A General Network Pharmacodynamic Model–Based Design Pipeline for Customized Cancer Therapy Applied to the VEGFR Pathway
A unified approach to optimize multidrug chemotherapy using a pharmacokinetic (PK)/enhanced pharmacodynamic model was developed using the vascular endothelial growth factor receptor (VEGFR) signaling system. The base VEGFR network model, characterized by ligand–receptor interactions, enzyme recruitm...
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| description | A unified approach to optimize multidrug chemotherapy using a pharmacokinetic (PK)/enhanced pharmacodynamic model was developed using the vascular endothelial growth factor receptor (VEGFR) signaling system. The base VEGFR network model, characterized by ligand–receptor interactions, enzyme recruitment (Grb2‐Sos, phospholipase C γ (PLCγ), and phosphoinositide‐3 kinase (PI3K)), and downstream mitogen‐activated protein kinase and Akt cascade activation, was linked to a sunitinib (VEGFR inhibitor) PK model and underwent Sobol sensitivity analysis that revealed potential sunitinib‐enhancing mechanisms. Drugs targeting these mechanisms (a VEGF inhibitor, a PI3K inhibitor, a PLCγ inhibitor, and a mitogen‐activated protein kinase inhibitor) and sunitinib were input to optimization‐based control analyses to design multidrug regimens that maintained 80% pERK and pAkt inhibition for 28 days while minimizing drug dose. The resultant combination regimens contained both continuous and discontinuous schedules, mostly at low doses, and were altered by oncogenic mutations. This pipeline of computational analyses demonstrates how model‐based methods can capture the complexities of drug action, tailor cancer chemotherapy, and empower personalized medicine.
CPT Pharmacometrics Syst. Pharmacol. (2014) 3, e92; doi:10.1038/psp.2013.65; published online 15 January 2014 |
| doi_str_mv | 10.1038/psp.2013.65 |
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CPT Pharmacometrics Syst. Pharmacol. (2014) 3, e92; doi:10.1038/psp.2013.65; published online 15 January 2014</description><subject>Cancer</subject><subject>Chemotherapy</subject><subject>Confidence intervals</subject><subject>Feedback</subject><subject>Genetic algorithms</subject><subject>Kinases</subject><subject>Ligands</subject><subject>Optimization</subject><subject>Original</subject><subject>Parameter estimation</subject><subject>Pharmacodynamics</subject><subject>Proteins</subject><subject>Sensitivity analysis</subject><subject>Signal transduction</subject><subject>Vascular endothelial growth factor</subject><issn>2163-8306</issn><issn>2163-8306</issn><fulltext>true</fulltext><rsrctype>article</rsrctype><creationdate>2014</creationdate><recordtype>article</recordtype><sourceid>24P</sourceid><sourceid>WIN</sourceid><sourceid>ABUWG</sourceid><sourceid>AFKRA</sourceid><sourceid>AZQEC</sourceid><sourceid>BENPR</sourceid><sourceid>CCPQU</sourceid><sourceid>DWQXO</sourceid><recordid>eNp9kc9u1DAQxi0EotXSE3dkiSParf_ESXxBWpZ2i1QggsLV8jrjxiWJg51llZ54B96QJ8GrLVW5MJcZeT79ZjwfQs8pWVDCy9MhDgtGKF_k4hE6ZjTn85KT_PGD-gidxHhDUhQZoZI8RUcsy5gUkh-j2yVeQw9Bt_gDjDsfvuGq0aHTxtdTrztn8HtfQ_v75683OkKN30J01z2u3ACt6wFbH_BqG0ffudvUXuneQMBXTUIOE14OQ-vS8-jx2AD-erY-_4QrPTY7PT1DT6xuI5zc5Rn6cn52tbqYX35cv1stL-eGl1zMeb3hlgCreQllJgtqiNBSZiznuaU1E1zUhba0NFaYLC-LcmO4lYbUlmWF3PAZen3gDttNB7WBfkzfVUNwnQ6T8tqpfzu9a9S1_6G4pISkK87QyztA8N-3EEd147ehTzsrxiTJhdjfcoZeHVQm-BgD2PsJlKi9Vyp5pfZeqVwk9YuHS91r_zqTBPwg2LkWpv-xVPW5yvZ1wv4Bgv6hHQ</recordid><startdate>201401</startdate><enddate>201401</enddate><creator>Zhang, X‐Y</creator><creator>Birtwistle, MR</creator><creator>Gallo, JM</creator><general>John Wiley & Sons, Inc</general><general>Nature Publishing Group</general><scope>24P</scope><scope>WIN</scope><scope>NPM</scope><scope>AAYXX</scope><scope>CITATION</scope><scope>3V.</scope><scope>7X7</scope><scope>7XB</scope><scope>8FI</scope><scope>8FJ</scope><scope>8FK</scope><scope>ABUWG</scope><scope>AFKRA</scope><scope>AZQEC</scope><scope>BENPR</scope><scope>CCPQU</scope><scope>DWQXO</scope><scope>FYUFA</scope><scope>GHDGH</scope><scope>K9.</scope><scope>M0S</scope><scope>PIMPY</scope><scope>PQEST</scope><scope>PQQKQ</scope><scope>PQUKI</scope><scope>PRINS</scope><scope>5PM</scope></search><sort><creationdate>201401</creationdate><title>A General Network Pharmacodynamic Model–Based Design Pipeline for Customized Cancer Therapy Applied to the VEGFR Pathway</title><author>Zhang, X‐Y ; Birtwistle, MR ; Gallo, JM</author></sort><facets><frbrtype>5</frbrtype><frbrgroupid>cdi_FETCH-LOGICAL-c3835-3db3f0e2d38e84971c05a9942636f1d2535d7af18cf5c46878bc3f9c0df2479b3</frbrgroupid><rsrctype>articles</rsrctype><prefilter>articles</prefilter><language>eng</language><creationdate>2014</creationdate><topic>Cancer</topic><topic>Chemotherapy</topic><topic>Confidence intervals</topic><topic>Feedback</topic><topic>Genetic algorithms</topic><topic>Kinases</topic><topic>Ligands</topic><topic>Optimization</topic><topic>Original</topic><topic>Parameter estimation</topic><topic>Pharmacodynamics</topic><topic>Proteins</topic><topic>Sensitivity analysis</topic><topic>Signal transduction</topic><topic>Vascular endothelial growth factor</topic><toplevel>peer_reviewed</toplevel><toplevel>online_resources</toplevel><creatorcontrib>Zhang, X‐Y</creatorcontrib><creatorcontrib>Birtwistle, MR</creatorcontrib><creatorcontrib>Gallo, JM</creatorcontrib><collection>Wiley Online Library (Open Access Collection)</collection><collection>Wiley Online Library (Open Access Collection)</collection><collection>PubMed</collection><collection>CrossRef</collection><collection>ProQuest Central (Corporate)</collection><collection>Health & Medical Collection</collection><collection>ProQuest Central (purchase pre-March 2016)</collection><collection>Hospital Premium Collection</collection><collection>Hospital Premium Collection (Alumni Edition)</collection><collection>ProQuest Central (Alumni) (purchase pre-March 2016)</collection><collection>ProQuest Central (Alumni Edition)</collection><collection>ProQuest Central UK/Ireland</collection><collection>ProQuest Central Essentials</collection><collection>ProQuest Central</collection><collection>ProQuest One Community College</collection><collection>ProQuest Central Korea</collection><collection>Health Research Premium Collection</collection><collection>Health Research Premium Collection (Alumni)</collection><collection>ProQuest Health & Medical Complete (Alumni)</collection><collection>Health & Medical Collection (Alumni Edition)</collection><collection>Publicly Available Content Database</collection><collection>ProQuest One Academic Eastern Edition (DO NOT USE)</collection><collection>ProQuest One Academic</collection><collection>ProQuest One Academic UKI Edition</collection><collection>ProQuest Central China</collection><collection>PubMed Central (Full Participant titles)</collection><jtitle>CPT: pharmacometrics and systems pharmacology</jtitle></facets><delivery><delcategory>Remote Search Resource</delcategory><fulltext>fulltext</fulltext></delivery><addata><au>Zhang, X‐Y</au><au>Birtwistle, MR</au><au>Gallo, JM</au><format>journal</format><genre>article</genre><ristype>JOUR</ristype><atitle>A General Network Pharmacodynamic Model–Based Design Pipeline for Customized Cancer Therapy Applied to the VEGFR Pathway</atitle><jtitle>CPT: pharmacometrics and systems pharmacology</jtitle><addtitle>CPT Pharmacometrics Syst Pharmacol</addtitle><date>2014-01</date><risdate>2014</risdate><volume>3</volume><issue>1</issue><spage>1</spage><epage>9</epage><pages>1-9</pages><issn>2163-8306</issn><eissn>2163-8306</eissn><abstract>A unified approach to optimize multidrug chemotherapy using a pharmacokinetic (PK)/enhanced pharmacodynamic model was developed using the vascular endothelial growth factor receptor (VEGFR) signaling system. The base VEGFR network model, characterized by ligand–receptor interactions, enzyme recruitment (Grb2‐Sos, phospholipase C γ (PLCγ), and phosphoinositide‐3 kinase (PI3K)), and downstream mitogen‐activated protein kinase and Akt cascade activation, was linked to a sunitinib (VEGFR inhibitor) PK model and underwent Sobol sensitivity analysis that revealed potential sunitinib‐enhancing mechanisms. Drugs targeting these mechanisms (a VEGF inhibitor, a PI3K inhibitor, a PLCγ inhibitor, and a mitogen‐activated protein kinase inhibitor) and sunitinib were input to optimization‐based control analyses to design multidrug regimens that maintained 80% pERK and pAkt inhibition for 28 days while minimizing drug dose. The resultant combination regimens contained both continuous and discontinuous schedules, mostly at low doses, and were altered by oncogenic mutations. This pipeline of computational analyses demonstrates how model‐based methods can capture the complexities of drug action, tailor cancer chemotherapy, and empower personalized medicine.
CPT Pharmacometrics Syst. Pharmacol. (2014) 3, e92; doi:10.1038/psp.2013.65; published online 15 January 2014</abstract><cop>United States</cop><pub>John Wiley & Sons, Inc</pub><pmid>24429593</pmid><doi>10.1038/psp.2013.65</doi><tpages>9</tpages><oa>free_for_read</oa></addata></record> |
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| subjects | Cancer Chemotherapy Confidence intervals Feedback Genetic algorithms Kinases Ligands Optimization Original Parameter estimation Pharmacodynamics Proteins Sensitivity analysis Signal transduction Vascular endothelial growth factor |
| title | A General Network Pharmacodynamic Model–Based Design Pipeline for Customized Cancer Therapy Applied to the VEGFR Pathway |
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