Population PK analysis was performed by nonlinear mixed effect modelling using NONMEM. Results A three\compartment model with zero\order infusion was found to best describe temsirolimus PK. effect modelling using NONMEM. Results A three\compartment model with zero\order infusion was found to best describe temsirolimus PK. Allometrically scaled body weight was included in the model to account for body size differences. Temsirolimus dose was identified as a significant covariate on clearance. A sirolimus metabolite formation model was developed and integrated with the temsirolimus model. A two\compartment structure model adequately described the sirolimus data. Conclusion This study is the first to describe a population PK model of temsirolimus combined with sirolimus formation and disposition in paediatric patients. The developed model will facilitate PK model\based dose individualization of temsirolimus and the design of future clinical studies in children. (%) Female 8 (42.1) Male 11 (57.9) Race, n (%) Caucasian 17 (89.5) AfricanCAmerican 1 (5.3) Asian 1 (5.3) Temsirolimus dose level, (%) 8?mg?m ?2 11 (57.9) 10?mg?m ?2 3 (15.8) 15?mg?m ?2 5 (26.3) Open in a separate window SD, standard deviation Population PK modelling Population PK analysis was performed by nonlinear mixed effect modelling using NONMEM (version 7.2, ICON, Ellicott City, MD, USA) with Perl speaks NONMEM (PsN) version 3.6.2 31 and Pirana version 2.7.1 (Pirana Software & Consulting BV, http://pirana.sourceforge.net) as the interface. The Aucubin first\order conditional estimation with interaction method (FOCE\I) was applied for all runs. Different compartment models were explored to describe the temsirolimus and sirolimus blood concentration\time profiles. Model selection was based on goodness\of\fit diagnostic plots, comparisons based on the minimum objective function value (OFV) and evaluation IL-23A of the estimates of Aucubin population fixed and random effect parameters. Interpatient variability was assessed using an exponential variability model (Equation (1)): =?Ppop is the typical population value (geometric mean) of the PK parameters such as clearance and volume of distribution, i is an interindividual random effect for individual with the mean of zero and variance of 2. A proportional error model and a combined proportional and additive error model were examined to describe the residual error. All PK models were parameterized in terms of values Aucubin of clearance (CL), volume of distribution (V) and intercompartmental clearances (Q). Allometrically scaled body weight was used to account for differences in body size as follows (Equation (2)): =?individual predicted value (IPRED), conditional weighted residuals PRED and conditional weighted residuals (A) population\predicted and (B) individual\predicted temsirolimus concentrations (line of identity shown for clarity). The conditional weighted residuals (CWRES) (C) time after dose and (D) population\predicted temsirolimus concentration Open in a separate window Figure 3 Prediction\corrected visual predictive check (pcVPC) for the final model of temsirolimus. (A) All observations and (B) enlarged picture from 0 to 25?h. Open circle, observed blood concentrations; lines represent the median, 5th and 95th percentiles of the simulated data (time after dose (C) and population predict temsirolimus (open circles) and sirolimus (blue circles) concentrations (D) Open in a separate window Figure 5 Prediction\corrected visual predictive check (pcVPC) for the final model of temsirolimus with sirolimus. (A, C) All observations and (B, D) enlarged picture from 0 to 25?h. Open circles, observed temsirolimus concentrations (A, B) and sirolimus concentrations (C, D); lines represent the median, 5th and 95th percentiles of the simulated data (Bayesian estimation with NONMEM. When CL was standardized to allometrically scaled body weight, no age effects were observed over the age range of patients in this study (Age range 1C19 years, with only one patient younger than 2?years; Figure S1). Discussion This study generated a combined population PK model of temsirolimus with its metabolite sirolimus in paediatric patients with recurrent solid tumours. To the best of our knowledge, this is the first population PK modelling analysis of temsirolimus in children. The analysis confirms that temsirolimus PK is nonlinear with dose consistent Aucubin with that reported in adult patients 5, 22. Nonlinearity in the relationship between temsirolimus dose and systemic exposure has been well documented 10, 19, 20, 21, 23, 35. In a previous population PK analysis in 50 adult patients, Boni Bayesian temsirolimus clearance (CL) estimates. (A) CL (l?hC1) age (years) and (B) allometrically scaled CL (l?hC1?70?kgC1) age. Solid line represents.
Population PK analysis was performed by nonlinear mixed effect modelling using NONMEM