Deema Hilmi Adawi, Nadia Ben Fredj, Ahmad Al-Barghouthi, Ichrack Dridi, Mustafa Lubada, Mohammad Manasra, Karim Aouam
{"title":"巴勒斯坦慢性髓系白血病患者甲磺酸伊马替尼的药代动力学及有限采样策略的建立","authors":"Deema Hilmi Adawi, Nadia Ben Fredj, Ahmad Al-Barghouthi, Ichrack Dridi, Mustafa Lubada, Mohammad Manasra, Karim Aouam","doi":"10.1007/s13318-023-00868-y","DOIUrl":null,"url":null,"abstract":"<p><strong>Background and objective: </strong>Imatinib is a tyrosine kinase inhibitor used in the treatment of chronic myeloid leukemia (CML). The area under the concentration-time curve (AUC) is a pharmacokinetic parameter that symbolizes overall exposure to a drug, which is correlated with complete cytogenetic and treatment responses to imatinib, as well as its side effects in patients with CML. The limited sampling strategy (LSS) is considered a sufficiently precise and practical method that can be used to estimate pharmacokinetic parameters such as AUC, without the need for frequent, costly, and inconvenient blood sampling. This study aims to investigate the pharmacokinetic parameters of imatinib, develop and validate a reliable and practical LSS for estimating imatinib AUC<sub>0-24</sub>, and determine the optimum sampling points for predicting the imatinib AUC after the administration of once-daily imatinib in Palestinian patients with CML.</p><p><strong>Method: </strong>Pharmacokinetic profiles, involving six blood samples collected during a 24-h dosing interval, were obtained from 25 Palestinian patients diagnosed with CML who had been receiving imatinib for at least 7 days and had reached a steady-state level. Imatinib AUC<sub>0-24</sub> was calculated using the trapezoidal rule, and linear regression analysis was performed to assess the relationship between measured AUC<sub>0-24</sub> and concentrations at each sampling time. All developed models were analyzed to determine their effectiveness in predicting AUC<sub>0-24</sub> and to identify the optimal sampling time. To evaluate predictive performance, two error indices were employed: the percentage of root mean squared error (% RMSE) and the mean predictive error (% MPE). Bland and Altman plots, along with mountain plots, were utilized to assess the agreement between measured and predicted AUC.</p><p><strong>Results: </strong>Among the one-timepoint estimations, predicted AUC<sub>0-24</sub> based on concentration of imatinib at the eighth hour after administration (C<sub>8</sub>-predicted AUC<sub>0-24</sub>) demonstrated the highest correlation with the measured AUC (r<sup>2</sup> = 0.97, % RMSE = 6.3). In two-timepoint estimations, the model consisting of C<sub>0</sub> and C<sub>8</sub> yielded the highest correlation between predicted and measured imatinib AUC (r<sup>2</sup> = 0.993 and % RMSE = 3.0). In three-timepoint estimations, the combination of C<sub>0</sub>, C<sub>1</sub>, and C<sub>8</sub> provided the most robust multilinear regression for predicting imatinib AUC<sub>0-24</sub> (r<sup>2</sup> = 0.996, % RMSE = 2.2). This combination also outperformed all other models in predicting AUC. The use of a two-timepoint limited sampling strategy (LSS) for predicting AUC was found to be reliable and practical. While C<sub>0</sub>/C<sub>8</sub> exhibited the highest correlation, the use of C<sub>0</sub>/C<sub>4</sub> could be a more practical and equally accurate choice. Therapeutic drug monitoring of imatinib based on C<sub>0</sub> can also be employed in routine clinical practice owing to its reliability and practicality.</p><p><strong>Conclusion: </strong>The LSS using one timepoint, especially C<sub>0</sub>, can effectively predict imatinib AUC. This approach offers practical benefits in optimizing dose regimens and improving adherence. However, for more precise estimation of imatinib AUC, utilizing two- or three-timepoint concentrations is recommended over relying on a single point.</p>","PeriodicalId":11939,"journal":{"name":"European Journal of Drug Metabolism and Pharmacokinetics","volume":" ","pages":"43-55"},"PeriodicalIF":1.9000,"publicationDate":"2024-01-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":"0","resultStr":"{\"title\":\"Pharmacokinetics of Imatinib Mesylate and Development of Limited Sampling Strategies for Estimating the Area under the Concentration-Time Curve of Imatinib Mesylate in Palestinian Patients with Chronic Myeloid Leukemia.\",\"authors\":\"Deema Hilmi Adawi, Nadia Ben Fredj, Ahmad Al-Barghouthi, Ichrack Dridi, Mustafa Lubada, Mohammad Manasra, Karim Aouam\",\"doi\":\"10.1007/s13318-023-00868-y\",\"DOIUrl\":null,\"url\":null,\"abstract\":\"<p><strong>Background and objective: </strong>Imatinib is a tyrosine kinase inhibitor used in the treatment of chronic myeloid leukemia (CML). The area under the concentration-time curve (AUC) is a pharmacokinetic parameter that symbolizes overall exposure to a drug, which is correlated with complete cytogenetic and treatment responses to imatinib, as well as its side effects in patients with CML. The limited sampling strategy (LSS) is considered a sufficiently precise and practical method that can be used to estimate pharmacokinetic parameters such as AUC, without the need for frequent, costly, and inconvenient blood sampling. This study aims to investigate the pharmacokinetic parameters of imatinib, develop and validate a reliable and practical LSS for estimating imatinib AUC<sub>0-24</sub>, and determine the optimum sampling points for predicting the imatinib AUC after the administration of once-daily imatinib in Palestinian patients with CML.</p><p><strong>Method: </strong>Pharmacokinetic profiles, involving six blood samples collected during a 24-h dosing interval, were obtained from 25 Palestinian patients diagnosed with CML who had been receiving imatinib for at least 7 days and had reached a steady-state level. Imatinib AUC<sub>0-24</sub> was calculated using the trapezoidal rule, and linear regression analysis was performed to assess the relationship between measured AUC<sub>0-24</sub> and concentrations at each sampling time. All developed models were analyzed to determine their effectiveness in predicting AUC<sub>0-24</sub> and to identify the optimal sampling time. To evaluate predictive performance, two error indices were employed: the percentage of root mean squared error (% RMSE) and the mean predictive error (% MPE). Bland and Altman plots, along with mountain plots, were utilized to assess the agreement between measured and predicted AUC.</p><p><strong>Results: </strong>Among the one-timepoint estimations, predicted AUC<sub>0-24</sub> based on concentration of imatinib at the eighth hour after administration (C<sub>8</sub>-predicted AUC<sub>0-24</sub>) demonstrated the highest correlation with the measured AUC (r<sup>2</sup> = 0.97, % RMSE = 6.3). In two-timepoint estimations, the model consisting of C<sub>0</sub> and C<sub>8</sub> yielded the highest correlation between predicted and measured imatinib AUC (r<sup>2</sup> = 0.993 and % RMSE = 3.0). In three-timepoint estimations, the combination of C<sub>0</sub>, C<sub>1</sub>, and C<sub>8</sub> provided the most robust multilinear regression for predicting imatinib AUC<sub>0-24</sub> (r<sup>2</sup> = 0.996, % RMSE = 2.2). This combination also outperformed all other models in predicting AUC. The use of a two-timepoint limited sampling strategy (LSS) for predicting AUC was found to be reliable and practical. While C<sub>0</sub>/C<sub>8</sub> exhibited the highest correlation, the use of C<sub>0</sub>/C<sub>4</sub> could be a more practical and equally accurate choice. Therapeutic drug monitoring of imatinib based on C<sub>0</sub> can also be employed in routine clinical practice owing to its reliability and practicality.</p><p><strong>Conclusion: </strong>The LSS using one timepoint, especially C<sub>0</sub>, can effectively predict imatinib AUC. This approach offers practical benefits in optimizing dose regimens and improving adherence. However, for more precise estimation of imatinib AUC, utilizing two- or three-timepoint concentrations is recommended over relying on a single point.</p>\",\"PeriodicalId\":11939,\"journal\":{\"name\":\"European Journal of Drug Metabolism and Pharmacokinetics\",\"volume\":\" \",\"pages\":\"43-55\"},\"PeriodicalIF\":1.9000,\"publicationDate\":\"2024-01-01\",\"publicationTypes\":\"Journal Article\",\"fieldsOfStudy\":null,\"isOpenAccess\":false,\"openAccessPdf\":\"\",\"citationCount\":\"0\",\"resultStr\":null,\"platform\":\"Semanticscholar\",\"paperid\":null,\"PeriodicalName\":\"European Journal of Drug Metabolism and Pharmacokinetics\",\"FirstCategoryId\":\"3\",\"ListUrlMain\":\"https://doi.org/10.1007/s13318-023-00868-y\",\"RegionNum\":4,\"RegionCategory\":\"医学\",\"ArticlePicture\":[],\"TitleCN\":null,\"AbstractTextCN\":null,\"PMCID\":null,\"EPubDate\":\"2023/11/25 0:00:00\",\"PubModel\":\"Epub\",\"JCR\":\"Q3\",\"JCRName\":\"PHARMACOLOGY & PHARMACY\",\"Score\":null,\"Total\":0}","platform":"Semanticscholar","paperid":null,"PeriodicalName":"European Journal of Drug Metabolism and Pharmacokinetics","FirstCategoryId":"3","ListUrlMain":"https://doi.org/10.1007/s13318-023-00868-y","RegionNum":4,"RegionCategory":"医学","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":null,"EPubDate":"2023/11/25 0:00:00","PubModel":"Epub","JCR":"Q3","JCRName":"PHARMACOLOGY & PHARMACY","Score":null,"Total":0}
Pharmacokinetics of Imatinib Mesylate and Development of Limited Sampling Strategies for Estimating the Area under the Concentration-Time Curve of Imatinib Mesylate in Palestinian Patients with Chronic Myeloid Leukemia.
Background and objective: Imatinib is a tyrosine kinase inhibitor used in the treatment of chronic myeloid leukemia (CML). The area under the concentration-time curve (AUC) is a pharmacokinetic parameter that symbolizes overall exposure to a drug, which is correlated with complete cytogenetic and treatment responses to imatinib, as well as its side effects in patients with CML. The limited sampling strategy (LSS) is considered a sufficiently precise and practical method that can be used to estimate pharmacokinetic parameters such as AUC, without the need for frequent, costly, and inconvenient blood sampling. This study aims to investigate the pharmacokinetic parameters of imatinib, develop and validate a reliable and practical LSS for estimating imatinib AUC0-24, and determine the optimum sampling points for predicting the imatinib AUC after the administration of once-daily imatinib in Palestinian patients with CML.
Method: Pharmacokinetic profiles, involving six blood samples collected during a 24-h dosing interval, were obtained from 25 Palestinian patients diagnosed with CML who had been receiving imatinib for at least 7 days and had reached a steady-state level. Imatinib AUC0-24 was calculated using the trapezoidal rule, and linear regression analysis was performed to assess the relationship between measured AUC0-24 and concentrations at each sampling time. All developed models were analyzed to determine their effectiveness in predicting AUC0-24 and to identify the optimal sampling time. To evaluate predictive performance, two error indices were employed: the percentage of root mean squared error (% RMSE) and the mean predictive error (% MPE). Bland and Altman plots, along with mountain plots, were utilized to assess the agreement between measured and predicted AUC.
Results: Among the one-timepoint estimations, predicted AUC0-24 based on concentration of imatinib at the eighth hour after administration (C8-predicted AUC0-24) demonstrated the highest correlation with the measured AUC (r2 = 0.97, % RMSE = 6.3). In two-timepoint estimations, the model consisting of C0 and C8 yielded the highest correlation between predicted and measured imatinib AUC (r2 = 0.993 and % RMSE = 3.0). In three-timepoint estimations, the combination of C0, C1, and C8 provided the most robust multilinear regression for predicting imatinib AUC0-24 (r2 = 0.996, % RMSE = 2.2). This combination also outperformed all other models in predicting AUC. The use of a two-timepoint limited sampling strategy (LSS) for predicting AUC was found to be reliable and practical. While C0/C8 exhibited the highest correlation, the use of C0/C4 could be a more practical and equally accurate choice. Therapeutic drug monitoring of imatinib based on C0 can also be employed in routine clinical practice owing to its reliability and practicality.
Conclusion: The LSS using one timepoint, especially C0, can effectively predict imatinib AUC. This approach offers practical benefits in optimizing dose regimens and improving adherence. However, for more precise estimation of imatinib AUC, utilizing two- or three-timepoint concentrations is recommended over relying on a single point.
期刊介绍:
Hepatology International is a peer-reviewed journal featuring articles written by clinicians, clinical researchers and basic scientists is dedicated to research and patient care issues in hepatology. This journal focuses mainly on new and emerging diagnostic and treatment options, protocols and molecular and cellular basis of disease pathogenesis, new technologies, in liver and biliary sciences.
Hepatology International publishes original research articles related to clinical care and basic research; review articles; consensus guidelines for diagnosis and treatment; invited editorials, and controversies in contemporary issues. The journal does not publish case reports.