4.6 Article

Feasibility of Extrapolating Randomly Taken Plasma Samples to Trough Levels for Therapeutic Drug Monitoring Purposes of Small Molecule Kinase Inhibitors

Journal

PHARMACEUTICALS
Volume 14, Issue 2, Pages -

Publisher

MDPI
DOI: 10.3390/ph14020119

Keywords

small molecule kinase inhibitor; trough level; C-min; therapeutic drug monitoring; implementation

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Therapeutic drug monitoring (TDM) is important in oncology for small molecule kinase inhibitors (SMKIs) to ensure optimal drug exposure, and extrapolating randomly taken drug concentrations to trough concentrations using elimination half-life is a feasible method for multiple SMKIs. Timing of blood withdrawals and specific extrapolation methods may vary for different SMKIs.
Small molecule kinase inhibitors (SMKIs) are widely used in oncology. Therapeutic drug monitoring (TDM) for SMKIs could reduce underexposure or overexposure. However, logistical issues such as timing of blood withdrawals hamper its implementation into clinical practice. Extrapolating a random concentration to a trough concentration using the elimination half-life could be a simple and easy way to overcome this problem. In our study plasma concentrations observed during 24 h blood sampling were used for extrapolation to trough levels. The objective was to demonstrate that extrapolation of randomly taken blood samples will lead to equivalent estimated trough samples compared to measured C-min values. In total 2241 blood samples were analyzed. The estimated C-trough levels of afatinib and sunitinib fulfilled the equivalence criteria if the samples were drawn after T-max. The calculated C-trough levels of erlotinib, imatinib and sorafenib met the equivalence criteria if they were taken, respectively, 12 h, 3 h and 10 h after drug intake. For regorafenib extrapolation was not feasible. In conclusion, extrapolation of randomly taken drug concentrations to a trough concentration using the mean elimination half-life is feasible for multiple SMKIs. Therefore, this simple method could positively contribute to the implementation of TDM in oncology.

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