BACKGROUND: Correlation analyses have demonstrated that maintaining an adequate imatinib (IM) trough concentration would be important for clinical response in patients with chronic myeloid leukemia (CML) and Kit-positive gastrointestinal stromal tumors. The objectives of the current work were to use a pharmacokinetic model to refine the trough levels obtained at different sampling times and to propose a therapeutic drug monitoring algorithm and an acceptable sampling time window for imatinib trough sampling. METHODS: The pharmacokinetics of IM in patients (pts) with CML were characterized based on historical data from a Phase III study. In the elimination phase the concentration of IM (C(t)) follows a mono-exponential decline, and the standardized trough concentration (C(min,std) = C(tau)) can be described by a simple algorithm C(min,std) = C(t)* exp(k(e) x Delta t), where Delta t = t - tau, and tau is 24 hours for qd or 12 hours for bid dosing and k(e) is the elimination rate constant. The percent deviation of C(t) from C(min,std) was simulated for different Delta t and k(e) values to define a sampling time window Delta t, within which the percent deviation is <20%. RESULTS: Simulation analysis shows that C(t) is largely dependent on Delta t and k(e). The percent deviation of C(t) at 3 hours before or after tau from C(min,std) will be 7.1%, 13.1%, and 23.4% for pts with low, typical, and high k(e) values, 0.023/hour, 0.041/hour, and 0.070/hour, respectively. However, if a correction is made for C(t) by the algorithm using the typical k(e) value of 0.041 per hour, the percent deviation at 3 hours will be reduced to 5.3%, 0%, and 9.1% for pts with low, typical, and high k(e) values, respectively. Even if the sampling window is extended to +/-6 hours, the corresponding percent deviation will still be reasonable: 10.2%, 0%, and 19.0%, respectively. CONCLUSION: By using the algorithm, the pharmacokinetic sampling window can be extended to a wider window to make the trough sampling easy to implement in the clinical setting, provided that the sampling time and dosing time are accurately recorded.
BACKGROUND: Correlation analyses have demonstrated that maintaining an adequate imatinib (IM) trough concentration would be important for clinical response in patients with chronic myeloid leukemia (CML) and Kit-positive gastrointestinal stromal tumors. The objectives of the current work were to use a pharmacokinetic model to refine the trough levels obtained at different sampling times and to propose a therapeutic drug monitoring algorithm and an acceptable sampling time window for imatinib trough sampling. METHODS: The pharmacokinetics of IM in patients (pts) with CML were characterized based on historical data from a Phase III study. In the elimination phase the concentration of IM (C(t)) follows a mono-exponential decline, and the standardized trough concentration (C(min,std) = C(tau)) can be described by a simple algorithm C(min,std) = C(t)* exp(k(e) x Delta t), where Delta t = t - tau, and tau is 24 hours for qd or 12 hours for bid dosing and k(e) is the elimination rate constant. The percent deviation of C(t) from C(min,std) was simulated for different Delta t and k(e) values to define a sampling time window Delta t, within which the percent deviation is <20%. RESULTS: Simulation analysis shows that C(t) is largely dependent on Delta t and k(e). The percent deviation of C(t) at 3 hours before or after tau from C(min,std) will be 7.1%, 13.1%, and 23.4% for pts with low, typical, and high k(e) values, 0.023/hour, 0.041/hour, and 0.070/hour, respectively. However, if a correction is made for C(t) by the algorithm using the typical k(e) value of 0.041 per hour, the percent deviation at 3 hours will be reduced to 5.3%, 0%, and 9.1% for pts with low, typical, and high k(e) values, respectively. Even if the sampling window is extended to +/-6 hours, the corresponding percent deviation will still be reasonable: 10.2%, 0%, and 19.0%, respectively. CONCLUSION: By using the algorithm, the pharmacokinetic sampling window can be extended to a wider window to make the trough sampling easy to implement in the clinical setting, provided that the sampling time and dosing time are accurately recorded.
Authors: Stefanie L Groenland; Ron H J Mathijssen; Jos H Beijnen; Alwin D R Huitema; Neeltje Steeghs Journal: Eur J Clin Pharmacol Date: 2019-06-07 Impact factor: 2.953
Authors: Marie-Rose B S Crombag; Jacobine G C van Doremalen; Julie M Janssen; Hilde Rosing; Jan H M Schellens; Jos H Beijnen; Neeltje Steeghs; Alwin D R Huitema Journal: Br J Clin Pharmacol Date: 2018-09-26 Impact factor: 4.335
Authors: Nienke A G Lankheet; Ingrid M E Desar; Sasja F Mulder; David M Burger; Dinemarie M Kweekel; Carla M L van Herpen; Winette T A van der Graaf; Nielka P van Erp Journal: Br J Clin Pharmacol Date: 2017-07-04 Impact factor: 4.335