The process of drug discovery is costly and many promising compounds fail during clinical trials. By then, expenses upward of $500 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: (1) 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; (2) By characterizing existing drugs more thoroughly, we can discover novel off-label usages for existing drugs, thus expanding treatment options of FDA-approved compounds; (3) 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.
Public Health Relevance Statement: PROJECT NARRATIVE We propose to develop a drug-screening platform and database that will allow improved prediction of a drug'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.
Project Terms: Adverse drug effect; Adverse effects; Animal Model; antimicrobial; Award; Biological; Biological Assay; Biotechnology; Cancerous; cell growth; Cell Line; Cells; Characteristics; Chromosome Mapping; Classification; Clinical; clinical application; Clinical Research; Clinical Trials; commercialization; Complex; cost; cost effective; CRISPR/Cas technology; cross reactivity; Data; Data Set; Databases; Dependency; design; Development; DNA Markers; DNA Repair; drug candidate; drug development; drug discovery; Drug Evaluation; drug testing; Economics; experience; FDA approved; Gene Expression; gene interaction; Gene Mutation; Genes; genetic makeup; genetic manipulation; Genome; genotoxicity; Goals; Growth; high throughput screening; Human; improved; individual patient; induced pluripotent stem cell; innovation; insight; Kinetics; Knowledge; knowledge base; Label; Lead; Libraries; Link; Machine Learning; Maps; Medical; Microtubules; Mitochondria; Molecular; molecular modeling; Molecular Models; molecular phenotype; Molecular Structure; mutant; Mutate; novel; novel therapeutics; Ontology; Pathway interactions; Patients; Pharmaceutical Preparations; Pharmacogenomics; Pharmacology; Pharmacotherapy; Phenotype; phenotypic biomarker; Positioning Attribute; pre-clinical research; Preclinical Drug Evaluation; Process; protein expression; Protein Kinase; Proteins; Rare Diseases; Reporter; Research; Research Proposals; response; Risk; Saccharomyces cerevisiae; Scientist; screening; Services; Shapes; small molecule; stable cell line; stem; System; Technology; Testing; tool; Toxicology; treatment planning; treatment strategy; Work; Yeasts
