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Yoon, Yi, Rhee, Lee, Kim, Yu, and Chung: Development of a physiologically-based pharmacokinetic model for cyclosporine in Asian children with renal impairment
Original Article

Translational and Clinical Pharmacology 2019; 27(3): 107-114.

Published online: 30 September 2019

DOI: https://doi.org/10.12793/tcp.2019.27.3.107

Development of a physiologically-based pharmacokinetic model for cyclosporine in Asian children with renal impairment

Sumin Yoon1, Sojeong Yi1, Su-jin Rhee1, Hyun A Lee1, Yun Kim1, Kyung-Sang Yu1, Jae-Yong Chung2

1Department of Clinical Pharmacology and Therapeutics, Seoul National University College of Medicine and Hospital, Seoul 03080, Republic of Korea.

2Department of Clinical Pharmacology and Therapeutics, Seoul National University College of Medicine and Bundang Hospital, Seongnam 13620, Republic of Korea.

Correspondence: JY Chung; Tel: +82-31-787-3955, Fax: +82-31-787-4901, jychung@snubh.org

Received 25 July 2019       Revised 30 August 2019       Accepted 3 September 2019

Copyright © 2019 Translational and Clinical Pharmacology

Translational and Clinical Pharmacology

(open-access, http://creativecommons.org/licenses/by-nc/3.0/):

It is identical to the Creative Commons Attribution Non-Commercial License (http://creativecommons.org/licenses/by-nc/3.0/).

Abstract

This study aimed to assess the pharmacokinetics of cyclosporine A (CsA) in Asian children with renal impairment (RI) by developing a physiologically-based pharmacokinetic (PBPK) model with Simcyp Simulator. The PBPK model of Asian children with RI was developed by modifying the physiological parameters of the built-in population libraries in Simcyp Simulator. The ratio of healthy and RI populations was obtained for each parameter showing a difference between the populations. Each ratio was multiplied by the corresponding parameter in healthy Asian children. The model verification was performed with published data of Korean children with kidney disease given multiple CsA administrations. Simulations were performed with different combinations of ethnicity, age, and renal function to identify the net impact of each factor. The simulated results suggested that the effect of RI was higher in children than adults for both Caucasian and Asian. In conclusion, the constructed model adequately characterized CsA pharmacokinetics in Korean children with RI. Simulations with populations categorized by ethnicity, age, and renal function enabled to assess the net impact of each factor on specific populations.

Keywords: Cyclosporine, Ethnicity, PBPK, Pediatrics, Renal impairment

Introduction

Cyclosporine A (CsA), a calcineurin inhibitor, is a commonly prescribed immunosuppressant for glomerular disease in children.[12] Even though its primary target organ is kidney, CsA has nephrotoxicity, which requires caution when using the drug. According to the ‘Kidney Disease: Improving Global Outcomes (KDIGO) Clinical Practice Guideline for Glomerulonephritis’, CsA is not suggested for patients with an estimated glomerular filtration rate (GFR) below 30 mL/min/1.73 m2, which is classified as severe renal impairment.[3] For patients with moderate renal impairment (GFR ranging 30–60 mL/min/1.73 m2), CsA is used following a rigorous evaluation of the risks and benefits. Due to its dose-related nephrotoxicity, maintaining the therapeutic concentration range is essential.[4] However, its narrow therapeutic range and high inter-individual and intra-individual variability in pharmacokinetics (PK) make it challenging to maintain the therapeutic concentration range which, in turn, may lead to poor clinical outcomes.[45]
In addition, there are general sources of variability in PK of CsA, which adds complications to providing appropriate therapy. CsA is primarily metabolized by cytochrome P-450 3A (CYP3A) and is a substrate of multidrug resistance efflux transporter, P-glycoprotein (P-gp). Previous studies have shown that polymorphisms in genes encoding CYP3A and P-gp may affect PK of drugs.[6] Ethnicity, linked to genetic polymorphisms of CYP3A and P-gp, has been reported to influence the PK profile of CsA.[78] The concomitant disease could affect the PK of the drug. Although only 0.1% of CsA is eliminated via the kidney,[9] not only hepatic impairment but also renal impairment affects the metabolism of the liver and eventually result in PK alterations. Accumulated uremic toxins in renally impaired patients are associated with alteration of the transporters and metabolic enzymes by genetic modification.[1011] Age is another known source of PK variability of CsA.[12] Despite its high utility in children, there are limited sources describing the pediatric PK of CsA.[131415] In accordance with labeling information, the pediatric dosage for transplantation or renal disease is adjusted by one's body weight.[16] However, CsA dose based on body weight alone does not efficiently achieve concentration targets in children.[17] Inter-individual variability from ethnicity, disease, and age all cause PK changes for the drug, which eventually may lead to a failed therapeutic response.[18]
Considering multiple factors are linked to PK variability, the whole-body physiologically-based pharmacokinetic (PBPK) model could be a useful tool for predicting PK of CsA. The whole-body PBPK model describes the PK of a drug by integrating known physiological information with biochemical processes and interactions.[19] Simcyp Simulator is the PBPK modeling and simulation platform that enables mechanistic and quantitative prediction of the impact of concomitant diseases, age, and even ethnicity. However, there are no details available yet on the specific ethnicity of children with renal impairment in the software, which limits PK prediction within the relevant population. This study primarily aimed to predict PK of CsA in Asian (Korean and Japanese) children with renal impairment by developing a PBPK model for the corresponding population. In addition, the net effect of ethnicity, age, and renal function on PK of CsA was assessed with the developed PBPK models.

Methods

Initial PBPK model development for specific populations

To assess the PK of CsA of Asian children with renal impairment, the PBPK models of Asian children with moderate and severe renal impairment were developed. The severity of renal impairment was based on GFR. A GFR ranging from 30 to 60 mL/min/1.73 m2 was considered ‘moderate renal impairment’, and a GFR less than 30 mL/min/1.73 m2 was considered ‘severe renal impairment’.
The whole-body PBPK model was developed using the Simcyp Simulator (Version 14 Release 1, Simcyp Ltd, a Certara Company, Sheffield, UK). Drug-specific parameters – physicochemical, blood binding, absorption, distribution, elimination, interaction, and transport – were taken from the compound file of the Simcyp Simulator. Table 1 presents the compound-related parameters integrated into this study. The physiological parameters were based on predetermined parameters of the Simcyp built-in population libraries. The built-in population libraries used in this study were as follows: ‘Sim-Healthy Volunteers’ for healthy Caucasians, ‘Sim-RenalGFR30-60’ and ‘Sim-RenalGFR less than 30’, which were based on North European Caucasian data, for Caucasians with moderate and severe renal impairment, respectively, and ‘Sim-Japanese’ for healthy Asians. For each population, a pediatric module is provided in the software. Changes in physiological parameters with age, such as ontogeny of drug-metabolizing enzymes, are applied to the simulations with a pediatric module.
To determine the physiological parameters that need to be modified, parameter values of healthy Caucasian adults were compared with those of Caucasian adults with renal impairment. The ratio of the healthy Caucasian adult population and the renal impairment population was calculated for each parameter that was different between the populations (renal impairment/healthy). Each ratio was then multiplied by the corresponding parameter for healthy Asian children.

Model verification

The constructed model was verified by observed clinical data from previously published literature.[20] Clinical data were comprised of PK data from 34 Korean children of age ranging from 2.3 to 17 (mean±standard deviation: 8.7±4.0 years) with nephrotic syndrome or glomerular diseases (male-female ratio of 2.2:1). Baseline mean creatinine clearance was 97.5±22.3 mL/min/1.73m2. Either capsules or the syrup formulation of CsA microemulsion (Cipol-N, Chong Keun Dang, Seoul, Republic of Korea) was given at 5 mg/kg/day, twice daily. The steady-state PK parameters were calculated with plasma CsA concentrations of 3–5 days after the first dose.
The same age and dosing regimen with observed data were applied to the simulation for verification. The simulated PK profile of 0–12 h of day 5 (from 96 h to 108 h after the first dose) was observed. In addition, PK parameters, including maximum plasma concentration at steady-state (Cmax,ss), area under the curve over 12 h after the last dosing (AUCτ,ss) and time at which Cmax,ss was observed (Tmax), were assessed.
The predictive performance using PBPK models was determined by visual predictive check. The mean and its 5–95% confidence intervals of simulated plasma concentration-time profiles were plotted with observed data for a visual predictive check.

Comparison of PK of CsA by ethnicity, age, and renal function

To identify the net impact of ethnicity, age, and renal impairment on PK of CsA, simulations were performed with the following subpopulations:

  • i. Healthy Caucasian adults and Caucasian adults with moderate and severe renal impairment.

  • ii. Healthy Caucasian children and Caucasian children with moderate and severe renal impairment.

  • iii. Healthy Asian adults and Asian adults with moderate and severe renal impairment.

  • iv. Healthy Asian children and Asian children with moderate and severe renal impairment.

The built-in population libraries of the Simcyp Simulator were utilized for simulations of Caucasian adults (i) and children (ii). Simulations of Asian children with renal impairment (iv) were conducted with the newly developed PBPK model of this study. Using the PBPK model development method for Asian children with renal impairment, PBPK models for Asian adults with renal impairment were developed. Specifically, the ratio of the healthy Caucasian adult population and renal impairment population for parameters showing discrepancies between the populations was calculated (renal impairment/healthy). Then, each ratio was multiplied by the corresponding parameter of the healthy Asian adult population. In the built-in Simcyp population libraries, the body weight and height of the population with impaired renal function were lower than that of the healthy population. To minimize confounding factors, body weight and height (baseline and coefficient of variation) were adjusted to the same as the healthy population.
A single simulation was composed of 10 trials of 10 subjects per trial. Age ranges for adults and children were 20–50 years and 0–15 years, respectively. All virtual individuals generated from the simulation were set as receiving 5 mg/kg/day capsule formulation, twice daily. PK parameters including Cmax,ss, AUCτ,ss, Tmax and overall plasma concentration-time profile were compared.

Results

Initial PBPK model development

PBPK models integrating ethnicity, age and renal impairment were developed. Several parameter values of kidney, liver and gastro-intestinal and tissue composition parts showed discrepancies between healthy and renally impaired populations in the Simcyp Simulator.
The abundance of cytochrome P450 enzymes in the liver was lower in the renal impairment populations than the healthy population. The CYP3A4 extensive metabolizers decreased 0.524-fold and 0.437-fold in moderate and severe renal impairment populations, respectively. CYP3A4-CYP3A5 correlation showed moderate differences between healthy and renal impairment populations. Parameters related to serum creatinine increased while kidney size decreased in renal impairment populations. The baseline serum creatinine increased approximately 2-fold and 4-fold, whereas baseline kidney size lowered 0.545-fold and 0.370-fold in moderate and severe renal impairment populations, respectively. Hematocrit and serum albumin values were slightly decreased in renal impairment populations. Among gastrointestinal parameters, the mean colon transit time was prolonged in the renal impairment populations. The altered physiological parameters in the renal impairment population compared to the healthy population is summarized in Table 2.

Model verification and optimization

Simulations of healthy Asian children and Asian children with renal impairment were performed with the built-in and newly developed PBPK model, respectively. Simulations with renally impaired PBPK models presented approximately 1 hour of lag time in the absorption phase. To optimize the absorption profiles, the lag time before absorption of the built-in compound file was modified. Based on the published literature, 1 h of lag time was replaced by 0.576 h.[212223]

Simulations with final PBPK models

The final simulation results with modified lag times are shown in Figure 1. Asian children with moderate renal impairment reflected the observed data better than severe renal impairment population by means of visual predictive check. PK parameters from simulation and observed data were also compared. The Tmax values were 1.10 h, 1.20 h, 1.25 h for healthy, moderate RI and severe RI Asian pediatric populations, respectively. The Cmax,ss and AUCτ,ss tended to increase as the severity of disease increased (Fig. 2). Simulations with PBPK models of renal impairment population presented higher Cmax,ss and AUCτ,ss values than the observed data.

Ethnicity, age, and renal function

Simulations were conducted for combinations of different ethnicities and age groups (Table 3, Fig. 3). Asians had lower Cmax,ss and higher AUCτ,ss when compared with Caucasians. In the healthy and moderate renal impairment populations, Cmax,ss and AUCτ,ss values of children were lower than those of adults. However, in severe renal impairment populations, Cmax,ss and AUCτ,ss were higher in children than those of adults. In addition, the degrees of PK parameter change between the healthy and renally impaired groups were higher in children than those of adults.

Discussion

The primary objective of this study was to identify PK of CsA in Asian children with renal impairment by means of a PBPK model. There was no corresponding built-in population in Simcyp Simulator to perform simulations. Thus, a PBPK model for Asian children with impaired renal function was developed. Final models were developed with the following processes: initial model development, verification and optimization. The lag time before absorption was modified in the optimization process. When clinical data obtained from the published literature were plotted with the simulated results, it was well-fitted within the simulation results of healthy and moderate renal impairment populations.
In addition, the coefficients of variation (CV) of observed and simulated Cmax,ss and AUCτ,ss were compared to assess the predictive performance of variability. The CV of Cmax,ss for the PBPK model was 52–53% which were slightly higher than the CV obtained from observed data (45%). However, the CV for AUCτ,ss was 92–93% which was highly overestimated when compared to the observed data (39%). The overestimation of variability in exposure could be a limitation of this PBPK model.
Furthermore, simulations with the PBPK models were conducted to assess the net influence of ethnicity and age with the disease. Slightly lower Cmax,ss and higher AUCτ,ss in Asian children compared to Caucasian children were observed. Polymorphisms of the CYP3A isozyme are known to be related to the PK of the drug. Little inter-ethnic variability of the CYP3A polymorphism between Asians and Caucasians may explain the simulation results.[24] The age effect on the PK of CsA differed between healthy and renal impairment populations. In populations with severe renal impairment, the Cmax,ss and AUCτ,ss were both higher for children than adults, whereas healthy and moderate renally impaired children demonstrated lower AUCτ,ss than healthy adults. It could be interpreted that the effect of severe renal impairment on CsA exposure is higher in children. Further clinical investigation is required to prove this hypothesis.
To our knowledge, there are limited publications or guidance on designing PBPK simulations, including the number of subjects and trials with PBPK simulations. In the validation procedure, we used the same number of subjects with observed data. For simulations examining the contribution of ethnicity, age, and disease to PK variability, the number of subjects and trials was determined considering the real-world clinical trial design. A total of 100 virtual subjects were created for each simulation as follows: 10 trials with 10 subjects per trial.
There were several limitations in this study. First, the ‘Asian’ population in this study needs to be more specific because Asian covers a broad spectrum of populations. However, there has been no available data for pediatric PBPK population model except Japanese. We defined our model as Asian because it was developed using two ethnic groups Japanese and Korean. Therefore, our population model could be more appropriate to the East Asian population. Second, the observed data used for validation did not contain individual GFR values. Visual inspection was consistent with that observed data were obtained from children with mean GFRs higher than 90 mL/min/1.73 m2. Clinical data with individual GFR values may enable more quantitative verification of the PBPK model. In addition, the relevance of physiological parameters modified for the newly developed PBPK model needs to be qualified by previous studies. For instance, albumin and total plasma protein levels increase from birth to 3 years. It causes alteration of unbound concentration of CsA (plasma binding>95%) and may affect the drug effect.[2526] The change of plasma drug binding during maturation reported in recent literature was not reflected in the PBPK models in this study. For a more convincing model, however, considering quantitative physiological information is essential. Third, all of the simulation concentrations were plasma concentrations that are default in the simulator. However, the observed clinical data[20] were whole blood concentrations in the verification process. Considering that blood concentrations of cyclosporine are about 50–60% higher than their plasma values and the GFR of the children population was within normal range, the verification confirmed the healthy population model. Attention should be paid in the interpretation of Figure 1. Lastly, characterizing physiological differences by the ratio of parameters may not reflect the whole precise mechanism of disease progression. Moreover, the Simcyp Simulator uses the same transporter related parameters in both healthy and renal impairment population, which might be unrealistic. Explaining the nonlinear relationship of physiologic conditions only by ratio could be insufficient, especially for the extremes of the population. However, there is no qualified method of parameter adjustment for a specific population in PBPK modeling so far. In this state, multiplying the ratio can be the most straightforward and not much biased approach we can derive. This work has a particular significance in trying this parameter adjustment strategy for the first time and comparing the simulation results with clinical data.
Furthermore, the typical PK profiles obtained from the simulation could be utilized as representative of the specific population. Accordingly, simulation results could support designing early clinical trials, such as a selection of dose and subject number. The PK profile for populations lacking in the built-in library of the Simcyp Simulator could be predicted by applying a parameter adjustment strategy used in this study. This strategy enabled identification of the net effect of several coexisting physiologic conditions by integrating into a single model. Ethical and practical issues, which are considerable in conducting clinical trials, are emphasized in particular patient populations, such as pediatric patients. Modeling and simulation enable the assessment of the mean parameter estimates of the specific populations; thus, it could be highly utilized for populations with difficulties in conducting real-world clinical trials.
In summary, the developed PBPK model of Asian children with renal impairment adequately characterized the PK profiles of CsA. Simulations with populations categorized by ethnicity, age, and renal function enabled the assessment of PK differences between each population. This model may be a useful tool to predict the PK of CsA and support dose adjustment or other relevant decision-making in clinical settings.

Acknowledgments

This research was supported by a grant of the Korea Health Technology R&D Project through the Korea Health Industry Development Institute (KHIDI), funded by the Ministry of Health & Welfare, Republic of Korea [Grant: HI14C2770]. None of the authors have any conflicts of interest to disclose. Sojeong Yi is currently employed by the U.S. Food and Drug Administration. Her contribution to the manuscript was based on their prior employment, and the current manuscript does not reflect any position of the U.S. Food and Drug Administration or the U.S. government. Portions of this work were previously present at the Population Approach Group in Korea Annual Meeting, Daejeon, Republic of Korea, February 2016.

Notes

Reviewer: This article was reviewed by peer experts who are not TCP editors.

Conflict of interest: - Authors: Nothing to declare

- Reviewers: Nothing to declare

- Editors: Nothing to declare

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Figure 1

Overlay of observed (circles) blood concentration and simulated (lines) steady-state plasma concentration-time profile of cyclosporine A. Dotted lines indicate a healthy population. Dashed lines indicate a population with moderate renal impairment. Solid lines indicate a population with severe renal impairment. Black lines indicate the overall mean for the virtual populations. The gray lines indicate the 5–95% confidence intervals.

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Figure 2

Comparison of simulated (A) Cmax,ss and (B) AUCτ,ss of CsA in three populations: healthy Asian children, Asian children with moderate renal impairment and Asian children with severe renal impairment.

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Figure 3

Comparison of PBPK model simulated steady-state plasma concentration-time profiles of cyclosporine A in (A) Caucasian adults and (B) children and (C) Asian adults and (D) children. Dotted lines indicate a healthy population. Dashed lines indicate a population with moderate renal impairment. Solid lines indicate a population with severe renal impairment.

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Table 1

Physicochemical properties and pharmacokinetic parameters of cyclosporine A used for the development of the PBPK model

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Parameter Input value
Physicochemical properties
 Molecular weight (g/mol) 1202.000
 log P 2.960
 Compound type Small molecule
 Blood-to-plasma partition ratio 1.620
 Hematocrit 45.000
 Fraction unbound in plasma 0.036
Absorption
 Absorption model First order
 Absorption rate constant (h-1) 1.659
 Lag time (h)* 0.576
 Caco-2 cell permeation (10-6 cm/s) 17.000
Distribution
 Distribution model Full PBPK model
 Vss (L/kg) Predicted**
 Kp scalar 1.000
Elimination
 Clearance type Enzyme kinetics
 Renal clearance (L/h) 0.029
 Active uptake into hepatocyte 1.534
 Hepatic clearance (µL/min/106) 0.690

All parameters except lag time are predetermined values of the Simcyp Simulator.

*Built-in substrate model of CsA (1 h) was optimized based on the literature.

**Rodgers and Rowland prediction method was used.[27]

Abbreviations: P, octanol-water partition coefficient; PBPK, physiologically-based pharmacokinetic; Vss, volume of distribution at steady-state; Kp, partition coefficient.

Table 2

Summary of altered physiological parameters in the renal impairment population compared to the healthy population

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Parameters Healthy Asian children* Ratio from Caucasian adult population Renal Impairment Asian children
Moderate* / Healthy* Severe*/ Healthy* Moderate Severe
Liver
 Metabolic enzyme abundance (mean, pmol/mg)
  CYP1A2 EM 44.000 0.546 0.527 24.031 23.185
  CYP2A6 EM 35.000 0.545 0.470 19.075 16.450
  CYP2B6 EM 3.000 0.547 0.471 1.641 1.412
  CYP2B6 PM 1.000 0.550 0.467 0.550 0.467
  CYP2C8 EM 14.000 0.546 0.471 7.642 6.592
  CYP2C9 EM 73.000 0.547 0.473 39.900 34.500
  CYP2C9 PM 29.000 0.548 0.472 15.900 13.700
  CYP2C18 EM 1.000 0.680 0.570 0.680 0.570
  CYP2C19 EM 1.000 0.543 0.429 0.543 0.429
  CYP2D6 EM 5.000 0.575 0.450 2.875 2.250
  CYP2D6 UM 9.000 0.575 0.450 5.175 4.050
  CYP2E1 EM 44.000 0.611 0.423 26.905 18.610
  CYP2J2 EM 2.000 0.725 0.558 1.450 1.117
  CYP3A4 EM** 122.000 0.524 0.437 63.939 53.342
  CYP3A4-CYP3A5 Correlation (%)** 49.980 0.724 0.555 36.202 27.755
Tissue composition
 Blood composition**
  Hematocrit Mean (male) (%) 43.000 0.923 0.772 39.700 33.200
  Hematocrit Mean (female) (%) 38.000 0.961 0.824 36.500 31.300
  Serum Albumin (male) (g/L) 50.340 0.936 0.856 47.100 43.080
  Serum Albumin (female) (g/L) 49.380 0.909 0.765 44.900 37.800
  Serum Albumin C1 (female) -0.037 1.000 1.554 -0.037 -0.058
Kidney
 Serum creatinine (µmol/L)**
  Male Baseline 76.500 1.987 3.922 152.000 300.000
  Male Baseline CV (%) 16.100 0.497 0.497 8.000 8.000
  Male Age Cut-off Baseline 81.200 1.761 3.695 143.000 300.000
  Male Age Cut-Off CV1 (%) 27.400 0.292 0.292 8.000 8.000
  Male Age Cut-Off CV2 (%) 21.200 0.377 0.377 8.000 8.000
  Female Baseline 57.000 2.667 5.263 152.000 300.000
  Female Baseline CV (%) 20.400 0.441 0.490 9.000 10.000
  Female Age Cut-Off 1 48.000 1.250 1.000 60.000 48.000
  Female Age Cut-Off Baseline 2 66.200 2.236 4.532 148.000 300.000
  Female Age Cut-off 1 CV 1 (%) 26.500 0.340 0.377 9.000 10.000
  Female Age Cut-off 1 CV 2 (%) 22.800 0.395 0.439 9.000 10.000
  Female Age Cut-Off 2 78.000 0.962 1.000 75.000 78.000
  Female Age Cut-Off Baseline 3 79.500 1.811 3.774 144.000 300.000
  Female Age Cut-Off 2 CV1 (%) 38.300 0.235 0.261 9.000 10.000
  Female Age Cut-Off 2 CV2 (%) 31.600 0.285 0.316 9.000 10.000
 Kidney Size
  Baseline 15.400 0.545 0.370 8.400 5.700
  BW coefficient 2.040 0.804 0.510 1.640 1.000
  BH coefficient 51.800 0.633 0.575 32.800 29.800
Gastrointestinal Tract
 Mean colon transit time (h) 13.8 1.250 1.250 17.250 17.250

*Parameter values are from built-in model of Simcyp Simulator.

**Physiological parameters used for developing the renally impaired pediatric model in this study.

Abbreviations: CV, coefficient of variations; EM, extensive metabolizers; PM, poor metabolizers; UM, ultra-rapid metabolizers; BW, Baseline width; BH, Baseline height.

Table 3

PBPK model-simulated steady-state pharmacokinetic parameters of cyclosporine A for subpopulations administered 5 mg/kg/day twice daily cyclosporine A

tcp-27-107-i003
Adult (20–50 years) Children (0–15 years)
Cmax,ss (ng/mL) AUCτ,ss (ng/mL·h) Cmax,ss (ng/mL) AUCτ,ss (ng/mL·h)
Caucasian
 Healthy 769.13 ± 374.18 2752.31 ± 1950.09 749.25 ± 320.66 2294.06 ± 1463.19
 Moderate RI* 1011.84 ± 494.15 (1.32) 4842.14 ± 3536.44 (1.76) 1016.87 ± 423.46 (1.36) 4382.19 ± 2851.01 (1.91)
 Severe RI* 1009.34 ± 503.19 (1.31) 5000.82 ± 3731.04 (1.82) 1113.65 ± 460.79 (1.49) 5285.23 ± 3414.46 (2.30)
Asian
 Healthy 716.02 ± 392.47 2776.89 ± 2398.42 736.85 ± 344.71 2333.15 ± 1868.38
 Moderate RI* 945.05 ± 534.32 (1.32) 4874.2 ± 4361.26 (1.76) 1001.64 ± 471.57 (1.36) 4398.79 ± 3656.58 (1.89)
 Severe RI* 950.49 ± 545.97 (1.33) 5106.51 ± 4588.51 (1.84) 1103.5 ± 520.23 (1.50) 5324.4 ± 4376.57 (2.28)

*Data are presented as mean ± standard deviation (ratio of RI population to heathy population RI/healthy).

Abbreviations: RI, renal impairment; Cmax,ss, the maximum plasma concentration at steady-state; AUCτ,ss, area under the curve over 12 h after the last dosing.

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