Determination of a Quantitative Relationship Between Hepatic CYP3A5*1/*3 and CYP3A4 Expression for Use in the Prediction of Metabolic Clearance in Virtual Populations

The creation of virtual populations allows the estimation of pharmacokinetic parameters, such as metabolic clearance in extreme individuals rather than the ‘average human’. Prediction of variability in metabolic clearance within genetically diverse populations relies on understanding the covariation in the expression of enzymes. A number of statistically significant positive correlations have been observed in the hepatic expression of cytochrome P450 drug metabolising enzymes. However, these rarely provided a quantitative description of the relationships which is required in creating virtual populations. Collation of data from 40 human liver microsomal samples in the current study indicated a significant positive relationship between hepatic microsomal CYP3A5*1/*3 and CYP3A4 content. Having developed a model describing the relationship between hepatic CYP3A4 and CYP3A5*1/*3, the Simcyp Population-based Simulator(®) was used to investigate the consequences of either incorporating or ignoring the relationship between the two enzymes on estimates of drug clearance. Simulations indicated that for a compound with greater metabolism by CYP3A5 than CYP3A4, such as tacrolimus, incorporation of the correlation between CYP3A4 and CYP3A5 does have an impact on the prediction of oral clearance. Failure to consider the relationship between CYP3A4 and CYP3A5 when creating the virtual population led to a 32% lower estimate of oral clearance in individuals possessing both the CYP3A5*1/*3 genotype and high basal concentrations of CYP3A4. Potential clinical implications may include an inadequate dose estimation during clinical study design, the consequences of which may include organ rejection in transplant recipients using immunosuppressants such as tacrolimus or toxicity due to elevated concentrations of circulating metabolites.

Author(s): Zoe Barter, H. F. Perrett, Karen Rowland Yeo, Delphine Allorge, Martin Lennard, Amin Rostami-Hodjegan

Year: November 1, 2010

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