Body fat composition impacts the hematologic toxicities and pharmacokinetics of doxorubicin in Asian breast cancer patients.

Body surface area (BSA)-based dosing leads to wide inter-individual variations in drug pharmacokinetics and pharmacodynamics, whereas body composition has been shown to be a more robust determinant of efficacy and toxicity of certain chemotherapeutic agents. We correlated various parameters of body composition with doxorubicin pharmacokinetics and hematologic toxicities in Asian patients with locally advanced or metastatic breast cancer. Our analysis included 84 patients from two studies who received pre- or post-operative single-agent doxorubicin; pharmacokinetic parameters were available for 44 patients. Body composition parameters were derived from CT cross-sectional images and population pharmacokinetic analysis was conducted using mixed-effects modeling. Higher intra-abdominal fat volume and fat ratio (intra-abdominal:total abdominal fat volume) correlated with greater incidence of grade 4 leukopenia on cycle 1 day 15 (mean intra-abdominal fat volume: 97.4 ± 46.5 cm(3) vs 63.4 ± 30.9 cm(3), p = 0.014; mean fat ratio: 0.43 ± 0.11 vs 0.33 ± 0.09, p = 0.012, grade 4 vs grade 0-3 leukopenia). On subset analysis, this relationship was maintained even in underweight patients. Concordantly, there were positive correlations between doxorubicin AUC and intra-abdominal fat volume as well as total abdominal fat volume (r (2) = 0.324 and 0.262, respectively, all p < 0.001). BSA and muscle volume did not predict for doxorubicin pharmacokinetics or toxicities. High-intra-abdominal fat volume but not BSA predicted for greater doxorubicin exposure and hematologic toxicities, suggesting that body composition is superior to BSA in determining doxorubicin pharmacokinetics and pharmacodynamics. Body composition has an emerging role in chemotherapy dose determination.

Population pharmacokinetics and pharmacogenetics of alcohol in Chinese and Indians in Singapore.

WHAT IS KNOWN AND OBJECTIVE:   Interindividual variability in alcohol pharmacokinetics is influenced by a number of factors, including polymorphisms in genes mediating alcohol pharmacology, ethnicity, sex and body size. Several studies have evaluated the population pharmacokinetics of alcohol from breath alcohol measures. None of these studies, however, have evaluated ethnicity and alcohol-metabolizing enzyme genotypes as covariates in their population pharmacokinetic modelling. We aimed to develop a population pharmacokinetic model using clinical and genetic factors and to identify covariates that influenced interindividual variability in alcohol clearance and volume of distribution. METHODS:   Hundred and eighty healthy subjects (90 Chinese and 90 Indians; 45 males and 45 females from each ethnic group) ingested a vodka-orange juice mixture to simulate social drinking. Subjects were genotyped for the ADH1B (Arg48His), ALDH2 (Glu504Lys) and CYP2E1 (c.-1293G>C and c.-1053C>T) polymorphisms. A base pharmacokinetic model was developed using the nonmem software (NONMEM Project Group, University of California, San Francisco, San Francisco, CA, USA) to determine the alcohol clearance and volume of distribution. The model was extended to include covariates that influenced the between-subject variability. RESULTS AND DISCUSSION:   Body weight and sex significantly influenced absorption rate and volume of distribution of alcohol. Body weight and ADH1B Arg48His polymorphism significantly influenced alcohol clearance. The Michaelis-Menten elimination rate (Vmax ) was decreased by 10% in homozygous ADH1B*1/*1 subjects. Ethnicity was not determined to be a significant covariate in the final population pharmacokinetic model. WHAT IS NEW AND CONCLUSION:   Gender and body weight were covariates that contributed most to explaining the observed interindividual alcohol pharmacokinetic variability. Of the four SNPs examined in this study, only ADH1B Arg48His polymorphism had a significant, though modest, effect on the pharmacokinetics of alcohol.

Population pharmacokinetics of caffeine in healthy male adults using mixed-effects models.

OBJECTIVE: Caffeine has been shown to maintain or improve the performance of individuals, but its pharmacokinetic profile for Asians has not been well characterized. In this study, a population pharmacokinetic model for describing the pharmacokinetics of caffeine in Singapore males was developed. The data were also analysed using non-compartmental models. METHODS: Data gathered from 59 male volunteers, who each ingested a single caffeine capsule in two clinical trials (3 or 5 mg/kg), were analysed via non-linear mixed-effects modelling. The participants' covariates, including age, body weight, and regularity of caffeinated-beverage consumption or smoking, were analysed in a stepwise fashion to identify their potential influence on caffeine pharmacokinetics. The final pharmacostatistical model was then subjected to stochastic simulation to predict the plasma concentrations of caffeine after oral (204, 340 and 476 mg) dosing regimens (repeated dosing every 6, 8 or 12 h) over a hypothetical 3-day period. RESULTS: The data were best described by a one-compartmental model with first-order absorption and first-order elimination. Smoking status was an influential covariate for clearance: clearance (mL/min) = 110*SMOKE + 114, where SMOKE was 0 and 1 for the non-smoker and the smoker respectively. Interoccasion variability was smaller compared to interindividual variability in clearance, volume and absorption rate (27% vs. 33%, 10% vs. 15% and 23% vs. 51% respectively). The extrapolated elimination half-lives of caffeine in the non-smokers and the smokers were 4.3 +/- 1.5 and 3.0 +/- 0.7 h respectively. Dosing simulations indicated that dosing regimens of 340 mg (repeated every 8 h) and 476 mg (repeated every 6 h) should achieve population-averaged caffeine concentrations within the reported beneficial range (4.5-9 microg/mL) in the non-smokers and the smokers respectively over 72 h. CONCLUSION: The population pharmacokinetic model satisfactorily described the disposition and variability of caffeine in the data. Mixed-effects modelling showed that the dose of caffeine depended on cigarette smoking status.