Limited sampling strategies for predicting area under the concentration-time curve of mycophenolic acid in islet transplant recipients

Abstract

Background: Mycophenolate mofetil is widely used in islet transplant recipients and its active metabolite, mycophenolic acid (MPA), exhibits wide pharmacokinetic variability. However, to our knowledge, no limited sampling strategy (LSS) exists for monitoring MPA in this subpopulation.

Objective: To define optimal LSSs for MPA monitoring and to test their predictive performance in islet transplant recipients.

Methods: After written informed consent was obtained and upon administration of a steady-state morning mycophenolate mofetil dose, blood samples were collected at 0, 0.3, 0.6, 1, 1.5, 2, 3, 4, 6, 8, 10, and 12 hours from 16 stable islet transplant recipients. MPA concentrations were measured by a validated high-performance liquid chromatography method with ultraviolet detection and pharmacokinetic parameters analyzed by noncompartmental modeling. All 16 patients' profiles were used to develop the LSSs via multiple regression analysis. Potential LSSs were restricted to ones having R(2) 0.90 or greater and 3 or fewer time points within the first 4 hours postdose. Resulting equations were validated for their predictive performance using the jackknife method, with acceptable criteria for bias and precision preset to within +/-15%. In addition, 14 published LSSs (in the renal transplant population) were tested in our islet transplant patients.

Results: Five LSSs met preset criteria and had conventional sampling times: AUC = 1.783 + 1.248C1 + 0.888C2 + 8.027C4 (R2 = 0.98, bias = -3.09%, precision = 9.53%) AUC = 2.778 + 1.413C1 + 0.963C3 + 7.511C4 (R2 = 0.97, bias = -3.22%, precision = 11.02%) AUC = 1.448 + 1.239C1 + 0.271C1.5 + 9.108 C4 (R2 = 0.96, bias = -1.90%, precision = 11.46) AUC = 1.410 - 0.259C0 + 1.443C1 + 9.622C4 (R2 = 0.96, bias = -2.68%, precision = 11.53%) AUC = 1.547 + 1.417C1 + 9.448C4 (R2 = 0.96, bias = -2.46%, precision = 11.14%) where AUC = area under the concentration-time curve. None of the other published LSSs in the renal transplant population met the preset criteria for bias and precision.

Conclusions: To our knowledge, these are the first precise and accurate LSSs for predicting MPA AUC developed specifically for islet transplant recipients. The LSS that we recommend is the one utilizing 2 concentrations: AUC = 1.547 + 1.417C1 + 9.448C4. This equation is convenient and clinically feasible. Other islet transplant centers may wish to validate our equation in their population or use our template as a guide to develop accurate and precise LSSs specific to their patient population.