Summary/Abstract Drug exposure and therapeutic outcomes are greatly affected by substantial variability in drug metabolism. CYP3A drug metabolizing enzymes, encoded by four genes clustered into one gene locus (most important CYP3A4, and 3A5, 3A7 and 3A43), are abundant and clinically important, metabolizing nearly half of all current drugs. However, CYP3A enzyme expression displays substantial (>10-fold) inter-individual variability, largely of unknown causes and therefore poorly predictable ? leading to suboptimal treatment outcomes. Built on novel concepts in functional genomics, this proposal focuses on the genomic architecture and transcription factor (TF) regulation of the CYP3A locus, applying cutting edge methodologies and novel mathematical modeling of regulatory networks. The goal is to identify the combined effect of all main regulatory factors determining highly variable CYP3A enzymes ? a critical step toward guiding personalized therapeutics of a substantial portion of current drugs. Our central hypothesis is that long-range interactions between regulatory elements (e.g., enhancers with genetic variants) within the CYP3A locus and perturbation of the relevant TF network, i.e., the CYP3A interactome, account for much of variable CYP3A expression. Applying a comprehensive experimental and computational approach, we have already demonstrated the presence of yet unknown regulatory genetic variants in several new enhancer regions that are spread across the CYP3A locus and interact with each other (via DNA looping), strong support of the need to study the CYP3A locus as an interactome, rather than each gene in isolation. Similarly, we need to study TFs regulating CYP3A expression as a dynamic network ? an approach already taken previously. However, using a novel mathematical modeling approach, we identify new key TFs and characterize their dynamic interactions. For example, our modeling predicts that the estrogen receptor alpha (ESR1) (in an unliganded form present in males and females) is a critical determinant of CYP3A4 expression in liver cells. Indeed, our genome-editing methods with CRISPR demonstrate that ESR1 exerts profound influence on CYP3A4 expression, possibly accounting for a substantial portion of variability. Since robust ESR1 genetic variation has been implicated in numerous diseases while the causative variants remain uncertain, this project (aim3) will focus on the molecular genetics of ESR1, with largely unknown functions in the liver. The proposed research is innovative in studying gene regulation of the whole CYP3A interactome rather than single genes, and employing a novel mathematical modeling approach capable of identifying TFs, such as ESR1, as key factors in molding local chromatin architecture. Collectively, the results are expected to advance the field of pharmacogenomics by revealing fundamental mechanisms regulating complex gene loci, broadly applicable to similar gene clusters. The results will account for a substantial portion of CYP3A variability, guiding biomarker development for CYP3A activity.