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The design, analysis and application of mouse clinical trials in oncology drug development
BACKGROUND: Mouse clinical trials (MCTs) are becoming wildly used in pre-clinical oncology drug development, but a statistical framework is yet to be developed. In this study, we establish such as framework and provide general guidelines on the design, analysis and application of MCTs. METHODS: We s...
Autores principales: | , , , |
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Formato: | Online Artículo Texto |
Lenguaje: | English |
Publicado: |
BioMed Central
2019
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Materias: | |
Acceso en línea: | https://www.ncbi.nlm.nih.gov/pmc/articles/PMC6643318/ https://www.ncbi.nlm.nih.gov/pubmed/31331301 http://dx.doi.org/10.1186/s12885-019-5907-7 |
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author | Guo, Sheng Jiang, Xiaoqian Mao, Binchen Li, Qi-Xiang |
author_facet | Guo, Sheng Jiang, Xiaoqian Mao, Binchen Li, Qi-Xiang |
author_sort | Guo, Sheng |
collection | PubMed |
description | BACKGROUND: Mouse clinical trials (MCTs) are becoming wildly used in pre-clinical oncology drug development, but a statistical framework is yet to be developed. In this study, we establish such as framework and provide general guidelines on the design, analysis and application of MCTs. METHODS: We systematically analyzed tumor growth data from a large collection of PDX, CDX and syngeneic mouse tumor models to evaluate multiple efficacy end points, and to introduce statistical methods for modeling MCTs. RESULTS: We established empirical quantitative relationships between mouse number and measurement accuracy for categorical and continuous efficacy endpoints, and showed that more mice are needed to achieve given accuracy for syngeneic models than for PDXs and CDXs. There is considerable disagreement between methods on calling drug responses as objective response. We then introduced linear mixed models (LMMs) to describe MCTs as clustered longitudinal studies, which explicitly model growth and drug response heterogeneities across mouse models and among mice within a mouse model. Case studies were used to demonstrate the advantages of LMMs in discovering biomarkers and exploring drug’s mechanisms of action. We introduced additive frailty models to perform survival analysis on MCTs, which more accurately estimate hazard ratios by modeling the clustered mouse population. We performed computational simulations for LMMs and frailty models to generate statistical power curves, and showed that power is close for designs with similar total number of mice. Finally, we showed that MCTs can explain discrepant results in clinical trials. CONCLUSIONS: Methods proposed in this study can make the design and analysis of MCTs more rational, flexible and powerful, make MCTs a better tool in oncology research and drug development. ELECTRONIC SUPPLEMENTARY MATERIAL: The online version of this article (10.1186/s12885-019-5907-7) contains supplementary material, which is available to authorized users. |
format | Online Article Text |
id | pubmed-6643318 |
institution | National Center for Biotechnology Information |
language | English |
publishDate | 2019 |
publisher | BioMed Central |
record_format | MEDLINE/PubMed |
spelling | pubmed-66433182019-07-29 The design, analysis and application of mouse clinical trials in oncology drug development Guo, Sheng Jiang, Xiaoqian Mao, Binchen Li, Qi-Xiang BMC Cancer Research Article BACKGROUND: Mouse clinical trials (MCTs) are becoming wildly used in pre-clinical oncology drug development, but a statistical framework is yet to be developed. In this study, we establish such as framework and provide general guidelines on the design, analysis and application of MCTs. METHODS: We systematically analyzed tumor growth data from a large collection of PDX, CDX and syngeneic mouse tumor models to evaluate multiple efficacy end points, and to introduce statistical methods for modeling MCTs. RESULTS: We established empirical quantitative relationships between mouse number and measurement accuracy for categorical and continuous efficacy endpoints, and showed that more mice are needed to achieve given accuracy for syngeneic models than for PDXs and CDXs. There is considerable disagreement between methods on calling drug responses as objective response. We then introduced linear mixed models (LMMs) to describe MCTs as clustered longitudinal studies, which explicitly model growth and drug response heterogeneities across mouse models and among mice within a mouse model. Case studies were used to demonstrate the advantages of LMMs in discovering biomarkers and exploring drug’s mechanisms of action. We introduced additive frailty models to perform survival analysis on MCTs, which more accurately estimate hazard ratios by modeling the clustered mouse population. We performed computational simulations for LMMs and frailty models to generate statistical power curves, and showed that power is close for designs with similar total number of mice. Finally, we showed that MCTs can explain discrepant results in clinical trials. CONCLUSIONS: Methods proposed in this study can make the design and analysis of MCTs more rational, flexible and powerful, make MCTs a better tool in oncology research and drug development. ELECTRONIC SUPPLEMENTARY MATERIAL: The online version of this article (10.1186/s12885-019-5907-7) contains supplementary material, which is available to authorized users. BioMed Central 2019-07-22 /pmc/articles/PMC6643318/ /pubmed/31331301 http://dx.doi.org/10.1186/s12885-019-5907-7 Text en © The Author(s). 2019 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. The Creative Commons Public Domain Dedication waiver (http://creativecommons.org/publicdomain/zero/1.0/) applies to the data made available in this article, unless otherwise stated. |
spellingShingle | Research Article Guo, Sheng Jiang, Xiaoqian Mao, Binchen Li, Qi-Xiang The design, analysis and application of mouse clinical trials in oncology drug development |
title | The design, analysis and application of mouse clinical trials in oncology drug development |
title_full | The design, analysis and application of mouse clinical trials in oncology drug development |
title_fullStr | The design, analysis and application of mouse clinical trials in oncology drug development |
title_full_unstemmed | The design, analysis and application of mouse clinical trials in oncology drug development |
title_short | The design, analysis and application of mouse clinical trials in oncology drug development |
title_sort | design, analysis and application of mouse clinical trials in oncology drug development |
topic | Research Article |
url | https://www.ncbi.nlm.nih.gov/pmc/articles/PMC6643318/ https://www.ncbi.nlm.nih.gov/pubmed/31331301 http://dx.doi.org/10.1186/s12885-019-5907-7 |
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