Lack of correlation between in vitro and in vivo studies on the effects of tangeretin and tangerine juice on midazolam hydroxylation.

BACKGROUND: Tangeretin is a flavonoid that stimulates the catalytic activity of cytochrome P450 3A4 (CYP3A4) and is found in high levels in tangerine juice. METHODS: The effect of tangeretin on hydroxylation of midazolam, a CYP3A4 probe, was examined in vitro with human liver microsomes and recombinant CYP3A4. In addition, the effect of tangerine juice on the pharmacokinetics and pharmacodynamics of orally administered midazolam (15 mg) and its active 1'-hydroxymetabolite was studied in a randomized crossover study in eight healthy volunteers. RESULTS: In microsomes from three human livers, tangeretin (1 to 100 micromol/L) increased 1'-hydroxymidazolam formation (12.5 micromol/L midazolam) by up to 212%. In complementary deoxyribonucleic acid-expressed CYP3A4, a 52% stimulation of midazolam 1'-hydroxylation was reached at 50 micromol/L tangeretin with no effect on midazolam 4-hydroxylation. In the pharmacokinetic-pharmacodynamic study, 200 mL tangerine juice reduced the area under the concentration versus time curve to 1.5 hours [AUC(O-1.5h)] of midazolam and 1'-hydroxymidazolam by 39% and 46%, respectively, and prolonged the time to reach peak concentration (P < .05) without affecting the total AUC values, elimination half-life values, or AUC ratios (1'-hydroxymidazolam/midazolam). These findings are consistent with a small delay in the absorption of midazolam and lack of effect on midazolam 1'-hydroxylation. Accordingly, tangerine juice slightly postponed the maximum pharmacodynamic effects of midazolam (P < .05). CONCLUSION: Tangeretin is a potent regioselective stimulator of midazolam 1'-hydroxylation by human liver microsomes and complementary deoxyribonucleic acid-expressed CYP3A4. However, tangerine juice is unlikely to have any appreciable effect on CYP3A4 in humans. Further studies are required to assess whether in vitro stimulators of CYP3A4 can influence drug metabolism in vivo.

Present and future in vitro approaches for drug metabolism.

The 1980s through 1990s witnessed the widespread incorporation of in vitro absorption, distribution, metabolism, and excretion (ADME) approaches into drug development by drug companies. This has been exemplified by the integration of the basic science of cytochrome P450s (CYPs) into most drug metabolism departments so that information on the metabolic pathways of drugs and drug-drug interactions (DDIs) is no longer an academic exercise, but essential for regulatory submission. This has come about due to the application of a variety of new technologies and in vitro models. For example, subcellular fractions have been widely used in metabolism studies since the 1960s. The last two decades has seen the increased use of hepatocytes as the reproducibility of cell isolations improved. The 1990s saw the rejuvenation of liver slices (as new slicers were developed) and the utilization of cDNA expressed enzymes as these technologies matured. In addition, there has been considerable interest in extrapolating in vitro data to in vivo for parameters such as absorption, clearance and DDIs. The current philosophy of drug development is moving to a 'fail early--fail cheaply' paradigm. Therefore, in vitro ADME approaches are being applied to drug candidates earlier in development since they are essential for identifying compounds likely to present ADME challenges in the latter stages of drug development. These in vitro tools are also being used earlier in lead optimization biology, in parallel with approaches for optimizing target structure activity relationships, as well as identification of DDI and the involvement of metabolic pathways that demonstrate genetic polymorphisms. This would suggest that the line between discovery and development drug metabolism has blurred. In vitro approaches to ADME are increasingly being linked with high-throughput automation and analysis. Further, if we think of perhaps the fastest available way to screen for successful drugs with optimal ADME characteristics, then we arrive at predictive computational algorithms, which are only now being generated and validated in parallel with in vitro and in vivo methods. In addition, as we increase the number of ADME parameters determined early, the overall amount of data generated for both discovery and development will increase. This will present challenges for the efficient and fast interpretation of such data, as well as incorporation and communication to chemistry, biology, and clinical colleagues. This review will focus on and assess the nature of present in vitro metabolism approaches and indicate how they are likely to develop in the future.

A population approach to enzyme characterization and identification: application to phenacetin O-deethylation.

PURPOSE: To determine the enzyme kinetics (EK) and identify the human cytochrome(s) P450 (CYP) involved in the deethylation of phenacetin to acetaminophen using a population-based method. METHODS: A sparse data set was generated from incubations containing human liver microsomes (n = 19) with phenacetin. Estimates of the EK parameters were obtained by fitting the concentration-velocity data to Michaelis-Menten models by using nonlinear mixed effects modeling. Relationships between the EK parameters and the CYP activities determined for these liver microsomes were examined. RESULTS: A two-enzyme kinetic model with a saturated, low KM enzyme and an unsaturated, high KM enzyme capable of forming acetaminophen best fit the data. The population estimates of the EK parameters were Vmax1, 911 pmol/min/mg protein; KM1, 11.3 microM; and Cl(int2), 0.4 microl/min/mg. The coefficients of variation for interliver variability in Vmax1 and residual error of the model were 39% and 15%, respectively. When the selective catalytic activities were examined as potential covariates, 7-ethoxyresorufin O-deethylation (CYP1A2) activity was found to be associated with the low KM enzyme, however, the high KM enzyme(s) could not be identified. CONCLUSIONS: The population approach characterized the EK parameters and identified the low KM enzyme responsible for phenacetin O-deethylation as CYP1A2. Population modeling of EK provides valuable information on inter- and intraliver variability in CYP dependent activities.