Álvaro Corral Alaejos1, Aránzazu Zarzuelo Castañeda2, Silvia Jiménez Cabrera1, Fermín Sánchez-Guijo3,4,5, María José Otero1,3, Jonás Samuel Pérez-Blanco2,3. 1. Pharmacy Service, University Hospital of Salamanca, Salamanca, Spain. 2. Department of Pharmaceutical Sciences, Pharmacy Faculty, University of Salamanca, Salamanca, Spain. 3. Institute for Biomedical Research of Salamanca, Salamanca, Spain. 4. Haematology Department, University Hospital of Salamanca, Salamanca, Spain. 5. Department of Medicine, University of Salamanca, Salamanca, Spain.
Abstract
AIMS: Imatinib is considered the standard first-line treatment in newly diagnosed patients with chronic-phase myeloid leukaemia (CML). Several imatinib population pharmacokinetic (popPK) models have been developed. However, their predictive performance has not been well established when extrapolated to different populations. Therefore, this study aimed to perform an external evaluation of available imatinib popPK models developed mainly in adult patients, and to evaluate the improvement in individual model-based predictions through Bayesian forecasting computed by each model at different treatment occasions. METHODS: A literature review was conducted through PubMed and Scopus to identify popPK models. Therapeutic drug monitoring data collected in adult CML patients treated with imatinib was used for external evaluation, including prediction- and simulated-based diagnostics together with Bayesian forecasting analysis. RESULTS: Fourteen imatinib popPK studies were included for model-performance evaluation. A total of 99 imatinib samples were collected from 48 adult CML patients undergoing imatinib treatment with a minimum of one plasma concentration measured at steady-state between January 2016 and December 2020. The model proposed by Petain et al showed the best performance concerning prediction-based diagnostics in the studied population. Bayesian forecasting demonstrated a significant improvement in predictive performance at the second visit. Inter-occasion variability contributed to reducing bias and improving individual model-based predictions. CONCLUSIONS: Imatinib popPK studies developed in Caucasian subjects including α1-acid glycoprotein showed the best model performance in terms of overall bias and precision. Moreover, two imatinib samples from different visits appear sufficient to reach an adequate model-based individual prediction performance trough Bayesian forecasting.
AIMS: Imatinib is considered the standard first-line treatment in newly diagnosed patients with chronic-phase myeloid leukaemia (CML). Several imatinib population pharmacokinetic (popPK) models have been developed. However, their predictive performance has not been well established when extrapolated to different populations. Therefore, this study aimed to perform an external evaluation of available imatinib popPK models developed mainly in adult patients, and to evaluate the improvement in individual model-based predictions through Bayesian forecasting computed by each model at different treatment occasions. METHODS: A literature review was conducted through PubMed and Scopus to identify popPK models. Therapeutic drug monitoring data collected in adult CML patients treated with imatinib was used for external evaluation, including prediction- and simulated-based diagnostics together with Bayesian forecasting analysis. RESULTS: Fourteen imatinib popPK studies were included for model-performance evaluation. A total of 99 imatinib samples were collected from 48 adult CML patients undergoing imatinib treatment with a minimum of one plasma concentration measured at steady-state between January 2016 and December 2020. The model proposed by Petain et al showed the best performance concerning prediction-based diagnostics in the studied population. Bayesian forecasting demonstrated a significant improvement in predictive performance at the second visit. Inter-occasion variability contributed to reducing bias and improving individual model-based predictions. CONCLUSIONS: Imatinib popPK studies developed in Caucasian subjects including α1-acid glycoprotein showed the best model performance in terms of overall bias and precision. Moreover, two imatinib samples from different visits appear sufficient to reach an adequate model-based individual prediction performance trough Bayesian forecasting.
Authors: Hinojal Zazo; Eduardo Lagarejos; Manuel Prado-Velasco; Sergio Sánchez-Herrero; Jenifer Serna; Almudena Rueda-Ferreiro; Ana Martín-Suárez; M Victoria Calvo; Jonás Samuel Pérez-Blanco; José M Lanao Journal: Front Pharmacol Date: 2022-09-28 Impact factor: 5.988
Authors: Alex K Lyashchenko; Yifan Yu; Donald J McMahon; Robert Bies; Michael T Yin; Serge Cremers Journal: Br J Clin Pharmacol Date: 2022-08-12 Impact factor: 3.716