Mighty, But How Useful? The Emerging Role of Genetically Engineered Mice in Cancer Drug Discovery and Development

  • Reinhard Ebner
  • Jeffrey W. Strovel
  • Stephen K. Horrigan
  • Kenneth C. Carter


Studies on genetically engineered mouse models (GEMMs) have provided invaluable insights to our understanding of human tumors for nearly three decades. The ability to manipulate the murine genome with ever increasing sophistication has generated great expectations for their successful use in developing novel therapeutics. Indeed, GEMMs have shown remarkable power to faithfully recapitulate some key aspects of human tumorigenesis and therapy response. Yet, despite much enthusiasm generated during the early years of the new millennium, their use in drug discovery and development has remained limited. Economic, practical, licensing, historical, and regulatory considerations remain as hurdles to the robust utility of GEMMs in drug discovery and development as does the modest predictive abilities of any single model. Because cancer is a cellular and genetic disorder, advancing treatment options and cure rates will very likely continue to depend on the intelligent use of a combination of simple and complex experimental model systems, including biochemical, cell- and tissue-based and animal models. To date, the most pronounced impact that GEMMs have had on the biomedical industry has been in the areas of target and pathway validation, disease history elucidation, and the discovery and refinement of pharmacodynamic and toxicity biomarkers. There are also areas where GEMMs have tremendous potential but are currently underused, such as modeling metastatic disease spread, stem cell targeting, predictive marker testing, adaptive resistance modeling in vivo or ex vivo or the pharmacogenetic representation of heterogeneous patient populations.


Drug Discovery Preclinical Testing Therapeutic Development Candidate Therapeutics Engineer Mouse Model 
These keywords were added by machine and not by the authors. This process is experimental and the keywords may be updated as the learning algorithm improves.


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Copyright information

© Springer Science+Business Media, LLC 2012

Authors and Affiliations

  • Reinhard Ebner
    • 1
  • Jeffrey W. Strovel
    • 2
  • Stephen K. Horrigan
    • 2
  • Kenneth C. Carter
    • 2
  1. 1.National Cancer Institute, Section of Cancer Genomics, National Institutes of HealthBethesdaUSA
  2. 2.Noble Life Sciences, Inc.GaithersburgUSA

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