An Effective Approach for Obtaining Optimal Sampling Windows for Population Pharmacokinetic Experiments

Optimal Sampling Strategies for Irinotecan (CPT-11) and its Active Metabolite (SN-38) in Cancer Patients

Irinotecan (CPT-11) is an anticancer agent widely used in the treatment of a variety of adult solid tumors. The objective of this study was to develop an optimal sampling strategy model that accurately estimates pharmacokinetic parameters of CPT-11 and its active metabolite, SN-38. This study included 221 patients with advanced solid tumors or lymphoma receiving CPT-11 single or combination therapy with 5-fluorouracil (5-FU)/leucovorin (LV) (FOLFIRI) plus bevacizumab from 4 separate clinical trials. Population pharmacokinetic analysis of CPT-11 and SN-38 was performed by non-linear mixed effects modeling. The optimal sampling strategy model was developed using D-optimality with expected distribution approach. The pharmacokinetic profiles of CPT-11 and SN-38 were best described by a 3- and 2-compartment model, respectively, with first-order elimination. Body surface area and co-administration with 5-FU/LV plus bevacizumab were significant covariates (p < 0.01) for volumes of the central compartment of CPT-11 and SN-38, and clearance of CPT-11. Pre-treatment total bilirubin and co-administration with 5-FU/LV and bevacizumab were significant covariates (p < 0.01) for clearance of SN-38. Accurate and precise predictive performance (r² > 0.99, -2 < bias (%ME) < 0, precision (% RMSE) < 12) of both CPT-11 and SN-38 was achieved using: (i) 6 fixed sampling times collected at 1.5, 3.5, 4, 5.75, 22, 23.5 hours post-infusion; or (ii) 1 fixed time and 2 sampling windows collected at 1.5, [3-5.75], [22-23.5] hours post-infusion. The present study demonstrates that an optimal sampling design with three blood samples achieves accurate and precise pharmacokinetic parameter estimates for both CPT-11 and SN-38.

A general method to determine sampling windows for nonlinear mixed effects models with an application to population pharmacokinetic studies

Optimal design methods have been proposed to determine the best sampling times when sparse blood sampling is required in clinical pharmacokinetic studies. However, the optimal blood sampling time points may not be feasible in clinical practice. Sampling windows, a time interval for blood sample collection, have been proposed to provide flexibility in blood sampling times while preserving efficient parameter estimation. Because of the complexity of the population pharmacokinetic models, which are generally nonlinear mixed effects models, there is no analytical solution available to determine sampling windows. We propose a method for determination of sampling windows based on MCMC sampling techniques. The proposed method attains a stationary distribution rapidly and provides time-sensitive windows around the optimal design points. The proposed method is applicable to determine sampling windows for any nonlinear mixed effects model although our work focuses on an application to population pharmacokinetic models.

Population pharmacokinetics and optimal design of paediatric studies for famciclovir

AIMS

To develop a population pharmacokinetic model for penciclovir (famciclovir is a prodrug of penciclovir) in adults and children and suggest an appropriate dose for children. Furthermore, to develop a limited sampling design based on sampling windows for three different paediatric age groups (1-2, 2-5 and 5-12 years) using an adequate number of subjects for future pharmacokinetic studies.

METHODS

Penciclovir plasma data from six different adult and paediatric studies were supplied by Novartis. Population pharmacokinetic modelling was undertaken in NONMEM version VI. Simulations in MATLAB were used to select an oral paediatric dose that gives similar exposure to 500 mg in adults. Optimal sampling times and sampling windows were obtained in MATLAB and simulations in NONMEM were used to select adequate sample sizes for three paediatric age groups.

RESULTS

A two-compartment, first-order absorption model with an absorption lag time, allometric weight models on V(1), V(2) and Q, and an allometric weight model, age and creatinine clearance as covariates on CL adequately describe the pharmacokinetics of penciclovir in adults and children. Estimated CL (l h(-1) 70 kg(-1)) and V(ss) (l.70 kg(-1)) were 31.2 and 83.1, respectively. An oral dose of 10 mg kg(-1) body weight in children was predicted to give similar exposure as 500 mg in adults. A single sampling windows design (0.25-0.4, 0.5-1, 1.25-1.75, 2.75-3.5 and 7.25-8 h) for five samples per subject and 10 subjects in each of the paediatric age groups is recommended for future studies.

CONCLUSIONS

A population pharmacokinetic model of penciclovir in adults and children has been developed. A prospective study design, including dose adjustment, cohort size and blood sampling design has been recommended.