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Published on: November 8, 2015
Population pharmacokinetic model and limited sampling strategy for clozapine using plasma and dried blood spot
Lisanne M Geers1, Dan Cohen2, Laura M Wehkamp3
1Department of Clinical Pharmacy and Pharmacology, University Medical Center Groningen, University of Groningen, Groningen, The Netherlands.
Background:
To improve efficacy, therapeutic drug monitoring is often used in clozapine therapy. Trough level monitoring is regular, but trough levels provide limited information about the pharmacokinetics of clozapine and exposure in time. The area under the concentration time curve (AUC) is generally valued as better marker of drug exposure in time but calculating AUC needs multiple sampling. An alternative approach is a limited sampling scheme in combination with a population pharmacokinetic model meant for Bayesian forecasting. Furthermore, multiple venepunctions can be a burden for the patient, whereas collecting samples by means of dried blood spot (DBS) sampling can facilitate AUC-monitoring, making it more patient friendly.
Objective:
Development of a population pharmacokinetic model and limited sampling strategy for estimating AUC0-12h (a twice-daily dosage regimen) and AUC0-24h (a once-daily dosage regimen) of clozapine, using a combination of results from venepunctions and DBS sampling.
Method:
From 15 schizophrenia patients, plasma and DBS samples were obtained before administration and 2, 4, 6, and 8 h after clozapine intake. MwPharm® pharmacokinetic software was used to parameterize a population pharmacokinetic model and calculate limited sampling schemes.
Results:
A three-point sampling strategy with samples at 2, 6, and 8 h after clozapine intake gave the best estimation of the clozapine AUC0-12h and at 4, 10, and 11 h for the AUC0-24h. For clinical practice, however, a two-point sampling strategy with sampling points at 2 and 6 h was sufficient to estimate AUC0-12h and at 4 and 11 h for AUC0-24h.
Conclusion:
A pharmacokinetic model with a two-time point limited sampling strategy meant for Bayesian forecasting using DBS sampling gives a better prediction of the clozapine exposure in time, expressed as AUC, compared to trough level monitoring. This limited sampling strategy might therefore provide a more accurate prediction of effectiveness and occurrence of side effects compared to trough level monitoring. The use of DBS samples also makes the collection of clozapine samples easier and wider applicable.
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