashibru ethnomedicineclaimdirectedinsilicopredictionofanticanceractivity AT amelowote ethnomedicineclaimdirectedinsilicopredictionofanticanceractivity |