The collection of newly discovered clinically valuable genetic tests is far outpacing our ability to use clinical trials to determine their clinical efficacy and determine which of the collection of tests and associated algorithms are best suited for any given clinical setting. For example, a review of the literature demonstrates that at least 35 published algorithms for the prediction of therapeutic warfarin dosing have been published in the past ten years, nine of which include genotype data (unpublished review). Expenditures to determine the most clinical useful of these algorithms include a total of 19 clinical trials (clinicaltrials.gov, accessed Feb, 2008) Compounding this complexity and adding to the delay of the successful medical use of the rapidly expanding collection of clinically valuable genetic discoveries is the lack of clinical and biomedical informatic methods, tools and infrastructure required to facilitate the successful translation of the discoveries to practical clinical use. Efforts to translate important biomedical informatics methods, tools and processes required to implement important new genetic discoveries in the clinical setting are severely hindered by regulatory, technical and validation barriers not easily resolved in the current clinical-research or clinical enterprise environments. This proposal will test an environment and methodology that creates clinical avatars with data statistically consistent with actual patient electronic medical records. Once created, these clinical avatar medical records will be used to conduct insilico experiments to compare genetic- based algorithms and their predicted value in the clinical setting. We will develop and test the methodology and create the applications by conducting a series of clinical avatar simulations and subsequent analysis of genetic-based warfarin dosing prediction. Once developed, we will conduct a series of insilico clinical trials comparing various clinical and genetic factors to demonstrate the efficacy of the warfarin dosing algorithms. This work is representative of similar projects that may be designed to test other personalized medicine devices such as genetic tests used to quantify risk to cancers, pharmacogenetic problems, and other FDA labeled """"""""IVDMIA"""""""" devices (e.g. Genetic Health's Oncotype DX(R)) projected to improve predictive and preventive personalized medicine.
There exists a significant knowledge and process gap between the discovery of genetic tests at the research bench and the use of those tests and supporting technology to improve healthcare at the clinical bedside. This gap impedes translation and thus delays the gain in clinical value of predictive, preventive and personalized medicine obtained from an individual's genetic data. We propose to create insilico methods using clinical 'avatars'to conduct systematic evaluation and provide evidence to determine those genetic tests and predictive algorithms most likely to yield high clinical impact.
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