Dynamics of Acquired Resistance to Nivolumab Therapies Varies From Administration Strategies
•We developed innovative model combining evolutionary dynamics and pharmacokinetics.•We adopted modeling method to simulate resistance which is difficult to achieve in vivo experiments.•The results indicate increasing the dose or shortening the doses interval can promote drug resistance.•The paramet...
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Veröffentlicht in: | Clinical therapeutics 2021-12, Vol.43 (12), p.2088-2103 |
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creator | Ji, Jiatong Wu, Hong Feng, Xiaobing Liu, Xiaoquan Huang, Chenrong Zheng, Shuyun Zou, Jianjun Liao, Jun |
description | •We developed innovative model combining evolutionary dynamics and pharmacokinetics.•We adopted modeling method to simulate resistance which is difficult to achieve in vivo experiments.•The results indicate increasing the dose or shortening the doses interval can promote drug resistance.•The parameters are derived from clinical trials, so the conclusion is reliable.
The identification of optimal drug administration schedules to overcome the emergence of resistance that causes treatment failure is a major challenge in cancer research. We report the outcomes of a computational strategy to assess the dynamics of tumor progression as a function of time under different treatment regimens.
We developed an evolutionary game theory model that combined Lotka-Volterra equations and pharmacokinetic properties with 2 competing cancer species: nivolumab-response cells and Janus kinase (JAK1/2) mutation cells. We selected 3 therapeutic schemes that have been tested in the clinical trials: 3 mg/kg Q2w, 10 mg/kg Q2w, and 480 mg Q4w. The simulation was performed under the intervals of 75, 125, and 175 days, respectively, for each regimen. The data sources of the pharmacokinetic parameters used in this study were collected from previous published clinical trials. Other parameters in the evolutionary model come from the existing references.
Predictions under various dose schedules indicated a strong selection for nivolumab-independent cells. Under the 3 mg/kg dose strategy, the reproduction rate of JAK mutation cells was highest, with strongest tumor elimination ability at a 75-day interval between treatments. Prolonged drug intervals to 125 or 175 days delayed tumor evolution but accelerated tumor recurrence. Although 10 mg/kg Q2w had an obvious clinical effect in a short time, it further promotes the progress of resistant population compared with the 3 mg/kg dose. Our model suggests that 480 mg Q4w would be more valuable in terms of clinical efficacy, but complete resistant occurs earlier regardless the interval.
The results of this study emphasize that increasing the dose or shortening the interval between doses accelerates the evolution of heterogeneous populations, although the short-term effect is significant. In practice, the therapeutic regimen should be balanced according to the evolutionary principle. |
doi_str_mv | 10.1016/j.clinthera.2021.10.004 |
format | Article |
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The identification of optimal drug administration schedules to overcome the emergence of resistance that causes treatment failure is a major challenge in cancer research. We report the outcomes of a computational strategy to assess the dynamics of tumor progression as a function of time under different treatment regimens.
We developed an evolutionary game theory model that combined Lotka-Volterra equations and pharmacokinetic properties with 2 competing cancer species: nivolumab-response cells and Janus kinase (JAK1/2) mutation cells. We selected 3 therapeutic schemes that have been tested in the clinical trials: 3 mg/kg Q2w, 10 mg/kg Q2w, and 480 mg Q4w. The simulation was performed under the intervals of 75, 125, and 175 days, respectively, for each regimen. The data sources of the pharmacokinetic parameters used in this study were collected from previous published clinical trials. Other parameters in the evolutionary model come from the existing references.
Predictions under various dose schedules indicated a strong selection for nivolumab-independent cells. Under the 3 mg/kg dose strategy, the reproduction rate of JAK mutation cells was highest, with strongest tumor elimination ability at a 75-day interval between treatments. Prolonged drug intervals to 125 or 175 days delayed tumor evolution but accelerated tumor recurrence. Although 10 mg/kg Q2w had an obvious clinical effect in a short time, it further promotes the progress of resistant population compared with the 3 mg/kg dose. Our model suggests that 480 mg Q4w would be more valuable in terms of clinical efficacy, but complete resistant occurs earlier regardless the interval.
The results of this study emphasize that increasing the dose or shortening the interval between doses accelerates the evolution of heterogeneous populations, although the short-term effect is significant. In practice, the therapeutic regimen should be balanced according to the evolutionary principle.</description><identifier>ISSN: 0149-2918</identifier><identifier>EISSN: 1879-114X</identifier><identifier>DOI: 10.1016/j.clinthera.2021.10.004</identifier><identifier>PMID: 34782163</identifier><language>eng</language><publisher>United States: Elsevier Inc</publisher><subject>Antigens ; Birth rate ; Cancer ; Cancer research ; Cancer therapies ; Clinical trials ; Cloning ; Competition ; Computer applications ; Computer Simulation ; Dosage ; dosing schedule ; Drug Administration Schedule ; Drug dosages ; drug resistance ; drug sensitivity ; Evolution ; Game theory ; Genotype & phenotype ; Humans ; Immunotherapy ; Janus kinase ; Kinases ; Lung cancer ; Lymphocytes ; Monoclonal antibodies ; Mutation ; Neoplasms - drug therapy ; nivolumab ; Nivolumab - therapeutic use ; Pharmacokinetics ; Pharmacology ; Schedules ; Strategy ; Targeted cancer therapy ; Treatment Outcome ; tumor ; Tumors ; Volterra integral equations</subject><ispartof>Clinical therapeutics, 2021-12, Vol.43 (12), p.2088-2103</ispartof><rights>2021 Elsevier Inc.</rights><rights>Copyright © 2021 Elsevier Inc. All rights reserved.</rights><rights>2021. Elsevier Inc.</rights><lds50>peer_reviewed</lds50><woscitedreferencessubscribed>false</woscitedreferencessubscribed><cites>FETCH-LOGICAL-c345t-b87aa5bc2258c3beb067de8d76827bb80a4ef5b8e4af5a398eca4bb2f479bc013</cites><orcidid>0000-0002-7994-0323 ; 0000-0003-0617-5840</orcidid></display><links><openurl>$$Topenurl_article</openurl><openurlfulltext>$$Topenurlfull_article</openurlfulltext><thumbnail>$$Tsyndetics_thumb_exl</thumbnail><linktohtml>$$Uhttps://www.proquest.com/docview/2610050660?pq-origsite=primo$$EHTML$$P50$$Gproquest$$H</linktohtml><link.rule.ids>314,780,784,3550,27924,27925,45995,64385,64387,64389,72469</link.rule.ids><backlink>$$Uhttps://www.ncbi.nlm.nih.gov/pubmed/34782163$$D View this record in MEDLINE/PubMed$$Hfree_for_read</backlink></links><search><creatorcontrib>Ji, Jiatong</creatorcontrib><creatorcontrib>Wu, Hong</creatorcontrib><creatorcontrib>Feng, Xiaobing</creatorcontrib><creatorcontrib>Liu, Xiaoquan</creatorcontrib><creatorcontrib>Huang, Chenrong</creatorcontrib><creatorcontrib>Zheng, Shuyun</creatorcontrib><creatorcontrib>Zou, Jianjun</creatorcontrib><creatorcontrib>Liao, Jun</creatorcontrib><title>Dynamics of Acquired Resistance to Nivolumab Therapies Varies From Administration Strategies</title><title>Clinical therapeutics</title><addtitle>Clin Ther</addtitle><description>•We developed innovative model combining evolutionary dynamics and pharmacokinetics.•We adopted modeling method to simulate resistance which is difficult to achieve in vivo experiments.•The results indicate increasing the dose or shortening the doses interval can promote drug resistance.•The parameters are derived from clinical trials, so the conclusion is reliable.
The identification of optimal drug administration schedules to overcome the emergence of resistance that causes treatment failure is a major challenge in cancer research. We report the outcomes of a computational strategy to assess the dynamics of tumor progression as a function of time under different treatment regimens.
We developed an evolutionary game theory model that combined Lotka-Volterra equations and pharmacokinetic properties with 2 competing cancer species: nivolumab-response cells and Janus kinase (JAK1/2) mutation cells. We selected 3 therapeutic schemes that have been tested in the clinical trials: 3 mg/kg Q2w, 10 mg/kg Q2w, and 480 mg Q4w. The simulation was performed under the intervals of 75, 125, and 175 days, respectively, for each regimen. The data sources of the pharmacokinetic parameters used in this study were collected from previous published clinical trials. Other parameters in the evolutionary model come from the existing references.
Predictions under various dose schedules indicated a strong selection for nivolumab-independent cells. Under the 3 mg/kg dose strategy, the reproduction rate of JAK mutation cells was highest, with strongest tumor elimination ability at a 75-day interval between treatments. Prolonged drug intervals to 125 or 175 days delayed tumor evolution but accelerated tumor recurrence. Although 10 mg/kg Q2w had an obvious clinical effect in a short time, it further promotes the progress of resistant population compared with the 3 mg/kg dose. Our model suggests that 480 mg Q4w would be more valuable in terms of clinical efficacy, but complete resistant occurs earlier regardless the interval.
The results of this study emphasize that increasing the dose or shortening the interval between doses accelerates the evolution of heterogeneous populations, although the short-term effect is significant. In practice, the therapeutic regimen should be balanced according to the evolutionary principle.</description><subject>Antigens</subject><subject>Birth rate</subject><subject>Cancer</subject><subject>Cancer research</subject><subject>Cancer therapies</subject><subject>Clinical trials</subject><subject>Cloning</subject><subject>Competition</subject><subject>Computer applications</subject><subject>Computer Simulation</subject><subject>Dosage</subject><subject>dosing schedule</subject><subject>Drug Administration Schedule</subject><subject>Drug dosages</subject><subject>drug resistance</subject><subject>drug sensitivity</subject><subject>Evolution</subject><subject>Game theory</subject><subject>Genotype & phenotype</subject><subject>Humans</subject><subject>Immunotherapy</subject><subject>Janus kinase</subject><subject>Kinases</subject><subject>Lung cancer</subject><subject>Lymphocytes</subject><subject>Monoclonal antibodies</subject><subject>Mutation</subject><subject>Neoplasms - drug therapy</subject><subject>nivolumab</subject><subject>Nivolumab - therapeutic use</subject><subject>Pharmacokinetics</subject><subject>Pharmacology</subject><subject>Schedules</subject><subject>Strategy</subject><subject>Targeted cancer therapy</subject><subject>Treatment Outcome</subject><subject>tumor</subject><subject>Tumors</subject><subject>Volterra integral equations</subject><issn>0149-2918</issn><issn>1879-114X</issn><fulltext>true</fulltext><rsrctype>article</rsrctype><creationdate>2021</creationdate><recordtype>article</recordtype><sourceid>EIF</sourceid><sourceid>8G5</sourceid><sourceid>ABUWG</sourceid><sourceid>AFKRA</sourceid><sourceid>AZQEC</sourceid><sourceid>BENPR</sourceid><sourceid>CCPQU</sourceid><sourceid>DWQXO</sourceid><sourceid>GNUQQ</sourceid><sourceid>GUQSH</sourceid><sourceid>M2O</sourceid><recordid>eNqFkU1r3DAQhkVoSTZp_kIq6KUXb0eyZMvHJWk-ILTQpqWHgpDkcavFtjaSHci_j8wmOfTS0wwzz3zwvoS8Z7BmwKpP27Xr_Tj9xWjWHDjL1TWAOCArpuqmYEz8ekNWwERT8IapI3Kc0hYAykbyQ3JUilpxVpUr8vvicTSDd4mGjm7c_ewjtvQbJp8mMzqkU6Bf_EPo58FYercc3HlM9KeJS7iMYaCbdvBj5qOZfBjp9yXBP7n9jrztTJ_w9DmekB-Xn-_Or4vbr1c355vbwpVCToVVtTHSOs6lcqVFC1XdomrrSvHaWgVGYCetQmE6acpGoTPCWt6JurEOWHlCPu737mK4nzFNevDJYd-bEcOcNJeNAgVSiIx--AfdhjmO-TvNKwYgoaogU_WecjGkFLHTu-gHEx81A70YoLf61QC9GLA0sgF58ux5_2wHbF_nXhTPwGYPYBbkwWPUyXnMSrdZeTfpNvj_HnkCEluccA</recordid><startdate>202112</startdate><enddate>202112</enddate><creator>Ji, Jiatong</creator><creator>Wu, Hong</creator><creator>Feng, Xiaobing</creator><creator>Liu, Xiaoquan</creator><creator>Huang, Chenrong</creator><creator>Zheng, Shuyun</creator><creator>Zou, Jianjun</creator><creator>Liao, Jun</creator><general>Elsevier Inc</general><general>Elsevier Limited</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>3V.</scope><scope>7RV</scope><scope>7X7</scope><scope>7XB</scope><scope>88C</scope><scope>88E</scope><scope>8AO</scope><scope>8FI</scope><scope>8FJ</scope><scope>8FK</scope><scope>8G5</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>GNUQQ</scope><scope>GUQSH</scope><scope>K9.</scope><scope>KB0</scope><scope>M0S</scope><scope>M0T</scope><scope>M1P</scope><scope>M2O</scope><scope>M7N</scope><scope>MBDVC</scope><scope>NAPCQ</scope><scope>PQEST</scope><scope>PQQKQ</scope><scope>PQUKI</scope><scope>PRINS</scope><scope>Q9U</scope><scope>7X8</scope><orcidid>https://orcid.org/0000-0002-7994-0323</orcidid><orcidid>https://orcid.org/0000-0003-0617-5840</orcidid></search><sort><creationdate>202112</creationdate><title>Dynamics of Acquired Resistance to Nivolumab Therapies Varies From Administration Strategies</title><author>Ji, Jiatong ; Wu, Hong ; Feng, Xiaobing ; Liu, Xiaoquan ; Huang, Chenrong ; Zheng, Shuyun ; Zou, Jianjun ; Liao, Jun</author></sort><facets><frbrtype>5</frbrtype><frbrgroupid>cdi_FETCH-LOGICAL-c345t-b87aa5bc2258c3beb067de8d76827bb80a4ef5b8e4af5a398eca4bb2f479bc013</frbrgroupid><rsrctype>articles</rsrctype><prefilter>articles</prefilter><language>eng</language><creationdate>2021</creationdate><topic>Antigens</topic><topic>Birth rate</topic><topic>Cancer</topic><topic>Cancer research</topic><topic>Cancer therapies</topic><topic>Clinical trials</topic><topic>Cloning</topic><topic>Competition</topic><topic>Computer applications</topic><topic>Computer Simulation</topic><topic>Dosage</topic><topic>dosing schedule</topic><topic>Drug Administration Schedule</topic><topic>Drug dosages</topic><topic>drug resistance</topic><topic>drug sensitivity</topic><topic>Evolution</topic><topic>Game theory</topic><topic>Genotype & phenotype</topic><topic>Humans</topic><topic>Immunotherapy</topic><topic>Janus kinase</topic><topic>Kinases</topic><topic>Lung cancer</topic><topic>Lymphocytes</topic><topic>Monoclonal antibodies</topic><topic>Mutation</topic><topic>Neoplasms - drug therapy</topic><topic>nivolumab</topic><topic>Nivolumab - therapeutic use</topic><topic>Pharmacokinetics</topic><topic>Pharmacology</topic><topic>Schedules</topic><topic>Strategy</topic><topic>Targeted cancer therapy</topic><topic>Treatment Outcome</topic><topic>tumor</topic><topic>Tumors</topic><topic>Volterra integral equations</topic><toplevel>peer_reviewed</toplevel><toplevel>online_resources</toplevel><creatorcontrib>Ji, Jiatong</creatorcontrib><creatorcontrib>Wu, Hong</creatorcontrib><creatorcontrib>Feng, Xiaobing</creatorcontrib><creatorcontrib>Liu, Xiaoquan</creatorcontrib><creatorcontrib>Huang, Chenrong</creatorcontrib><creatorcontrib>Zheng, Shuyun</creatorcontrib><creatorcontrib>Zou, Jianjun</creatorcontrib><creatorcontrib>Liao, Jun</creatorcontrib><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>Nursing & Allied Health Database</collection><collection>Health & Medical Collection</collection><collection>ProQuest Central (purchase pre-March 2016)</collection><collection>Healthcare Administration Database (Alumni)</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>Research Library (Alumni Edition)</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 Central Student</collection><collection>Research Library Prep</collection><collection>ProQuest Health & Medical Complete (Alumni)</collection><collection>Nursing & Allied Health Database (Alumni Edition)</collection><collection>Health & Medical Collection (Alumni Edition)</collection><collection>Healthcare Administration Database</collection><collection>Medical Database</collection><collection>Research Library</collection><collection>Algology Mycology and Protozoology Abstracts (Microbiology C)</collection><collection>Research Library (Corporate)</collection><collection>Nursing & Allied Health Premium</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>ProQuest Central Basic</collection><collection>MEDLINE - Academic</collection><jtitle>Clinical therapeutics</jtitle></facets><delivery><delcategory>Remote Search Resource</delcategory><fulltext>fulltext</fulltext></delivery><addata><au>Ji, Jiatong</au><au>Wu, Hong</au><au>Feng, Xiaobing</au><au>Liu, Xiaoquan</au><au>Huang, Chenrong</au><au>Zheng, Shuyun</au><au>Zou, Jianjun</au><au>Liao, Jun</au><format>journal</format><genre>article</genre><ristype>JOUR</ristype><atitle>Dynamics of Acquired Resistance to Nivolumab Therapies Varies From Administration Strategies</atitle><jtitle>Clinical therapeutics</jtitle><addtitle>Clin Ther</addtitle><date>2021-12</date><risdate>2021</risdate><volume>43</volume><issue>12</issue><spage>2088</spage><epage>2103</epage><pages>2088-2103</pages><issn>0149-2918</issn><eissn>1879-114X</eissn><abstract>•We developed innovative model combining evolutionary dynamics and pharmacokinetics.•We adopted modeling method to simulate resistance which is difficult to achieve in vivo experiments.•The results indicate increasing the dose or shortening the doses interval can promote drug resistance.•The parameters are derived from clinical trials, so the conclusion is reliable.
The identification of optimal drug administration schedules to overcome the emergence of resistance that causes treatment failure is a major challenge in cancer research. We report the outcomes of a computational strategy to assess the dynamics of tumor progression as a function of time under different treatment regimens.
We developed an evolutionary game theory model that combined Lotka-Volterra equations and pharmacokinetic properties with 2 competing cancer species: nivolumab-response cells and Janus kinase (JAK1/2) mutation cells. We selected 3 therapeutic schemes that have been tested in the clinical trials: 3 mg/kg Q2w, 10 mg/kg Q2w, and 480 mg Q4w. The simulation was performed under the intervals of 75, 125, and 175 days, respectively, for each regimen. The data sources of the pharmacokinetic parameters used in this study were collected from previous published clinical trials. Other parameters in the evolutionary model come from the existing references.
Predictions under various dose schedules indicated a strong selection for nivolumab-independent cells. Under the 3 mg/kg dose strategy, the reproduction rate of JAK mutation cells was highest, with strongest tumor elimination ability at a 75-day interval between treatments. Prolonged drug intervals to 125 or 175 days delayed tumor evolution but accelerated tumor recurrence. Although 10 mg/kg Q2w had an obvious clinical effect in a short time, it further promotes the progress of resistant population compared with the 3 mg/kg dose. Our model suggests that 480 mg Q4w would be more valuable in terms of clinical efficacy, but complete resistant occurs earlier regardless the interval.
The results of this study emphasize that increasing the dose or shortening the interval between doses accelerates the evolution of heterogeneous populations, although the short-term effect is significant. In practice, the therapeutic regimen should be balanced according to the evolutionary principle.</abstract><cop>United States</cop><pub>Elsevier Inc</pub><pmid>34782163</pmid><doi>10.1016/j.clinthera.2021.10.004</doi><tpages>16</tpages><orcidid>https://orcid.org/0000-0002-7994-0323</orcidid><orcidid>https://orcid.org/0000-0003-0617-5840</orcidid></addata></record> |
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subjects | Antigens Birth rate Cancer Cancer research Cancer therapies Clinical trials Cloning Competition Computer applications Computer Simulation Dosage dosing schedule Drug Administration Schedule Drug dosages drug resistance drug sensitivity Evolution Game theory Genotype & phenotype Humans Immunotherapy Janus kinase Kinases Lung cancer Lymphocytes Monoclonal antibodies Mutation Neoplasms - drug therapy nivolumab Nivolumab - therapeutic use Pharmacokinetics Pharmacology Schedules Strategy Targeted cancer therapy Treatment Outcome tumor Tumors Volterra integral equations |
title | Dynamics of Acquired Resistance to Nivolumab Therapies Varies From Administration Strategies |
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