Practical Recommendations for Therapeutic Drug Monitoring of Kinase Inhibitors in Oncology

Verheijen, Remy B.; Yu, Huixin; Schellens, Jan H.M.; Beijnen, Jos H.; Steeghs, Neeltje; Huitema, Alwin D. R.

doi:10.1002/cpt.787

Cited by 235 publications

(321 citation statements)

References 80 publications

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“…Additionally, TDM has been shown to improve safety and efficacy of many targeted oral anticancer drugs. 13,14 A relationship between plasma exposure and treatment outcome has been retrospectively established for imatinib in patients with GIST, 10 supporting the rationale for the use of TDM in GIST patients treated with imatinib. While the effect of TDM to redistribute patients to adequate plasma concentrations has been proven, it is currently unknown what the financial consequences of TDM is compared with fixed dosing.…”

Section: Introductionmentioning

confidence: 90%

Optimizing the dose in patients treated with imatinib as first line treatment for gastrointestinal stromal tumours: A cost‐effectiveness study

Zuidema

Desar

Erp

et al. 2019

Brit J Clinical Pharma

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Aims Patients with metastatic gastrointestinal stromal tumours (GIST) are treated in first line with the oral tyrosine kinase inhibitor, imatinib, until progressive disease. With this fixed dosing regimen, only approximately 40% of patients reach adequate plasma levels within the therapeutic index. Therapeutic drug monitoring (TDM) is a solution to reach plasma levels within the therapeutic index. However, introducing TDM will also increase costs, due to prolonged imatinib use and laboratory costs. The aim of this study was to evaluate the cost‐effectiveness of TDM in patients with metastatic/unresectable GIST treated with imatinib as a first line treatment, compared with fixed dosing. Methods A survival model was created to simulate progression, mortality and treatment costs over a 5‐year time horizon, comparing fixed dosing vs TDM‐guided dosing. The outcomes measured were treatments costs, life‐years and quality‐adjusted life‐years. Results Total costs over the 5‐year time horizon were estimated to be €106 994.85 and €150 477.08 for fixed dosing vs TDM‐guided dosing, respectively. A quality‐adjusted life year gain of 0.74 (95% confidence interval 0.66–0.90) was estimated with TDM‐guided dosing compared to fixed dosing. An average incremental cost‐effectiveness ratio of €58 785.70 per quality‐adjusted life year gained was found, mainly caused by longer use and higher dosages of imatinib. Conclusion Based on the currently available data, this analysis suggests that TDM‐guided dosing may be a cost‐effective intervention for patients with metastatic/unresectable GIST treated with imatinib which will be improved when imatinib losses its patency.

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Section: Introductionmentioning

confidence: 90%

Optimizing the dose in patients treated with imatinib as first line treatment for gastrointestinal stromal tumours: A cost‐effectiveness study

Zuidema

Desar

Erp

et al. 2019

Brit J Clinical Pharma

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“…A pooled population PK analysis of the steady‐state exposure‐response relationship for axitinib in a cohort of 168 renal cell carcinoma patients demonstrated that an axitinib AUC SS of >300 ng/mL/hr was associated with superior progression‐free survival of 13.8 vs 7.4 months (hazard ratio, 0.558 [95%CI, 0.379‐0.823]) and overall survival of 37.4 vs 15.8 months (hazard ratio, 0.489 [95%CI, 0.324‐0.738]) ( P < .001) . This result has not translated into a robust “threshold” C trough for axitinib to facilitate standard therapeutic drug monitoring–guided precision dosing for this drug . Similarly, the relationship between axitinib exposure and tolerability remains unclear.…”

Section: Discussionmentioning

confidence: 99%

Use of Physiologically Based Pharmacokinetic Modeling to Identify Physiological and Molecular Characteristics Driving Variability in Axitinib Exposure: A Fresh Approach to Precision Dosing in Oncology

Sorich

Mutlib

Dyk

et al. 2019

The Journal of Clinical Pharma

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Axitinib is a second‐generation small‐molecule vascular endothelial growth factor receptor inhibitor. An axitinib steady‐state area under the plasma concentration–time curve (AUCSS) >300 ng/mL/hr is associated with superior progression‐free and overall survival. This study sought to characterize the physiological and molecular characteristics driving variability in axitinib AUCSS using physiologically based pharmacokinetic modeling to identify exposure biomarkers for this drug. The capacity to predict subjects likely to fail to achieve an axitinib AUCSS >300 ng/mL/hr was evaluated as a secondary outcome. A full physiologically based pharmacokinetic model incorporating mechanistic absorption was developed and verified for axitinib in accordance with the US Food and Drug Administration Guidance using Simcyp (Version 17.1). This model was used to simulate axitinib exposure over 7 days with twice‐daily dosing (5 mg) in a cohort of 1000 virtual cancer patients. Multiple linear regression modeling was used to identify patient characteristics associated with differences in axitinib exposure. A multivariable linear regression model incorporating hepatic cytochrome P450 (CYP) 3A4 abundance, albumin concentration, hepatic CYP1A2 abundance, hepatic CYP2C19 abundance, and intestinal CYP2C19 abundance provided robust prediction of axitinib AUCSS (R2 = 0.890; P < .001). By accounting for these variables, it was possible to identify subjects who would fail to achieve an effective axitinib AUCSS with a specificity of 88.7% and a sensitivity of 92.6%. Variability in axitinib AUCSS is primarily driven by differences in hepatic CYP3A4 abundance and albumin concentration. Consideration of these 2 characteristic is likely to be sufficient to individualize axitinib dosing.

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“…The TDM of anticancer drugs is becoming an important tool in treatment of patients with cancer, especially with increased use of oral anticancer drugs with highly variable bioavailability (B. Gao et al, ; Herbrink et al, ; Lankheet et al, ; Paci et al, ; Widmer et al, ; Yu et al, ). TDM has been shown to be a valuable intervention to optimize dosing of some anticancer drugs (Groenland et al, ; Paci et al, ; Verheijen et al, ; Widmer et al, ; Yu et al, ); however, prospective research is needed to further confirm these TDM targets. With the growing class of oral anticancer therapies, there is an increasing demand for TDM for which new assays have to be developed and validated.…”

Section: Future Perspectivesmentioning

confidence: 99%

Bioanalytical LC–MS/MS validation of therapeutic drug monitoring assays in oncology

Nuland

Rosing

Schellens

et al. 2019

Biomedical Chromatography

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Therapeutic drug monitoring (TDM) has shown to benefit patients treated with drugs of many drug classes, among which is oncology. With an increasing demand for drug monitoring, new assays have to be developed and validated. Guidelines for bioanalytical validation issued by the European Medicines Agency and US Food and Drug Administration are applicable for clinical trials and toxicokinetic studies and demand fully validated bioanalytical methods to yield reliable results. However, for TDM assays a limited validation approach is suggested based on the intended use of these methods. This review presents an overview of publications that describe method validation of assays specifically designed for TDM. In addition to evaluating current practice, we provide recommendations that could serve as a guide for future validations of TDM assays.

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Practical Recommendations for Therapeutic Drug Monitoring of Kinase Inhibitors in Oncology

Cited by 235 publications

References 80 publications

Optimizing the dose in patients treated with imatinib as first line treatment for gastrointestinal stromal tumours: A cost‐effectiveness study

Optimizing the dose in patients treated with imatinib as first line treatment for gastrointestinal stromal tumours: A cost‐effectiveness study

Use of Physiologically Based Pharmacokinetic Modeling to Identify Physiological and Molecular Characteristics Driving Variability in Axitinib Exposure: A Fresh Approach to Precision Dosing in Oncology

Bioanalytical LC–MS/MS validation of therapeutic drug monitoring assays in oncology

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