POPULATION PHARMACOKINETICS OF CYCLOSPORINE IN CLINICAL RENAL TRANSPLANT PATIENTS Ke-Hua Wu, Yi-Min Cui, Jin-Feng Guo, Ying Zhou, Suo-Di Zhai, Fu-De Cui, and Wei Lu Department of Pharmaceutics, School of Pharmaceutical Sciences, Peking University, Beijing, China (K.-H.W., W.L.); Shenyang Pharmaceutical University, Shenyang, China (K.-H.W., F.-D.C.); Department of Pharmacy, Peking University First Hospital, Beijing, China (Y.-M.C., J.-F.G., Y.Z.); and Peking University Third Hospital, Beijing, China (S.-D.Z.) Received March 1, 2005; accepted May 24, 2005 ABSTRACT: Population pharmacokinetics of cyclosporine (CsA) in clinical renal transplant patients has been reported in the present study. A total of 2548 retrospective drug monitoring data points were collected from 120 renal transplant patients receiving CsA. Population mod- eling was performed using the NONMEM (nonlinear mixed-effect modeling) program, using a one-compartment model with first- order absorption and elimination. The final regression model for CsA clearance (CL/F) with the influence of six significant covari- ates, comprising postoperative days (POD), total bilirubin level (TBIL, micromolar concentration), current body weight (CBW, kilo- grams), age (years), concurrent metabolic inhibitors of cyclospor- ine (INHI), and hematocrit (HCT, percentage), has been established and expressed as CL/F  28.5  1.24  POD  0.252  (TBIL  11)  0.188  (CBW  58) 0.191  (Age  42)  2.45  INHI  0.212  (HCT  28) (liters per hour). The values in parentheses represent the me- dian level for each of the corresponding covariates. The population estimates for CL/F (28.5 l/h), V/F (volume of distribution, 133 l), and interpatient variability (CV%  19.7%) for CL/F were achieved, respectively. The population model was further validated by inter- nal and external approaches, and was demonstrated to be effec- tive and stable. Moreover, simulation was conducted to facilitate the individualized treatment based on patient information and the final model. Cyclosporine (CsA) has been introduced into organ transplantation since the early 1980s, and has been shown to largely reduce the rate and severity of graft-versus-host disease and to increase success in graft and survival of patients (Hesselink et al., 2004). Today, CsA has become the backbone of immunosuppression in clinical organ trans- plantation (Kyriakides and Miller, 2004). As a result, short-term and medium-term kidney allograft survivals have been greatly increased. However, CsA application has exhibited a high degree of interindi- vidual and intraindividual variability in pharmacokinetic and/or phar- macodynamic aspects. Furthermore, the therapeutic window (range of drug concentration for desired therapeutic effect) with acceptable tolerability is very limited (Armstrong and Oellerich, 2001; Aben- droth, 2004). Levels below the window are associated with a high risk of organ rejection, whereas levels above the window correlate with side effects, such as nephrotoxicity, infection, hepatotoxicity, and tumor (Kasiske et al., 1988). Many clinical pharmacokinetic studies of CsA have been conducted using ordinary pharmacokinetic methods, which were focused on individual parameter estimates, with multiple blood-sampling points (Banner et al., 2002; Trompeter et al., 2003). However, the pharma- cokinetic properties of CsA changed greatly between patients and between investigations. It has been difficult to predict its disposition in a specific individual, although pharmacokinetics and pharmacody- namics of CsA have been well reported in the literature. In contrast to the traditional pharmacokinetic approach, population pharmacokinet- ics has great advantages in estimation of the population parameters and analysis of factors (i.e., influence of demographic parameters and physiological conditions on the pharmacokinetic parameters). The population method is robust to predict a drug’s behavior based on specific individual information and, moreover, is ideal in analyzing the sparse data commonly obtained in the clinic (Sheiner et al., 1977). In the present study, the medical histories of 120 patients receiving renal transplant were retrospectively analyzed, and a population phar- macokinetics study of CsA in the patients was performed using NONMEM (nonlinear mixed-effect modeling). Consequently, the pharmacokinetic model was defined, using routing drug monitoring data, and could be used to improve the clinical application of CsA. Materials and Methods Patients and Data Collection. Plasma concentration data of CsA from 120 patients receiving renal transplantation in the past 4 years in Peking University First Hospital, Beijing, China, were collected. The patients were divided into two groups: 99 in the index group for the construction of the model and 21 in the validation group for external validation. All the patients were treated with Article, publication date, and citation information can be found at http://dmd.aspetjournals.org. doi:10.1124/dmd.105.004358. ABBREVIATIONS: CsA, cyclosporine; NONMEM, nonlinear mixed-effect modeling; POD, postoperative days; TBIL, total bilirubin level; CBW, current body weight; BMI, body mass index; HCT, hematocrit; INHI, concurrent metabolic inhibitors of cyclosporine; ALT, alanine aminotrans- ferase; ALP, alkaline phosphatase; OFV, objective function value; CL/F, oral clearance; V/F, apparent volume of distribution; CI, confidence interval; ME, mean predicted error; MSE, mean squared prediction error; RMSE, root mean squared prediction error; SPE, standardized prediction error; RSE, relative standard error; OBS, observed; PRED, population model-predicted; IPRED, individual model-predicted; WRES, weighted residual; COVR, covariate. 0090-9556/05/3309-1268–1275$20.00 DRUG METABOLISM AND DISPOSITION Vol. 33, No. 9 Copyright © 2005 by The American Society for Pharmacology and Experimental Therapeutics 4358/3045524 DMD 33:1268–1275, 2005 Printed in U.S.A. 1268 at ASPET Journals on September 2, 2016 dmd.aspetjournals.org Downloaded from