Optimizing the design of population-based patient-derived tumor xenograft studies to better predict clinical response
The high attrition rate of preclinical agents entering oncology clinical trials has been associated with poor understanding of the heterogeneous patient response, arising from limitations in the preclinical pipeline with cancer models. Patient-derived tumor xenograft (PDX) models have been shown to...
| Main Authors: | , , , , , |
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| Format: | Article |
| Language: | English |
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The Company of Biologists
2018-11-01
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| Series: | Disease Models & Mechanisms |
| Subjects: | |
| Online Access: | http://dmm.biologists.org/content/11/11/dmm036160 |
| _version_ | 1828845099154407424 |
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| author | Nicolas Floc'h Maria Luisa Guerriero Antonio Ramos-Montoya Barry R. Davies Jonathan Cairns Natasha A. Karp |
| author_facet | Nicolas Floc'h Maria Luisa Guerriero Antonio Ramos-Montoya Barry R. Davies Jonathan Cairns Natasha A. Karp |
| author_sort | Nicolas Floc'h |
| collection | DOAJ |
| description | The high attrition rate of preclinical agents entering oncology clinical trials has been associated with poor understanding of the heterogeneous patient response, arising from limitations in the preclinical pipeline with cancer models. Patient-derived tumor xenograft (PDX) models have been shown to better recapitulate the patient drug response. However, the platform of evidence generated to support clinical development in a drug discovery project typically employs a limited number of models, which may not accurately predict the response at a population level. Population PDX studies, large-scale screens of PDX models, have been proposed as a strategy to model the patient inter-tumor heterogeneity. Here, we present a freely available interactive tool that explores the design of a population PDX study and how it impacts the sensitivity and false-positive rate experienced. We discuss the reflection process needed to optimize the design for the therapeutic landscape being studied and manage the risk of false-negative and false-positive outcomes that the sponsor is willing to take. The tool has been made freely available to allow the optimal design to be determined for each drug-disease area. This will allow researchers to improve their understanding of treatment efficacy in the presence of genetic variability before taking a drug to clinic. In addition, the tool serves to refine the number of animals to be used for population-based PDX studies, ensuring researchers meet their ethical obligation when performing animal research. |
| first_indexed | 2024-12-12T21:20:21Z |
| format | Article |
| id | doaj.art-fbe31529bc76498fa1d09b256d8e05d0 |
| institution | Directory Open Access Journal |
| issn | 1754-8403 1754-8411 |
| language | English |
| last_indexed | 2024-12-12T21:20:21Z |
| publishDate | 2018-11-01 |
| publisher | The Company of Biologists |
| record_format | Article |
| series | Disease Models & Mechanisms |
| spelling | doaj.art-fbe31529bc76498fa1d09b256d8e05d02022-12-22T00:11:37ZengThe Company of BiologistsDisease Models & Mechanisms1754-84031754-84112018-11-01111110.1242/dmm.036160036160Optimizing the design of population-based patient-derived tumor xenograft studies to better predict clinical responseNicolas Floc'h0Maria Luisa Guerriero1Antonio Ramos-Montoya2Barry R. Davies3Jonathan Cairns4Natasha A. Karp5 Bioscience, Oncology, Discovery Sciences, IMED Biotech Unit, AstraZeneca, Cambridge CB4 0WG, UK Quantitative Biology, Discovery Sciences, IMED Biotech Unit, AstraZeneca, Cambridge CB4 0WG, UK Bioscience, Oncology, Discovery Sciences, IMED Biotech Unit, AstraZeneca, Cambridge CB4 0WG, UK Bioscience, Oncology, Discovery Sciences, IMED Biotech Unit, AstraZeneca, Cambridge CB4 0WG, UK Quantitative Biology, Discovery Sciences, IMED Biotech Unit, AstraZeneca, Cambridge CB4 0WG, UK Quantitative Biology, Discovery Sciences, IMED Biotech Unit, AstraZeneca, Cambridge CB4 0WG, UK The high attrition rate of preclinical agents entering oncology clinical trials has been associated with poor understanding of the heterogeneous patient response, arising from limitations in the preclinical pipeline with cancer models. Patient-derived tumor xenograft (PDX) models have been shown to better recapitulate the patient drug response. However, the platform of evidence generated to support clinical development in a drug discovery project typically employs a limited number of models, which may not accurately predict the response at a population level. Population PDX studies, large-scale screens of PDX models, have been proposed as a strategy to model the patient inter-tumor heterogeneity. Here, we present a freely available interactive tool that explores the design of a population PDX study and how it impacts the sensitivity and false-positive rate experienced. We discuss the reflection process needed to optimize the design for the therapeutic landscape being studied and manage the risk of false-negative and false-positive outcomes that the sponsor is willing to take. The tool has been made freely available to allow the optimal design to be determined for each drug-disease area. This will allow researchers to improve their understanding of treatment efficacy in the presence of genetic variability before taking a drug to clinic. In addition, the tool serves to refine the number of animals to be used for population-based PDX studies, ensuring researchers meet their ethical obligation when performing animal research.http://dmm.biologists.org/content/11/11/dmm036160Patient-derived tumor xenograftsPopulation studiesPreclinical trial |
| spellingShingle | Nicolas Floc'h Maria Luisa Guerriero Antonio Ramos-Montoya Barry R. Davies Jonathan Cairns Natasha A. Karp Optimizing the design of population-based patient-derived tumor xenograft studies to better predict clinical response Disease Models & Mechanisms Patient-derived tumor xenografts Population studies Preclinical trial |
| title | Optimizing the design of population-based patient-derived tumor xenograft studies to better predict clinical response |
| title_full | Optimizing the design of population-based patient-derived tumor xenograft studies to better predict clinical response |
| title_fullStr | Optimizing the design of population-based patient-derived tumor xenograft studies to better predict clinical response |
| title_full_unstemmed | Optimizing the design of population-based patient-derived tumor xenograft studies to better predict clinical response |
| title_short | Optimizing the design of population-based patient-derived tumor xenograft studies to better predict clinical response |
| title_sort | optimizing the design of population based patient derived tumor xenograft studies to better predict clinical response |
| topic | Patient-derived tumor xenografts Population studies Preclinical trial |
| url | http://dmm.biologists.org/content/11/11/dmm036160 |
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