Prediction of in vivo clearance and associated variability of CYP2C19 substrates by genotypes in populations utilizing a pharmacogenetics-based mechanistic model.

It is important to examine the cytochrome P450 2C19 (CYP2C19) genetic contribution to drug disposition and responses of CYP2C19 substrates during drug development. Design of such clinical trials requires projection of genotype-dependent in vivo clearance and associated variabilities of the investigational drug, which is not generally available during early stages of drug development, but is essential for CYP2C19 substrates with multiple clearance pathways. This study evaluated the utility of pharmacogenetics-based mechanistic modeling in predicting such parameters. Hepatic CYP2C19 activity and variability within genotypes were derived from in vitro S-mephenytoin metabolic activity in genotyped human liver microsomes (N = 128). These data were then used in mechanistic models to predict genotype-dependent disposition of CYP2C19 substrates (i.e., S-mephenytoin, citalopram, pantoprazole, and voriconazole) by incorporating in vivo clearance or pharmacokinetics of wild-type subjects and parameters of other clearance pathways. Relative to the wild-type, the CYP2C19 abundance (coefficient of variation percentage) in CYP2C19*17/*17, *1/*17, *1/*1, *17/null, *1/null, and null/null microsomes was estimated as 1.85 (117%), 1.79 (155%), 1.00 (138%), 0.83 (80%), 0.38 (130%), and 0 (0%), respectively. The subsequent modeling and simulations predicted, within 2-fold of the observed, the means and variabilities of urinary S/R-mephenytoin ratio (36 of 37 genetic groups), the oral clearance of citalopram (9 of 9 genetic groups) and pantoprazole (6 of 6 genetic groups), and voriconazole oral clearance (4 of 4 genetic groups). Thus, relative CYP2C19 genotype-dependent hepatic activity and variability were quantified in vitro and used in a mechanistic model to predict pharmacokinetic variability, thus allowing the design of pharmacogenetics and drug-drug interaction trials for CYP2C19 substrates.

Pharmacogenomics of gemcitabine metabolism: functional analysis of genetic variants in cytidine deaminase and deoxycytidine kinase.

Gemcitabine (dFdC, 2',2'-difluorodeoxycytidine) is metabolized by cytidine deaminase (CDA) and deoxycytidine kinase (DCK), but the contribution of genetic variation in these enzymes to the variability in systemic exposure and response observed in cancer patients is unclear. Wild-type enzymes and variants of CDA (Lys27Gln and Ala70Thr) and DCK (Ile24Val, Ala119Gly, and Pro122Ser) were expressed in and purified from Escherichia coli, and enzyme kinetic parameters were estimated for cytarabine (Ara-C), dFdC, and its metabolite 2',2'-difluorodeoxyuridine (dFdU) as substrates. All three CDA proteins showed similar K(m) and V(max) for Ara-C and dFdC deamination, except for CDA70Thr, which had a 2.5-fold lower K(m) and 6-fold lower V(max) for Ara-C deamination. All four DCK proteins yielded comparable metabolic activity for Ara-C and dFdC monophosphorylation, except for DCK24Val, which demonstrated an approximately 2-fold increase (P < 0.05) in the intrinsic clearance of dFdC monophosphorylation due to a 40% decrease in K(m) (P < 0.05). DCK did not significantly contribute to dFdU monophosphorylation. In conclusion, the Lys27Gln substitution does not significantly modulate CDA activity toward dFdC, and therefore would not contribute to interindividual variability in response to gemcitabine. The higher in vitro catalytic efficiency of DCK24Val toward dFdC monophosphorylation may be relevant to dFdC clinical response. The substrate-dependent alterations in activities of CDA70Thr and DCK24Val in vitro were observed for the first time, and demonstrate that the in vivo consequences of these genetic variations should not be extrapolated from one substrate of these enzymes to another.