Factors Influencing the Steady-State Plasma Concentration of Imatinib Mesylate in Patients With Gastrointestinal Stromal Tumors and Chronic Myeloid Leukemia

Abstract

Imatinib mesylate (IM) is the standard treatment for advanced, metastatic gastrointestinal stromal tumors (GISTs) and chronic myeloid leukemia (CML) with a fixed daily standard dosage via the oral route. Interindividual and intraindividual variability in plasma concentrations have been closely linked to the efficacy of IM therapy. Therefore, this review identifies and describes the key factors influencing the plasma concentration of IM in patients with GISTs and CML. We used the following keywords to search the PubMed, EMBASE, Ovid, Wangfang, and CNKI databases to identify published reports: IM, plasma concentration, GISTs, CML, drug combination/interaction, pathology, and genotype/genetic polymorphism, either alone or in combination. This literature review revealed that only 10 countries have reported the mean concentrations of IM in GISTs or CML patients and the clinical outcomes in different ethnic groups and populations. There were totally 24 different gene polymorphisms, which were examined for any potential influence on the steady-state plasma concentration of IM. As a result, some genotype locus made discrepant conclusion. Herein, the more sample capacity, multicenter, long-term study was worthy to carry out. Eleven reports were enumerated on clinical drug interactions with IM, while there is not sufficient information on the pharmacokinetic parameters altered by drug combinations with IM that could help in investigating the actual drug interactions. The drug interaction with IM should be paid more attention in the future research.

External evaluation of population pharmacokinetic models of imatinib in adults diagnosed with chronic myeloid leukaemia

“…A large BSV has been reported for imatinib, which might be directly related to treatment response variability. In addition to the previously mentioned α1‐acid glycoprotein and ethnicity, total body weight, 7,48,53 white blood count and haemoglobin alterations 7,48,65 might have a significant impact on imatinib exposure and most likely on treatment response. Age, gender and genetic polymorphisms (ie drug transport genes, CYP polymorphisms) have also been suggested to influence imatinib exposure 7,18,53,65 …”

“…showed an overall model performance improvement for those models including α1-acid glycoprotein, which most likely is related to the high protein binding shown for imatinib and the saturable nature of this process. 7,65 Indeed, an increase in imatinib clearance of 50% has been shown for α1-acid glycoprotein values lower than 50 g/dL. 66 Thus, the usefulness of α1-acid glycoprotein as a potential index of treatment efficacy for CML patients has already been proposed.…”

“…The external evaluation carried out in our study presents several limitations, such as a reduced number of patients (n = 48) and imatinib plasma samples (n = 99), which can limit the robustness of the results derived, differences across structural and stochastic popPK models due to the high BSV in the PK of imatinib previously described in the literature, 7,19,65 the popPK model selection criteria, the adequate implementation of IOV from previous popPK models developed, the exclusion criteria of the popPK models (ie, models including free and total imatinib plasma concentrations), the potential missing values of covariates included in the popPK models considered, which might not have an important impact in this specific work as only missing WBC were higher than 10% in the external dataset, or the adherence to imatinib treatment and differences in treatment duration follow-up (models not considering time-varying clearance). 71 In addition, the impact of ethnicity on model performance could not be evaluated as all the subjects included in this analysis were and would be recommended for imatinib MIPD in the target population studied in this work and very similar ones.…”

“…26 Several imatinib popPK models have been developed in patients diagnosed with CML, which, together with Bayesian forecasting methodology, are able to reduce the uncertainty surrounding individual PK exposures based on a priori and individualized information. 7,12 The identification of the covariates is usually useful for explaining part of the variability of the C min across the studied population. However, the bias and precision of model predictions improved substantially by including imatinib concentrations using Bayesian forecasting.…”

Concurrence of imatinib-induced massive pleural/pericardial effusion and Campylobacter bacteremia in an adolescent with chronic myeloid leukemia

“…Notably, AGP plays an essential role in binding with imatinib. The trough concentration of imatinib proved to be synchronously associated with AGP [22]. Therefore, it is reasonable to assume that the risk of severe fluid accumulation increases when the imatinib concentration is elevated.…”