A comprehensive evaluation of exposure–response relationships in clinical trials: application to support guselkumab dose selection for patients with psoriasis
Guselkumab, a human IgG1 monoclonal antibody that blocks interleukin-23, has been evaluated in one Phase 2 and two Phase 3 trials in patients with moderate-to-severe psoriasis, in which disease severity was assessed using Psoriasis Area and Severity Index (PASI) and Investigator’s Global Assessment...
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| Veröffentlicht in: | Journal of pharmacokinetics and pharmacodynamics 2018-08, Vol.45 (4), p.523-535 |
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| container_title | Journal of pharmacokinetics and pharmacodynamics |
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| creator | Hu, Chuanpu Yao, Zhenling Chen, Yang Randazzo, Bruce Zhang, Liping Xu, Zhenhua Sharma, Amarnath Zhou, Honghui |
| description | Guselkumab, a human IgG1 monoclonal antibody that blocks interleukin-23, has been evaluated in one Phase 2 and two Phase 3 trials in patients with moderate-to-severe psoriasis, in which disease severity was assessed using Psoriasis Area and Severity Index (PASI) and Investigator’s Global Assessment (IGA) scores. Through the application of landmark and longitudinal exposure–response (E–R) modeling analyses, we sought to predict the guselkumab dose–response (D–R) relationship using data from 1459 patients who participated in these trials. A recently developed novel latent-variable Type I Indirect Response joint model was applied to PASI75/90/100 and IGA response thresholds, with placebo effect empirically modeled. An effect of body weight on E–R, independent of pharmacokinetics, was identified. Thorough landmark analyses also were implemented using the same dataset. The E–R models were combined with a population pharmacokinetic model to generate D–R predictions. The relative merits of longitudinal and landmark analysis also are discussed. The results provide a comprehensive and robust evaluation of the D–R relationship. |
| doi_str_mv | 10.1007/s10928-018-9581-1 |
| format | Article |
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Through the application of landmark and longitudinal exposure–response (E–R) modeling analyses, we sought to predict the guselkumab dose–response (D–R) relationship using data from 1459 patients who participated in these trials. A recently developed novel latent-variable Type I Indirect Response joint model was applied to PASI75/90/100 and IGA response thresholds, with placebo effect empirically modeled. An effect of body weight on E–R, independent of pharmacokinetics, was identified. Thorough landmark analyses also were implemented using the same dataset. The E–R models were combined with a population pharmacokinetic model to generate D–R predictions. The relative merits of longitudinal and landmark analysis also are discussed. 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All Rights Reserved.</rights><lds50>peer_reviewed</lds50><woscitedreferencessubscribed>false</woscitedreferencessubscribed><citedby>FETCH-LOGICAL-c398t-78e85c2999460c2db464c4ac08b993a3e64917286fb56eb993488ae2a154f1c93</citedby><cites>FETCH-LOGICAL-c398t-78e85c2999460c2db464c4ac08b993a3e64917286fb56eb993488ae2a154f1c93</cites><orcidid>0000-0001-7812-2778</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/s10928-018-9581-1$$EPDF$$P50$$Gspringer$$H</linktopdf><linktohtml>$$Uhttps://link.springer.com/10.1007/s10928-018-9581-1$$EHTML$$P50$$Gspringer$$H</linktohtml><link.rule.ids>314,776,780,27901,27902,41464,42533,51294</link.rule.ids><backlink>$$Uhttps://www.ncbi.nlm.nih.gov/pubmed/29549540$$D View this record in MEDLINE/PubMed$$Hfree_for_read</backlink></links><search><creatorcontrib>Hu, Chuanpu</creatorcontrib><creatorcontrib>Yao, Zhenling</creatorcontrib><creatorcontrib>Chen, Yang</creatorcontrib><creatorcontrib>Randazzo, Bruce</creatorcontrib><creatorcontrib>Zhang, Liping</creatorcontrib><creatorcontrib>Xu, Zhenhua</creatorcontrib><creatorcontrib>Sharma, Amarnath</creatorcontrib><creatorcontrib>Zhou, Honghui</creatorcontrib><title>A comprehensive evaluation of exposure–response relationships in clinical trials: application to support guselkumab dose selection for patients with psoriasis</title><title>Journal of pharmacokinetics and pharmacodynamics</title><addtitle>J Pharmacokinet Pharmacodyn</addtitle><addtitle>J Pharmacokinet Pharmacodyn</addtitle><description>Guselkumab, a human IgG1 monoclonal antibody that blocks interleukin-23, has been evaluated in one Phase 2 and two Phase 3 trials in patients with moderate-to-severe psoriasis, in which disease severity was assessed using Psoriasis Area and Severity Index (PASI) and Investigator’s Global Assessment (IGA) scores. Through the application of landmark and longitudinal exposure–response (E–R) modeling analyses, we sought to predict the guselkumab dose–response (D–R) relationship using data from 1459 patients who participated in these trials. A recently developed novel latent-variable Type I Indirect Response joint model was applied to PASI75/90/100 and IGA response thresholds, with placebo effect empirically modeled. An effect of body weight on E–R, independent of pharmacokinetics, was identified. Thorough landmark analyses also were implemented using the same dataset. The E–R models were combined with a population pharmacokinetic model to generate D–R predictions. The relative merits of longitudinal and landmark analysis also are discussed. 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Yao, Zhenling ; Chen, Yang ; Randazzo, Bruce ; Zhang, Liping ; Xu, Zhenhua ; Sharma, Amarnath ; Zhou, Honghui</author></sort><facets><frbrtype>5</frbrtype><frbrgroupid>cdi_FETCH-LOGICAL-c398t-78e85c2999460c2db464c4ac08b993a3e64917286fb56eb993488ae2a154f1c93</frbrgroupid><rsrctype>articles</rsrctype><prefilter>articles</prefilter><language>eng</language><creationdate>2018</creationdate><topic>Biochemistry</topic><topic>Biomedical and Life Sciences</topic><topic>Biomedical Engineering and Bioengineering</topic><topic>Biomedicine</topic><topic>Body weight</topic><topic>Clinical trials</topic><topic>Data processing</topic><topic>Evaluation</topic><topic>Exposure</topic><topic>Immunoglobulin A</topic><topic>Immunoglobulin G</topic><topic>Interleukin 23</topic><topic>Monoclonal antibodies</topic><topic>Original Paper</topic><topic>Pharmacokinetics</topic><topic>Pharmacology/Toxicology</topic><topic>Pharmacy</topic><topic>Placebo effect</topic><topic>Placebos</topic><topic>Psoriasis</topic><topic>Veterinary Medicine/Veterinary Science</topic><toplevel>peer_reviewed</toplevel><toplevel>online_resources</toplevel><creatorcontrib>Hu, Chuanpu</creatorcontrib><creatorcontrib>Yao, Zhenling</creatorcontrib><creatorcontrib>Chen, Yang</creatorcontrib><creatorcontrib>Randazzo, Bruce</creatorcontrib><creatorcontrib>Zhang, Liping</creatorcontrib><creatorcontrib>Xu, Zhenhua</creatorcontrib><creatorcontrib>Sharma, Amarnath</creatorcontrib><creatorcontrib>Zhou, Honghui</creatorcontrib><collection>PubMed</collection><collection>CrossRef</collection><collection>ProQuest Central (Corporate)</collection><collection>Virology and AIDS Abstracts</collection><collection>Health & Medicine (ProQuest)</collection><collection>ProQuest Central (purchase pre-March 2016)</collection><collection>Medical Database (Alumni Edition)</collection><collection>ProQuest Pharma Collection</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)</collection><collection>ProQuest Central</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>AIDS and Cancer Research Abstracts</collection><collection>ProQuest Health & Medical Complete (Alumni)</collection><collection>Health & Medical Collection (Alumni Edition)</collection><collection>Medical Database</collection><collection>Algology Mycology and Protozoology Abstracts (Microbiology C)</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>MEDLINE - Academic</collection><jtitle>Journal of pharmacokinetics and pharmacodynamics</jtitle></facets><delivery><delcategory>Remote Search Resource</delcategory><fulltext>fulltext</fulltext></delivery><addata><au>Hu, Chuanpu</au><au>Yao, Zhenling</au><au>Chen, Yang</au><au>Randazzo, Bruce</au><au>Zhang, Liping</au><au>Xu, Zhenhua</au><au>Sharma, Amarnath</au><au>Zhou, Honghui</au><format>journal</format><genre>article</genre><ristype>JOUR</ristype><atitle>A comprehensive evaluation of exposure–response relationships in clinical trials: application to support guselkumab dose selection for patients with psoriasis</atitle><jtitle>Journal of pharmacokinetics and pharmacodynamics</jtitle><stitle>J Pharmacokinet Pharmacodyn</stitle><addtitle>J Pharmacokinet Pharmacodyn</addtitle><date>2018-08-01</date><risdate>2018</risdate><volume>45</volume><issue>4</issue><spage>523</spage><epage>535</epage><pages>523-535</pages><issn>1567-567X</issn><eissn>1573-8744</eissn><abstract>Guselkumab, a human IgG1 monoclonal antibody that blocks interleukin-23, has been evaluated in one Phase 2 and two Phase 3 trials in patients with moderate-to-severe psoriasis, in which disease severity was assessed using Psoriasis Area and Severity Index (PASI) and Investigator’s Global Assessment (IGA) scores. Through the application of landmark and longitudinal exposure–response (E–R) modeling analyses, we sought to predict the guselkumab dose–response (D–R) relationship using data from 1459 patients who participated in these trials. A recently developed novel latent-variable Type I Indirect Response joint model was applied to PASI75/90/100 and IGA response thresholds, with placebo effect empirically modeled. An effect of body weight on E–R, independent of pharmacokinetics, was identified. Thorough landmark analyses also were implemented using the same dataset. The E–R models were combined with a population pharmacokinetic model to generate D–R predictions. The relative merits of longitudinal and landmark analysis also are discussed. 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| subjects | Biochemistry Biomedical and Life Sciences Biomedical Engineering and Bioengineering Biomedicine Body weight Clinical trials Data processing Evaluation Exposure Immunoglobulin A Immunoglobulin G Interleukin 23 Monoclonal antibodies Original Paper Pharmacokinetics Pharmacology/Toxicology Pharmacy Placebo effect Placebos Psoriasis Veterinary Medicine/Veterinary Science |
| title | A comprehensive evaluation of exposure–response relationships in clinical trials: application to support guselkumab dose selection for patients with psoriasis |
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