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Pharmacomicrobiomics informs clinical pharmacogenomics
Microbiota–host–xenobiotics interactions in humans become of prime interest when clinical pharmacogenomics is to be implemented. Despite the advent of technology, information still needs to be translated into knowledge for optimum patient stratification and disease management. Herein, we mined metag...
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| Published in: | Pharmacogenomics 2019-07, Vol.20 (10), p.731-739 |
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| Main Authors: | , , , |
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| cites | cdi_FETCH-LOGICAL-c371t-736e368825c44677b7c00cb16b96a1540e6c2de019ca551919ed8f5a312edf4f3 |
| container_end_page | 739 |
| container_issue | 10 |
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| container_title | Pharmacogenomics |
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| creator | Katsila, Theodora Balasopoulou, Angeliki Tsagaraki, Ioanna Patrinos, George P |
| description | Microbiota–host–xenobiotics interactions in humans become of prime interest when clinical pharmacogenomics is to be implemented. Despite the advent of technology, information still needs to be translated into knowledge for optimum patient stratification and disease management.
Herein, we mined metagenomic, pharmacometagenomic and pharmacomicrobiomic datasets to map microbiota–host–drugs networks.
Datasets were multifaceted and voluminous. Interoperability, data sparsity and scarcity remain a challenge. Mapping microbiota–host–drugs networks allowed the prediction of drug response/toxicity and modulation of the microbiota–host–drugs interplay.
Our approach triangulated microbiota, host and drug networks revealing the need for contextual data and open science via microattribution to accelerate knowledge growth. Our findings may serve as a data storehouse for a user-friendly query system, coupled with databanks and databases. |
| doi_str_mv | 10.2217/pgs-2019-0027 |
| format | article |
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Herein, we mined metagenomic, pharmacometagenomic and pharmacomicrobiomic datasets to map microbiota–host–drugs networks.
Datasets were multifaceted and voluminous. Interoperability, data sparsity and scarcity remain a challenge. Mapping microbiota–host–drugs networks allowed the prediction of drug response/toxicity and modulation of the microbiota–host–drugs interplay.
Our approach triangulated microbiota, host and drug networks revealing the need for contextual data and open science via microattribution to accelerate knowledge growth. Our findings may serve as a data storehouse for a user-friendly query system, coupled with databanks and databases.</description><identifier>ISSN: 1462-2416</identifier><identifier>EISSN: 1744-8042</identifier><identifier>DOI: 10.2217/pgs-2019-0027</identifier><identifier>PMID: 31368841</identifier><language>eng</language><publisher>England: Future Medicine Ltd</publisher><subject>Antibiotics ; Citation indexes ; Collaboration ; Content analysis ; contextual data ; Data banks ; Databases, Factual ; Drug-Related Side Effects and Adverse Reactions - genetics ; Drugs ; Ecosystems ; Genomes ; Genomics ; Health care ; Host Microbial Interactions - genetics ; Humans ; metagenomics ; Microbiota ; Microbiota - physiology ; microbiota–host–xenobiotics networks ; Microorganisms ; open science ; Pharmaceutical Preparations - administration & dosage ; Pharmacogenetics - methods ; Pharmacogenomics ; pharmacometagenomics ; pharmacomicrobiomics ; Public health ; Toxicity ; Xenobiotics</subject><ispartof>Pharmacogenomics, 2019-07, Vol.20 (10), p.731-739</ispartof><rights>2019 Future Medicine Ltd</rights><rights>Copyright Future Medicine Ltd Jul 2019</rights><lds50>peer_reviewed</lds50><woscitedreferencessubscribed>false</woscitedreferencessubscribed><citedby>FETCH-LOGICAL-c371t-736e368825c44677b7c00cb16b96a1540e6c2de019ca551919ed8f5a312edf4f3</citedby><cites>FETCH-LOGICAL-c371t-736e368825c44677b7c00cb16b96a1540e6c2de019ca551919ed8f5a312edf4f3</cites><orcidid>0000-0002-6263-4231</orcidid></display><links><openurl>$$Topenurl_article</openurl><openurlfulltext>$$Topenurlfull_article</openurlfulltext><thumbnail>$$Tsyndetics_thumb_exl</thumbnail><link.rule.ids>314,780,784,27924,27925</link.rule.ids><backlink>$$Uhttps://www.ncbi.nlm.nih.gov/pubmed/31368841$$D View this record in MEDLINE/PubMed$$Hfree_for_read</backlink></links><search><creatorcontrib>Katsila, Theodora</creatorcontrib><creatorcontrib>Balasopoulou, Angeliki</creatorcontrib><creatorcontrib>Tsagaraki, Ioanna</creatorcontrib><creatorcontrib>Patrinos, George P</creatorcontrib><title>Pharmacomicrobiomics informs clinical pharmacogenomics</title><title>Pharmacogenomics</title><addtitle>Pharmacogenomics</addtitle><description>Microbiota–host–xenobiotics interactions in humans become of prime interest when clinical pharmacogenomics is to be implemented. Despite the advent of technology, information still needs to be translated into knowledge for optimum patient stratification and disease management.
Herein, we mined metagenomic, pharmacometagenomic and pharmacomicrobiomic datasets to map microbiota–host–drugs networks.
Datasets were multifaceted and voluminous. Interoperability, data sparsity and scarcity remain a challenge. Mapping microbiota–host–drugs networks allowed the prediction of drug response/toxicity and modulation of the microbiota–host–drugs interplay.
Our approach triangulated microbiota, host and drug networks revealing the need for contextual data and open science via microattribution to accelerate knowledge growth. 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Despite the advent of technology, information still needs to be translated into knowledge for optimum patient stratification and disease management.
Herein, we mined metagenomic, pharmacometagenomic and pharmacomicrobiomic datasets to map microbiota–host–drugs networks.
Datasets were multifaceted and voluminous. Interoperability, data sparsity and scarcity remain a challenge. Mapping microbiota–host–drugs networks allowed the prediction of drug response/toxicity and modulation of the microbiota–host–drugs interplay.
Our approach triangulated microbiota, host and drug networks revealing the need for contextual data and open science via microattribution to accelerate knowledge growth. Our findings may serve as a data storehouse for a user-friendly query system, coupled with databanks and databases.</abstract><cop>England</cop><pub>Future Medicine Ltd</pub><pmid>31368841</pmid><doi>10.2217/pgs-2019-0027</doi><tpages>9</tpages><orcidid>https://orcid.org/0000-0002-6263-4231</orcidid></addata></record> |
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| language | eng |
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| source | PubMed (Medline) |
| subjects | Antibiotics Citation indexes Collaboration Content analysis contextual data Data banks Databases, Factual Drug-Related Side Effects and Adverse Reactions - genetics Drugs Ecosystems Genomes Genomics Health care Host Microbial Interactions - genetics Humans metagenomics Microbiota Microbiota - physiology microbiota–host–xenobiotics networks Microorganisms open science Pharmaceutical Preparations - administration & dosage Pharmacogenetics - methods Pharmacogenomics pharmacometagenomics pharmacomicrobiomics Public health Toxicity Xenobiotics |
| title | Pharmacomicrobiomics informs clinical pharmacogenomics |
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