Considerations and Caveats when Applying Global Sensitivity Analysis Methods to Physiologically Based Pharmacokinetic Models
Three global sensitivity analysis (GSA) methods (Morris, Sobol and extended Sobol) are applied to a minimal physiologically based PK (mPBPK) model using three model drugs given orally, namely quinidine, alprazolam, and midazolam. We investigated how correlations among input parameters affect the det...
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description | Three global sensitivity analysis (GSA) methods (Morris, Sobol and extended Sobol) are applied to a minimal physiologically based PK (mPBPK) model using three model drugs given orally, namely quinidine, alprazolam, and midazolam. We investigated how correlations among input parameters affect the determination of the key parameters influencing pharmacokinetic (PK) properties of general interest, i.e., the maximal plasma concentration (
C
max
) time at which
C
max
is reached (
T
max
), and area under plasma concentration (AUC). The influential parameters determined by the Morris and Sobol methods (suitable for independent model parameters) were compared to those determined by the extended Sobol method (which considers model parameter correlations). For the three drugs investigated, the Morris method was as informative as the Sobol method. The extended Sobol method identified different sets of influential parameters to Morris and Sobol. These methods overestimated the influence of volume of distribution at steady state (
V
ss
) on AUC24h for quinidine and alprazolam. They also underestimated the effect of volume of liver (
V
liver
) for all three drugs, the impact of enzyme intrinsic clearance of CYP2C9 and CYP2E1 for quinidine, and that of UGT1A4 abundance for midazolam. Our investigation showed that the interpretation of GSA results is not straightforward. Dismissing existing model parameter correlations, GSA methods such as Morris and Sobol can lead to biased determination of the key parameters for the selected outputs of interest. Decisions regarding parameters’ influence (or otherwise) should be made in light of available knowledge including the model assumptions, GSA method limitations, and inter-correlations between model parameters, particularly in complex models.
Graphical abstract |
doi_str_mv | 10.1208/s12248-020-00480-x |
format | Article |
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C
max
) time at which
C
max
is reached (
T
max
), and area under plasma concentration (AUC). The influential parameters determined by the Morris and Sobol methods (suitable for independent model parameters) were compared to those determined by the extended Sobol method (which considers model parameter correlations). For the three drugs investigated, the Morris method was as informative as the Sobol method. The extended Sobol method identified different sets of influential parameters to Morris and Sobol. These methods overestimated the influence of volume of distribution at steady state (
V
ss
) on AUC24h for quinidine and alprazolam. They also underestimated the effect of volume of liver (
V
liver
) for all three drugs, the impact of enzyme intrinsic clearance of CYP2C9 and CYP2E1 for quinidine, and that of UGT1A4 abundance for midazolam. Our investigation showed that the interpretation of GSA results is not straightforward. Dismissing existing model parameter correlations, GSA methods such as Morris and Sobol can lead to biased determination of the key parameters for the selected outputs of interest. Decisions regarding parameters’ influence (or otherwise) should be made in light of available knowledge including the model assumptions, GSA method limitations, and inter-correlations between model parameters, particularly in complex models.
Graphical abstract</description><identifier>ISSN: 1550-7416</identifier><identifier>EISSN: 1550-7416</identifier><identifier>DOI: 10.1208/s12248-020-00480-x</identifier><identifier>PMID: 32681207</identifier><language>eng</language><publisher>Cham: Springer International Publishing</publisher><subject>Biochemistry ; Biomedical and Life Sciences ; Biomedicine ; Biotechnology ; Pharmacology/Toxicology ; Pharmacy ; Research Article</subject><ispartof>The AAPS journal, 2020-07, Vol.22 (5), p.93-93, Article 93</ispartof><rights>The Author(s) 2020</rights><lds50>peer_reviewed</lds50><oa>free_for_read</oa><woscitedreferencessubscribed>false</woscitedreferencessubscribed><citedby>FETCH-LOGICAL-c446t-257c8b1f0161d1f832a0be3cbdae4310e19c84db3d65011c0ee5291c3d72cc43</citedby><cites>FETCH-LOGICAL-c446t-257c8b1f0161d1f832a0be3cbdae4310e19c84db3d65011c0ee5291c3d72cc43</cites></display><links><openurl>$$Topenurl_article</openurl><openurlfulltext>$$Topenurlfull_article</openurlfulltext><thumbnail>$$Tsyndetics_thumb_exl</thumbnail><linktopdf>$$Uhttps://link.springer.com/content/pdf/10.1208/s12248-020-00480-x$$EPDF$$P50$$Gspringer$$Hfree_for_read</linktopdf><linktohtml>$$Uhttps://link.springer.com/10.1208/s12248-020-00480-x$$EHTML$$P50$$Gspringer$$Hfree_for_read</linktohtml><link.rule.ids>230,314,776,780,881,27901,27902,41464,42533,51294</link.rule.ids><backlink>$$Uhttps://www.ncbi.nlm.nih.gov/pubmed/32681207$$D View this record in MEDLINE/PubMed$$Hfree_for_read</backlink></links><search><creatorcontrib>Liu, Dan</creatorcontrib><creatorcontrib>Li, Linzhong</creatorcontrib><creatorcontrib>Rostami-Hodjegan, Amin</creatorcontrib><creatorcontrib>Bois, Frederic Y.</creatorcontrib><creatorcontrib>Jamei, Masoud</creatorcontrib><title>Considerations and Caveats when Applying Global Sensitivity Analysis Methods to Physiologically Based Pharmacokinetic Models</title><title>The AAPS journal</title><addtitle>AAPS J</addtitle><addtitle>AAPS J</addtitle><description>Three global sensitivity analysis (GSA) methods (Morris, Sobol and extended Sobol) are applied to a minimal physiologically based PK (mPBPK) model using three model drugs given orally, namely quinidine, alprazolam, and midazolam. We investigated how correlations among input parameters affect the determination of the key parameters influencing pharmacokinetic (PK) properties of general interest, i.e., the maximal plasma concentration (
C
max
) time at which
C
max
is reached (
T
max
), and area under plasma concentration (AUC). The influential parameters determined by the Morris and Sobol methods (suitable for independent model parameters) were compared to those determined by the extended Sobol method (which considers model parameter correlations). For the three drugs investigated, the Morris method was as informative as the Sobol method. The extended Sobol method identified different sets of influential parameters to Morris and Sobol. These methods overestimated the influence of volume of distribution at steady state (
V
ss
) on AUC24h for quinidine and alprazolam. They also underestimated the effect of volume of liver (
V
liver
) for all three drugs, the impact of enzyme intrinsic clearance of CYP2C9 and CYP2E1 for quinidine, and that of UGT1A4 abundance for midazolam. Our investigation showed that the interpretation of GSA results is not straightforward. Dismissing existing model parameter correlations, GSA methods such as Morris and Sobol can lead to biased determination of the key parameters for the selected outputs of interest. Decisions regarding parameters’ influence (or otherwise) should be made in light of available knowledge including the model assumptions, GSA method limitations, and inter-correlations between model parameters, particularly in complex models.
Graphical abstract</description><subject>Biochemistry</subject><subject>Biomedical and Life Sciences</subject><subject>Biomedicine</subject><subject>Biotechnology</subject><subject>Pharmacology/Toxicology</subject><subject>Pharmacy</subject><subject>Research Article</subject><issn>1550-7416</issn><issn>1550-7416</issn><fulltext>true</fulltext><rsrctype>article</rsrctype><creationdate>2020</creationdate><recordtype>article</recordtype><sourceid>C6C</sourceid><recordid>eNp9kU1vVCEUhonR2Fr9Ay4MSzdXOcD92piME60mbTSxe8KFMzNUBkZgxt7EHy91alM3rjg55z0PhIeQl8DeAGfD2wycy6FhnDWMyYE1N4_IKbQta3oJ3eMH9Ql5lvM1Y4ILgKfkRPBuqIj-lPxaxpCdxaSLqxXVwdKlPqAumf7cYKCL3c7PLqzpuY-T9vQb1nxxB1dmugjaz9lleollE22mJdKvm9qJPq6d0d7P9L3OaGtXp6028bsLWJyhl9Giz8_Jk5X2GV_cnWfk6uOHq-Wn5uLL-efl4qIxUnal4W1vhglWDDqwsBoE12xCYSarUQpgCKMZpJ2E7VoGYBhiy0cwwvbcGCnOyLsjdreftmgNhpK0V7vktjrNKmqn_p0Et1HreFC96PoRbgGv7wAp_thjLmrrskHvdcC4z4pLLsdRdtDWKD9GTYo5J1zdXwNM3VpTR2uqWlN_rKmbuvTq4QPvV_5qqgFxDOQ6CmtM6jruU_39_D_sb24lp1M</recordid><startdate>20200717</startdate><enddate>20200717</enddate><creator>Liu, Dan</creator><creator>Li, Linzhong</creator><creator>Rostami-Hodjegan, Amin</creator><creator>Bois, Frederic Y.</creator><creator>Jamei, Masoud</creator><general>Springer International Publishing</general><scope>C6C</scope><scope>NPM</scope><scope>AAYXX</scope><scope>CITATION</scope><scope>7X8</scope><scope>5PM</scope></search><sort><creationdate>20200717</creationdate><title>Considerations and Caveats when Applying Global Sensitivity Analysis Methods to Physiologically Based Pharmacokinetic Models</title><author>Liu, Dan ; Li, Linzhong ; Rostami-Hodjegan, Amin ; Bois, Frederic Y. ; Jamei, Masoud</author></sort><facets><frbrtype>5</frbrtype><frbrgroupid>cdi_FETCH-LOGICAL-c446t-257c8b1f0161d1f832a0be3cbdae4310e19c84db3d65011c0ee5291c3d72cc43</frbrgroupid><rsrctype>articles</rsrctype><prefilter>articles</prefilter><language>eng</language><creationdate>2020</creationdate><topic>Biochemistry</topic><topic>Biomedical and Life Sciences</topic><topic>Biomedicine</topic><topic>Biotechnology</topic><topic>Pharmacology/Toxicology</topic><topic>Pharmacy</topic><topic>Research Article</topic><toplevel>peer_reviewed</toplevel><toplevel>online_resources</toplevel><creatorcontrib>Liu, Dan</creatorcontrib><creatorcontrib>Li, Linzhong</creatorcontrib><creatorcontrib>Rostami-Hodjegan, Amin</creatorcontrib><creatorcontrib>Bois, Frederic Y.</creatorcontrib><creatorcontrib>Jamei, Masoud</creatorcontrib><collection>SpringerOpen</collection><collection>PubMed</collection><collection>CrossRef</collection><collection>MEDLINE - Academic</collection><collection>PubMed Central (Full Participant titles)</collection><jtitle>The AAPS journal</jtitle></facets><delivery><delcategory>Remote Search Resource</delcategory><fulltext>fulltext</fulltext></delivery><addata><au>Liu, Dan</au><au>Li, Linzhong</au><au>Rostami-Hodjegan, Amin</au><au>Bois, Frederic Y.</au><au>Jamei, Masoud</au><format>journal</format><genre>article</genre><ristype>JOUR</ristype><atitle>Considerations and Caveats when Applying Global Sensitivity Analysis Methods to Physiologically Based Pharmacokinetic Models</atitle><jtitle>The AAPS journal</jtitle><stitle>AAPS J</stitle><addtitle>AAPS J</addtitle><date>2020-07-17</date><risdate>2020</risdate><volume>22</volume><issue>5</issue><spage>93</spage><epage>93</epage><pages>93-93</pages><artnum>93</artnum><issn>1550-7416</issn><eissn>1550-7416</eissn><abstract>Three global sensitivity analysis (GSA) methods (Morris, Sobol and extended Sobol) are applied to a minimal physiologically based PK (mPBPK) model using three model drugs given orally, namely quinidine, alprazolam, and midazolam. We investigated how correlations among input parameters affect the determination of the key parameters influencing pharmacokinetic (PK) properties of general interest, i.e., the maximal plasma concentration (
C
max
) time at which
C
max
is reached (
T
max
), and area under plasma concentration (AUC). The influential parameters determined by the Morris and Sobol methods (suitable for independent model parameters) were compared to those determined by the extended Sobol method (which considers model parameter correlations). For the three drugs investigated, the Morris method was as informative as the Sobol method. The extended Sobol method identified different sets of influential parameters to Morris and Sobol. These methods overestimated the influence of volume of distribution at steady state (
V
ss
) on AUC24h for quinidine and alprazolam. They also underestimated the effect of volume of liver (
V
liver
) for all three drugs, the impact of enzyme intrinsic clearance of CYP2C9 and CYP2E1 for quinidine, and that of UGT1A4 abundance for midazolam. Our investigation showed that the interpretation of GSA results is not straightforward. Dismissing existing model parameter correlations, GSA methods such as Morris and Sobol can lead to biased determination of the key parameters for the selected outputs of interest. Decisions regarding parameters’ influence (or otherwise) should be made in light of available knowledge including the model assumptions, GSA method limitations, and inter-correlations between model parameters, particularly in complex models.
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subjects | Biochemistry Biomedical and Life Sciences Biomedicine Biotechnology Pharmacology/Toxicology Pharmacy Research Article |
title | Considerations and Caveats when Applying Global Sensitivity Analysis Methods to Physiologically Based Pharmacokinetic Models |
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