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PKPD modeling of acquired resistance to anti-cancer drug treatment
Non-small cell lung cancer (NSCLC) patients greatly benefit from the treatment with tyrosine kinase inhibitors (TKIs) targeting the epidermal growth factor receptor (EGFR). However, emergence of acquired resistance inevitable occurs after long-term treatment in most patients and limits clinical impr...
Autores principales: | , , , |
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Formato: | Online Artículo Texto |
Lenguaje: | English |
Publicado: |
Springer US
2017
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Materias: | |
Acceso en línea: | https://www.ncbi.nlm.nih.gov/pmc/articles/PMC5686279/ https://www.ncbi.nlm.nih.gov/pubmed/29090407 http://dx.doi.org/10.1007/s10928-017-9553-x |
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author | Eigenmann, Miro J. Frances, Nicolas Lavé, Thierry Walz, Antje-Christine |
author_facet | Eigenmann, Miro J. Frances, Nicolas Lavé, Thierry Walz, Antje-Christine |
author_sort | Eigenmann, Miro J. |
collection | PubMed |
description | Non-small cell lung cancer (NSCLC) patients greatly benefit from the treatment with tyrosine kinase inhibitors (TKIs) targeting the epidermal growth factor receptor (EGFR). However, emergence of acquired resistance inevitable occurs after long-term treatment in most patients and limits clinical improvement. In the present study, resistance to drug treatment in patient-derived NSCLC xenograft mice was assessed and modeling and simulation was applied to understand the dynamics of drug resistance as a basis to explore more beneficial drug regimen. Two semi-mechanistic models were fitted to tumor growth inhibition profiles during and after treatment with erlotinib or gefitinib. The base model proposes that as a result of drug treatment, tumor cells stop proliferating and undergo several stages of damage before they eventually die. The acquired resistance model adds a resistance term to the base model which assumes that resistant cells are emerging from the pool of damaged tumor cells. As a result, tumor cells sensitive to drug treatment will either die or be converted to a drug resistant cell population which is proliferating at a slower growth rate as compared to the sensitive cells. The observed tumor growth profiles were better described by the resistance model and emergence of resistance was concluded. In simulation studies, the selection of resistant cells was explored as well as the time-variant fraction of resistant over sensitive cells. The proposed model provides insight into the dynamic processes of emerging resistance. It predicts tumor regrowth during treatment driven by the selection of resistant cells and explains why faster tumor regrowth may occur after discontinuation of TKI treatment. Finally, it is shown how the semi-mechanistic model can be used to explore different scenarios and to identify optimal treatment schedules in clinical trials. ELECTRONIC SUPPLEMENTARY MATERIAL: The online version of this article (10.1007/s10928-017-9553-x) contains supplementary material, which is available to authorized users. |
format | Online Article Text |
id | pubmed-5686279 |
institution | National Center for Biotechnology Information |
language | English |
publishDate | 2017 |
publisher | Springer US |
record_format | MEDLINE/PubMed |
spelling | pubmed-56862792017-11-28 PKPD modeling of acquired resistance to anti-cancer drug treatment Eigenmann, Miro J. Frances, Nicolas Lavé, Thierry Walz, Antje-Christine J Pharmacokinet Pharmacodyn Original Paper Non-small cell lung cancer (NSCLC) patients greatly benefit from the treatment with tyrosine kinase inhibitors (TKIs) targeting the epidermal growth factor receptor (EGFR). However, emergence of acquired resistance inevitable occurs after long-term treatment in most patients and limits clinical improvement. In the present study, resistance to drug treatment in patient-derived NSCLC xenograft mice was assessed and modeling and simulation was applied to understand the dynamics of drug resistance as a basis to explore more beneficial drug regimen. Two semi-mechanistic models were fitted to tumor growth inhibition profiles during and after treatment with erlotinib or gefitinib. The base model proposes that as a result of drug treatment, tumor cells stop proliferating and undergo several stages of damage before they eventually die. The acquired resistance model adds a resistance term to the base model which assumes that resistant cells are emerging from the pool of damaged tumor cells. As a result, tumor cells sensitive to drug treatment will either die or be converted to a drug resistant cell population which is proliferating at a slower growth rate as compared to the sensitive cells. The observed tumor growth profiles were better described by the resistance model and emergence of resistance was concluded. In simulation studies, the selection of resistant cells was explored as well as the time-variant fraction of resistant over sensitive cells. The proposed model provides insight into the dynamic processes of emerging resistance. It predicts tumor regrowth during treatment driven by the selection of resistant cells and explains why faster tumor regrowth may occur after discontinuation of TKI treatment. Finally, it is shown how the semi-mechanistic model can be used to explore different scenarios and to identify optimal treatment schedules in clinical trials. ELECTRONIC SUPPLEMENTARY MATERIAL: The online version of this article (10.1007/s10928-017-9553-x) contains supplementary material, which is available to authorized users. Springer US 2017-10-31 2017 /pmc/articles/PMC5686279/ /pubmed/29090407 http://dx.doi.org/10.1007/s10928-017-9553-x Text en © The Author(s) 2017 Open AccessThis article is distributed under the terms of the Creative Commons Attribution 4.0 International License (http://creativecommons.org/licenses/by/4.0/), which permits unrestricted use, distribution, and reproduction in any medium, provided you give appropriate credit to the original author(s) and the source, provide a link to the Creative Commons license, and indicate if changes were made. |
spellingShingle | Original Paper Eigenmann, Miro J. Frances, Nicolas Lavé, Thierry Walz, Antje-Christine PKPD modeling of acquired resistance to anti-cancer drug treatment |
title | PKPD modeling of acquired resistance to anti-cancer drug treatment |
title_full | PKPD modeling of acquired resistance to anti-cancer drug treatment |
title_fullStr | PKPD modeling of acquired resistance to anti-cancer drug treatment |
title_full_unstemmed | PKPD modeling of acquired resistance to anti-cancer drug treatment |
title_short | PKPD modeling of acquired resistance to anti-cancer drug treatment |
title_sort | pkpd modeling of acquired resistance to anti-cancer drug treatment |
topic | Original Paper |
url | https://www.ncbi.nlm.nih.gov/pmc/articles/PMC5686279/ https://www.ncbi.nlm.nih.gov/pubmed/29090407 http://dx.doi.org/10.1007/s10928-017-9553-x |
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