Population pharmacokinetics of mycophenolic acid in children and young people undergoing blood or marrow and solid organ transplantation.

AIMS: To characterize the population pharmacokinetics of mycophenolic acid (MPA) and evaluate dose regimens using a simulation approach and accepted therapeutic drug monitoring targets in children and young people undergoing blood or marrow, kidney and liver transplantation. METHODS: MPA concentration-time data were collected using an age specific sampling protocol over 12h. Some patients provided randomly timed but accurately recorded blood samples. Total and unbound MPA were measured by HPLC. NONMEM was employed to analyze MPA pharmacokinetic data. Simulations (n= 1000) were conducted to assess the suitability of the MPA dose regimens to maintain total MPA AUC(0,12h) within the range 30 and 60mg l(-1) h associated with optimal outcome. RESULTS: A two-compartment pharmacokinetic model with first-order elimination best described MPA concentration-time data. Population mean estimates of MPA clearance, inter-compartmental clearance, volumes of distribution in the central and peripheral compartments, absorption rate constant and bioavailability were 6.42 l h(-1) , 3.74 l h(-1) , 7.24 l, 16.8l, 0.39h(-1) and 0.48, respectively. Inclusion of bodyweight and concomitant ciclosporin reduced the inter-individual variability in CL from 54.3% to 31.6%. Children with a bodyweight of 10kg receiving standard MPA dose regimens achieve an MPA AUC below the target range suggesting they may be at a greater risk of acute rejection. CONCLUSIONS: The population pharmacokinetic model for MPA can be used to explore dosing guidelines for safe and effective immunotherapy in children and young people undergoing transplantation.

Investigation of the effects of herbal medicines on warfarin response in healthy subjects: a population pharmacokinetic-pharmacodynamic modeling approach.

Systematic evidence regarding herb-drug interactions is lacking. This study investigated herb-drug interactions with warfarin. S-warfarin concentration and response (prothrombin complex activity) data from healthy subjects (n = 24) who received a single warfarin dose (25 mg) and either St John's wort, Asian ginseng, Ginkgo biloba, or ginger were analyzed using a population pharmacokinetic-pharmacodynamic modeling approach. The ratio of S-warfarin apparent clearance (CL/F) compared to control was 1.39 +/- 0.06 and 1.14 +/- 0.04 after St John's wort and Asian ginseng pretreatment, respectively. Other pharmacokinetic and pharmacodynamic parameters were unaffected. Coadministration of St John's wort significantly increased S-warfarin CL/F, whereas treatment with Asian ginseng produced only a moderate increase in CL/F. Ginkgo and ginger did not affect the pharmacokinetics of warfarin in healthy subjects. None of the herbs studied had a direct effect on warfarin pharmacodynamics. Studies in anticoagulated patients are warranted to assess the clinical significance of these herb-drug interactions.

Predictors of vinorelbine pharmacokinetics and pharmacodynamics in patients with cancer.

PURPOSE: Marked interindividual variation in drug disposition and toxicity pose an ongoing challenge to chemotherapy dosage individualization. The aim of this study was to evaluate pretreatment clinical features, genotype and functional indicators of drug clearance as predictors of vinorelbine clearance, and myelotoxicity that could inform dosage optimization. PATIENTS AND METHODS: Forty-one patients with cancer received a 60 mg intravenous dose of vinorelbine. Pretreatment routine body size measurements and blood tests were performed. Midazolam clearance and hepatic technetium labeled sestamibi (99mTc-MIBI) clearance were used to investigate CYP3A and ABCB1 (MDR1, P-glycoprotein) phenotype respectively and selected single nucleotide polymorphisms in CYP3A and ABCB1 were documented. A limited blood sampling strategy was employed and vinorelbine concentrations were determined by high-performance liquid chromatography. Posterior Bayesian estimates of vinorelbine clearance were obtained for each patient using population pharmacokinetic modeling. Myelotoxicity was estimated from the fractional survival of neutrophils post-treatment. RESULTS: There was 4.3-fold variation in vinorelbine clearance across the cohort. In a multivariable analysis, pretreatment estimated creatinine clearance (P < .01) and hepatic (99m)Tc-MIBI clearance (P = .01) were independent predictors of vinorelbine clearance. Fractional survival of neutrophils ranged from 1.3% to 100% and was significantly correlated with vinorelbine clearance (P < .01). Body-surface area was the only pretreatment predictor of fractional survival of neutrophils independent of vinorelbine clearance (P = .02). CONCLUSION: Specific indicators of drug clearance provide predictive information about vinorelbine pharmacokinetics, and body-surface area, probably reflecting normal bone marrow reserve, provides an additional pharmacodynamic indicator. Use of a fixed dose of vinorelbine with modifications guided by pretreatment measures is worthy of prospective evaluation.

Population pharmacokinetics of raltitrexed in patients with advanced solid tumours.

AIMS: To investigate the population pharmacokinetics of raltitrexed in patients with advanced solid tumours and to identify patient covariates contributing to the interpatient variability in the pharmacokinetics of raltitrexed. METHODS: Patient covariate and concentration-time data were collected from patients receiving 0.1-4.5 mg m(-2) raltitrexed during the early clinical trials of raltitrexed. Data were fitted using nonlinear mixed effects modelling to generate population mean estimates for clearance (CL) and central volume of distribution (V). The relationship between individual estimates of the pharmacokinetic parameters and patient covariates was examined and the influence of significant covariates on the population parameter estimates and their variance was investigated using stepwise multiple linear regression. The performance of the developed model was tested using an independent validation dataset. All patient data were pooled in the total cohort to refine the population pharmacokinetic model for raltitrexed. RESULTS: three-compartment pharmacokinetic model was used to fit the concentration-time data of raltitrexed. Estimated creatinine clearance (CL(CR)) was found to influence significantly the CL of raltitrexed and explained 35% of variability in this parameter, whilst body weight (WT) and serum albumin concentrations (ALB) accounted for 56% of the variability in V. Satisfactory prediction (mean prediction error 0.17 micro g l(-1) and root mean square prediction error 4.99 micro g l(-1)) of the observed raltitrexed concentrations was obtained in the model validation step. The final population mean estimates were 2.17 l h(-1)[95% confidence interval (CI) 2.06, 2.28] and 6.36 l (95% CI 6.02, 6.70) for CL and V, respectively. Interpatient variability in the pharmacokinetic parameters was reduced (CL 28%, V 25%) when influential covariates were included in the final model. The following covariate relationships with raltitrexed parameters were described by the final population model: CL (l h(-1)) = 0.54 + 0.02 CL(CR) (ml min(-1)) and V (l) = 6.64 + 0.08 WT (kg) - 0.16 ALB (g l(-1)). CONCLUSIONS: A population pharmacokinetic model has been developed for raltitrexed in patients with advanced cancer. Pharmacokinetic parameters of raltitrexed are markedly influenced by the patient's renal function, body weight and serum albumin levels, which may be taken into account in dose individualization. The use of influential covariates to guide anticancer dosage selection may result in less variability in drug exposure and potentially a better clinical outcome.

Population pharmacokinetics of weekly docetaxel in patients with advanced cancer.

AIMS: Previous pharmacokinetic studies of the 3-weekly regimen (100 mg m(-2) every 3 weeks) of docetaxel have shown that docetaxel clearance is affected by liver function, body surface area, age, serum alpha1-acid glycoprotein and cytochrome P450 3A4 (CYP3A4) activity. However, the pharmacokinetics of a weekly docetaxel (40 mg m(-2) week(-1)) schedule are not well characterized. The aims of this study were (a) to investigate the pharmacokinetics of docetaxel (40 mg m(-2) week(-1)) using sparse concentration-time data collected from patients with advanced cancer and (b) to utilize a population pharmacokinetic approach to identify patient covariates that significantly influence the clearance of docetaxel when administered according to this regimen. METHODS: A two-compartment pharmacokinetic model was used to describe the docetaxel concentration-time data from 54 patients with advanced cancer. The mean population and individual posterior Bayesian estimates of docetaxel clearance were estimated using P-PHARM. The relationships between docetaxel clearance and 21 covariates were investigated. This included estimates of CYP3A4 function in each patient using the erythromycin breath test (1/tmax). Significant covariates were included into the final population pharmacokinetic model. Pharmacokinetic models were validated using a data splitting approach with a dataset consisting of 16 patients. RESULTS: Significant relationships were found between docetaxel clearance and 1/tmax (erythromycin breath test parameter) and several of the liver function enzymes and CL was best described by the equation; CL = 21.51 + 217 (1/tmax) - 0.13 (ALT). This final population pharmacokinetic model provided both precise and unbiased predictions of docetaxel concentrations in a validation group of patients and an estimate of the population mean (95% confidence interval) clearance of docetaxel was 30.13 l h(-1) (12.54, 46.04 l h(-1)) with an intersubject variability 30%. CONCLUSIONS: A population pharmacokinetic model has been developed and validated for weekly docetaxel (40 mg m(-2)) in patients with advanced cancer. These results indicate that CYP3A4 activity and hepatic function have an impact on the pharmacokinetics of docetaxel when administered weekly.