SITC 2024 Poster 1252

Incorporating Virtual Control Groups in Preclinical Mouse Studies: A Historical Data Approach

Huajun Zhou, Ludovic Bourre, Sheng Guo

Explore Crown Bioscience’s innovative approach to using Virtual Control Groups (VCGs) in preclinical mouse studies. By leveraging historical tumor growth data, this methodology can reduce the number of required control groups, improving the efficiency of preclinical trials while maintaining statistical power. Discover how VCGs can support faster and more reliable insights in early-stage drug development, particularly in oncology research.

Download this Poster to Discover:

• Overview of VCG Implementation: Learn about the development and application of Virtual Control Groups, using historical data to create robust control benchmarks in preclinical mouse studies.
  
  
• Data Collection and Standardization: Discover how tumor growth data from syngeneic mouse models is standardized and categorized based on key factors such as mouse strain, age, dosing route, and isotype.
  
  
• Methodological Comparison: Review the differences between tumor growth rate and tumor growth inhibition (TGI) methods for quantifying drug efficacy.
  
  
• Statistical Approaches: Explore the statistical methods, including random sampling and linear mixed-effects modeling (LMM), used to incorporate historical data as virtual controls.
  
  
• Key Findings on Tumor Growth Variability: See results that demonstrate how factors such as mouse model, dosing, and study timing can impact tumor growth rate, with implications for VCG reliability.
  
  
• Efficacy of VCG Integration: Discover findings on how VCGs, created through historical data, can increase statistical significance in treatment efficacy evaluations.
  
  
• Clinical and Research Implications: Learn how incorporating VCGs can reduce the reliance on live control groups, improving preclinical study efficiency and ethical considerations in animal research.

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