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

Abstract

AbstractMouse clinical trials (MCTs) are becoming widely used in pre-clinical oncology drug development. In this study, we provide some general guidelines on the design, analysis and application of MCTs. We first established empirical quantitative relationships between mouse number and measurement accuracy for both 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 categorical methods on calling drug responses as objective response, indicating limitations of such approaches. We then introduced linear mixed models, or 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. Several case studies were used to demonstrate the advantages of LMMs in discovering biomarkers and exploring a drug’s mechanism of action. We also introduced the additive frailty models to perform survival analysis on MCTs, which more accurately estimate hazard ratios by modeling the clustered population structures in MCTs. We performed computational simulations for LMMs and frailty models to generate statistical power curves, and showed that statistical power is close for designs with similar total number of mice at given drug efficacy. Finally, we explained how MCTs can explain discrepant results in clinical trials, hence, MCTs are more than preclinical versions of clinical trials but possess their unique values. Results in the report will make MCTs a better tool for oncology drug development.