Merging artificial intelligence (AI) and pharmacometrics to elucidate gene-drug interactions linked to clopidogrel responsiveness in genetically heterogeneous patients - 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 genetically heterogeneous 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 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 genetically heterogeneous 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 genetically heterogeneous individuals who are likely to have combinations of pharmacogenomics variants and exposure to interacting co-medications that may eventually affect their health care. There is a paucity of data on genetically heterogeneous individuals in existing databases, which limits the ability to extrapolate findings from more genetically homogeneous groups and hinders the broader adoption of precision medicine initiatives. 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 evolving area of pharmacogenetics in genetically heterogeneous patients will generate results that apply to personalize antiplatelet therapy in the wider population as it moves, inevitably, toward increasing heterogeneity through admixed genomes.
