An investigation of CYP2E1 phenotype determination utilizing modeling and simulation studies
Date of Completion
Health Sciences, Pharmacology
Phenotyping individuals for specific cytochrome P450 enzymes has been performed to assess possible clinical outcomes, including the prevention of drug-drug interactions. Clinical P450 phenotyping is done by administering a probe substrate to an individual and then measuring specific parameters related to the metabolism of that probe. A model has been established for phenotyping of Cytochrome P450 2E1 (CYP2E1) that utilizes the probe substrate chlorzoxazone (CZ) and monitors the metabolite to parent ratio at 4 hours post dose (1). This ratio correlates well with actual clearance by CYP2E1 in subjects with healthy renal function (p<0.01), but in subjects, that have impaired renal function, the correlation fails (p>0.05). This faulty estimate of CYP2E1 activity would result in a misdiagnosis of the correct phenotype and may lead to possible clinical problems such as: the faulty assessment of hepatic activity pre- and post-transplantation. An accurate and precise method to estimate CYP2E1 phenotype is necessary for all individuals regardless of renal competency. ^ Novel models and design schemes are explored through simulation. Through simulation, an optimal design using a 2-h or 4-h time point is shown to accurately estimate CYP2E1 phenotype even in subjects with impaired renal function. These design schemes are based upon a population mixed effects model and are robust with respect to measurement errors in CLcr and sampling time, but not with body weight. ^ Newly derived fixed effect models are also presented. Both novel models accurately estimate CYP2E1 phenotype regardless of renal function. The final fixed effects model incorporates individual body weight and CLcr. It is both accurate and precise, %mean prediction error of -7.82% and %mean absolute prediction error of 16.8%, for individuals with CLcr > 10 mL/min and can replace the current metabolic ratio. ^
Nicholas, Timothy Matthew, "An investigation of CYP2E1 phenotype determination utilizing modeling and simulation studies" (2007). Doctoral Dissertations. AAI3252592.