Model validity for preclinical studies in precision medicine: precisely how precise do we need to be?

Abby LD Tadenev
Robert W. Burgess

We would like to thank Drs. Dave Serreze, Jeremy Racine, and Mark Krebs for their input on this manuscript and Kristen Davidson-Brady for assistance with graphic design. We would also like to thank other members of the Jackson Laboratory Center for Precision Genetics and colleagues at The Jackson Laboratory for their work towards developing precision models for preclinical research.


The promise of personalized medicine is that each patient's treatment can be optimally tailored to their disease. In turn, their disease, as well as their response to the treatment, is determined by their genetic makeup and the "environment," which relates to their general health, medical history, personal habits, and surroundings. Developing such optimized treatment strategies is an admirable goal and success stories include examples such as switching chemotherapy agents based on a patient's tumor genotype. However, it remains a challenge to apply precision medicine to diseases for which there is no known effective treatment. Such diseases require additional research, often using experimentally tractable models. Presumably, models that recapitulate as much of the human pathophysiology as possible will be the most predictive. Here we will discuss the considerations behind such "precision models." What sort of precision is required and under what circumstances? How can the predictive validity of such models be improved? Ultimately, there is no perfect model, but our continually improving ability to genetically engineer a variety of systems allows the generation of more and more precise models. Furthermore, our steadily increasing awareness of risk alleles, genetic background effects, multifactorial disease processes, and gene by environment interactions also allows increasingly sophisticated models that better reproduce patients' conditions. In those cases where the research has progressed sufficiently far, results from these models appear to often be translating to effective treatments for patients.