Polymorphism in Cytochrome P450 3A4 Is Ethnicity Related

Can mutations in Cytochrome P450 3A4 (CYP3A4), the major food- and drug-metabolizing enzyme, serve as biomarkers for personalized precise medicine? Classical genetic studies provide only limited data regarding the frequencies of CYP3A4 mutations and their role in food–drug interactions. Here, in an analysis of one large database of 141,456 individuals, we found 856 SNPs (single nucleotide polymorphism), of which 312 are missense mutations, far more than the previously reported dozens. Analyzing the data further, it is demonstrated that the frequency of mutations differs among ethnic groups. Hierarchical clustering divided the mutations to seven groups, each corresponding to a specific ethnicity. To the best of our knowledge this is the first comprehensive analysis of CYP3A4 allele frequencies in distinct ethnic groups. We suggest ethnicity based classification of CYP3A4 SNPs as the first step toward precise diet and medicine. Understanding which and when polymorphism might have clinical significance is a tremendously complex task. Using modeling approach, we could predict changes in the binding poses of ligands in the active site of single variants. These changes might imply clinical effects of the overlooked protein-altering CYP3A4 mutations, by modifying drug metabolism and FDI. It may be concluded that dietary habits, and hence FDI, are matters of ethnicity. Consequently, ethnic-related polymorphism in CYP3A4 and diet may be one underlying mechanism of response to medical regimes. The approaches presented here have the power to highlight mutations of clinical relevance in any gene of interest, thus to complement the arsenal of classic genetic screening tools

Computer-Aided (In Silico) Modeling of Cytochrome P450-Mediated Food–Drug Interactions (FDI)

Modifications of the activity of Cytochrome 450 (CYP) enzymes by compounds in food might impair medical treatments. These CYP-mediated food–drug interactions (FDI) play a major role in drug clearance in the intestine and liver. Inter-individual variation in both CYP expression and structure is an important determinant of FDI. Traditional targeted approaches have highlighted a limited number of dietary inhibitors and single-nucleotide variations (SNVs), each determining personal CYP activity and inhibition. These approaches are costly in time, money and labor. Here, we review computational tools and databases that are already available and are relevant to predicting CYP-mediated FDIs. Computer-aided approaches such as protein–ligand interaction modeling and the virtual screening of big data narrow down hundreds of thousands of items in databanks to a few putative targets, to which the research resources could be further directed. Structure-based methods are used to explore the structural nature of the interaction between compounds and CYP enzymes. However, while collections of chemical, biochemical and genetic data are available today and call for the implementation of big-data approaches, ligand-based machine-learning approaches for virtual screening are still scarcely used for FDI studies. This review of CYP-mediated FDIs promises to attract scientists and the general public