Abstract
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.
Highlights
Food–drug interactions (FDI) and herb–drug interactions have been known to limit the success of medical treatments
Modern tools such as big-data analysis, machine learning, and simulation of protein–ligand interactions may help us to answer a whole set of questions: Might food choices contribute to the failure of therapeutic regimes and, if so, how? Which food(s) should be consumed prior to taking a prescribed drug? And probably the most exciting question: How can we use these tools to predict personal food–drug interactions (FDI)? Clearly, many answers lie in the Polymorphism in Cytochrome P450 3A4 metabolism of drugs, foods, and herbs by cytochrome P450 3A4 (CYP3A4) in the liver and digestive tract (Galetin et al, 2010; Basheer and Kerem, 2015)
The small number of subjects in all previously published works on CYP3A4 mutations provides us with limited data regarding true frequencies of CYP3A4 mutations in the whole population and in defined groups
Summary
Food–drug interactions (FDI) and herb–drug interactions have been known to limit the success of medical treatments. The enormous number of possible interactions between genetic variations, medical regimes, and the numerous bioactive compounds found in food and herbs result in overwhelming complexity. Modern tools such as big-data analysis, machine learning, and simulation of protein–ligand interactions may help us to answer a whole set of questions: Might food choices contribute to the failure of therapeutic regimes and, if so, how? Many answers lie in the Polymorphism in Cytochrome P450 3A4 metabolism of drugs, foods, and herbs by cytochrome P450 3A4 (CYP3A4) in the liver and digestive tract (Galetin et al, 2010; Basheer and Kerem, 2015). The small number of subjects in all previously published works on CYP3A4 mutations provides us with limited data regarding true frequencies of CYP3A4 mutations in the whole population and in defined groups
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