AACR 2022 Poster 2743

Tumor Growth Modeling and Its Applications in Preclinical Pharmacology Studies to Improve Translatability of Animal Models

Huajun Zhou, BinchenMao, Sheng Guo

Successful oncology drug development hinges on proper use of preclinical xenograft and allograft mouse models that faithfully reflect patient tumor histopathology, genomic features, and exhibit clinically similar drug responses. For in vivo pharmacology studies, tumor-bearing mice treated with drugs are monitored for efficacy by continuingly measuring tumor volumes, resulting in mouse-specific tumor growth data.

In a previous study, we used linear mixed model (LMM) to fit tumor growth data and demonstrated its superiority to simple readouts, such as tumor growth inhibition (TGI), in discovering biomarkers and mechanism of action for drugs. Here we extended the study to more parametric statistical models on an expanded dataset with over 100,000 tumor growth curves.

Download this Poster to Discover:

• Which of the six tested parametric mathematical models were best for tumor growth modeling
  
  
• Which models had the best fit for identifying genes linked to mechanisms of action
  
  
• How exponential or exponential square models can be applied to most in vivo mouse studies

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