Drug off-target effects predicted using structural analysis in the context of a metabolic network model
Recent advances in structural bioinformatics have enabled the prediction of protein-drug off-targets based on their ligand binding sites. Concurrent developments in systems biology allow for prediction of the functional effects of system perturbations using large-scale network models. Integration of...
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description | Recent advances in structural bioinformatics have enabled the prediction of protein-drug off-targets based on their ligand binding sites. Concurrent developments in systems biology allow for prediction of the functional effects of system perturbations using large-scale network models. Integration of these two capabilities provides a framework for evaluating metabolic drug response phenotypes in silico. This combined approach was applied to investigate the hypertensive side effect of the cholesteryl ester transfer protein inhibitor torcetrapib in the context of human renal function. A metabolic kidney model was generated in which to simulate drug treatment. Causal drug off-targets were predicted that have previously been observed to impact renal function in gene-deficient patients and may play a role in the adverse side effects observed in clinical trials. Genetic risk factors for drug treatment were also predicted that correspond to both characterized and unknown renal metabolic disorders as well as cryptic genetic deficiencies that are not expected to exhibit a renal disorder phenotype except under drug treatment. This study represents a novel integration of structural and systems biology and a first step towards computational systems medicine. The methodology introduced herein has important implications for drug development and personalized medicine. |
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Concurrent developments in systems biology allow for prediction of the functional effects of system perturbations using large-scale network models. Integration of these two capabilities provides a framework for evaluating metabolic drug response phenotypes in silico. This combined approach was applied to investigate the hypertensive side effect of the cholesteryl ester transfer protein inhibitor torcetrapib in the context of human renal function. A metabolic kidney model was generated in which to simulate drug treatment. Causal drug off-targets were predicted that have previously been observed to impact renal function in gene-deficient patients and may play a role in the adverse side effects observed in clinical trials. Genetic risk factors for drug treatment were also predicted that correspond to both characterized and unknown renal metabolic disorders as well as cryptic genetic deficiencies that are not expected to exhibit a renal disorder phenotype except under drug treatment. This study represents a novel integration of structural and systems biology and a first step towards computational systems medicine. The methodology introduced herein has important implications for drug development and personalized medicine.</description><identifier>ISSN: 1553-7358</identifier><identifier>ISSN: 1553-734X</identifier><identifier>EISSN: 1553-7358</identifier><identifier>DOI: 10.1371/journal.pcbi.1000938</identifier><identifier>PMID: 20957118</identifier><language>eng</language><publisher>United States: Public Library of Science</publisher><subject>Algorithms ; Anticholesteremic agents ; Biochemistry/Biomacromolecule-Ligand Interactions ; Bioinformatics ; Cardiovascular Disorders/Cardiovascular Pharmacology ; Cardiovascular Disorders/Hypertension ; Chemical properties ; Complications and side effects ; Computational biology ; Computational Biology - methods ; Computational Biology/Metabolic Networks ; Computational Biology/Systems Biology ; Drug Discovery - methods ; Drug metabolism ; Gene Expression Profiling ; Genetics and Genomics/Bioinformatics ; Genetics and Genomics/Gene Expression ; Genetics and Genomics/Genetics of Disease ; Humans ; Kidney - drug effects ; Kidney - metabolism ; Kidney diseases ; Kidney Diseases - metabolism ; Kidney Function Tests ; Medical research ; Metabolic disorders ; Metabolic Networks and Pathways - drug effects ; Metabolic Networks and Pathways - genetics ; Metabolic Networks and Pathways - physiology ; Metabolomics ; Models, Biological ; Pharmaceutical industry ; Pharmacokinetics ; Pharmacology/Adverse Reactions ; Pharmacology/Drug Development ; Pharmacology/Personalized Medicine ; Physiology/Renal, Fluid, and Electrolyte Physiology ; Protein Binding ; Proteins ; Proteome - drug effects ; Proteome - genetics ; Proteome - metabolism ; Quinolines - pharmacology ; Risk factors ; ROC Curve ; Structural analysis (Engineering) ; Studies ; Technology application</subject><ispartof>PLoS computational biology, 2010-09, Vol.6 (9), p.e1000938-e1000938</ispartof><rights>COPYRIGHT 2010 Public Library of Science</rights><rights>Chang et al. 2010</rights><rights>2010 Chang 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: Chang RL, Xie L, Xie L, Bourne PE, Palsson BØ (2010) Drug Off-Target Effects Predicted Using Structural Analysis in the Context of a Metabolic Network Model. 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Concurrent developments in systems biology allow for prediction of the functional effects of system perturbations using large-scale network models. Integration of these two capabilities provides a framework for evaluating metabolic drug response phenotypes in silico. This combined approach was applied to investigate the hypertensive side effect of the cholesteryl ester transfer protein inhibitor torcetrapib in the context of human renal function. A metabolic kidney model was generated in which to simulate drug treatment. Causal drug off-targets were predicted that have previously been observed to impact renal function in gene-deficient patients and may play a role in the adverse side effects observed in clinical trials. Genetic risk factors for drug treatment were also predicted that correspond to both characterized and unknown renal metabolic disorders as well as cryptic genetic deficiencies that are not expected to exhibit a renal disorder phenotype except under drug treatment. This study represents a novel integration of structural and systems biology and a first step towards computational systems medicine. The methodology introduced herein has important implications for drug development and personalized medicine.</description><subject>Algorithms</subject><subject>Anticholesteremic agents</subject><subject>Biochemistry/Biomacromolecule-Ligand Interactions</subject><subject>Bioinformatics</subject><subject>Cardiovascular Disorders/Cardiovascular Pharmacology</subject><subject>Cardiovascular Disorders/Hypertension</subject><subject>Chemical properties</subject><subject>Complications and side effects</subject><subject>Computational biology</subject><subject>Computational Biology - methods</subject><subject>Computational Biology/Metabolic Networks</subject><subject>Computational Biology/Systems Biology</subject><subject>Drug Discovery - methods</subject><subject>Drug metabolism</subject><subject>Gene Expression Profiling</subject><subject>Genetics and Genomics/Bioinformatics</subject><subject>Genetics and Genomics/Gene Expression</subject><subject>Genetics and Genomics/Genetics of Disease</subject><subject>Humans</subject><subject>Kidney - drug effects</subject><subject>Kidney - metabolism</subject><subject>Kidney diseases</subject><subject>Kidney Diseases - metabolism</subject><subject>Kidney Function Tests</subject><subject>Medical research</subject><subject>Metabolic disorders</subject><subject>Metabolic Networks and Pathways - drug effects</subject><subject>Metabolic Networks and Pathways - genetics</subject><subject>Metabolic Networks and Pathways - physiology</subject><subject>Metabolomics</subject><subject>Models, Biological</subject><subject>Pharmaceutical industry</subject><subject>Pharmacokinetics</subject><subject>Pharmacology/Adverse Reactions</subject><subject>Pharmacology/Drug Development</subject><subject>Pharmacology/Personalized Medicine</subject><subject>Physiology/Renal, Fluid, and Electrolyte Physiology</subject><subject>Protein Binding</subject><subject>Proteins</subject><subject>Proteome - drug effects</subject><subject>Proteome - genetics</subject><subject>Proteome - metabolism</subject><subject>Quinolines - pharmacology</subject><subject>Risk factors</subject><subject>ROC Curve</subject><subject>Structural analysis (Engineering)</subject><subject>Studies</subject><subject>Technology application</subject><issn>1553-7358</issn><issn>1553-734X</issn><issn>1553-7358</issn><fulltext>true</fulltext><rsrctype>article</rsrctype><creationdate>2010</creationdate><recordtype>article</recordtype><sourceid>EIF</sourceid><sourceid>DOA</sourceid><recordid>eNqVkkuLFDEQxxtR3HX1G4gGPKiHGfPodJKLsKyvgUXBxzmk05XejD2d2SSt7rc37cwuOyCC1CGVyq_-Raqqqh4TvCRMkFfrMMXRDMutbf2SYIwVk3eqY8I5WwjG5d1b_lH1IKU1xsVVzf3qiGLFBSHyuOrfxKlHwblFNrGHjMA5sDmhbYTO2wwdmpIfe5RynGyeohmQKWWvkk_IjyhfALJhzPArFxVk0AayacPgLRoh_wzxO9qEDoaH1T1nhgSP9udJ9e3d269nHxbnn96vzk7PF1YwkhegZAOiUZwIp0jbAOHGEYlBtNThWkqLOQEDytXcOSqUorShEnfMCKloy06qpzvd7RCS3vcoacKKNYLUdSFWO6ILZq230W9MvNLBeP0nEGKvTczeDqBrQjDlTd10hNXG2FY1mMmW147LplyK1ut9tandQGdhzKVBB6KHL6O_0H34oaniuBG8CDzfC8RwOUHKeuOThWEwI4QpacEVo3XD5lIv_kkSKaisCzyLPtuhvSmf8KMLpbadcX1KmVSEUjpTy79QxTrY-DJScL7EDxJeHiTsx96bKSW9-vL5P9iPh2y9Y20MKUVwN_0jWM-rfj1GPa-63q96SXtyu_c3Sde7zX4DKfr5Ug</recordid><startdate>20100901</startdate><enddate>20100901</enddate><creator>Chang, Roger L</creator><creator>Xie, Li</creator><creator>Xie, Lei</creator><creator>Bourne, Philip E</creator><creator>Palsson, Bernhard Ø</creator><general>Public Library of Science</general><general>Public Library of Science (PLoS)</general><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>ISN</scope><scope>ISR</scope><scope>7QO</scope><scope>8FD</scope><scope>FR3</scope><scope>P64</scope><scope>7X8</scope><scope>5PM</scope><scope>DOA</scope></search><sort><creationdate>20100901</creationdate><title>Drug off-target effects predicted using structural analysis in the context of a metabolic network model</title><author>Chang, Roger L ; Xie, Li ; Xie, Lei ; Bourne, Philip E ; Palsson, Bernhard Ø</author></sort><facets><frbrtype>5</frbrtype><frbrgroupid>cdi_FETCH-LOGICAL-c731t-e986e769517f91b6e15af180e7b2f0488c051eae9f45ff2799226280d3a7892b3</frbrgroupid><rsrctype>articles</rsrctype><prefilter>articles</prefilter><language>eng</language><creationdate>2010</creationdate><topic>Algorithms</topic><topic>Anticholesteremic agents</topic><topic>Biochemistry/Biomacromolecule-Ligand Interactions</topic><topic>Bioinformatics</topic><topic>Cardiovascular Disorders/Cardiovascular Pharmacology</topic><topic>Cardiovascular Disorders/Hypertension</topic><topic>Chemical properties</topic><topic>Complications and side effects</topic><topic>Computational biology</topic><topic>Computational Biology - methods</topic><topic>Computational Biology/Metabolic Networks</topic><topic>Computational Biology/Systems Biology</topic><topic>Drug Discovery - methods</topic><topic>Drug metabolism</topic><topic>Gene Expression Profiling</topic><topic>Genetics and Genomics/Bioinformatics</topic><topic>Genetics and Genomics/Gene Expression</topic><topic>Genetics and Genomics/Genetics of Disease</topic><topic>Humans</topic><topic>Kidney - drug effects</topic><topic>Kidney - metabolism</topic><topic>Kidney diseases</topic><topic>Kidney Diseases - metabolism</topic><topic>Kidney Function Tests</topic><topic>Medical research</topic><topic>Metabolic disorders</topic><topic>Metabolic Networks and Pathways - drug effects</topic><topic>Metabolic Networks and Pathways - genetics</topic><topic>Metabolic Networks and Pathways - physiology</topic><topic>Metabolomics</topic><topic>Models, Biological</topic><topic>Pharmaceutical industry</topic><topic>Pharmacokinetics</topic><topic>Pharmacology/Adverse Reactions</topic><topic>Pharmacology/Drug Development</topic><topic>Pharmacology/Personalized Medicine</topic><topic>Physiology/Renal, Fluid, and Electrolyte Physiology</topic><topic>Protein Binding</topic><topic>Proteins</topic><topic>Proteome - drug effects</topic><topic>Proteome - genetics</topic><topic>Proteome - metabolism</topic><topic>Quinolines - pharmacology</topic><topic>Risk factors</topic><topic>ROC Curve</topic><topic>Structural analysis (Engineering)</topic><topic>Studies</topic><topic>Technology application</topic><toplevel>peer_reviewed</toplevel><toplevel>online_resources</toplevel><creatorcontrib>Chang, Roger L</creatorcontrib><creatorcontrib>Xie, Li</creatorcontrib><creatorcontrib>Xie, Lei</creatorcontrib><creatorcontrib>Bourne, Philip E</creatorcontrib><creatorcontrib>Palsson, Bernhard Ø</creatorcontrib><collection>Medline</collection><collection>MEDLINE</collection><collection>MEDLINE (Ovid)</collection><collection>MEDLINE</collection><collection>MEDLINE</collection><collection>PubMed</collection><collection>CrossRef</collection><collection>Gale In Context: Canada</collection><collection>Gale In Context: Science</collection><collection>Biotechnology Research Abstracts</collection><collection>Technology Research Database</collection><collection>Engineering Research Database</collection><collection>Biotechnology and BioEngineering Abstracts</collection><collection>MEDLINE - Academic</collection><collection>PubMed Central (Full Participant titles)</collection><collection>DOAJ Directory of Open Access Journals</collection><jtitle>PLoS computational biology</jtitle></facets><delivery><delcategory>Remote Search Resource</delcategory><fulltext>fulltext</fulltext></delivery><addata><au>Chang, Roger L</au><au>Xie, Li</au><au>Xie, Lei</au><au>Bourne, Philip E</au><au>Palsson, Bernhard Ø</au><au>Dunbrack, Roland L.</au><format>journal</format><genre>article</genre><ristype>JOUR</ristype><atitle>Drug off-target effects predicted using structural analysis in the context of a metabolic network model</atitle><jtitle>PLoS computational biology</jtitle><addtitle>PLoS Comput Biol</addtitle><date>2010-09-01</date><risdate>2010</risdate><volume>6</volume><issue>9</issue><spage>e1000938</spage><epage>e1000938</epage><pages>e1000938-e1000938</pages><issn>1553-7358</issn><issn>1553-734X</issn><eissn>1553-7358</eissn><abstract>Recent advances in structural bioinformatics have enabled the prediction of protein-drug off-targets based on their ligand binding sites. Concurrent developments in systems biology allow for prediction of the functional effects of system perturbations using large-scale network models. Integration of these two capabilities provides a framework for evaluating metabolic drug response phenotypes in silico. This combined approach was applied to investigate the hypertensive side effect of the cholesteryl ester transfer protein inhibitor torcetrapib in the context of human renal function. A metabolic kidney model was generated in which to simulate drug treatment. Causal drug off-targets were predicted that have previously been observed to impact renal function in gene-deficient patients and may play a role in the adverse side effects observed in clinical trials. Genetic risk factors for drug treatment were also predicted that correspond to both characterized and unknown renal metabolic disorders as well as cryptic genetic deficiencies that are not expected to exhibit a renal disorder phenotype except under drug treatment. This study represents a novel integration of structural and systems biology and a first step towards computational systems medicine. The methodology introduced herein has important implications for drug development and personalized medicine.</abstract><cop>United States</cop><pub>Public Library of Science</pub><pmid>20957118</pmid><doi>10.1371/journal.pcbi.1000938</doi><oa>free_for_read</oa></addata></record> |
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subjects | Algorithms Anticholesteremic agents Biochemistry/Biomacromolecule-Ligand Interactions Bioinformatics Cardiovascular Disorders/Cardiovascular Pharmacology Cardiovascular Disorders/Hypertension Chemical properties Complications and side effects Computational biology Computational Biology - methods Computational Biology/Metabolic Networks Computational Biology/Systems Biology Drug Discovery - methods Drug metabolism Gene Expression Profiling Genetics and Genomics/Bioinformatics Genetics and Genomics/Gene Expression Genetics and Genomics/Genetics of Disease Humans Kidney - drug effects Kidney - metabolism Kidney diseases Kidney Diseases - metabolism Kidney Function Tests Medical research Metabolic disorders Metabolic Networks and Pathways - drug effects Metabolic Networks and Pathways - genetics Metabolic Networks and Pathways - physiology Metabolomics Models, Biological Pharmaceutical industry Pharmacokinetics Pharmacology/Adverse Reactions Pharmacology/Drug Development Pharmacology/Personalized Medicine Physiology/Renal, Fluid, and Electrolyte Physiology Protein Binding Proteins Proteome - drug effects Proteome - genetics Proteome - metabolism Quinolines - pharmacology Risk factors ROC Curve Structural analysis (Engineering) Studies Technology application |
title | Drug off-target effects predicted using structural analysis in the context of a metabolic network model |
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