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Growth rate analysis and efficient experimental design for tumor xenograft studies
Gregory Hather1, Ray Liu1, Syamala Bandi2
1Department of Global Statistics, Takeda Pharmaceuticals International Co., Cambridge, MA, USA.
Abstract:
Human tumor xenograft studies are the primary means to evaluate the biological activity of anticancer agents in late-stage preclinical drug discovery. The variability in the growth rate of human tumors established in mice and the small sample sizes make rigorous statistical analysis critical. The most commonly used summary of antitumor activity for these studies is the T/C ratio. However, alternative methods based on growth rate modeling can be used. Here, we describe a summary metric called the rate-based T/C, derived by fitting each animal's tumor growth to a simple exponential model. The rate-based T/C uses all of the data, in contrast with the traditional T/C, which only uses a single measurement. We compare the rate-based T/C with the traditional T/C and assess their performance through a bootstrap analysis of 219 tumor xenograft studies. We find that the rate-based T/C requires fewer animals to achieve the same power as the traditional T/C. We also compare 14-day studies with 21-day studies and find that 14-day studies are more cost efficient. Finally, we perform a power analysis to determine an appropriate sample size.
Insights
A new rate-based T/C metric improves anticancer drug evaluation in preclinical xenograft studies. This method requires fewer animals and shorter study durations, enhancing efficiency in drug discovery.
Area of Science:
- Preclinical drug discovery
- Oncology
- Pharmacodynamics
Background:
- Human tumor xenograft studies are crucial for evaluating anticancer agents.
- Tumor growth variability and small sample sizes necessitate robust statistical analysis.
- The traditional T/C ratio is the standard metric for antitumor activity.
Purpose of the Study:
- To introduce and evaluate a novel rate-based T/C metric.
- To compare the performance of the rate-based T/C with the traditional T/C.
- To assess the impact of study duration on statistical power and cost-efficiency.
Main Methods:
- Developed a rate-based T/C metric using exponential growth modeling of tumor data.
- Analyzed 219 human tumor xenograft studies using bootstrap analysis.
- Compared 14-day and 21-day study durations.
Main Results:
- The rate-based T/C metric utilizes all available tumor growth data.
- Fewer animals are needed with the rate-based T/C to achieve equivalent statistical power compared to the traditional T/C.
- 14-day studies are more cost-efficient than 21-day studies.
Conclusions:
- The rate-based T/C offers a more powerful and efficient method for analyzing xenograft study data.
- Optimizing study duration and sample size can improve preclinical drug discovery efficiency.
- This metric enhances the statistical rigor of anticancer agent evaluation.
