Evaluating the utility of therapeutic drug monitoring in the clinical use of small molecule kinase inhibitors: a review of the literature

Fahmy, Alia; Hopkins, Ashley M.; Sorich, Michael J.; Rowland, Andrew

doi:10.1080/17425255.2021.1943357

Cited by 11 publications

(8 citation statements)

References 136 publications

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“…IM is an anti-cancer drug administered primarily to outpatients because blood samples are not always available at the end of the administration interval. Thus, IM C min is the most widely used pharmacokinetic proxy for predicting clinical outcomes [ 7 , 37 ], and C min is naturally used as a focus for TDM [ 38 ]. TDM for IM may reassure patients and physicians about full exposure to the drug and improve long-term adherence to this chronic treatment, which may be a promising approach for fine-tuning the IM dosage for better tolerability and optimal clinical outcomes in patients with GISTs [ 7 , 37 ].…”

Section: Discussionmentioning

confidence: 99%

“…Thus, IM C min is the most widely used pharmacokinetic proxy for predicting clinical outcomes [ 7 , 37 ], and C min is naturally used as a focus for TDM [ 38 ]. TDM for IM may reassure patients and physicians about full exposure to the drug and improve long-term adherence to this chronic treatment, which may be a promising approach for fine-tuning the IM dosage for better tolerability and optimal clinical outcomes in patients with GISTs [ 7 , 37 ]. It is widely known that high IM C min increases the risk of adverse effects and toxicity, which can reduce medication adherence rates and quality of life.…”

Section: Discussionmentioning

confidence: 99%

“…Imatinib (IM), a tyrosine kinase inhibitor (TKI), blocks KIT receptor activity and has become the conventional first-line therapy for patients with advanced GISTs [ 4 ], which inhibits proliferation and promotes apoptosis of GIST cells [ 4 – 6 ]. Therefore, the IM plasma trough concentration (C min ) is intimately linked to the effectiveness of treatment [ 7 ].…”

Section: Introductionmentioning

confidence: 99%

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Advanced gastrointestinal stromal tumor: reliable classification of imatinib plasma trough concentration via machine learning

Ran,

Tan,

et al. 2024

BMC Cancer

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Aim Patients with advanced gastrointestinal stromal tumors (GISTs) exhibiting an imatinib plasma trough concentration (IM Cmin) under 1100 ng/ml may show a reduced drug response rate, leading to the suggestion of monitoring for IM Cmin. Consequently, the objective of this research was to create a customized IM Cmin classification model for patients with advanced GISTs from China. Methods Initial data and laboratory indicators from patients with advanced GISTs were gathered, and the above information was segmented into a training set, validation set, and testing set in a 6:2:2 ratio. Key variables associated with IM Cmin were identified to construct the classification model using the least absolute shrinkage and selection operator (LASSO) regression and forward stepwise binary logistic regression. Within the training and validation sets, nine ML classification models were constructed via the resampling method and underwent comparison through the Brier scores, the areas under the receiver-operating characteristic curve (AUROC), the decision curve, and the precision-recall (AUPR) curve to determine the most suitable model for this dataset. Two methods of internal validation were used to assess the most suitable model's classification performance: tenfold cross-validation and random split-sample validation (test set), and the value of the test set AUROC was used to evaluate the model's classification performance. Results Six key variables (gender, daily IM dose, metastatic site, red blood cell count, platelet count, and percentage of neutrophils) were ultimately selected to construct the classification model. In the validation set, it is found by comparison that the Extreme Gradient Boosting (XGBoost) model has the largest AUROC, the lowest Brier score, the largest area under the decision curve, and the largest AUPR value. Furthermore, as evaluated via internal verification, it also performed well in the test set (AUROC = 0.725). Conclusion For patients with advanced GISTs who receive IM, initial data and laboratory indicators could be used to accurately estimate whether the IM Cmin is below 1100 ng/ml. The XGBoost model may stand a chance to assist clinicians in directing the administration of IM.

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Section: Discussionmentioning

confidence: 99%

Section: Discussionmentioning

confidence: 99%

Section: Introductionmentioning

confidence: 99%

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Advanced gastrointestinal stromal tumor: reliable classification of imatinib plasma trough concentration via machine learning

Ran,

Tan,

et al. 2024

BMC Cancer

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“…Development of PK/PD models can be successfully deployed to inform TDM; however, the infrequent dosing schedule of epirubicin and relatively short terminal half-life (18-45 h) limit practicality in this setting [9,10]. Additionally, while TDM is appropriate to guide on-treatment dose adjustments for antineoplastic drugs [11,12], it does not support optimal initial (cycle 1) dose selection.…”

Section: Introductionmentioning

confidence: 99%

A Physiologically Based Pharmacokinetic Model to Predict Determinants of Variability in Epirubicin Exposure and Tissue Distribution

2023

Self Cite

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Background: Epirubicin is an anthracycline antineoplastic drug that is primarily used in combination therapies for the treatment of breast, gastric, lung and ovarian cancers and lymphomas. Epirubicin is administered intravenously (IV) over 3 to 5 min once every 21 days with dosing based on body surface area (BSA; mg/m2). Despite accounting for BSA, marked inter-subject variability in circulating epirubicin plasma concentration has been reported. Methods: In vitro experiments were conducted to determine the kinetics of epirubicin glucuronidation by human liver microsomes in the presence and absence of validated UGT2B7 inhibitors. A full physiologically based pharmacokinetic model was built and validated using Simcyp® (version 19.1, Certara, Princeton, NJ, USA). The model was used to simulate epirubicin exposure in 2000 Sim-Cancer subjects over 158 h following a single intravenous dose of epirubicin. A multivariable linear regression model was built using simulated demographic and enzyme abundance data to determine the key drivers of variability in systemic epirubicin exposure. Results: Multivariable linear regression modelling demonstrated that variability in simulated systemic epirubicin exposure following intravenous injection was primarily driven by differences in hepatic and renal UGT2B7 expression, plasma albumin concentration, age, BSA, GFR, haematocrit and sex. By accounting for these factors, it was possible to explain 87% of the variability in epirubicin in a simulated cohort of 2000 oncology patients. Conclusions: The present study describes the development and evaluation of a full-body PBPK model to assess systemic and individual organ exposure to epirubicin. Variability in epirubicin exposure was primarily driven by hepatic and renal UGT2B7 expression, plasma albumin concentration, age, BSA, GFR, haematocrit and sex.

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“…We firmly advocate that both qualitative and quantitative aspects of cancer treatment must be considered as part of the precision oncology concept: it encompasses not only the selection of the best anti-cancer agents on the basis of tumor genetic biomarkers, but also the precise individualization of their dosage in each patient. Dosage individualization should be based on both the thorough adaptation to influential patient characteristics (a priori dosage adjustment) and the measurement of plasma circulating drug concentrations (a posteriori adjustment, i.e., therapeutic drug monitoring; TDM) [ 9 , 10 , 11 ]. These quantitative issues are the focus of pharmacometrics, a discipline that has undergone a remarkable evolution over the last few decades, but of which oncology and cancer patients have yet to take full advantage of.…”

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confidence: 99%

Population Pharmacokinetics in Oncology and Its Clinical Applications

Widmer,

Guidi,

Buclin

2024

Pharmaceutics

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Evaluating the utility of therapeutic drug monitoring in the clinical use of small molecule kinase inhibitors: a review of the literature

Cited by 11 publications

References 136 publications

Advanced gastrointestinal stromal tumor: reliable classification of imatinib plasma trough concentration via machine learning

Advanced gastrointestinal stromal tumor: reliable classification of imatinib plasma trough concentration via machine learning

A Physiologically Based Pharmacokinetic Model to Predict Determinants of Variability in Epirubicin Exposure and Tissue Distribution

Population Pharmacokinetics in Oncology and Its Clinical Applications

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