A comprehensive regulatory and industry review of modeling and simulation practices in oncology clinical drug development
© 2023. The Author(s)..
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.
| Medienart: |
E-Artikel |
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| Erscheinungsjahr: |
2023 |
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| Erschienen: |
2023 |
| Enthalten in: |
Zur Gesamtaufnahme - volume:50 |
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| Enthalten in: |
Journal of pharmacokinetics and pharmacodynamics - 50(2023), 3 vom: 04. Juni, Seite 147-172 |
| Sprache: |
Englisch |
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| Beteiligte Personen: |
Ruiz-Garcia, Ana [VerfasserIn] |
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| Links: |
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| Themen: |
C-QT |
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| Anmerkungen: |
Date Completed 11.05.2023 Date Revised 20.06.2023 published: Print-Electronic Citation Status MEDLINE |
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| doi: |
10.1007/s10928-023-09850-2 |
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| funding: |
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| Förderinstitution / Projekttitel: |
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| PPN (Katalog-ID): |
NLM353758973 |
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| 520 | |a 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 | ||
| 650 | 4 | |a Journal Article | |
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| 650 | 4 | |a Disease progression | |
| 650 | 4 | |a Exposure–response | |
| 650 | 4 | |a Logistic regression | |
| 650 | 4 | |a Markov | |
| 650 | 4 | |a Oncology | |
| 650 | 4 | |a Semi-mechanistic | |
| 650 | 4 | |a Time-to-event | |
| 650 | 4 | |a Tumor growth dynamics | |
| 700 | 1 | |a Baverel, Paul |e verfasserin |4 aut | |
| 700 | 1 | |a Bottino, Dean |e verfasserin |4 aut | |
| 700 | 1 | |a Dolton, Michael |e verfasserin |4 aut | |
| 700 | 1 | |a Feng, Yan |e verfasserin |4 aut | |
| 700 | 1 | |a González-García, Ignacio |e verfasserin |4 aut | |
| 700 | 1 | |a Kim, Jaeyeon |e verfasserin |4 aut | |
| 700 | 1 | |a Robey, Seth |e verfasserin |4 aut | |
| 700 | 1 | |a Singh, Indrajeet |e verfasserin |4 aut | |
| 700 | 1 | |a Turner, David |e verfasserin |4 aut | |
| 700 | 1 | |a Wu, Shu-Pei |e verfasserin |4 aut | |
| 700 | 1 | |a Yin, Donghua |e verfasserin |4 aut | |
| 700 | 1 | |a Zhou, Di |e verfasserin |4 aut | |
| 700 | 1 | |a Zhu, Hao |e verfasserin |4 aut | |
| 700 | 1 | |a Bonate, Peter |e verfasserin |4 aut | |
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