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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...

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Autores principales: 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
Formato: Online Artículo Texto
Lenguaje:English
Publicado: Springer US 2023
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.
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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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