Purpose: To develop a pharmacokinetic limited sampling model (LSM) for temozolomide and its metabolite MTIC in infants and children. Methods: LSMs consisting of either two or four samples were determined using a modification of the D-optimality algorithm. This accounted for prior distribution of temozolomide and MTIC pharmacokinetic parameters based on full pharmacokinetic sampling from 38 patients with 120 pharmacokinetic studies (dosage range 145-200 mg/m2 per day orally). Accuracy and bias of each LSM were determined relative to the full sampling method. We also assessed the predictive performance of the LSMs using Monte-Carlo simulations. Results: The four strategies generated from the D-optimality algorithm were as follows: LSM 1=0.25, 1.25, and 3 h; LSM 2=0.25, 1.25, and 6 h; LSM 3=0.25, 0.5, 1.25, and 3 h; LSM 4=0.25, 0.5, 1.25, and 6 h. LSM 2 demonstrated the best combination of low bias [0.1% (-8.9%, 11%) and 11% (4.3%, 15%)] and high accuracy [-1.0% (-12%, 24%) and 14% (7.9%, 37%)] for temozolomide clearance and MTIC AUC, respectively. Furthermore, adding a fourth sample (e.g., LSM 4) did not substantially decrease the bias or increase the accuracy for temozolomide clearance or MTIC AUC. Results from Monte-Carlo simulations also revealed that LSM 2 had the best combination of lowest bias (0.1±6.1% and -0.8±6.5%), and the highest accuracy (4.5±4.1% and 5.0±4.3%) for temozolomide clearance and MTIC apparent clearance, respectively. Conclusions: Using data derived from our population analysis, the sampling times for a limited sample pharmacokinetic model for temozolomide and MTIC in children are prior to the temozolomide dose, and 15 min, 1.25 h and 6 h after the dose.
|Original language||English (US)|
|Number of pages||6|
|Journal||Cancer chemotherapy and pharmacology|
|State||Published - May 2005|
Bibliographical noteFunding Information:
Acknowledgements The authors would like to thank Lisa Walters, Terri Kuehner, Paula Condy, Margaret Edwards, and Sheri Ring for assistance in obtaining plasma samples, Suzan Hanna for her invaluable technical support in the laboratory, and Kathy Probst for her support with data management. This work was supported in part by USPHS awards CA23099 and Cancer Center CORE grant CA21765, the Schering Plough Institute, and the American Lebanese Syrian Associated Charities (ALSAC).
- D-optimal sampling
- Monte-Carlo simulation