A genomic-pharmacometabolomic investigation of tenofovir, a commonly prescribed anti-HIV medication
Abstract
For the proposed project, we are leveraging the unprecedented time-series plasma samples collected during intensive pharmacokinetic (iPK) studies of tenofovir (TFV), which circulates in blood after administration of tenofovir disoproxil fumarate (TDF), in 123 representative and understudied HIV+ women of Women’s Interagency HIV Study (WIHS) cohort. Pharmacogenetic (PGx) studies of TFV have been completed in 123 WIHS participants, and they explain 40% of the variability in TFV plasma concentration. We aim to identify other genetic and plasma metabolic factors that can explain additional variability. We plan to measure plasma metabolome profile at 2 different times to identify metabolites under genetic control correlated with TFV plasma concentration. To achieve our goal, we have two specific aims. In aim 1, we plan to identify endogenous metabolites correlated with plasma concentration of TFV in adherent, virally suppressed women treated with TDF. Using these correlated metabolites as biomarkers of medication activity, in aim 2, we plan to identify, through genome-wide association analyses, novel genes impacting metabolite concentrations in 16,322 individuals from two independent population-based cohorts from the United Kingdom (UK): The UK National Institute of Health Research and Twins UK. Using the large UK sample size provides sufficient power to identify drug-metabolite correlations reliably. The effect of identified novel genes will be validated in WIHS women who completed the iPK for TDF. This novel approach will help identify new genes and genetic markers impacting TFV plasma exposure. The deliverables from this study will positively impact future studies in the field of PGx of anti-HIV medication and beyond in two ways: First, we will characterize for the first time and with much higher resolution the primary and secondary metabolic pathways relevant to TFV, and second, it will provide a novel multi-omic study design that can be implemented to discover genetic factors associated with drug variability.