The Challenge

 For over half a century, the regulatory agencies that oversee drug  development and testing of new chemical and molecular entities that  address human disease has relied on a requirement for animal testing.   While this may have represented the most reliable method of predicting  clinical safety and efficacy in humans in the past, it is now recognized  that scientific advances made in the past decades, such as those  elucidated from The Human Genome Project, have lead to a deeper  understanding of the genetic basis of disease.  Many of the most  promising therapies of today are genetic-based, making a human relevant  model for assessing their safety and efficacy a logical and desirable  one.  Today, we also have human-derived assay tools in the toolkit, such  as 2D and 3D cell cultures and "organs on chips"-  living cell systems  architecturally accurate and functionally supported, for studying the  effects of drugs and chemicals in vitro.   This presents an  opportunity to examine the potential for a model with greater  predictability of human clinical outcome than the current animal  models.  While these newer technologies are being utilized and tested  in industry and academia today, there is currently no organized and  meaningful pathway to validation of them.  Clearing this hurdle has not  yet been solved.  Thus, these newer technologies remain largely  curiosities, the topic of a  few working groups and committees.    A  much broader conversation, industry-wide adoption and application, with  peer review process would be needed to facilitate a formal pathway to  their validation.  The greatest barrier to be overcome in a potential  shift from animal models to a human-relevant model is the enormous  quantity and quality of data required to meet the burden of validating a  new model.   GIVVISH is a proposed platform for organizing and  analyzing all the subsets of data these technologies are generating -  from industry, academia, consortia, government, and existing databases  for predictive modeling.   With  real-time data review, predictive modeling generation , peer review, and a feedback loop continually updating between industry and regulators on validation criteria parameters,  the platform would continue to refine, aligning state-of-science with  state-of-regulatory.  A natural shift to a human relevant model would  unfold organically based on the science.  "Big Data" analyzed from  human-relevant models is the logical pathway to a 21st century solution.