Population Pharmacokinetics of Imipenem in Critically Ill Patients: A Parametric and Nonparametric Model Converge on CKD-EPI Estimated Glomerular Filtration Rate as an Impactful Covariate
Background Population pharmacokinetic (popPK) models for antibiotics are used to improve dosing strategies and individualize dosing by therapeutic drug monitoring. Little is known about the differences in results of parametric versus nonparametric popPK models and their potential consequences in cli...
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Veröffentlicht in: | Clinical pharmacokinetics 2020-07, Vol.59 (7), p.885-898 |
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creator | de Velde, Femke de Winter, Brenda C. M. Neely, Michael N. Yamada, Walter M. Koch, Birgit C. P. Harbarth, Stephan von Dach, Elodie van Gelder, Teun Huttner, Angela Mouton, Johan W. |
description | Background
Population pharmacokinetic (popPK) models for antibiotics are used to improve dosing strategies and individualize dosing by therapeutic drug monitoring. Little is known about the differences in results of parametric versus nonparametric popPK models and their potential consequences in clinical practice. We developed both parametric and nonparametric models of imipenem using data from critically ill patients and compared their results.
Methods
Twenty-six critically ill patients treated with intravenous imipenem/cilastatin were included in this study. Median estimated glomerular filtration rate (eGFR) measured by the Chronic Kidney Disease Epidemiology Collaboration (CKD-EPI) equation was 116 mL/min/1.73 m
2
(interquartile range 104–124) at inclusion. The usual dosing regimen was 500 mg/500 mg four times daily. On average, five imipenem levels per patient (138 levels in total) were drawn as peak, intermediate, and trough levels. Imipenem concentration-time profiles were analyzed using parametric (NONMEM 7.2) and nonparametric (Pmetrics 1.5.2) popPK software.
Results
For both methods, data were best described by a model with two distribution compartments and the CKD-EPI eGFR equation unadjusted for body surface area as a covariate on the elimination rate constant (
K
e
). The parametric population parameter estimates were
K
e
0.637 h
−1
(between-subject variability [BSV]: 19.0% coefficient of variation [CV]) and central distribution volume (
V
c
) 29.6 L (without BSV). The nonparametric values were
K
e
0.681 h
−1
(34.0% CV) and
V
c
31.1 L (42.6% CV).
Conclusions
Both models described imipenem popPK well; the parameter estimates were comparable and the included covariate was identical. However, estimated BSV was higher in the nonparametric model. This may have consequences for estimated exposure during dosing simulations and should be further investigated in simulation studies. |
doi_str_mv | 10.1007/s40262-020-00859-1 |
format | Article |
fullrecord | <record><control><sourceid>proquest_pubme</sourceid><recordid>TN_cdi_pubmedcentral_primary_oai_pubmedcentral_nih_gov_7329758</recordid><sourceformat>XML</sourceformat><sourcesystem>PC</sourcesystem><sourcerecordid>2422404240</sourcerecordid><originalsourceid>FETCH-LOGICAL-c474t-252cdacbb106976f90eb6ebebd5c8517b99d1dfe58e35f80a5b38c77ebae23e43</originalsourceid><addsrcrecordid>eNp9UU1v1DAQtRCILoU_wAFZ4hxqO5_mgFSFbbuiwArB2XKcydYlsYPtrNTfxp9jypYWLhwsj-a9efNGj5CXnL3hjNUnsWCiEhkTLGOsKWXGH5EV5zUWUlSPyYrlXGSlrPIj8izGa4YswdhTcpRzWVaykivyc-vnZdTJeke3VzpM2vjv1kGyJlI_0M1kZ3AwUetoGyy29Tje0M040i1OgUvxLT3FOugJUrCGatfTT97ND52PvoeRtt7tIeyA4qb2w_tsvd3QdUx20gl6ej76CQI6CfTMjikcHH1BjOqImmhk1iYNy63QXgeLyHPyZNBjhBd3_zH5drb-2l5kl5_PN-3pZWaKukiZKIXptek6zipZV4Nk0FXQQdeXpil53UnZ836AsoG8HBqmyy5vTF1Dp0HkUOTH5N1Bd166CXqDRwc9qjmg93CjvLbqX8TZK7Xze1XnQtZlgwKv7wSC_7FATOraL8GhZyUKIQpW4EOWOLBM8DEGGO43cKZuA1eHwBUGrn4HrjgOvfrb2_3In4SRkB8IESG3g_Cw-z-yvwC5cbt0</addsrcrecordid><sourcetype>Open Access Repository</sourcetype><iscdi>true</iscdi><recordtype>article</recordtype><pqid>2422404240</pqid></control><display><type>article</type><title>Population Pharmacokinetics of Imipenem in Critically Ill Patients: A Parametric and Nonparametric Model Converge on CKD-EPI Estimated Glomerular Filtration Rate as an Impactful Covariate</title><source>MEDLINE</source><source>Springer Nature - Complete Springer Journals</source><creator>de Velde, Femke ; de Winter, Brenda C. M. ; Neely, Michael N. ; Yamada, Walter M. ; Koch, Birgit C. P. ; Harbarth, Stephan ; von Dach, Elodie ; van Gelder, Teun ; Huttner, Angela ; Mouton, Johan W.</creator><creatorcontrib>de Velde, Femke ; de Winter, Brenda C. M. ; Neely, Michael N. ; Yamada, Walter M. ; Koch, Birgit C. P. ; Harbarth, Stephan ; von Dach, Elodie ; van Gelder, Teun ; Huttner, Angela ; Mouton, Johan W. ; COMBACTE-NET consortium</creatorcontrib><description>Background
Population pharmacokinetic (popPK) models for antibiotics are used to improve dosing strategies and individualize dosing by therapeutic drug monitoring. Little is known about the differences in results of parametric versus nonparametric popPK models and their potential consequences in clinical practice. We developed both parametric and nonparametric models of imipenem using data from critically ill patients and compared their results.
Methods
Twenty-six critically ill patients treated with intravenous imipenem/cilastatin were included in this study. Median estimated glomerular filtration rate (eGFR) measured by the Chronic Kidney Disease Epidemiology Collaboration (CKD-EPI) equation was 116 mL/min/1.73 m
2
(interquartile range 104–124) at inclusion. The usual dosing regimen was 500 mg/500 mg four times daily. On average, five imipenem levels per patient (138 levels in total) were drawn as peak, intermediate, and trough levels. Imipenem concentration-time profiles were analyzed using parametric (NONMEM 7.2) and nonparametric (Pmetrics 1.5.2) popPK software.
Results
For both methods, data were best described by a model with two distribution compartments and the CKD-EPI eGFR equation unadjusted for body surface area as a covariate on the elimination rate constant (
K
e
). The parametric population parameter estimates were
K
e
0.637 h
−1
(between-subject variability [BSV]: 19.0% coefficient of variation [CV]) and central distribution volume (
V
c
) 29.6 L (without BSV). The nonparametric values were
K
e
0.681 h
−1
(34.0% CV) and
V
c
31.1 L (42.6% CV).
Conclusions
Both models described imipenem popPK well; the parameter estimates were comparable and the included covariate was identical. However, estimated BSV was higher in the nonparametric model. This may have consequences for estimated exposure during dosing simulations and should be further investigated in simulation studies.</description><identifier>ISSN: 0312-5963</identifier><identifier>EISSN: 1179-1926</identifier><identifier>DOI: 10.1007/s40262-020-00859-1</identifier><identifier>PMID: 31956969</identifier><language>eng</language><publisher>Cham: Springer International Publishing</publisher><subject>Adult ; Anti-Bacterial Agents - pharmacokinetics ; Anti-Bacterial Agents - therapeutic use ; Antibiotics ; Antimicrobial agents ; Creatinine ; Critical Illness ; Drug dosages ; Female ; Glomerular Filtration Rate ; Humans ; Imipenem - pharmacokinetics ; Imipenem - therapeutic use ; Internal Medicine ; Male ; Medicine ; Medicine & Public Health ; Middle Aged ; Nonparametric statistics ; Original ; Original Research Article ; Pharmacokinetics ; Pharmacology/Toxicology ; Pharmacotherapy ; Population ; Renal Insufficiency, Chronic - drug therapy ; Therapeutic drug monitoring</subject><ispartof>Clinical pharmacokinetics, 2020-07, Vol.59 (7), p.885-898</ispartof><rights>The Author(s) 2020</rights><rights>Copyright Springer Nature B.V. Jul 2020</rights><lds50>peer_reviewed</lds50><oa>free_for_read</oa><woscitedreferencessubscribed>false</woscitedreferencessubscribed><citedby>FETCH-LOGICAL-c474t-252cdacbb106976f90eb6ebebd5c8517b99d1dfe58e35f80a5b38c77ebae23e43</citedby><cites>FETCH-LOGICAL-c474t-252cdacbb106976f90eb6ebebd5c8517b99d1dfe58e35f80a5b38c77ebae23e43</cites><orcidid>0000-0003-4581-9688</orcidid></display><links><openurl>$$Topenurl_article</openurl><openurlfulltext>$$Topenurlfull_article</openurlfulltext><thumbnail>$$Tsyndetics_thumb_exl</thumbnail><linktopdf>$$Uhttps://link.springer.com/content/pdf/10.1007/s40262-020-00859-1$$EPDF$$P50$$Gspringer$$Hfree_for_read</linktopdf><linktohtml>$$Uhttps://link.springer.com/10.1007/s40262-020-00859-1$$EHTML$$P50$$Gspringer$$Hfree_for_read</linktohtml><link.rule.ids>230,314,776,780,881,27903,27904,41467,42536,51297</link.rule.ids><backlink>$$Uhttps://www.ncbi.nlm.nih.gov/pubmed/31956969$$D View this record in MEDLINE/PubMed$$Hfree_for_read</backlink></links><search><creatorcontrib>de Velde, Femke</creatorcontrib><creatorcontrib>de Winter, Brenda C. M.</creatorcontrib><creatorcontrib>Neely, Michael N.</creatorcontrib><creatorcontrib>Yamada, Walter M.</creatorcontrib><creatorcontrib>Koch, Birgit C. P.</creatorcontrib><creatorcontrib>Harbarth, Stephan</creatorcontrib><creatorcontrib>von Dach, Elodie</creatorcontrib><creatorcontrib>van Gelder, Teun</creatorcontrib><creatorcontrib>Huttner, Angela</creatorcontrib><creatorcontrib>Mouton, Johan W.</creatorcontrib><creatorcontrib>COMBACTE-NET consortium</creatorcontrib><title>Population Pharmacokinetics of Imipenem in Critically Ill Patients: A Parametric and Nonparametric Model Converge on CKD-EPI Estimated Glomerular Filtration Rate as an Impactful Covariate</title><title>Clinical pharmacokinetics</title><addtitle>Clin Pharmacokinet</addtitle><addtitle>Clin Pharmacokinet</addtitle><description>Background
Population pharmacokinetic (popPK) models for antibiotics are used to improve dosing strategies and individualize dosing by therapeutic drug monitoring. Little is known about the differences in results of parametric versus nonparametric popPK models and their potential consequences in clinical practice. We developed both parametric and nonparametric models of imipenem using data from critically ill patients and compared their results.
Methods
Twenty-six critically ill patients treated with intravenous imipenem/cilastatin were included in this study. Median estimated glomerular filtration rate (eGFR) measured by the Chronic Kidney Disease Epidemiology Collaboration (CKD-EPI) equation was 116 mL/min/1.73 m
2
(interquartile range 104–124) at inclusion. The usual dosing regimen was 500 mg/500 mg four times daily. On average, five imipenem levels per patient (138 levels in total) were drawn as peak, intermediate, and trough levels. Imipenem concentration-time profiles were analyzed using parametric (NONMEM 7.2) and nonparametric (Pmetrics 1.5.2) popPK software.
Results
For both methods, data were best described by a model with two distribution compartments and the CKD-EPI eGFR equation unadjusted for body surface area as a covariate on the elimination rate constant (
K
e
). The parametric population parameter estimates were
K
e
0.637 h
−1
(between-subject variability [BSV]: 19.0% coefficient of variation [CV]) and central distribution volume (
V
c
) 29.6 L (without BSV). The nonparametric values were
K
e
0.681 h
−1
(34.0% CV) and
V
c
31.1 L (42.6% CV).
Conclusions
Both models described imipenem popPK well; the parameter estimates were comparable and the included covariate was identical. However, estimated BSV was higher in the nonparametric model. This may have consequences for estimated exposure during dosing simulations and should be further investigated in simulation studies.</description><subject>Adult</subject><subject>Anti-Bacterial Agents - pharmacokinetics</subject><subject>Anti-Bacterial Agents - therapeutic use</subject><subject>Antibiotics</subject><subject>Antimicrobial agents</subject><subject>Creatinine</subject><subject>Critical Illness</subject><subject>Drug dosages</subject><subject>Female</subject><subject>Glomerular Filtration Rate</subject><subject>Humans</subject><subject>Imipenem - pharmacokinetics</subject><subject>Imipenem - therapeutic use</subject><subject>Internal Medicine</subject><subject>Male</subject><subject>Medicine</subject><subject>Medicine & Public Health</subject><subject>Middle Aged</subject><subject>Nonparametric statistics</subject><subject>Original</subject><subject>Original Research Article</subject><subject>Pharmacokinetics</subject><subject>Pharmacology/Toxicology</subject><subject>Pharmacotherapy</subject><subject>Population</subject><subject>Renal Insufficiency, Chronic - drug therapy</subject><subject>Therapeutic drug monitoring</subject><issn>0312-5963</issn><issn>1179-1926</issn><fulltext>true</fulltext><rsrctype>article</rsrctype><creationdate>2020</creationdate><recordtype>article</recordtype><sourceid>C6C</sourceid><sourceid>EIF</sourceid><sourceid>BENPR</sourceid><recordid>eNp9UU1v1DAQtRCILoU_wAFZ4hxqO5_mgFSFbbuiwArB2XKcydYlsYPtrNTfxp9jypYWLhwsj-a9efNGj5CXnL3hjNUnsWCiEhkTLGOsKWXGH5EV5zUWUlSPyYrlXGSlrPIj8izGa4YswdhTcpRzWVaykivyc-vnZdTJeke3VzpM2vjv1kGyJlI_0M1kZ3AwUetoGyy29Tje0M040i1OgUvxLT3FOugJUrCGatfTT97ND52PvoeRtt7tIeyA4qb2w_tsvd3QdUx20gl6ej76CQI6CfTMjikcHH1BjOqImmhk1iYNy63QXgeLyHPyZNBjhBd3_zH5drb-2l5kl5_PN-3pZWaKukiZKIXptek6zipZV4Nk0FXQQdeXpil53UnZ836AsoG8HBqmyy5vTF1Dp0HkUOTH5N1Bd166CXqDRwc9qjmg93CjvLbqX8TZK7Xze1XnQtZlgwKv7wSC_7FATOraL8GhZyUKIQpW4EOWOLBM8DEGGO43cKZuA1eHwBUGrn4HrjgOvfrb2_3In4SRkB8IESG3g_Cw-z-yvwC5cbt0</recordid><startdate>20200701</startdate><enddate>20200701</enddate><creator>de Velde, Femke</creator><creator>de Winter, Brenda C. M.</creator><creator>Neely, Michael N.</creator><creator>Yamada, Walter M.</creator><creator>Koch, Birgit C. P.</creator><creator>Harbarth, Stephan</creator><creator>von Dach, Elodie</creator><creator>van Gelder, Teun</creator><creator>Huttner, Angela</creator><creator>Mouton, Johan W.</creator><general>Springer International Publishing</general><general>Springer Nature B.V</general><scope>C6C</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>3V.</scope><scope>4T-</scope><scope>7X7</scope><scope>7XB</scope><scope>88E</scope><scope>8FI</scope><scope>8FJ</scope><scope>8FK</scope><scope>ABUWG</scope><scope>AFKRA</scope><scope>BENPR</scope><scope>CCPQU</scope><scope>FYUFA</scope><scope>GHDGH</scope><scope>K9.</scope><scope>M0S</scope><scope>M1P</scope><scope>PQEST</scope><scope>PQQKQ</scope><scope>PQUKI</scope><scope>PRINS</scope><scope>5PM</scope><orcidid>https://orcid.org/0000-0003-4581-9688</orcidid></search><sort><creationdate>20200701</creationdate><title>Population Pharmacokinetics of Imipenem in Critically Ill Patients: A Parametric and Nonparametric Model Converge on CKD-EPI Estimated Glomerular Filtration Rate as an Impactful Covariate</title><author>de Velde, Femke ; de Winter, Brenda C. M. ; Neely, Michael N. ; Yamada, Walter M. ; Koch, Birgit C. P. ; Harbarth, Stephan ; von Dach, Elodie ; van Gelder, Teun ; Huttner, Angela ; Mouton, Johan W.</author></sort><facets><frbrtype>5</frbrtype><frbrgroupid>cdi_FETCH-LOGICAL-c474t-252cdacbb106976f90eb6ebebd5c8517b99d1dfe58e35f80a5b38c77ebae23e43</frbrgroupid><rsrctype>articles</rsrctype><prefilter>articles</prefilter><language>eng</language><creationdate>2020</creationdate><topic>Adult</topic><topic>Anti-Bacterial Agents - pharmacokinetics</topic><topic>Anti-Bacterial Agents - therapeutic use</topic><topic>Antibiotics</topic><topic>Antimicrobial agents</topic><topic>Creatinine</topic><topic>Critical Illness</topic><topic>Drug dosages</topic><topic>Female</topic><topic>Glomerular Filtration Rate</topic><topic>Humans</topic><topic>Imipenem - pharmacokinetics</topic><topic>Imipenem - therapeutic use</topic><topic>Internal Medicine</topic><topic>Male</topic><topic>Medicine</topic><topic>Medicine & Public Health</topic><topic>Middle Aged</topic><topic>Nonparametric statistics</topic><topic>Original</topic><topic>Original Research Article</topic><topic>Pharmacokinetics</topic><topic>Pharmacology/Toxicology</topic><topic>Pharmacotherapy</topic><topic>Population</topic><topic>Renal Insufficiency, Chronic - drug therapy</topic><topic>Therapeutic drug monitoring</topic><toplevel>peer_reviewed</toplevel><toplevel>online_resources</toplevel><creatorcontrib>de Velde, Femke</creatorcontrib><creatorcontrib>de Winter, Brenda C. M.</creatorcontrib><creatorcontrib>Neely, Michael N.</creatorcontrib><creatorcontrib>Yamada, Walter M.</creatorcontrib><creatorcontrib>Koch, Birgit C. P.</creatorcontrib><creatorcontrib>Harbarth, Stephan</creatorcontrib><creatorcontrib>von Dach, Elodie</creatorcontrib><creatorcontrib>van Gelder, Teun</creatorcontrib><creatorcontrib>Huttner, Angela</creatorcontrib><creatorcontrib>Mouton, Johan W.</creatorcontrib><creatorcontrib>COMBACTE-NET consortium</creatorcontrib><collection>Springer Nature OA Free Journals</collection><collection>Medline</collection><collection>MEDLINE</collection><collection>MEDLINE (Ovid)</collection><collection>MEDLINE</collection><collection>MEDLINE</collection><collection>PubMed</collection><collection>CrossRef</collection><collection>ProQuest Central (Corporate)</collection><collection>Docstoc</collection><collection>Health & Medical Collection</collection><collection>ProQuest Central (purchase pre-March 2016)</collection><collection>Medical Database (Alumni Edition)</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</collection><collection>ProQuest One Community College</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>Medical 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>Clinical pharmacokinetics</jtitle></facets><delivery><delcategory>Remote Search Resource</delcategory><fulltext>fulltext</fulltext></delivery><addata><au>de Velde, Femke</au><au>de Winter, Brenda C. M.</au><au>Neely, Michael N.</au><au>Yamada, Walter M.</au><au>Koch, Birgit C. P.</au><au>Harbarth, Stephan</au><au>von Dach, Elodie</au><au>van Gelder, Teun</au><au>Huttner, Angela</au><au>Mouton, Johan W.</au><aucorp>COMBACTE-NET consortium</aucorp><format>journal</format><genre>article</genre><ristype>JOUR</ristype><atitle>Population Pharmacokinetics of Imipenem in Critically Ill Patients: A Parametric and Nonparametric Model Converge on CKD-EPI Estimated Glomerular Filtration Rate as an Impactful Covariate</atitle><jtitle>Clinical pharmacokinetics</jtitle><stitle>Clin Pharmacokinet</stitle><addtitle>Clin Pharmacokinet</addtitle><date>2020-07-01</date><risdate>2020</risdate><volume>59</volume><issue>7</issue><spage>885</spage><epage>898</epage><pages>885-898</pages><issn>0312-5963</issn><eissn>1179-1926</eissn><abstract>Background
Population pharmacokinetic (popPK) models for antibiotics are used to improve dosing strategies and individualize dosing by therapeutic drug monitoring. Little is known about the differences in results of parametric versus nonparametric popPK models and their potential consequences in clinical practice. We developed both parametric and nonparametric models of imipenem using data from critically ill patients and compared their results.
Methods
Twenty-six critically ill patients treated with intravenous imipenem/cilastatin were included in this study. Median estimated glomerular filtration rate (eGFR) measured by the Chronic Kidney Disease Epidemiology Collaboration (CKD-EPI) equation was 116 mL/min/1.73 m
2
(interquartile range 104–124) at inclusion. The usual dosing regimen was 500 mg/500 mg four times daily. On average, five imipenem levels per patient (138 levels in total) were drawn as peak, intermediate, and trough levels. Imipenem concentration-time profiles were analyzed using parametric (NONMEM 7.2) and nonparametric (Pmetrics 1.5.2) popPK software.
Results
For both methods, data were best described by a model with two distribution compartments and the CKD-EPI eGFR equation unadjusted for body surface area as a covariate on the elimination rate constant (
K
e
). The parametric population parameter estimates were
K
e
0.637 h
−1
(between-subject variability [BSV]: 19.0% coefficient of variation [CV]) and central distribution volume (
V
c
) 29.6 L (without BSV). The nonparametric values were
K
e
0.681 h
−1
(34.0% CV) and
V
c
31.1 L (42.6% CV).
Conclusions
Both models described imipenem popPK well; the parameter estimates were comparable and the included covariate was identical. However, estimated BSV was higher in the nonparametric model. This may have consequences for estimated exposure during dosing simulations and should be further investigated in simulation studies.</abstract><cop>Cham</cop><pub>Springer International Publishing</pub><pmid>31956969</pmid><doi>10.1007/s40262-020-00859-1</doi><tpages>14</tpages><orcidid>https://orcid.org/0000-0003-4581-9688</orcidid><oa>free_for_read</oa></addata></record> |
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subjects | Adult Anti-Bacterial Agents - pharmacokinetics Anti-Bacterial Agents - therapeutic use Antibiotics Antimicrobial agents Creatinine Critical Illness Drug dosages Female Glomerular Filtration Rate Humans Imipenem - pharmacokinetics Imipenem - therapeutic use Internal Medicine Male Medicine Medicine & Public Health Middle Aged Nonparametric statistics Original Original Research Article Pharmacokinetics Pharmacology/Toxicology Pharmacotherapy Population Renal Insufficiency, Chronic - drug therapy Therapeutic drug monitoring |
title | Population Pharmacokinetics of Imipenem in Critically Ill Patients: A Parametric and Nonparametric Model Converge on CKD-EPI Estimated Glomerular Filtration Rate as an Impactful Covariate |
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