A side-effect free method for identifying cancer drug targets

Abstract Identifying effective drug targets, with little or no side effects, remains an ever challenging task. A potential pitfall of failing to uncover the correct drug targets, due to side effect of pleiotropic genes, might lead the potential drugs to be illicit and withdrawn. Simplifying disease...

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Autores principales:	Md. Izhar Ashraf, Seng-Kai Ong, Shama Mujawar, Shrikant Pawar, Pallavi More, Somnath Paul, Chandrajit Lahiri
Formato:	article
Lenguaje:	EN
Publicado:	Nature Portfolio 2018
Materias:	Medicine R Science Q
Acceso en línea:	https://doaj.org/article/646e2a0ba9394df089c4ab16254ef5b6
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spelling	oai:doaj.org-article:646e2a0ba9394df089c4ab16254ef5b62021-12-02T15:08:53ZA side-effect free method for identifying cancer drug targets10.1038/s41598-018-25042-22045-2322https://doaj.org/article/646e2a0ba9394df089c4ab16254ef5b62018-04-01T00:00:00Zhttps://doi.org/10.1038/s41598-018-25042-2https://doaj.org/toc/2045-2322Abstract Identifying effective drug targets, with little or no side effects, remains an ever challenging task. A potential pitfall of failing to uncover the correct drug targets, due to side effect of pleiotropic genes, might lead the potential drugs to be illicit and withdrawn. Simplifying disease complexity, for the investigation of the mechanistic aspects and identification of effective drug targets, have been done through several approaches of protein interactome analysis. Of these, centrality measures have always gained importance in identifying candidate drug targets. Here, we put forward an integrated method of analysing a complex network of cancer and depict the importance of k-core, functional connectivity and centrality (KFC) for identifying effective drug targets. Essentially, we have extracted the proteins involved in the pathways leading to cancer from the pathway databases which enlist real experimental datasets. The interactions between these proteins were mapped to build an interactome. Integrative analyses of the interactome enabled us to unearth plausible reasons for drugs being rendered withdrawn, thereby giving future scope to pharmaceutical industries to potentially avoid them (e.g. ESR1, HDAC2, F2, PLG, PPARA, RXRA, etc). Based upon our KFC criteria, we have shortlisted ten proteins (GRB2, FYN, PIK3R1, CBL, JAK2, LCK, LYN, SYK, JAK1 and SOCS3) as effective candidates for drug development.Md. Izhar AshrafSeng-Kai OngShama MujawarShrikant PawarPallavi MoreSomnath PaulChandrajit LahiriNature PortfolioarticleMedicineRScienceQENScientific Reports, Vol 8, Iss 1, Pp 1-9 (2018)
institution	DOAJ
collection	DOAJ
language	EN
topic	Medicine R Science Q
spellingShingle	Medicine R Science Q Md. Izhar Ashraf Seng-Kai Ong Shama Mujawar Shrikant Pawar Pallavi More Somnath Paul Chandrajit Lahiri A side-effect free method for identifying cancer drug targets
description	Abstract Identifying effective drug targets, with little or no side effects, remains an ever challenging task. A potential pitfall of failing to uncover the correct drug targets, due to side effect of pleiotropic genes, might lead the potential drugs to be illicit and withdrawn. Simplifying disease complexity, for the investigation of the mechanistic aspects and identification of effective drug targets, have been done through several approaches of protein interactome analysis. Of these, centrality measures have always gained importance in identifying candidate drug targets. Here, we put forward an integrated method of analysing a complex network of cancer and depict the importance of k-core, functional connectivity and centrality (KFC) for identifying effective drug targets. Essentially, we have extracted the proteins involved in the pathways leading to cancer from the pathway databases which enlist real experimental datasets. The interactions between these proteins were mapped to build an interactome. Integrative analyses of the interactome enabled us to unearth plausible reasons for drugs being rendered withdrawn, thereby giving future scope to pharmaceutical industries to potentially avoid them (e.g. ESR1, HDAC2, F2, PLG, PPARA, RXRA, etc). Based upon our KFC criteria, we have shortlisted ten proteins (GRB2, FYN, PIK3R1, CBL, JAK2, LCK, LYN, SYK, JAK1 and SOCS3) as effective candidates for drug development.
format	article
author	Md. Izhar Ashraf Seng-Kai Ong Shama Mujawar Shrikant Pawar Pallavi More Somnath Paul Chandrajit Lahiri
author_facet	Md. Izhar Ashraf Seng-Kai Ong Shama Mujawar Shrikant Pawar Pallavi More Somnath Paul Chandrajit Lahiri
author_sort	Md. Izhar Ashraf
title	A side-effect free method for identifying cancer drug targets
title_short	A side-effect free method for identifying cancer drug targets
title_full	A side-effect free method for identifying cancer drug targets
title_fullStr	A side-effect free method for identifying cancer drug targets
title_full_unstemmed	A side-effect free method for identifying cancer drug targets
title_sort	side-effect free method for identifying cancer drug targets
publisher	Nature Portfolio
publishDate	2018
url	https://doaj.org/article/646e2a0ba9394df089c4ab16254ef5b6
work_keys_str_mv	AT mdizharashraf asideeffectfreemethodforidentifyingcancerdrugtargets AT sengkaiong asideeffectfreemethodforidentifyingcancerdrugtargets AT shamamujawar asideeffectfreemethodforidentifyingcancerdrugtargets AT shrikantpawar asideeffectfreemethodforidentifyingcancerdrugtargets AT pallavimore asideeffectfreemethodforidentifyingcancerdrugtargets AT somnathpaul asideeffectfreemethodforidentifyingcancerdrugtargets AT chandrajitlahiri asideeffectfreemethodforidentifyingcancerdrugtargets AT mdizharashraf sideeffectfreemethodforidentifyingcancerdrugtargets AT sengkaiong sideeffectfreemethodforidentifyingcancerdrugtargets AT shamamujawar sideeffectfreemethodforidentifyingcancerdrugtargets AT shrikantpawar sideeffectfreemethodforidentifyingcancerdrugtargets AT pallavimore sideeffectfreemethodforidentifyingcancerdrugtargets AT somnathpaul sideeffectfreemethodforidentifyingcancerdrugtargets AT chandrajitlahiri sideeffectfreemethodforidentifyingcancerdrugtargets
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A side-effect free method for identifying cancer drug targets

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