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Cited by 68Statistical Methods in Clinical TrialsAnimal testing and alternativesStatistical Methods and Bayesian Inference

The design, analysis and application of mouse clinical trials in oncology drug development

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Guo, Sheng, Jiang, Xiaoqian, Mao, Binchen, Li, Qi-Xiang·2019·BMC Cancer
DOI 10.1186/s12885-019-5907-7PMC PMC6643318

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Abstract

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.

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  • eGR makes no assumption on tumor growth kinetics and is invariant to starting TV.

    “We previously defined an efficacy readout called normalized AUC that makes no assumption on tumor growth kinetics and is invariant to starting TV (31).”

    From Statistical Assessment of Drug Synergy from <i>In Vivo</i> Combination Studies Using Mouse Tumor Models (2023) · cites this paper as [31]

    The Methods section on continuous efficacy endpoints introduces an AUC ratio as a more general readout that reduces to the growth-rate ratio under exponential kinetics. It does not call the readout normalized AUC or eGR, or state that it is invariant to starting tumor volume. The latter part of the attributed claim is therefore unsupported by our copy. (LLM, conf=0.76)

    Experimental — accuracy not yet measured

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Cited by

Sorted by citations

Authors
Guo, S., Jiang, X., Mao, B., & Li, Q.-X.
Affiliations
  • Crown Bioscience (China) (CN)
  • Peking University (CN)
Corresponding author
  • Guo, Sheng
  • Li, Qi-Xiang
ORCID
Venue
BMC Cancer
Publisher
Springer Science and Business Media LLC
Published
2019-07-22
Volume
19
Issue
1
Pages
718-718
ISSN
1471-2407 (ISSN-L)
DOI
10.1186/s12885-019-5907-7
PMC
PMC6643318
OpenAlex
W2963576773
Cited by
68
References
8
Citations by year
2026: 7 · 2025: 10 · 2024: 3 · 2023: 22 · 2022: 5 · 2021: 10 · 2020: 9 · 2019: 2
FWCI
5.07
Type
journal article
Language
en
Open access
Licence
Locations
  • BMC Cancer (publishedVersion, cc-by)
  • bioRxiv (Cold Spring Harbor Laboratory) (acceptedVersion, cc-by-nd)
  • PubMed Central (publishedVersion, cc-by)
  • PubMed (publishedVersion)
  • DOAJ (DOAJ: Directory of Open Access Journals) (submittedVersion, cc-by-sa)
Topics
  • Physical Sciences › Mathematics › Statistics and Probability › Statistical Methods in Clinical Trials
  • Health Sciences › Veterinary › Small Animals › Animal testing and alternatives
  • Physical Sciences › Mathematics › Statistics and Probability › Statistical Methods and Bayesian Inference
Keywords
anticancer drug development, preclinical cancer models, patient-derived xenografts, cell line-derived xenografts, syngeneic mouse model, tumor growth modeling, linear mixed models, statistical power, sample size determination, objective response, biomarker discovery, mechanism of action, survival analysis
MeSH terms
Drug Development, Progression-Free Survival, Animals, Biopsy, Clinical Trials as Topic, Computer Simulation, Disease Models, Animal, Drug Screening Assays, Antitumor, Humans, Medical Oncology, Neoplasms, Research Design, Biomarkers, Tumor, Linear Models, Xenograft Model Antitumor Assays, Cell Line, Tumor, Mice, Isografts

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