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A comprehensive regulatory and industry review of modeling and simulation practices in oncology clinical drug development
Exposure–response (E–R) analyses are an integral component in the development of oncology products. Characterizing the relationship between drug exposure metrics and response allows the sponsor to use modeling and simulation to address both internal and external drug development questions (e.g., opt...
Autores principales: | , , , , , , , , , , , , , , |
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Formato: | Online Artículo Texto |
Lenguaje: | English |
Publicado: |
Springer US
2023
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Materias: | |
Acceso en línea: | https://www.ncbi.nlm.nih.gov/pmc/articles/PMC10169901/ https://www.ncbi.nlm.nih.gov/pubmed/36870005 http://dx.doi.org/10.1007/s10928-023-09850-2 |
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author | Ruiz-Garcia, Ana Baverel, Paul Bottino, Dean Dolton, Michael Feng, Yan González-García, Ignacio Kim, Jaeyeon Robey, Seth Singh, Indrajeet Turner, David Wu, Shu-Pei Yin, Donghua Zhou, Di Zhu, Hao Bonate, Peter |
author_facet | Ruiz-Garcia, Ana Baverel, Paul Bottino, Dean Dolton, Michael Feng, Yan González-García, Ignacio Kim, Jaeyeon Robey, Seth Singh, Indrajeet Turner, David Wu, Shu-Pei Yin, Donghua Zhou, Di Zhu, Hao Bonate, Peter |
author_sort | Ruiz-Garcia, Ana |
collection | PubMed |
description | Exposure–response (E–R) analyses are an integral component in the development of oncology products. Characterizing the relationship between drug exposure metrics and response allows the sponsor to use modeling and simulation to address both internal and external drug development questions (e.g., optimal dose, frequency of administration, dose adjustments for special populations). This white paper is the output of an industry-government collaboration among scientists with broad experience in E–R modeling as part of regulatory submissions. The goal of this white paper is to provide guidance on what the preferred methods for E–R analysis in oncology clinical drug development are and what metrics of exposure should be considered. |
format | Online Article Text |
id | pubmed-10169901 |
institution | National Center for Biotechnology Information |
language | English |
publishDate | 2023 |
publisher | Springer US |
record_format | MEDLINE/PubMed |
spelling | pubmed-101699012023-05-11 A comprehensive regulatory and industry review of modeling and simulation practices in oncology clinical drug development Ruiz-Garcia, Ana Baverel, Paul Bottino, Dean Dolton, Michael Feng, Yan González-García, Ignacio Kim, Jaeyeon Robey, Seth Singh, Indrajeet Turner, David Wu, Shu-Pei Yin, Donghua Zhou, Di Zhu, Hao Bonate, Peter J Pharmacokinet Pharmacodyn Review Paper Exposure–response (E–R) analyses are an integral component in the development of oncology products. Characterizing the relationship between drug exposure metrics and response allows the sponsor to use modeling and simulation to address both internal and external drug development questions (e.g., optimal dose, frequency of administration, dose adjustments for special populations). This white paper is the output of an industry-government collaboration among scientists with broad experience in E–R modeling as part of regulatory submissions. The goal of this white paper is to provide guidance on what the preferred methods for E–R analysis in oncology clinical drug development are and what metrics of exposure should be considered. Springer US 2023-03-04 2023 /pmc/articles/PMC10169901/ /pubmed/36870005 http://dx.doi.org/10.1007/s10928-023-09850-2 Text en © The Author(s) 2023 https://creativecommons.org/licenses/by/4.0/Open AccessThis article is licensed under a Creative Commons Attribution 4.0 International License, which permits use, sharing, adaptation, distribution and reproduction in any medium or format, as long as you give appropriate credit to the original author(s) and the source, provide a link to the Creative Commons licence, and indicate if changes were made. The images or other third party material in this article are included in the article's Creative Commons licence, unless indicated otherwise in a credit line to the material. If material is not included in the article's Creative Commons licence and your intended use is not permitted by statutory regulation or exceeds the permitted use, you will need to obtain permission directly from the copyright holder. To view a copy of this licence, visit http://creativecommons.org/licenses/by/4.0/ (https://creativecommons.org/licenses/by/4.0/) . |
spellingShingle | Review Paper Ruiz-Garcia, Ana Baverel, Paul Bottino, Dean Dolton, Michael Feng, Yan González-García, Ignacio Kim, Jaeyeon Robey, Seth Singh, Indrajeet Turner, David Wu, Shu-Pei Yin, Donghua Zhou, Di Zhu, Hao Bonate, Peter A comprehensive regulatory and industry review of modeling and simulation practices in oncology clinical drug development |
title | A comprehensive regulatory and industry review of modeling and simulation practices in oncology clinical drug development |
title_full | A comprehensive regulatory and industry review of modeling and simulation practices in oncology clinical drug development |
title_fullStr | A comprehensive regulatory and industry review of modeling and simulation practices in oncology clinical drug development |
title_full_unstemmed | A comprehensive regulatory and industry review of modeling and simulation practices in oncology clinical drug development |
title_short | A comprehensive regulatory and industry review of modeling and simulation practices in oncology clinical drug development |
title_sort | comprehensive regulatory and industry review of modeling and simulation practices in oncology clinical drug development |
topic | Review Paper |
url | https://www.ncbi.nlm.nih.gov/pmc/articles/PMC10169901/ https://www.ncbi.nlm.nih.gov/pubmed/36870005 http://dx.doi.org/10.1007/s10928-023-09850-2 |
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