A.        SPECIFIC AIMS

            This grant submission proposes a partnership between the NIGMS funded “Metabolomics Network for Drug Response Phenotypes” and two centers of excellence within the Pharmacogenomics Network (PGRN) with the goal of integrating the rapidly evolving science of metabolomics with molecular pharmacology and pharmacogenomics to better define mechanisms involved in variation in response to antihypertensive and anti platelet therapies widely used for cardiovascular health. Specifically, the partnership between the multi-institution Metabolomics Network involving centers of excellence in metabolomics and metabolomic bioinformatics, together with centers for molecular pharmacologic and pharmacogenomic science will move beyond focused scientific collaboration to create an environment in which a cooperative, iterative process of hypothesis generation and testing will be applied to achieve the union of metabolomic and pharmacologic science.  That “union” would have the potential to accelerate advances in our understanding of mechanisms of drug action and individual variation in the drug response. We will enlist metabolomic analyses to study the drug response phenotypes to and to generate broadly based, biochemically precise “metabolomic signatures” to complement and extend the clinical response phenotypes currently being used to monitor drug therapy. 

     Specifically, metabolomic signatures will be determined for patients who do and do not respond to drug therapy and for patients who do and do not develop adverse events.  These signatures would reflect both mechanisms of drug action and variation in the drug response phenotype.  Therefore, they would serve as the basis for the generation and testing of mechanistic hypotheses that address the underlying basis for individual variation in drug response –making it possible to move toward a goal of truly “personalized” or “individualized” drug therapy.  Metabolomic analytical platforms and informatics tools have already been developed and the marriage of these advances in metabolomics with striking progress in molecular pharmacology and pharmacogenomics will result in novel insights into mechanisms of drug effect and mechanisms responsible for individual variation in response to these agents, i.e., variation in the drug response phenotype.

Specific Aims :

1. Determine metabolic signatures for the antihypertensive drugs atenolol (b-blocker) and hydrochlorothiazide (diuretic) and define sub signatures that correlate with or predict drug response phenotypes.

2. Determine metabolic signatures for antiplatelet drugs clopidogrel and aspirin and define sub signatures that correlate with or predict drug response phenotypes.

3. Create an environment  to achieve the union of metabolomics and pharmacologic and pharmacogenomic sciences to better define mechanisms of variation in response to selected therapies.

4. Build resources for metabolomics research to enable network achieve its goals and make available for metabolomics community, specifically, to build a standardized metabolome database (Fiehn lab, UC Davis).

The hypotheses being tested are: 1. Selected drugs will induce biochemical changes or “metabolic signatures” that can be detected in plasma; some of these changes could be related to therapeutic benefit while others to development of adverse effects; responders and non-responders and people who develop or do not develop metabolic side effects will have different metabolic profiles at baseline and post treatment; 2. Antihypertensives will elicit changes in the renin angiotensin aldosterone, dopamine, serotonin, purine and lipid metabolic pathways and anti-platelet drugs will elicit changes in purines, arachidonic acid and prostaglandins, lipids, amino acids and derivatives, and metabolites of intermediate metabolism, 3. Combined metabolomic and pharmacogenomic data will enable more effective mapping of pathways implicated in drug response phenotypes. 4. Several resources built through the implementation of the project such as metabolomics and biochemical pathway databases will be shared with the metabolomics community at large and will contribute to advances in the metabolomics field.

  To achieve these Specific Aims, we plan to perform metabolomic analyses using stored samples from large pharmacogenomics studies with blood samples selected from subjects who are being treated with atenolol, hydrochlorothiazide, clopidogrel or aspirin. Metabolomic analyses will be performed before and after initiation of drug therapy. This approach will make it possible to define metabolomic signatures for both drug exposure and drug response and for mapping of pathways implicated.