Optimizing Successful Outcomes for Repurposing Existing Pharmaceutical Compounds

摘要

The present invention provides a process and method for preventing and/or treating protein based diseases, including, but not limited to: Alzheimer's and other amyloid based disease groups. This method recognizes correlative similarities across genomics, phenotypic and pharmacologic analytics and data to identify a list of existing compounds as high probability targets to act as inhibitors and/or stimulants close to a disease of interest. Pertinent genetic or epigenetic variances, or protein expression anomalies, are used to assemble a list of existing compounds through the use of artificial intelligence, machine learning and algorithmic relationships. This invention applies current genomic, phenotypic and pharmacological data to leverage data previously obtained in pursuit of other disease treatments. Accordingly, the current invention collapses cycle time to discovery and dramatically reduces costs. Through this system of active compounds created from tenuous relationships exhibits an elevated probability of success. Using a selection of algorithms that coordinate relationships including, but not limited to: genomic, epigenomic, phenotypic, protein dysregulation, cultural, pharmacological, etc., data, the present invention collapses cycle time of development and dramatically reduces costs by accessing data on target rich groups of previously tested abstractly related compounds.