The Simcyp Population-Based ADME Simulator was used to predict median drug clearances and their associated variance from in vitro data. Fifteen drugs satisfied the entry criteria for the study and the relevant information (in vitro metabolism data and in vivo human clearance values) were collated from the literature. Predicted values of median clearances fell within 2-fold of observed values for 73% of the drugs (oral route) and 78% of the drugs (intravenous route) when microsomal binding was disregarded, and for 93% (oral) and 100% (intravenous) when it was considered. Irrespective of whether microsomal binding was considered, the predicted fold variability fell within 2-fold of the observed variability for 80% (oral) and 67% (intravenous) of the drugs.

Potential differences in drug clearance between Japanese and Caucasians were investigated by integrating data on demography, liver size, the abundance of the major cytochromes P450 and in vitro metabolic parameters. Eleven drugs (alprazolam, caffeine, chlorzoxazone, cyclosporine, midazolam, omeprazole, sildenafil, tolbutamide, triazolam, S-warfarin and zolpidem) fulfilled the entry criteria of the study (i.e. the necessary in vitro metabolism data were available and clearance values had been reported both in Caucasians and Japanese). Values of relevant biological variables were obtained from the literature, and clearance predictions were made using the Simcyp Population-Based ADME Simulator. The ratios of observed oral clearance (CLp.o.) values in Caucasians compared with Japanese ranged from 0.6 to 2.8 (integrating data from 82 sources). The CLp.o. values for alprazolam, caffeine and zolpidem were not statistically different between Caucasian and Japanese (p>0.05), whereas those for chorzoxazone, cyclosporine, omeprazole, tolbutamide and triazolam were higher in Caucasians (p<0.05), and those for midazolam, sildenafil and S-warfarin were higher in Japanese (p<0.05). CLp.o. values, predicted from in vitro data, were within 3-fold of observed in vivo values for seven of the 11 drugs in Japanese. Values for the predicted ratios ranged from 1.6 to 4.9. The predicted ratios were not significantly different from observed ratios for cyclosporine, omeprazole, tolbutamide and triazolam. Only partial success in predicting ethnic differences in clearance indicates the need for larger and more reliable databases on relevant variables. With such information, in silico predictions might be used with more confidence to decrease the need for repeating pharmacokinetic studies in different ethnic groups.

Previously in vitro-in vivo extrapolation (IVIVE) with the Simcyp Clearance and Interaction Simulator has been used to predict the clearance of 15 clinically used drugs in humans. The criteria for the selection of the drugs were that they are used as probes for the activity of specific cytochromes P450 (CYPs) or have a single CYP isoform as the major or sole contributor to their metabolism and that they do not exhibit non-linear kinetics in vivo. Where data were available for the clearance of the drugs in at least three animal species, the predictions from IVIVE have now been compared with those based on allometric scaling (AS). Adequate data were available for estimating oral clearance (CLp.o.) in 9 cases (alprazolam, sildenafil, caffeine, clozapine, cyclosporine, dextromethorphan, midazolam, omeprazole and tolbutamide) and intravenous clearance in 6 cases (CLi.v.) (cyclosporine, diclofenac, midazolam, omeprazole, theophylline and tolterodine). AS predictions were based on five different methods: (1) simple allometry (clearance versus body weight); (2) correction for maximum life-span potential (CL x MLP); (3) correction for brain weight (CL x BrW); (4) the use of body surface area; and (5) the rule of exponents. A prediction accuracy was indicated by mean-fold error and the Pearson product moment correlation coefficient. Predictions were considered successful if the mean-fold error was

Prediction of metabolic clearance in extreme individuals rather than the 'average human' is becoming an attractive tool within the pharmaceutical industry. The current study involved prediction of variability in metabolic clearance for alprazolam, triazolam and midazolam with emphasis on the following factors: first, evaluation of clearance prediction accuracy using intrinsic clearance (CL(int)) data from in vitro metabolic data and back-calculation from in vivo clearance data. Second, the sensitivity of predicted in vivo variability to changes in variability for physiological parameters (e.g. liver weight, haematocrit, CYP3A abundance). Finally, reported estimates of variability in hepatic CYP3A4 abundance (coefficient of variation (CV) 95%) were refined by separating experimental from interindividual variability using a repeat measurement protocol in 52 human liver samples. Using in vitro metabolic data, predicted clearances were within 2-fold of observed for triazolam and midazolam. Clearance of alprazolam was overpredicted by 2.0- to 3.7-fold. Use of in vivo CL(int) values improved prediction of intravenous clearance to within 2-fold of observed for all drugs. Initially, the variability in clearance was overestimated for all drugs (by 1.8- to 3.6-fold). Use of a reduced hepatic CYP3A4 CV of 41%, representative of interindividual variability alone improved predictions of variability in clearance for all drugs to within 2-fold of observed.

AIMSThe aims of the study were to characterize the pharmacokinetics (PK) of selumetinib (AZD6244; ARRY-142886), a mitogenactivated protein kinase kinase (MEK) 1/2 inhibitor in clinical development for various indications, and its N-desmethyl metabolite in healthy volunteers, and evaluate clinically important covariates. METHODSA pooled-population PK analysis was performed using a nonlinear mixed-effects approach with plasma concentration data from 346 subjects who received single oral doses of selumetinib 20-75 mg across 10 phase I studies. Absolute bioavailability was determined using intravenous [ 14 C] selumetinib. RESULTSA two-compartment linear model with sequential zero-first order absorption and a lag time for the zero-order process was described for selumetinib PK. N-desmethyl metabolite disposition was described by a single compartment with linear elimination, without back transformation. The parent-only and joint models generally described pooled data adequately. For the median subject, not taking interacting drugs, estimates for clearance (CL) and central volume of distribution (V2) for selumetinib in the final joint model were 12.7 l h -1 and 35.6 l, respectively. Food effects, comedication with itraconazole [a cytochrome P450 (CYP) 3A4 inhibitor], fluconazole (a CYP2C19 inhibitor) and rifampicin (a CYP3A4 inducer) and formulation effects were incorporated into the base model a priori. Race and hepatic function were also influential in the PK model. Additional covariates affecting selumetinib disposition identified from covariate analysis were age on V2, bilirubin on CL, and weight on CL and V2. CONCLUSIONSAnalysis confirmed previous clinical pharmacology study findings of drug-drug interactions and food effects, with additional covariates that influence selumetinib and N-desmethyl selumetinib PK identified. Dose modifications based on these additional covariates were not considered necessary. British Journal of Clinical Pharmacology WHAT IS ALREADY KNOWN ABOUT THIS SUBJECT• Drug-drug interactions, bioavailability, food effects and hepatic/renal impairment effects of selumetinib have previously been evaluated in numerous clinical studies in healthy subjects. Our aim was to develop a population pharmacokinetic (PK) model of selumetinib and its N-desmethyl metabolite in healthy subjects, and to identify important covariates influencing the PK. WHAT THIS STUDY ADDS• We confirmed that food effects, comedication with cytochrome P450 inhibitors/inducers, race and hepatic function status were influential in the PK model, as indicated by previous studies. We identified additional covariates, including age, bilirubin level and weight, that influence selumetinib and N-desmethyl selumetinib PK.