Development of Limited Sampling Strategies for the Estimation of Tacrolimus Area Under the Curve in Adult Kidney Transplant Recipients According to the Posttransplantation Time. Issue 4 (August 2015)

• Record Type: Journal Article
• Authors: Aouam, Karim; Chadli, Zohra; Hammouda, Mouna; Fredj, Nadia Ben; Aloui, Sabra; May, Mezri El; Boughattas, Naceur; Skhiri, Habib; Chaabane, Amel
• Is Part Of: Therapeutic drug monitoring. Volume 37:Issue 4(2015:Aug.)
• Publication Date: 2015-08
• Subjects: Pharmacokinetics -- Periodicals; Patient monitoring -- Periodicals; Drugs -- Analysis -- Periodicals; Body fluids -- Analysis -- Periodicals; Drug Therapy -- Periodicals; Monitoring, Physiologic -- Periodicals; Pharmacology -- Periodicals; 615.7
• DOI: 10.1097/FTD.0000000000000173
• Languages: English
• ISSNs: 0163-4356

Abstract

<abstract> <title> <x xml:space="preserve">Abstract</x> </title> <sec> <title>Background:</title> <p>Limited sampling strategies (LSS), using few sampling times after dosing, have been used to reliably predict tacrolimus area under the 12-hour concentration–time curve (AUC). Because the pharmacokinetics of tacrolimus is subject to significant changes over the exposure time to this drug, it can be hypothesized that the reliability of the LSS would also change. This study aimed to develop a reliable and practical LSS allowing the estimation of tacrolimus AUC in Tunisian kidney transplant recipients taking into account the posttransplantation time.</p> </sec> <sec> <title>Methods:</title> <p>Thirty Tunisian patients were enrolled into 3 groups (10 in each group) according to the posttransplantation period: period 1: between 1 day and 3 months, period 2: between 3 and 12 months and period 3 over 12 months, as defined by the European consensus conference on the therapeutic drug monitoring of tacrolimus. Samples were collected just before and 0.5, 1, 2, 4, 6, 8, and 12 hours after tacrolimus administration. The full pharmacokinetic profiles obtained from these timed concentration data were used to choose the best sampling times. Error indices (mean absolute prediction error and the root mean squared prediction error) were used to evaluate the predictive performance.</p> </sec> <sec> <title>Results:</title> <p>Among the 1-point estimations, the C<sub>4</sub>-predicted AUC showed the highest correlation with the measured one during period 1 and period 2 (r<sup>2</sup> = 0.94 and 0.91, respectively) but not period 3 (r<sup>2</sup> = 0.76). The C<sub>0</sub>-predicted and the measured AUC become less and less correlated from period 1 to period 3 (r<sup>2</sup> = 0.81, 0.75, and 0.66), respectively. Only the model including the C<sub>0</sub>/C<sub>2</sub> provided a high correlation between predicted and measured tacrolimus AUC regardless of the posttransplant period (r<sup>2</sup> = 0.95, 0.96, 0.98 and root mean squared prediction error = 4.1, 5.8, 4.2 during periods 1, 2, and 3, respectively).</p> </sec> <sec> <title>Conclusions:</title> <p>Our data clearly indicate that the predictive performance of LSS is prone to change according to the posttransplantation time. A 2–time point LSS was found to be sufficient to predict tacrolimus AUC. The LSS using C<sub>0</sub> and C<sub>2</sub> is reliable, accurate, and practical to estimate the AUC of tacrolimus regardless of the posttransplantation time.</p> </sec> </abstract>