An integrative multi phenotype pipeline for drug evaluation pharmacogenomics and attribute prediction

PROJECT SUMMARY The process of drug discovery is costly and many promising compounds fail during clinical trials By then expenses upward of $ million dollars per failed drug may have incurred and these financial risks hamper research efforts and ultimately reduce the availability of treatment options In this research proposal we are using systematic approaches to map the relationships between drugs genes and phenotypes i e the ability of a drug to elicit a certain molecular response in a cell with a specific gene mutation These efforts aim at generating three important insights By performing these mapping systematically across many drugs and many phenotypes we generate phenotypic profiles that can aid in the classification of new compounds enabling us to predict how well these compounds may fare in later clinical stages thus reducing cost and risk in drug development By characterizing existing drugs more thoroughly we can discover novel off label usages for existing drugs thus expanding treatment options of FDA approved compounds By understanding gene drug phenotype relationships one by one we can assemble a complete picture of drug gene interactions an important milestone in the development of personalized pharmacogenomics that would allow patient specific treatment planning To accomplish these goals we will employ a novel yeast based phenotypic screening platform and use data driven ontologies to understand the similarities between drugs in the phenotype gene space Overall this work will move us closer to a comprehensive understanding of how phenotypes arise from the genome and how complex relationships between genes and drugs shape our medical treatment strategies PROJECT NARRATIVE We propose to develop a drug screening platform and database that will allow improved prediction of a drugandapos s side effects mode of action cross reactivity and other important pharmacological attributes in the context of gene mutations This system would reduce drug discovery cost encourage rare disease research and ultimately lead to personalized pharmacogenomics the ability to select drugs and therapies based on the genetic makeup of individual patients to optimize treatment results