Drug repurposing: Iron in the fire for older drugs
Repositioning or "repurposing" of existing therapies for indications of alternative disease is an attractive approach that can generate lower costs and require a shorter approval time than developing a de novo drug. The development of experimental drugs is time-consuming, expensive, and li...
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Veröffentlicht in: | Biomedicine & pharmacotherapy 2021-09, Vol.141, p.111638-111638, Article 111638 |
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creator | Sonaye, H.V. Sheikh, R.Y. Doifode, C.A. |
description | Repositioning or "repurposing" of existing therapies for indications of alternative disease is an attractive approach that can generate lower costs and require a shorter approval time than developing a de novo drug. The development of experimental drugs is time-consuming, expensive, and limited to a fairly small number of targets. The incorporation of separate and complementary data should be used, as each type of data set exposes a specific feature of organism knowledge Drug repurposing opportunities are often focused on sporadic findings or on time-consuming pre-clinical drug tests which are often not guided by hypothesis. In comparison, repurposing in-silico drugs is a new, hypothesis-driven method that takes advantage of big-data use. Nonetheless, the widespread use of omics technology, enhanced data storage, data sense, machine learning algorithms, and computational modeling all give unparalleled knowledge of the methods of action of biological processes and drugs, providing wide availability, for both disease-related data and drug-related data. This review has taken an in-depth look at the current state, possibilities, and limitations of further progress in the field of drug repositioning.
[Display omitted]
•Identify the most promising drugs for further clinical investigation.•Review drug-related data and bring it to the attention of clinical investigators.•Document about how such medications should be combined. |
doi_str_mv | 10.1016/j.biopha.2021.111638 |
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[Display omitted]
•Identify the most promising drugs for further clinical investigation.•Review drug-related data and bring it to the attention of clinical investigators.•Document about how such medications should be combined.</description><identifier>ISSN: 0753-3322</identifier><identifier>EISSN: 1950-6007</identifier><identifier>DOI: 10.1016/j.biopha.2021.111638</identifier><identifier>PMID: 34153846</identifier><language>eng</language><publisher>ISSY-LES-MOULINEAUX: Elsevier Masson SAS</publisher><subject><![CDATA[Animals ; Approaches ; Big Data ; Classification ; Computer Simulation - statistics & numerical data ; Drug Delivery Systems - methods ; Drug Delivery Systems - statistics & numerical data ; Drug Discovery - methods ; Drug Discovery - statistics & numerical data ; Drug Repositioning - methods ; Drug Repositioning - statistics & numerical data ; Drug repurposing ; Humans ; Life Sciences & Biomedicine ; Machine Learning - statistics & numerical data ; Medicine, Research & Experimental ; Methodology ; Pharmaceutical Preparations - administration & dosage ; Pharmacology & Pharmacy ; Research & Experimental Medicine ; Science & Technology]]></subject><ispartof>Biomedicine & pharmacotherapy, 2021-09, Vol.141, p.111638-111638, Article 111638</ispartof><rights>2021</rights><rights>Copyright © 2021. Published by Elsevier Masson SAS.</rights><lds50>peer_reviewed</lds50><oa>free_for_read</oa><woscitedreferencessubscribed>true</woscitedreferencessubscribed><woscitedreferencescount>21</woscitedreferencescount><woscitedreferencesoriginalsourcerecordid>wos000694948200008</woscitedreferencesoriginalsourcerecordid><citedby>FETCH-LOGICAL-c408t-75b3b591930e157bbb9369c5e1ea7f7e9aed3a3201dd46c6edef4623de3dce8f3</citedby><cites>FETCH-LOGICAL-c408t-75b3b591930e157bbb9369c5e1ea7f7e9aed3a3201dd46c6edef4623de3dce8f3</cites></display><links><openurl>$$Topenurl_article</openurl><openurlfulltext>$$Topenurlfull_article</openurlfulltext><thumbnail>$$Tsyndetics_thumb_exl</thumbnail><linktohtml>$$Uhttps://dx.doi.org/10.1016/j.biopha.2021.111638$$EHTML$$P50$$Gelsevier$$Hfree_for_read</linktohtml><link.rule.ids>315,781,785,3551,27929,27930,39263,46000</link.rule.ids><backlink>$$Uhttps://www.ncbi.nlm.nih.gov/pubmed/34153846$$D View this record in MEDLINE/PubMed$$Hfree_for_read</backlink></links><search><creatorcontrib>Sonaye, H.V.</creatorcontrib><creatorcontrib>Sheikh, R.Y.</creatorcontrib><creatorcontrib>Doifode, C.A.</creatorcontrib><title>Drug repurposing: Iron in the fire for older drugs</title><title>Biomedicine & pharmacotherapy</title><addtitle>BIOMED PHARMACOTHER</addtitle><addtitle>Biomed Pharmacother</addtitle><description>Repositioning or "repurposing" of existing therapies for indications of alternative disease is an attractive approach that can generate lower costs and require a shorter approval time than developing a de novo drug. The development of experimental drugs is time-consuming, expensive, and limited to a fairly small number of targets. The incorporation of separate and complementary data should be used, as each type of data set exposes a specific feature of organism knowledge Drug repurposing opportunities are often focused on sporadic findings or on time-consuming pre-clinical drug tests which are often not guided by hypothesis. In comparison, repurposing in-silico drugs is a new, hypothesis-driven method that takes advantage of big-data use. Nonetheless, the widespread use of omics technology, enhanced data storage, data sense, machine learning algorithms, and computational modeling all give unparalleled knowledge of the methods of action of biological processes and drugs, providing wide availability, for both disease-related data and drug-related data. This review has taken an in-depth look at the current state, possibilities, and limitations of further progress in the field of drug repositioning.
[Display omitted]
•Identify the most promising drugs for further clinical investigation.•Review drug-related data and bring it to the attention of clinical investigators.•Document about how such medications should be combined.</description><subject>Animals</subject><subject>Approaches</subject><subject>Big Data</subject><subject>Classification</subject><subject>Computer Simulation - statistics & numerical data</subject><subject>Drug Delivery Systems - methods</subject><subject>Drug Delivery Systems - statistics & numerical data</subject><subject>Drug Discovery - methods</subject><subject>Drug Discovery - statistics & numerical data</subject><subject>Drug Repositioning - methods</subject><subject>Drug Repositioning - statistics & numerical data</subject><subject>Drug repurposing</subject><subject>Humans</subject><subject>Life Sciences & Biomedicine</subject><subject>Machine Learning - statistics & numerical data</subject><subject>Medicine, Research & Experimental</subject><subject>Methodology</subject><subject>Pharmaceutical Preparations - administration & dosage</subject><subject>Pharmacology & Pharmacy</subject><subject>Research & Experimental Medicine</subject><subject>Science & Technology</subject><issn>0753-3322</issn><issn>1950-6007</issn><fulltext>true</fulltext><rsrctype>article</rsrctype><creationdate>2021</creationdate><recordtype>article</recordtype><sourceid>HGBXW</sourceid><sourceid>EIF</sourceid><recordid>eNqNkctKxDAUQIMoOj7-QKRLQTrm3caFIOMTBDe6Dm1yO5NhpqlJq_j3ZujoUtwkWZyTcE8QOiV4SjCRl8tp7Xy3qKYUUzIlhEhW7qAJUQLnEuNiF01wIVjOGKUH6DDGJcZYJGgfHTBOBCu5nCB6G4Z5FqAbQueja-dX2VPwbebarF9A1riQFh8yv7IQMpvgeIz2mmoV4WS7H6G3-7vX2WP-_PLwNLt5zg3HZZ8Xoma1UEQxDEQUdV0rJpURQKAqmgJUBZZVjGJiLZdGgoWGS8osMGugbNgROh_v7YJ_HyD2eu2igdWqasEPUVPB0xglUUVC-Yia4GMM0OguuHUVvjTBelNLL_VYS29q6bFW0s62Lwz1Guyv9JMnAeUIfELtm2gctAZ-sdRTKq54SdMJlzPXV73z7cwPbZ_Ui_-rib4eaUhBPxwEvTVs-gDTa-vd36N8A9h5nLc</recordid><startdate>202109</startdate><enddate>202109</enddate><creator>Sonaye, H.V.</creator><creator>Sheikh, R.Y.</creator><creator>Doifode, C.A.</creator><general>Elsevier Masson SAS</general><general>Elsevier</general><scope>6I.</scope><scope>AAFTH</scope><scope>BLEPL</scope><scope>DTL</scope><scope>HGBXW</scope><scope>CGR</scope><scope>CUY</scope><scope>CVF</scope><scope>ECM</scope><scope>EIF</scope><scope>NPM</scope><scope>AAYXX</scope><scope>CITATION</scope><scope>7X8</scope></search><sort><creationdate>202109</creationdate><title>Drug repurposing: Iron in the fire for older drugs</title><author>Sonaye, H.V. ; Sheikh, R.Y. ; Doifode, C.A.</author></sort><facets><frbrtype>5</frbrtype><frbrgroupid>cdi_FETCH-LOGICAL-c408t-75b3b591930e157bbb9369c5e1ea7f7e9aed3a3201dd46c6edef4623de3dce8f3</frbrgroupid><rsrctype>articles</rsrctype><prefilter>articles</prefilter><language>eng</language><creationdate>2021</creationdate><topic>Animals</topic><topic>Approaches</topic><topic>Big Data</topic><topic>Classification</topic><topic>Computer Simulation - statistics & numerical data</topic><topic>Drug Delivery Systems - methods</topic><topic>Drug Delivery Systems - statistics & numerical data</topic><topic>Drug Discovery - methods</topic><topic>Drug Discovery - statistics & numerical data</topic><topic>Drug Repositioning - methods</topic><topic>Drug Repositioning - statistics & numerical data</topic><topic>Drug repurposing</topic><topic>Humans</topic><topic>Life Sciences & Biomedicine</topic><topic>Machine Learning - statistics & numerical data</topic><topic>Medicine, Research & Experimental</topic><topic>Methodology</topic><topic>Pharmaceutical Preparations - administration & dosage</topic><topic>Pharmacology & Pharmacy</topic><topic>Research & Experimental Medicine</topic><topic>Science & Technology</topic><toplevel>peer_reviewed</toplevel><toplevel>online_resources</toplevel><creatorcontrib>Sonaye, H.V.</creatorcontrib><creatorcontrib>Sheikh, R.Y.</creatorcontrib><creatorcontrib>Doifode, C.A.</creatorcontrib><collection>ScienceDirect Open Access Titles</collection><collection>Elsevier:ScienceDirect:Open Access</collection><collection>Web of Science Core Collection</collection><collection>Science Citation Index Expanded</collection><collection>Web of Science - Science Citation Index Expanded - 2021</collection><collection>Medline</collection><collection>MEDLINE</collection><collection>MEDLINE (Ovid)</collection><collection>MEDLINE</collection><collection>MEDLINE</collection><collection>PubMed</collection><collection>CrossRef</collection><collection>MEDLINE - Academic</collection><jtitle>Biomedicine & pharmacotherapy</jtitle></facets><delivery><delcategory>Remote Search Resource</delcategory><fulltext>fulltext</fulltext></delivery><addata><au>Sonaye, H.V.</au><au>Sheikh, R.Y.</au><au>Doifode, C.A.</au><format>journal</format><genre>article</genre><ristype>JOUR</ristype><atitle>Drug repurposing: Iron in the fire for older drugs</atitle><jtitle>Biomedicine & pharmacotherapy</jtitle><stitle>BIOMED PHARMACOTHER</stitle><addtitle>Biomed Pharmacother</addtitle><date>2021-09</date><risdate>2021</risdate><volume>141</volume><spage>111638</spage><epage>111638</epage><pages>111638-111638</pages><artnum>111638</artnum><issn>0753-3322</issn><eissn>1950-6007</eissn><abstract>Repositioning or "repurposing" of existing therapies for indications of alternative disease is an attractive approach that can generate lower costs and require a shorter approval time than developing a de novo drug. The development of experimental drugs is time-consuming, expensive, and limited to a fairly small number of targets. The incorporation of separate and complementary data should be used, as each type of data set exposes a specific feature of organism knowledge Drug repurposing opportunities are often focused on sporadic findings or on time-consuming pre-clinical drug tests which are often not guided by hypothesis. In comparison, repurposing in-silico drugs is a new, hypothesis-driven method that takes advantage of big-data use. Nonetheless, the widespread use of omics technology, enhanced data storage, data sense, machine learning algorithms, and computational modeling all give unparalleled knowledge of the methods of action of biological processes and drugs, providing wide availability, for both disease-related data and drug-related data. This review has taken an in-depth look at the current state, possibilities, and limitations of further progress in the field of drug repositioning.
[Display omitted]
•Identify the most promising drugs for further clinical investigation.•Review drug-related data and bring it to the attention of clinical investigators.•Document about how such medications should be combined.</abstract><cop>ISSY-LES-MOULINEAUX</cop><pub>Elsevier Masson SAS</pub><pmid>34153846</pmid><doi>10.1016/j.biopha.2021.111638</doi><tpages>9</tpages><oa>free_for_read</oa></addata></record> |
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subjects | Animals Approaches Big Data Classification Computer Simulation - statistics & numerical data Drug Delivery Systems - methods Drug Delivery Systems - statistics & numerical data Drug Discovery - methods Drug Discovery - statistics & numerical data Drug Repositioning - methods Drug Repositioning - statistics & numerical data Drug repurposing Humans Life Sciences & Biomedicine Machine Learning - statistics & numerical data Medicine, Research & Experimental Methodology Pharmaceutical Preparations - administration & dosage Pharmacology & Pharmacy Research & Experimental Medicine Science & Technology |
title | Drug repurposing: Iron in the fire for older drugs |
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