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
Hauptverfasser: Ji, Jiatong, Wu, Hong, Feng, Xiaobing, Liu, Xiaoquan, Huang, Chenrong, Zheng, Shuyun, Zou, Jianjun, Liao, Jun
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container_end_page 2103
container_issue 12
container_start_page 2088
container_title Clinical therapeutics
container_volume 43
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
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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 &amp; 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. 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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. 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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.</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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