PROJECT SUMMARY Gene-drug-drug interactions (GDDIs) are becoming a new frontier in Precision Medicine (PM). Pharmacogenomics can certainly be a good predictive tool to guide drug therapies but is far from being a deterministic measure of phenotypes. A current limitation in most of existing guidelines is their lack of provisions to address the effect of drug combinations, co-medications and, therefore, the risk for GDDIs. Although the high inter-individual variability in the response to clopidogrel has primarily been associated with genetic polymorphisms, multivariate analyses suggest that additional factors (e.g., GDDIs) may contribute to the overall between-subject variability in treatment response. However, the extent to which each of these additional factors contributes to the overall variability, and how they are interrelated, is currently unclear. To this purpose, we propose to derive, for the first time ever, a weighted genetic risk score system based on a genome-wide association study in Caribbean Hispanic patients and machine learning methods. We also plan on developing a novel semi-mechanistic population-based pharmacokinetic/pharmacodynamics (PK-PD) modeling of clopidogrel in individuals with GDDIs with cilostazol in order to identify the clinically relevant factors affecting drug exposure and response, which may ultimately serve as a solid basis for dosing optimization and tailoring therapies. Based on strong preliminary data, we hypothesize that: “The enhancing effect of cilostazol over the clopidogrel-induced anti-platelet activity in Caribbean Hispanic patients exposed to these interacting co-medications is exclusive of those who also are CYP2C19 PMs and CYP3A5 non-expressers” This study is expected to provide important new information on the proportions of individuals from the Caribbean Hispanic population who are likely to have combinations of pharmacogenomics variants and exposure to interacting co-medications that may eventually affect their health care. Hispanics have been largely excluded from PM initiatives, which increase dramatically the disparities in translating benefits from new findings in pharmacogenomics to this medically underserved population, exacerbating the existing inequity in healthcare services. Accordingly, the proposed research will expand our current understanding of the PK-PD interactions between clopidogrel and cilostazol from a pharmacogenetics perspective. Advancing knowledge in the under- investigated area of pharmacogenetics in minority populations will generate results that apply to personalize antiplatelet therapy in the wider population as it moves, inevitably, toward increasing heterogeneity through admixed genomes.

PROJECT NARRATIVE Caribbean Hispanics are a population with a disproportionately high prevalence of cardio-metabolic disorders but with a limited expectation of benefits from current pharmacogenetic studies conducted mainly in subjects of mostly European ancestry. We focus on gene-drug interactions (GDDIs) to estimate the modifying effect of cilostazol on the weighted genetic risk score (wGRS)-driven prediction model for clopidogrel responsiveness and hence develop urgently-needed pharmacogenomic-driven prescription guidelines for this population while also using a semi-mechanistic population-based PK-PD analysis of such GDDI. Our project combines machine learning techniques (i.e., artificial intelligence), a pharmacokinetics and pharmacodynamics modeling approach, active metabolites measurements and genotyping with the development of more accurate rules for better predictability of GDDIs between clopidogrel and cilostazol in cardiovascular patients from this medically underserved population.