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약학석사 학위논문
Population Pharmacokinetics of Tacrolimus
in Glomerulonephritis Patients
사구체신염 환자에서 tacrolimus의
집단약동학 연구
2014년 2월
서울대학교 대학원
약학과 예방임상약학 전공
심 미 경
Population Pharmacokinetics of Tacrolimus
in Glomerulonephritis Patients
사구체신염 환자에서 tacrolimus의
집단약동학 연구
지도교수 오 정 미
이 논문을 약학석사 학위논문으로 제출함
2013년 11월
서울대학교 대학원
약학과 예방임상약학 전공
심 미 경
심미경의 석사학위논문을 인준함
2013 년 12 월
위 원 장 신 완 균 (인)
부 위 원 장 김 은 경 (인)
위 원 오 정 미 (인)
i
Abstract
Population Pharmacokinetics of Tacrolimus
in Glomerulonephritis Patients
Mi Kyong Shim
College of Pharmacy
The Graduate School
Seoul National University
Background: Tacrolimus as an immunosuppressant is used for certain
glomerulonephritis (GN) as pharmacotherapy of glomerulonephritis often
targets immune system. Narrow therapeutic window and huge
interindividual variability of tacrolimus make it impossible to predict
tacrolimus serum level. Hence, the objective of this study is to derive
population pharmacokinetic model of tacrolimus in GN patients and to
identify covariates that may be associated in tacrolimus PK.
Design and methods: Adult patients, diagnosed with glomerulonephritis
and treated with orally administered tacrolimus at Seoul National University
Hospital outpatient clinics, Seoul, South Korea between Jan 01, 2000 and
Oct 30, 2013, were included. Electronic medical records were reviewed
retrospectively to access clinical records, demographic data, and
medication profile that patients were taking. Population pharmacokinetics
of tacrolimus was evaluated using non-linear mixed effect model by first-
order conditional estimation with interaction algorithm.
ii
Results: One hundred and forty five patients were included in this study
and the number of tacrolimus blood samples was 1938. Correlations
between covariates were explored and found that urine protein creatinine
ratio showed moderate association with elevated cholesterol level and low
total protein level. For population pharmacokinetic analysis, one
compartment model with linear absorption and first order elimination were
selected. Serum creatinine level was selected as a covariate explaining
variability in clearance of tacrolimus. Serum albumin (ALB) and serum
creatinine levels (SCR) were found to be most significantly influencing
apparent volume (V/F). The derived final model was as follows;
TypicalValue θ .
TypicalValue θ . .
Conclusions: Various factors affecting tacrolimus pharmacokinetics were
explored. Findings of this study indicated that pharmacokinetics of
tacrolimus was associated with serum creatinine and serum albumin level.
Developed model may guide individualized tacrolimus therapy in GN
patients and improve pharmacotherapy in these populations.
Keywords: Population pharmacokinetics, Tacrolimus, Glomerulonephritis
Student number: 2012-21596
iii
Table of Contents
1. Introduction ..................................................................................................................... - 1 -
1.1. Glomerulonephritis .......................................................................................... - 1 -
1.2. Treatment issues in glomerulonephritis .................................................. - 3 -
1.3. Pharmacokinetic alteration in GN .............................................................. - 4 -
1.4. Pharmacokinetics of tacrolimus ................................................................. - 5 -
1.5. Drug interaction of tacrolimus ..................................................................... - 6 -
1.6 Rationale of proposed study and specific aims ..................................... - 7 -
2. Methods ........................................................................................................................... - 9 -
2.1. Study Design & Patients ............................................................................... - 9 -
2.2. Data Collection and Variables .................................................................... - 9 -
2.3. Population pharmacokinetic Analysis .................................................... - 10 -
2.3.1. Structural model ................................................................................ - 10 -
2.3.2. Covariate Model Building ............................................................... - 12 -
2.3.3. Model evaluation ............................................................................... - 13 -
2.4. Statistical Analysis ........................................................................................ - 13 -
3. Results ........................................................................................................................... - 15 -
3.1. Patient Characteristics ................................................................................ - 15 -
3.2 Correlation between covariates ................................................................. - 15 -
iv
3.3 Basic model development ........................................................................... - 16 -
3.4 Drug interaction ............................................................................................... - 17 -
3.5. Final model ...................................................................................................... - 18 -
3.6. Population prediction over time ................................................................ - 18 -
3.7. Validation .......................................................................................................... - 19 -
4. Discussion .................................................................................................................... - 20 -
5. Conclusion .................................................................................................................... - 26 -
6. References ................................................................................................................... - 27 -
7. Tables ............................................................................................................................. - 31 -
8. Figures ........................................................................................................................... - 46 -
초록 ...................................................................................................................................... - 69 -
v
List of Tables
TABLE 1. PATHOLOGIC FINDINGS IN GN 6 ........................................................................ - 31 -
TABLE 2. CLINICAL PRESENTATION OF GN 6 ..................................................................... - 32 -
TABLE 3. BASELINE CHARACTERISTIC OF PATIENTS (N=145) .......................................... - 33 -
TABLE 4. ETIOLOGY OF GLOMERULONEPHRITIS IN SELECTED PATIENTS .......................... - 34 -
TABLE 5. CORRELATIONS BETWEEN VARIABLES(1) ............................................................ - 35 -
TABLE 6. CORRELATIONS BETWEEN VARIABLES (2) .......................................................... - 36 -
TABLE 7. CORRELATIONS BETWEEN VARIABLES (3) .......................................................... - 37 -
TABLE 8. CORRELATIONS BETWEEN VARIABLES (4) .......................................................... - 38 -
TABLE 9. SUMMARY OF MODEL DEVELOPMENT PROCESS ............................................. - 39 -
TABLE 10. FINAL POPULATION PHARMACOKINETIC PARAMETERS OF TACROLIMUS AND
BOOTSTRAP VALIDATION ............................................................................................. - 45 -
vi
List of Figures
FIGURE 1. SCATTERPLOT MATRIX OF CLINICAL DATA (1) ................................................. - 46 -
FIGURE 2. SCATTERPLOT MATRIX OF CLINICAL DATA (2) ................................................. - 47 -
FIGURE 3. SCATTERPLOT MATRIX IN CLINICAL DATA (3) .................................................. - 48 -
FIGURE 4. BASE MODEL - POPULATION AND INDIVIDUAL PREDICTION VS. OBSERVATION ... -
49 -
FIGURE 5. FINAL MODEL - POPULATION AND INDIVIDUAL PREDICTIONS VS. OBSERVATION
(1) ................................................................................................................................. - 50 -
FIGURE 6. OBSERVATIONS/INDIVIDUAL PREDICTION/ POPULATION PREDICTIONS VS
OBSERVATION ............................................................................................................... - 50 -
FIGURE 7. FINAL MODEL – OBSERVATION VS. POPULATION PREDICTION .................... - 51 -
FIGURE 8. FINAL MODEL – OBSERVATION VS. INDIVIDUAL PREDICTION ...................... - 51 -
FIGURE 9. INDIVIDUAL OBSERVATION VS. PREDICTION .................................................... - 52 -
FIGURE 10. INDIVIDUAL PLOTS (1) .................................................................................... - 53 -
FIGURE 11. INDIVIDUAL PLOTS (2) .................................................................................... - 54 -
FIGURE 12. INDIVIDUAL PLOTS (3) .................................................................................... - 55 -
FIGURE 13. INDIVIDUAL PLOTS (4) .................................................................................... - 56 -
FIGURE 14. INDIVIDUAL PLOTS (5) .................................................................................... - 57 -
FIGURE 15. INDIVIDUAL PLOTS (6) .................................................................................... - 58 -
FIGURE 16. INDIVIDUAL PLOTS (7) .................................................................................... - 59 -
FIGURE 17. WEIGHTED RESIDUALS VS. POPULATION PREDICTIONS (1) ........................ - 60 -
FIGURE 18. POPULATION PREDICTION VS. WEIGHTED RESIDUALS.................................. - 61 -
FIGURE 19. CONDITIONAL WEIGHTED RESIDUALS VS. POPULATION PREDICTION ........ - 62 -
FIGURE 20. CONDITIONAL WEIGHTED RESIDUALS VS. COVARIATES ............................... - 63 -
FIGURE 21. OBSERVED CONCENTRATION OVER TIME ...................................................... - 64 -
FIGURE 22. POPULATION PREDICTIONS VS. OBSERVATION OVER TIME (1) .................. - 65 -
FIGURE 23. POPULATION PREDICTIONS VS. OBSERVATION OVER TIME (2) .................. - 66 -
FIGURE 24. POPULATION PREDICTIONS VS. OBSERVATION OVER TIME (3) .................. - 67 -
FIGURE 25. VISUAL PREDICTION CHECK ........................................................................... - 68 -
- 1 -
1. Introduction
1.1. Glomerulonephritis
Glomerular nephritis(GN) accounts for 14.5% of the 44,333 patients
with end-stage renal disease (ESRD) cases in the South Korea, after
diabetic nephropathy and hypertensive nephropathy according to the report
published by Korean Society of Nephrology in 2005.1 As the absolute
number of chronic kidney disease patients increases, the number of
patients with glomerulonephritis on dialysis is expected to rise. Readily
available regular checkups, mandatory health screenings and the progress
in diagnostic technology make it possible to detect early phase,
asymptomatic glomerulonephritis.2 Although the incidence of GN is not
quite high compared to major illnesses such as hypertension or diabetes,
the socio-economic burden is huge in its contribution to renal failure
requiring dialysis. For example, medical expenditure for GN increases
because patients with glomerulonephritis tend to have earlier onset of
dialysis (52.7±14.4 years) compared to patients with diabetic nephropathy
(61.3±11.7 years) and longer survival rate among other kidney disease
patients. The 10-year survival rate is higher in GN patients on dialysis
(66.4%) than in dialysis patients with diabetic nephropathy (29.5%) in South
Korea.2
By definition, glomerulonephritis is a group of illness damaging
glomeruli, a cluster of small vessels, which act as a first step of filtering
blood to produce urine.3 Many glomerulonephritis are characterized by the
inflammation in glomeruli or surrounding small blood vessels in the kidney
but not all the glomerulonephritis has inflammation component. They are
thought immune mediated kidney diseases caused by primary or secondary
reason.4 In primary GN, the etiology is not known but it is believed immune
disorder. Secondary GN includes several autoimmune, infection,
- 2 -
malignancy, metabolic disease related disorders. Urinary findings including
hematuria, red cell casts, lipiduria, and proteinuria brought medical
attentions. However to establish the correct diagnosis, it often requires
kidney biopsy. Pathologic classification of GN based on biopsy is listed in
(Table 1).5
Most of glomerulonephritis present with hematuria, proteinuria, and
possibly reduced renal function in acute phase. GN is classified into several
categories based on clinical presentations and it is managed accordingly.
These clinical presentations include asymptomatic urinary abnormalities,
nephritic syndrome, rapidly progressive glomerulonephritis (RPGN),
nephrotic syndrome, and chronic glomerulonephritis. Those are listed in
(Table 2).4 RPGN, nephrotic syndrome, and nephritic syndrome usually
need urgent workup for diagnosis to guide the management of illness.
Chronic GN has indication for renal biopsy as well. Asymptomatic urinary
abnormalities may need observation over time.4 In South Korea, the most
common diagnosis of primary GN between age 15 to 60 is IgA
nephropathy (46.1%), followed by FSGS (6.4%), minimal change disease
(MCD) (5.3%), membranous nephropathy (MN) (6.4%), membranous
proliferative glomerulonephritis (MPGN) (3.5%), and crescentic GN
(0.8%).2
Although pathophysiology of GN is not completely understood, it is
thought there are two major phases that glomerular damage develops. First,
immune reaction occurs in glomeruli and various mediators of tissue injury
are triggered.6 In the second phase, glomerular cells response to these
mediators such as complements, coagulation factors, growth factors, and
cytokines. It will result in chronic phase of glomerulonephritis by
overproduction of oxidants and extracellular matrix, phenotype changes
which leads to glomerular sclerosis and eventually cause impaired renal
- 3 -
function.6
1.2. Treatment issues in glomerulonephritis
Treatment of glomerulonephritis usually depends on specific diagnosis
of GN and severity of illness. According to clinical practice guideline for
glomerulonephritis published by KDIGO (Kidney Disease Improving Global
Outcomes), tacrolimus has indications for certain glomerulonephritis such
as minimal change disease (MCD), focal segmental glomerulosclerosis
(FSGS), idiopathic membranous nephropathy (IMN) and Lupus nephritis.7
However, tacrolimus is only approved for the refractory lupus nephritis
among other glomerulonephritis in South Korea and it is insured by
government owned prescription drug coverage.8 Unlike cyclosporin,
another calcineurin inhibitor, which is approved for steroid dependent or
steroid resistant nephrotic syndromes, the use of tacrolimus in GN other
than lupus nephritis is not covered by insurance. It is because cyclosporin
was discovered early and is more experienced than tacrolimus for the
treatment of GN. The use of tacrolimus is expected to rise as its use has
been increased in renal transplantation patients due to less nephrotoxicity
and different side effect profile compared to cyclosporin.9,10
Specifically, tacrolimus of 0.05-0.1mg/kg/day in divided doses for 1-2
years is recommended for frequently relapsing and steroid dependent MCD
patients as an alternative therapy for patients who failed cyclophosphamide
therapy or cannot use cyclophosphamide. For idiopathic focal segmental
glomerulosclerosis in adults, calcineurin inhibitors are the first line therapy
for patients who cannot tolerated to high-dose corticosteroids or
corticosteroid is contraindicated. In patients with MN, calcineurin inhibitors
are suggested as a second-line therapy when the use of first line therapy
- 4 -
such as corticosteroid/alkylating agents is not appropriate.11 It should be
used for at least six months and the dose needs to be reduced every 4-8
weeks to the final of 50%of initial dose. Tacrolimus treatment should be
maintained for at least twelve months. Class II Lupus nephritis (LN) with
proteinuria can be treated with corticosteroids or CNIs and, for Class III LN
or Class IV LN, calcineurin inhibitors are used for maintenance therapy
when a person cannot take MMF and azathioprine. Class V LN with
persistent nephrotic proteinuria is recommended to treat with corticosteroid
with cyclophosphamide, calcineurin inhibitors, MMF, or azathioprine.12
Although tacrolimus can be used for FSGS, cyclosporine is favored over
tacrolimus in that it is used longer period of time and thus have more
experience with cyclosporin in FSGS.13
Serum levels of calcineurin inhibitors should be monitored regularly to
avoid toxicity during initial therapy periods, and when serum creatinine level
increases more than 20%.14 According to Korea Food and Drug
Administration (KFDA), it is recommended that tacrolimus trough level is
checked every month during first three months after initiation of therapy,
and to be tested regularly thereafter.8
1.3. Pharmacokinetic alteration in GN
Pharmacokinetics of drugs in patients with chronic kidney disease
(CKD) will be different of the ones in other disease states. The absorption
of medications in CKD will be delayed due to decreased gastric emptying
time and prolonged elimination half-life of the drug.15,16 Distribution of the
medication will also be affected by renal dysfunction because of reduced
plasma binding caused by hypoalbuminemia. This is especially important in
glomerulonephritis patients since many of them have low albumin level due
- 5 -
to heavy proteinuria. Unbound free drug will be increased in these
populations and thus, it will change the clearance of the administered drug.
Anemia is another factor that leads to pharmacokinetic changes. Since
tacrolimus largely binds to erythrocytes, anemia of chronic kidney disease
can cause alteration in tacrolimus pharmacokinetics. For instance,
distribution of tacrolimus to red blood cell is at least four times more than
the one to plasma.17 Often edematous status will be caused by proteinuria
that might cause altered volume of distribution of the durg. There are some
studies that reported changes in pharmacokinetics in glomerulonephritis
patients.18,19 For example, Gugler R and his colleagues reported that
unbound fraction, plasma clearance, and volume of distribution of phenytoin
were increased in patients with nephrotic syndrome.18 Theoretically, renal
excretion of drugs would be varying by diagnosis of renal disease in that
glomeruli and tubules are differently influenced by the kidney disease.16
1.4. Pharmacokinetics of tacrolimus
Tacrolimus was first approved for the prophylaxis of organ rejection in
liver transplant in 1994. And then, its use has been extended to kidney and
heart transplantation. Tacrolimus is recently added to glomerulonephritis
treatment regimen.14 Consequently, most pharmacokinetics data regarding
tacrolimus were derived from transplant patients.
The absorption of oral tacrolimus is variable by individuals in that
bioavailability of tacrolimus is 17% ± 10% in renal transplant patients, 22%
± 6% in liver transplant patients, and 23% ± 10% in heart transplant patients.
When 0.3mg/kg/day of tacrolimus was given to both renal transplant patient
and liver transplant patient in one study, Cmax was 24.2±15.8 ng/ml and
68.5±30.3 ng/ml, respectively. AUC was 288±93ng·hr/mL in renal
- 6 -
transplantation and 519±179ng·hr/mL in liver transplantation.21 Co-
administration with food decreases the rate and extent of tacrolimus
absorption. The 99% of tacrolimus binds to protein and this protein bound
is not associated with tacrolimus concentration up to 50 mg/ml. Among
proteins, most of tacrolimus are bound to albumin and alpha-1-acid
glycoprotein. It is also extensively bound to erythrocytes and the distribution
of tacrolimus to erythrocytes in blood is four times stronger than distribution
to plasma.22 Volume of distribution of tacrolimus was 0.85 L/kg in adult, 1.07
L/Kg in renal dysfunction, and 3.1-3.9L/kg in patients with hepatic illness.
The 98%-99% of tacrolimus is metabolized in liver mainly by CYP3A4. It is
extensively eliminated in feces (92.4%-92.6%) via bile. Less than 1% of
tacrolimus is excreted through kidney as unchanged drug. The elimination
half-life of oral tacrolimus ranged from 8.7 to 11.3 hours but it is reported
18.2 hours in twelve bone marrow transplant patients.21
Population pharmacokinetics of tacrolimus was extensively studied in
early phase of kidney transplantation. Han NY, et al. demonstrated that
hematocrit level, CYP3A5 genotype, and post-operative days were all
influencing the clearance of tacrolimus and body weight affected on volume
of distribution in South Korean.23 Population pharmacokinetics of tacrolimus
in hematopoietic cell transplant (HCT) patients was also studied recently.24
The authors found that clearance of tacrolimus was significantly decreased
by raised total bilirubin, bilirubin, serum creatinine level, presence of grade
III/IV GVHD, and the presence of veno-occlusive disease in HCT population.
1.5. Drug interaction of tacrolimus
Tacrolimus is a substrate as well as weak inhibitor of CYP3A4 and P-
glycoprotein.25 Numerous medications are thought to have drug interactions
- 7 -
with tacrolimus and those are certain antacids, anticonvulsants,
antimycobacterials, antifungals, calcium-channel blocking agents,
potassium sparing diuretics, HIV protease inhibitors, macrolide antibiotics,
proton-pump inhibitors, nephrotoxic drugs, and sirolimus. Christians U, et
al. stated that drug interaction between tacrolimus and CYP3A/P-
glycoprotein inhibitors or inducers were more likely influenced by
interrupted oral bioavailability rather than clearance.26 The author also
reported that clinical drug interactions of tacrolimus were almost similar to
the ones of cyclosporin.27 However, majority of these studies were derived
from in-vitro analysis and thus, it may be poorly correlated with drug
interaction cases in real population. Gender and ethnic differences may also
contribute to tacrolimus drug interactions according to this article.
1.6 Rationale of proposed study and specific aims
In summary, there are not many tacrolimus pharmacokinetic studies
available in glomerulonephritis patients and no population pharmacokinetic
studies were performed so far. Due to huge inter-individual and intra-
individual variability, predicting therapeutic effect of tacrolimus based on
serum drug level may be difficult. Understating of pharmacokinetics of
tacrolimus is clinically important because it has narrow therapeutic window
and dose related toxicities. Finally, pharmacokinetic differences of
tacrolimus between transplantation and glomerulonephritis population are
expected. Therefore, population pharmacokinetic study of tacrolimus is
needed.
The objective of this thesis is to derive population pharmacokinetic
(PPK) model of tacrolimus in GN patients and to identify covariates that may
be associated in tacrolimus PK. The goal of this study is to understand
- 8 -
pharmacokinetics in glomerulonephritis patients and improve tacrolimus
treatment in these specific populations. The main hypothesis of this study
is that pharmacokinetics of tacrolimus in GN patients would be different than
the ones in other disease populations such as transplantation. Questions to
be explored in this study include i) How do laboratory results influence
population pharmacokinetics in GN?, ii) Dose demographic data affect
pharmacokinetics of tacrolimus in GN? iii) What are the effects of co-
administered medications in pharmacokinetics of tacrolimus? To answer
these questions, population pharmacokinetic analyses of tacrolimus using
serum trough drug concentration in GN were performed.
- 9 -
2. Methods
2.1. Study Design & Patients
This is a retrospective electronic medical record review study. All
patients, diagnosed with glomerulonephritis and treated with orally
administered tacrolimus as hard capsule formulations at Seoul National
University Hospital (SNUH) outpatient clinics, Seoul, South Korea between
Jan 01, 2000 and Oct 30, 2013, were included. Tacrolimus was given twice
daily in most of the patients and both brands, Prograf® (Astellas, Levallois-
Perret, France) and Tacrobell® (Chong Kun Dang Pharmaceutical Corp, Ltd.,
Seoul, Korea), were included. Patients under 18 years old as well as
patients who have no steady state tacrolimus trough level on record were
all excluded from this study.
2.2. Data Collection and Variables
Electronic medical records were reviewed retrospectively to access
clinical records, demographic data, and medication profile that patients
were taking.
Clinical data included Protein/Creatinine ratio (P/Cr), White Blood
Cell(WBC), Hemoglobin(Hb), Hematocrit(Hct), Platelet, INR, Sodium(Na),
Potassium(K), Calcium(Ca), phosphorous(P), magnesium(Mg), Glucose,
Blood Urea Nitrogen (BUN), Serum Creatinine (Scr), IDMS MDRD
Glomerular Filtration Rate (GFR), Cholesterol, Total Protein, Albumin, Uric
acid, Total bilirubin, Direct bilirubin, Alkaline phosphatase, Aspartate
Transaminase (AST), Alanine aminotransferase (ALT), Gamma-glutamyl
transferase (GGT), C-reactive protein (CRP), Alpha Feto Protein (AFP),
Cytomegalovirus (CMV) antigen, Body temperature (℃), Systolic Blood
Pressure (mmHg), Diastolic Blood Pressure (mmHg), Body Weight (kg),
- 10 -
tacrolimus, MPA, cyclosporin, Rapamune, SRL, FK506/CsA,
Mycophenolate, Prednisolone serum levels. Tacrolimus levels were
analyzed by liquid chromatography-mass spectrometry using Quattro
microTM API tandem mass spectrometer(Micromass, Manchester, UK)
after 2007. Until 2007, Microparticle enzyme immunoassay (IMX Tacrolimus
II assay, Abbott Lab, IL, USA) was utilized for the analysis of trough
tacrolimus level.
Age, gender and GN diagnosis were collected for demographic
analysis.
Concomitantly used medications were any known medications that will
increase or decrease serum tacrolimus level referenced by Micromedex®.
And then, collected medication profiles were categorized into two
categories for statistical analysis; 1= medication used, 2= not on medication
for each medications.
2.3. Population pharmacokinetic Analysis
Pharmacokinetics of tacrolimus were evaluated using non-linear mixed
effect model with double precision using NONMEM® software(NONMEM
Version 7.2.0, ICON Development Solutions, Ellicott City, MD). The first-
order conditional estimation algorithm with interaction (FOCE INTER),
considering interactions with the interindividual error and random error, was
used throughout the analysis. Detailed analytic methods are listed below;
2.3.1. Structural model
Two compartments model with or without lag time and one-
compartment linear model with first order absorption and elimination were
- 11 -
compared in this study.9,10,26 Typical value of the absorption rate constant
(Ka) could not be estimated from this analysis due to sparsity of data.
Therefore, Ka was fixed at 4.5 /h which was previously reported in liver
transplant patients.28 The bioavailability of tacrolimus (F) could not be
calculated in this analysis, tacrolimus clearance (CL) and volume of
distribution (V) was matched to CL/F and V/F, respectively. Analyses using
both log transformed concentration and base dataset were also compared.
Interindividual variability in structural model parameters was estimated
by an exponential error model (Equation 1).
Pj θ ∙ η (1)
Where P stands for pharmacokinetic parameter, Pj means the individual
value for P in the jth individual,θ is the population mean value of P (i.e. CL/F,
V/F), and ηj imply a random error; the difference between the typical value
and individual value. Residual variability (ith individual, jth concentration)
which is the difference between the individual observed , and the
respective individual model-predicted concentration ( , was
compared with additive, proportional, exponential error model (Equation 2-
4).
, , (2)
, , 1 (3)
, , (4)
Random effect parameter η and ε were all presumed to be randomly
distributed with a mean of 0 and variances of ω2 and σ2, respectively.
Different pharmacokinetic models were tested and the best structural model
- 12 -
was selected based on the objective function value (OFV) after visual check
of estimated parameters and physiologic plausibility of parameter estimates.
2.3.2. Covariate Model Building
Covariate models were created to evaluate the influence of patient
demographics (gender, body weight, age, etiology of glomerulonephritis),
clinical data (P/Cr, WBC, hemoglobin, hematocrit, platelet, BUN, serum
creatinine, cholesterol, total protein, albumin, uric acid, total bilirubin, AST,
ALT), concurrent use of medications that might interfere pharmacokinetics
of tacrolimus (i.e. prednisolone, omeprazole, nifedipine, rifampin,
fluconazole, rosuvastatin, omega-3-fatty acid). For continuous covariates
(age, body weight, P/Cr, WBC, hemoglobin, hematocrit, platelet, BUN,
serum creatinine, cholesterol, total protein, albumin, uric acid, total bilirubin,
AST, ALT), following covariate model was used to test the significance of
the covariate (Equation 5).
P= θ ∗ θ ∗ (5)
θ is the population mean value of pharmacokinetic parameter and
θ is the estimated effect for the covariate on parameter such as
CL/F or V/F.
For categorical variables (gender, etiology of glomerulonephritis,
concurrent medications), Equation 6 and 7 were used for covariate
modeling.
P = θ for reference covariate (6)
- 13 -
P = θ ∗ θ for exploratory covariate (7)
Where θ is the population mean value of pharmacokinetic
parameters such as CL/F and V/F, θcovariate is the assessed fractional
change in θ for the exploratory covariate.
Covariates were selected by forward addition and backward
elimination methods. Likelihood ratio tests to compare models were done
by comparing differences in OFV between models. A covariate was
selected if the reduction of OFV of 3.84 (χ2 distribution, P=0.05, degree of
freedom=1) or more from base model were archived. During backward
elimination, covariates at the p<0.01 (in case of difference of Objective
Function Value (dOFV) of 6.64 or more increase compared with previous
model) level were retained in the model.
2.3.3. Model evaluation
The reliability and stability of the PopPK model were tested by a
nonparametric bootstrap procedure and visual predictive check (VPC).
During bootstrap procedure, each patients were randomly resampled to
form a new data set from original ones. One thousands data sets were
simulated for the serum concentration of tacrolimus from the final model.
The observed data were covered with the 2.5th, 50th, and 97.5th
percentiles of the simulated data calculated at each time point.
2.4. Statistical Analysis
- 14 -
Baseline characteristics were presented with mean ± standard
deviation. Range and median value were also described as needed. All
statistical analysis were performed by SPSS ver.19 (SPSS Inc., Chicago,
Illinois, U.S.A ).
- 15 -
3. Results
3.1. Patient Characteristics
One hundred and forty five patients were included in this analysis and
total tacrolimus blood samples were 1938. The characteristics of included
glomerulonephritis patients are summarized in Table 1 and 2. Patients were
predominantly female (63.5%) and lupus nephritis was the most common
diagnosis among included patients. One hundred and forty three patients
were prescribed Prograf® (Astellas, Levallois-Perret, France) and only two
patients were taking Tacrobell® (Chong Kun Dang Pharmaceutical Corp,
Ltd., Seoul, Korea). The mean serum creatinine level was 1.12 ± 0.8 mg/dL
and the mean estimated glomerular filtration rate by MDRD methods was
76.58 ± 30.57 ml/min. Hence, the majority of patients seemed to have
normal to moderately impaired renal function. Many patients also showed
mild proteinuria and mild hypoalbuminemia with or without
hypercholesterolemia. For instance, average urine protein to creatinine
ratio and serum albumin level were 1.83 ± 3.0 and 3.82 ± 0.59 mg/dL,
respectively. The mean total serum cholesterol level was 195.7± 67 mg/dL.
Included medications were rosuvastatin (ROSU), steroids (STER),
omeprazole (OMEP), nifedipine (NIFE), rifampin (RIFA), and fluconazole
(FLUC).
3.2 Correlation between covariates
Correlations between covariates were explored (Table 5-8) (Figure 1-
3). As expected, hemoglobin level – Hematocrit level, BUN – S.Cr, and
serum BUN – Uric acid were highly correlated. Positive strong relationship
between total protein and uric acid was also reported (r = 0.875) whereas
moderate negative correlation between albumin and total protein level was
seen in GN patients (r = -0.445).
- 16 -
However, the correlation between protein creatinine ratio (P/Cr) and
cholesterol was not strong (r=0.353) and it was similar between P/Cr and
total protein level (r=-0.498). WBC was related to Platelet and hemoglobin
is strongly associated with hematocrit (r=0.840). Etiology of GN seems to
related to serum cholesterol level (r=0.322). Women seemed to have lower
serum creatinine level compared to men (r=-0.363).
3.3 Basic model development
Although two compartments model with lag time was known to give
best fit for tacrolimus, two compartments model and absorption lag time
model were tested and gave worse fit compared to one compartment model
in this study. Hence, one compartment model with linear absorption and first
order elimination were selected. Random effect model for both CL/F and
V/F were tried first but it turned out to have poor estimation of V/F. Hence,
V/F was estimated by fixed effect rather than random effect in that
intraindividual variability could not be considered. This is because the
concentration data were only consisted of trough levels. Absorption
coefficient (Ka) was fixed to 4.5 h-1 due to same reason. First-order
conditional estimation with interaction was utilized through all model
development process.
Exponential error model was found the best to describe the
interindividual as well as intraindividual variability. Figure 4 showed the base
model population prediction and individual prediction. Among the tested
covariates, PCR, WBC, HEMO, HCT, PLT, BUN, SCR, CHOL, TROP, ALBU,
URIC, TBIL, AST, ALT, BWT, AGE, ROSU, OMAC, Steroid, OMEP, NIFE,
RIFA, Dz were selected in CL/F (Table 9).
The PCR, WBC, HEMO, HCT, PLT, BUN, SCR, CHOL, TPRO, ALB,
- 17 -
URIC, TBIL, AST, ALT, BWT and AGE were selected as covariates for the
CL/F in full model (dOFV >3.84; P<0.05). Inclusion of HEMO, HCT, PLT,
BUN, SCR, CHOL, TPRO, ALB, URIC, TBIL, AGE, OMAC, Steroid, OMEP,
NIFE< RIFA, Dz in the covariate model for the V/F significantly improved
the model (dOFV >3.84; P<0.05). The resultant full model was as follows
(Equation 8, 9) :
TypicalValue θ . .
. . . .
. . . .
. . . .
.
(8)
TypicalValueVF
θ . .
. . .
. . .
. . .
(9)
3.4 Drug interaction
To evaluate the effect of concomitantly administered medication,
covariates as medication were tested. Several medications were selected
in the full model. When it is considered with continuous variables (i.e.
Clinical data), the effect was small enough to be eliminated for the final
- 18 -
model (Table 9).
3.5. Final model
During the backward elimination process to get the final model, all the
covariates except serum creatinine in CL/F and V/F, and serum albumin in
V/F were removed (dOFV 7.88; P<0.005) (Table 10). The final model was
as follows;
TypicalValue θ . (10)
TypicalValue θ . . (11)
The final parameter estimates are shown in Table 10. The
Interindividual variability of CL/F was 58.1%. The residual error was 0.266
mg/L. The scatter plots of the predicted concentrations versus observed
concentrations and the weighted residual versus predicted concentrations
are shown in Figure 5-9. Individual observed concentration versus
prediction plots were displayed in Figure 10 -16. Weighted residual errors
were improved after considering eta in that individual residual errors in final
model were equally distributed around zero (Figure 17).
3.6. Population prediction over time
Visual check of the concentration versus time did not show any
relationship between tacrolimus blood level and time (Figure 20). On the
other hand, population prediction displayed better prediction in early phase
after starting tacrolimus therapy when it is compared to later treatment
periods (Figure 21-24).
- 19 -
3.7. Validation
The reliability and stability of the population pharmacokinetic model
were evaluated by the nonparametric bootstrap method and one thousands
cases were resampled. The predicted population parameter estimates
obtained from the boot strap procedure were highly correlated with the
estimation from final model (Table 10). The estimated mean values were all
within VPC estimates of 2.5th and 97.5th percentile range. Visual prediction
check plot was shown in Figure 25.
- 20 -
4. Discussion
Although population pharmacokinetic studies were frequently
performed in transplantation patients, this is the first study examined
population pharmacokinetics of tacrolimus in Korean glomerulonephritis
patients and explored various parameters affecting tacrolimus
pharmacokinetics including clinical, demographic data and concomitantly
used medications.23,24 Previously, cyclosporin population pharmacokinetics
was studied in Chinese patients with nephrotic syndrome.29 Population
pharmacokinetics of mycophenolate mofetil, another immunosuppressant
medication frequently used for many glomerulonephritis, was also studied
in U.S. patients.30 Population pharmacokinetic studies were done in many
transplantation groups but not in glomerulonephritis patients who requires
immunosuppressant therapy.23,24
Since Tacrolimus seemed to be effective at low serum concentration
level in glomerulonephritis patients, Korea Food and Drug Administration
(KFDA) recommends checking the serum tacrolimus level regularly to avoid
drug toxicity in lupus nephritis patients.7 In practice, however, poor dose
and concentration relationships were frequently observed. Huge variation
in pharmacokinetics will cause not only the toxicity of the medication but
also the reduced efficacy to treat the glomerulonephritis. Because trial and
error approach was commonly utilized in the management of
glomerulonephritis patients on tacrolimus, pharmacokinetic prediction
would be helpful in deciding the tacrolimus dose and guiding the treatment
regimen to treat GN. This study is designed to make it possible to predict
pharmacokinetics of tacrolimus in glomerulonephritis patients.
In this study, correlation between covariates was explored. As
expected, serum creatinine levels were strongly associated with serum uric
acid and BUN level. That means people with impaired renal function likely
- 21 -
have hyperuricemia and azotemia. Hemoglobin level was highly related to
hematocrit level while hematocrit showed strong association with serum
creatinine, total protein, and cholesterol. This may be because hematocrit
is related to the fluid volume in the body and factors related to the volume
seemed to have related to hematocrit level in glomerulonephritis patients.
Although strong association was expected between serum albumin level
and total protein, BUN and uric acid was strongly related to total protein
level (r = 0.908 and r = 0.856, respectively). This might be explained by that
people who eat a lot of meat product or who are on high protein diet will
have elevated BUN, uric acid level, and total protein level in
glomerulonephritis patients. Increased total protein level and
hypoalbuminemia probably caused by proteinuria were associated with
elevated serum creatinine level, meaning reduced renal function in
glomerulonephritis patients. Consequently, low protein diet needs to be
emphasized especially in glomerulonephritis population with low serum
albumin level. Urine protein creatinine ratio which is quantitative
measurement of proteinuria shows moderate correlation with total protein
(r = -0.498) and with cholesterol (r = 0.353). That means proteinuria cause
decreased serum protein level and increased serum cholesterol level but
the association seemed to be weak. Serum albumin level was not
associated with urine protein creatinine ratio and this could not be explained
in this study.
During basic model development process, the raw data were visually
checked and thoroughly inspected due to huge variation in observed data.
Log transformed data vs original raw data were compared only to find
original one was better than log transformation. It looked like uncertainty
explained by the pharmacokinetic parameters and factors was much
smaller than the one of random error itself. Sparse concentration data
- 22 -
consisted by only serum trough level may be one of the reasons of this. In
other similarly designed population pharmacokinetic studies, Vd was fixed
or was replaced by previously reported values, and then selected the one
which gave lowest objective function value (OFV).24,29 Fixing Vd and Vd
estimation by fixed effect were compared each other in this study and fixed
effect model for Vd looked promising. So, random effect for CL/F and fixed
effect for V/F model was developed and used throughout this study.
Drug interactions between concomitantly used medications and
tacrolimus were also examined in this study. During forward selection, most
of the medications seemed to affect tacrolimus pharmacokinetics but all of
them were excluded after backward elimination. It is believed that the effect
of drug interaction was smaller than random errors or other clinical
covariates. Another possible explanation is that there are not many patients
who were on medications that cause drug interaction. Hence the power to
detect the difference may be not enough to show any significance.
According to study findings, final parameter estimate of clearance
(CL/F) of 19.5 L/h is similar to previous reported value of 22.9 L/h although
the volume of distribution (V/F) of 380L was lower than the previously
reported ones.23,31-37 This may be explained by that a lot of previous studies
were done in liver transplantation patients and the volume of tacrolimus was
increased as much as three times larger than the one with impaired kidney
disease. Referenced by Prograf® package insert, Volume of distribution of
tacrolimus was 0.85 L/kg in healthy volunteer, 1.07 L/Kg in renal impairment,
and 3.1-3.9L/kg in patients with hepatic disease. Therefore V/F of 380L
(Vd=1.1875 L/Kg in body weight of 64kg person when F (bioavailability) is
0.2) seems to be a reasonable outcome in that most of the study subjects
in this study showed mildly impaired to normal kidney function.
- 23 -
Population pharmacokinetic parameters were estimated and the
effects of covariates were examined. Serum creatinine level in clearance
and in volume as well as serum albumin level in volume were identified as
covariates to improve model prediction. Since majority of tacrolimus is
excreted in hepatic pathway, the influence of serum creatinine or renal
function on clearance of tacrolimus is not clear. In this study, it is
hypothesized that both decreased clearance and decreased volume mean
increased serum tacrolimus level and elevated serum tacrolimus level
results in kidney injury resulting elevated serum creatinine level. It is
presumed that serum creatinine level is not the factor influencing tacrolimus
pharmacokinetics but the results of elevated serum drug level. In addition,
other clinical factors related to kidney impairment such as BUN, was not
selected in this final model because BUN and serum creatinine are highly
associated each other. Though the exact mechanism of nephrotoxicity of
calcineurin inhibitors is not clearly understood, tacrolimus as a calcineurin
inhibitor results in vasoconstriction of the afferent and glomerular
arterioles.38-41 This nephrotoxicity of calcineurin inhibitors are reported as
dose related.38 Eliminating serum creatinine level in full model analysis
might produce somewhat different final model. However, further evaluation
was not performed as these are only hypotheses. Further
pharmacodynamics study to evaluate the relationship between serum
creatinine level and tacrolimus blood concentration to find optimized target
drug level may be warranted.
On the other hand, elevated serum albumin level causes volume
expansion and thus, increases the volume of distribution of tacrolimus in
glomerulonephritis patients. Protein creatinine ratio was expected to be
selected as covariates but the effect may be too small to show any
significance in pharmacokinetics of tacrolimus.
- 24 -
It looked like there were no correlations between observed drug
concentration and time in GN patients whereas postoperative day was
strongly associated in transplantation patients. However prediction of
population pharmacokinetics showed better estimation shortly after starting
tacrolimus therapy. Possible explanation for this may be patients were
closely monitors after initiating tacrolimus therapy and thus, trough level
were checked at the correct time and patients’ compliance was better than
the one in later treatment periods.
There are a couple of limitations in this study. First, trough blood
tacrolimus concentration was used in our analysis mainly due to
retrospective study design and authority recommendations. Korea Food
and Drug Administration recommend checking serum trough level regularly
in glomerulonephritis patients who is on tacrolimus to avoid toxicity. And
thus, only trough concentrations were checked in most of the included
patients every several months. Rich concentration data might estimate the
absorption characteristics of tacrolimus in GN and it is clinically important
in that absorption may be more vulnerable to drug interactions in tacrolimus.
Second, although pharmacogenomic factors are known to be strongly
associated with pharmacokinetic variation of tacrolimus, it could not be
tested in this study due to lack of data.23 Consideration of genetic factors
might have been improved the study result. Third, due to the retrospective
study design, patient could not be controlled that leads to huge variation in
our dataset. For instance, some patients took their blood level checked right
after taking medications which was coincidently found while reviewing
patient’s records. Poor compliance in GN patients compared to well-
educated transplant patients was also suspected. Transplant patients were
provided with man to man medication counselling sessions by pharmacist
at this practice site while GN patients were not. Additionally many
- 25 -
glomerular nephritis patients seemed to show poor understating of why they
are taking immunosuppressant medications and why it is important to take
medications correctly.
Further study with prospective controlled design considering
pharmacogenomics variables would give more predictive pharmacokinetic
explanation in GN patients.
- 26 -
5. Conclusion
Various factors affecting tacrolimus pharmacokinetics were explored.
Findings of this study indicated that pharmacokinetics of tacrolimus was
associated with serum creatinine and serum albumin level. Especially,
volume of distribution of tacrolimus was affected by serum albumin level
and serum creatinine level was related to both clearance and volume of
distribution of tacrolimus.
Developed model may guide individualized tacrolimus therapy in GN
patients and improve pharmacotherapy in these populations.
- 27 -
6. References
1. Korean Society of Nephrology RC. Renal Replacement Therapy in
Korea - Insan Memorial Dialysis Registry 2005 -. The Korean Society
of Nephrology. 2005;25(2):S425-S457.
2. Chae D-W. Current Status of Primary Glomerulonephritis. Korean J
Med. 2013;84(1):1-5.
3. Foundation NK. Glomerulonephritis. 2013;
http://www.kidney.org/atoz/content/glomerul.cfm. Accessed Dec.15,
2013.
4. Chadban SJ, Atkins RC. Glomerulonephritis. Lancet. May 21-27
2005;365(9473):1797-1806.
5. Naesens M, Kuypers DR, Sarwal M. Calcineurin inhibitor
nephrotoxicity. Clinical journal of the American Society of
Nephrology : CJASN. Feb 2009;4(2):481-508.
6. Couser WG. Glomerulonephritis. Lancet. May 1 1999;353(9163):1509-
1515.
7. Radhakrishnan J, Cattran DC. The KDIGO practice guideline on
glomerulonephritis: reading between the (guide)lines--application to
the individual patient. Kidney international. Oct 2012;82(8):840-856.
8. (KFDA) KFaDA. 허가사항 변경지시(안) 및 변경대비표(프로그랍®캅셀
에 한함, 타크로리무스 5mg 제외); Labeling revision of prograf®
(excluding tacrolimus 5mg). 2013.
9. Martins L VA, Branco A, Carvalho MJ, Henriques AC, Dias L, Sarmento
AM, Amil M. Cyclosporine versus tacrolimus in kidney transplantation:
are there differences in nephrotoxicity? Transplant Proc.
2004;36(4):877-879.
10. Webster A WR, Taylor RS, Chapman JR, Craig JC. Tacrolimus versus
cyclosporin as primary immunosuppression for kidney transplant
recipients. Cochrane Database Syst Rev. 2005;19(4):CD003961.
11. Cattran D. Management of membranous nephropathy: when and
what for treatment. Journal of the American Society of Nephrology :
JASN. May 2005;16(5):1188-1194.
12. Chen W, Tang X, Liu Q, et al. Short-term outcomes of induction
therapy with tacrolimus versus cyclophosphamide for active lupus
nephritis: A multicenter randomized clinical trial. American journal of
- 28 -
kidney diseases : the official journal of the National Kidney
Foundation. Feb 2011;57(2):235-244.
13. Duncan N, Dhaygude A, Owen J, et al. Treatment of focal and
segmental glomerulosclerosis in adults with tacrolimus monotherapy.
Nephrology, dialysis, transplantation : official publication of the
European Dialysis and Transplant Association - European Renal
Association. Dec 2004;19(12):3062-3067.
14. Praga M, Barrio V, Juarez GF, Luno J. Tacrolimus monotherapy in
membranous nephropathy: a randomized controlled trial. Kidney
international. May 2007;71(9):924-930.
15. Verbeeck RK, Musuamba FT. Pharmacokinetics and dosage
adjustment in patients with renal dysfunction. European journal of
clinical pharmacology. Aug 2009;65(8):757-773.
16. Joy MS, Frye RF, Nolin TD, et al. In Vivo Alterations in Drug
Metabolism and Transport Pathways in Patients with Chronic Kidney
Diseases. Pharmacotherapy. Sep 6 2013.
17. Venkataramanan R, Jain A, Warty VW, et al. Pharmacokinetics of FK
506 following oral administration: a comparison of FK 506 and
cyclosporine. Transplantation proceedings. Feb 1991;23(1 Pt 2):931-
933.
18. Gugler R, Shoeman DW, Huffman DH, Cohlmia JB, Azarnoff DL.
Pharmacokinetics of drugs in patients with the nephrotic syndrome.
The Journal of clinical investigation. Jun 1975;55(6):1182-1189.
19. Keller F, Maiga M, Neumayer HH, Lode H, Distler A. Pharmacokinetic
effects of altered plasma protein binding of drugs in renal disease.
European journal of drug metabolism and pharmacokinetics. Jul-Sep
1984;9(3):275-282.
20. Micromedex® Healthcare Series [Internet database]. Thomson
Reuters (Healthcare) Inc. . Accessed 05-01-2012.
21. Administration USFaD. Prograf® capsule label information.
http://www.accessdata.fda.gov/drugsatfda_docs/label/2013/050708s
043,050709s036lbl.pdf. Accessed Dec. 28, 2013.
22. Zahir H, McCaughan G, Gleeson M, Nand RA, McLachlan AJ. Factors
affecting variability in distribution of tacrolimus in liver transplant
recipients. British journal of clinical pharmacology. Mar
- 29 -
2004;57(3):298-309.
23. Han N, Yun HY, Hong JY, et al. Prediction of the tacrolimus population
pharmacokinetic parameters according to CYP3A5 genotype and
clinical factors using NONMEM in adult kidney transplant recipients.
European journal of clinical pharmacology. Jan 2013;69(1):53-63.
24. Jacobson P, Ng J, Ratanatharathorn V, Uberti J, Brundage RC. Factors
affecting the pharmacokinetics of tacrolimus (FK506) in
hematopoietic cell transplant (HCT) patients. Bone marrow
transplantation. Oct 2001;28(8):753-758.
25. Lexi-Comp. Lexi-Comp Online.Hudson, OH: . ; Retrieved from
http://online.lexi.com/
26. Christians U JW, Benet LZ, Lampen A. Mechanisms of clinically
relevant drug interactions associated with tacrolimus. Clin
Pharmacokinet. . 2002;41(11):813-851.
27. Christians U, Jacobsen W, Benet LZ, Lampen A. Mechanisms of
clinically relevant drug interactions associated with tacrolimus.
Clinical pharmacokinetics. 2002;41(11):813-851.
28. Jusko WJ, Piekoszewski W, Klintmalm GB, et al. Pharmacokinetics of
tacrolimus in liver transplant patients. Clinical pharmacology and
therapeutics. Mar 1995;57(3):281-290.
29. Xiaoli D QF. Population pharmacokinetic study of cyclosporine in
patients with nephrotic syndrome. Journal of clinical pharmacology.
2009;49(7):782-788.
30. Sam WJ JM. Population pharmacokinetics of mycophenolic acid and
metabolites in patients with glomerulonephritis. Ther Drug Monit.
2010;32(5):594-605.
31. Golubović B VK, Radivojević D, Kovačević SV, Prostran M, Miljković B.
Total plasma protein effect on tacrolimus elimination in kidney
transplant patients - Population pharmacokinetic approach. Eur J
Pharm Sci. 2014;52:34-40.
32. Antignac M BB, Farinotti R, Lechat P, Urien S. Population
pharmacokinetics and bioavailability of tacrolimus in kidney
transplant patients. Br J Clin Pharmacol. . 2007;64(6):750-757.
33. Antignac M HJ, Boleslawski E, Hannoun L, Touitou Y, Farinotti R,
Lechat P, Urien S. Population pharmacokinetics of tacrolimus in full
- 30 -
liver transplant patients: modelling of the post-operative clearance.
Eur J Clin Pharmacol. . 2005;61(5-6):409-416.
34. Fukatsu S YI, Igarashi T, Hashida T, Takayanagi K, Saito H, Uemoto S,
Kiuchi T, Tanaka K, Inui K, Tanaka K, Inui K. Population
pharmacokinetics of tacrolimus in adult recipients receiving living-
donor liver transplantation. Eur J Clin Pharmacol. 2001;57(6-7):479-
484.
35. Li D LW, Zhu JY, Gao J, Lou YQ, Zhang GL. Population
pharmacokinetics of tacrolimus and CYP3A5, MDR1 and IL-10
polymorphisms in adult liver transplant patients. J Clin Pharm Ther.
2007;32(5):505-515.
36. Sam WJ TL, Holmes MJ, Aw M, Quak SH, Lee KH, Lim SG, Prabhakaran
K, Chan SY, Ho PC. Population pharmacokinetics of tacrolimus in
whole blood and plasma in asian liver transplant patients. Clin
Pharmacokinet. 2006;45(1):59-75.
37. Staatz CE WC, Taylor PJ, Tett SE. Population pharmacokinetics of
tacrolimus in adult kidney transplant recipients. Clin Pharmacol Ther. .
2002;72(6):660-669.
38. Burdmann EA AT, Yu L, Bennett WM. Cyclosporine nephrotoxicity.
Semin Nephrol 2003;23:465.
39. Kopp JB KP. Cellular and molecular mechanisms of cyclosporin
nephrotoxicity. J Am Soc Nephrol 1990;1:162.
40. Shihab FS WT, Conti DJ, et al. A comparison of tacrolimus (FK 506)
and cyclosporine for immunosuppression in liver transplantation. The
U.S. Multicenter FK506 Liver Study Group. . N Engl J Med.
1994;331:1110.
41. Gerster JC DM, Halkic N, Gillet M. . Gout in liver transplant patients
receiving tacrolimus. Ann Rheum Dis 2004;63:894.
- 31 -
7. Tables
Table 1. Pathologic findings in GN 6
Pathological classification
Glomerular involvement
All glomeruli (diffuse) or only some (focal)
Extent of disease within involved glomeruli: patchy (segmental) or
general
Cell involvement
Increases in cell number (proliferative)
Neutrophil accumulation (exudative)
Cell damage
Cell necrosis visible by light microscopy (necrotising)
Ultrastructural damage visible only by electronmicroscopy (foot-
process effacement, membrane thinning)
Changes in non-cellular glomerular components
Matrix accumulation (hyalinosis) or immune deposits
Site of deposition (mesangial, subendothelial, subepithelial)
- 32 -
Table 2. Clinical Presentation of GN 6
Classification according to clinical presentation
Asymptomatic urinary abnormalities
Subnephrotic-range proteinuria, and/or microscopic haematuria, not
accompanied by renal impairment, oedema, or hypertension
Nephritic syndrome
Recent onset of haematuria and proteinuria, renal impairment, and
salt and water retention, causing hypertension
Rapidly progressive glomerulonephritis
Progression to renal failure over days to weeks, in most cases in the
context of a nephritic presentation, typically associated with the
pathological finding of extensive glomerular crescent formation on
renal biopsy
Nephrotic syndrome
Nephrotic-range proteinuria (more than 3·5 g per 1·73 m2 in 24 h),
hypoalbuminaemia, hyperlipidaemia, and oedema, in many cases
complicated by predisposition to venous thrombosis and bacterial
infection
Chronic glomerulonephritis
Persistent proteinuria with or without haematuria and slowly
progressive impairment of renal function
- 33 -
Table 3. Baseline characteristic of patients (n=145)
Characteristic Mean± SD Range
Gender Male [n (%)] 53 (36.5)
Period on Tacrolimus (days) 619.5 ± 518 14 - 886
Age (years) 39.2 ± 13.7 19 - 76
Body weight (kg) 64 ± 15.7 38.9 - 157
Tacrolimus serum level (ng/mL) 1.54 ± 2.95 0 - 32.6
Urine Protein/Creatinine ratio 1.83 ± 3.0 0.02 - 61.06
WBC (K/μL) 9.28 ± 3.38 1.49 - 24.72
Hemoglobin (g/dL) 13.44 ± 1.75 6.4 - 19.2
Hematocrit (%) 40.34 ± 4.7 22.2 - 55
Platelet (K/μL) 246.8 ± 67.7 5 - 585
BUN (mg/dL) 20.6 ± 16.7 4 - 496
Serum creatinine (mg/dL)
Estimated GFR (ml/min)
1.12 ± 0.8
76.58 ± 30.57
0.39 - 13.85
5-193
Choleterol (mg/dL) 195.7 ± 67 95 - 894
Total protein (mg/dL) 6.32 ± 0.83 3.1 - 8.9
Serum albumin (mg/dL) 3.82 ± 0.59 1.2 - 5.5
Uric acid (g/dL) 6.45 ± 1.8 1.8 - 17.3
Total bilirubin (mg/dL) 0.73 ± 0.33 0.2 - 3.5
AST (IU/L) 20.9 ± 14 7 - 442
ALT (IU/L) 25.25 ± 26 1 - 686
- 34 -
Table 4. Etiology of glomerulonephritis in selected patients
Etiology of glomerulonephritis n (%)
Nephrotic Minimal change disease (MCD) 28(19.31)
Membranous nephropathy (MN) 11(7.59)
Focal segmental glomerulosclerosis (FSGS) 9(6.21)
Nephritic
±
nephrotic
IgA nephropathy (IgAN) 35(24.14)
Minimal change disease (MCD) 28(19.31)
Membrano-proliferative glomerulonephritis (MPGN) 5(3.45)
Lupus nephritis 45(31.03)
Rapidly progressive glomerulonephritis (RPGN) 1(0.69)
Glomerulonephritis (GN) 10(6.90)
Henoch-Scho¨ nlein purpura nephritis (HSP) 5(3.45)
Alport syndrome 1(0.69)
Other GN 12(8.28)
- 35 -
Table 5. Correlations between variables(1)
DV PCRA WBCC HEMO HCTT PLTT BUNN SCRR CHOL TPRO ALBU URIC TBIL ASTT ALTT BWTT AGE MDV SEX DIAG
PearsonCorrelation
1 .028 .126 .146 .132 .034 -.089 -.075 .052 -.104 .158 -.074 .027 .026 .150 -.010 -.037 .017 .015 -.115
Sig. (2-tailed)
.256 .000 .000 .000 .136 .000 .001 .023 .000 .000 .001 .229 .253 .000 .672 .108 .455 .515 .066
N 1938 1622 1634 1882 1882 1882 1930 1936 1935 1932 1932 1935 1931 1932 894 1769 1902 1938 1938 256
PearsonCorrelation
.028 1 .056 -.067 -.066 .156 .186 .109 .353 -.498 .055 -.272 .001 .031 .028 .055 .191 -.025 -.043 .a
Sig. (2-tailed)
.256 .026 .008 .009 .000 .000 .000 .000 .000 .027 .000 .963 .207 .488 .027 .000 .306 .085
N 1622 1622 1574 1574 1574 1574 1622 1622 1621 1621 1621 1621 1621 1621 625 1622 1611 1622 1622 0
PearsonCorrelation
.126 .056 1 .053 .085 .357 .127 .095 .202 -.137 .110 -.058 -.038 .100 -.093 -.165 .037 .033 -.100 .a
Sig. (2-tailed)
.000 .026 .031 .001 .000 .000 .000 .000 .000 .000 .018 .126 .000 .019 .000 .139 .177 .000
N 1634 1574 1634 1634 1634 1634 1634 1634 1633 1633 1633 1633 1633 1633 636 1634 1598 1634 1634 0
PearsonCorrelation
.146 -.067 .053 1 .840 .413 -.595 -.516 .532 -.574 .205 -.518 .262 .054 .687 -.326 -.507 .319 .190 .011
Sig. (2-tailed)
.000 .008 .031 0.000 .000 .000 .000 .000 .000 .000 .000 .000 .020 .000 .000 .000 .000 .000 .867
N 1882 1574 1634 1882 1882 1882 1882 1882 1881 1878 1878 1881 1877 1878 881 1720 1846 1882 1882 248
PearsonCorrelation
.132 -.066 .085 .840 1 .650 -.876 -.765 .712 -.840 .362 -.811 .395 .075 .820 -.406 -.691 .608 .352 -.305
Sig. (2-tailed)
.000 .009 .001 0.000 .000 0.000 0.000 .000 0.000 .000 0.000 .000 .001 .000 .000 .000 .000 .000 .000
N 1882 1574 1634 1882 1882 1882 1882 1882 1881 1878 1878 1881 1877 1878 881 1720 1846 1882 1882 248
PearsonCorrelation
.034 .156 .357 .413 .650 1 -.725 -.632 .643 -.707 .314 -.718 .307 .046 .696 -.319 -.536 .614 .264 -.274
Sig. (2-tailed)
.136 .000 .000 .000 .000 .000 .000 .000 .000 .000 .000 .000 .047 .000 .000 .000 .000 .000 .000
N 1882 1574 1634 1882 1882 1882 1882 1882 1881 1878 1878 1881 1877 1878 881 1720 1846 1882 1882 248
PearsonCorrelation
-.089 .186 .127 -.595 -.876 -.725 1 .770 -.705 .908 -.377 .875 -.423 -.074 -.795 .401 .665 -.735 -.409 .023
Sig. (2-tailed)
.000 .000 .000 .000 0.000 .000 0.000 .000 0.000 .000 0.000 .000 .001 .000 .000 .000 0.000 .000 .713
N 1930 1622 1634 1882 1882 1882 1930 1930 1929 1926 1926 1929 1925 1926 888 1768 1894 1930 1930 248
PearsonCorrelation
-.075 .109 .095 -.516 -.765 -.632 .770 1 -.620 .764 -.348 .868 -.372 -.078 -.664 .348 .658 -.644 -.363 .274
Sig. (2-tailed)
.001 .000 .000 .000 0.000 .000 0.000 .000 0.000 .000 0.000 .000 .001 .000 .000 .000 .000 .000 .000
N 1936 1622 1634 1882 1882 1882 1930 1936 1935 1932 1932 1935 1931 1932 894 1769 1900 1936 1936 254
PearsonCorrelation
.052 .353 .202 .532 .712 .643 -.705 -.620 1 -.702 .344 -.709 .311 .093 .750 -.215 -.550 .535 .255 .322
Sig. (2-tailed)
.023 .000 .000 .000 .000 .000 .000 .000 .000 .000 .000 .000 .000 .000 .000 .000 .000 .000 .000
N 1935 1621 1633 1881 1881 1881 1929 1935 1935 1932 1931 1935 1931 1932 893 1768 1899 1935 1935 254
PearsonCorrelation
-.104 -.498 -.137 -.574 -.840 -.707 .908 .764 -.702 1 -.445 .856 -.418 -.076 -.749 .316 .693 -.720 -.411 -.192
Sig. (2-tailed)
.000 .000 .000 .000 0.000 .000 0.000 0.000 .000 .000 0.000 .000 .001 .000 .000 .000 0.000 .000 .002
N 1932 1621 1633 1878 1878 1878 1926 1932 1932 1932 1931 1932 1931 1932 893 1766 1896 1932 1932 251
PearsonCorrelation
.158 .055 .110 .205 .362 .314 -.377 -.348 .344 -.445 1 -.433 .257 .119 .529 -.122 -.341 .231 .202 .109
Sig. (2-tailed)
.000 .027 .000 .000 .000 .000 .000 .000 .000 .000 .000 .000 .000 .000 .000 .000 .000 .000 .086
N 1932 1621 1633 1878 1878 1878 1926 1932 1931 1931 1932 1931 1930 1931 893 1766 1896 1932 1932 251
PearsonCorrelation
-.074 -.272 -.058 -.518 -.811 -.718 .875 .868 -.709 .856 -.433 1 -.413 -.071 -.740 .339 .668 -.727 -.437 .201
Sig. (2-tailed)
.001 .000 .018 .000 0.000 .000 0.000 0.000 .000 0.000 .000 .000 .002 .000 .000 .000 0.000 .000 .001
N 1935 1621 1633 1881 1881 1881 1929 1935 1935 1932 1931 1935 1931 1932 893 1768 1899 1935 1935 254
PearsonCorrelation
.027 .001 -.038 .262 .395 .307 -.423 -.372 .311 -.418 .257 -.413 1 .811 .526 -.031 -.288 .358 .290 -.412
Sig. (2-tailed)
.229 .963 .126 .000 .000 .000 .000 .000 .000 .000 .000 .000 0.000 .000 .198 .000 .000 .000 .000
N 1931 1621 1633 1877 1877 1877 1925 1931 1931 1931 1930 1931 1931 1931 892 1765 1895 1931 1931 250
PearsonCorrelation
.026 .031 .100 .054 .075 .046 -.074 -.078 .093 -.076 .119 -.071 .811 1 .098 -.002 -.015 .049 .109 .026
Sig. (2-tailed)
.253 .207 .000 .020 .001 .047 .001 .001 .000 .001 .000 .002 0.000 .003 .946 .512 .030 .000 .677
N 1932 1621 1633 1878 1878 1878 1926 1932 1932 1932 1931 1932 1931 1932 893 1766 1896 1932 1932 251
PearsonCorrelation
.150 .028 -.093 .687 .820 .696 -.795 -.664 .750 -.749 .529 -.740 .526 .098 1 -.351 -.678 .656 .472 .060
Sig. (2-tailed)
.000 .488 .019 .000 .000 .000 .000 .000 .000 .000 .000 .000 .000 .003 .000 .000 .000 .000 .344
N 894 625 636 881 881 881 888 894 893 893 893 893 892 893 894 728 893 894 894 251
PearsonCorrelation
-.010 .055 -.165 -.326 -.406 -.319 .401 .348 -.215 .316 -.122 .339 -.031 -.002 -.351 1 .320 -.231 -.234 -.085
Sig. (2-tailed)
.672 .027 .000 .000 .000 .000 .000 .000 .000 .000 .000 .000 .198 .946 .000 .000 .000 .000 .432
N 1769 1622 1634 1720 1720 1720 1768 1769 1768 1766 1766 1768 1765 1766 728 1769 1733 1769 1769 87
PearsonCorrelation
-.037 .191 .037 -.507 -.691 -.536 .665 .658 -.550 .693 -.341 .668 -.288 -.015 -.678 .320 1 -.539 -.285 -.352
Sig. (2-tailed)
.108 .000 .139 .000 .000 .000 .000 .000 .000 .000 .000 .000 .000 .512 .000 .000 .000 .000 .000
N 1902 1611 1598 1846 1846 1846 1894 1900 1899 1896 1896 1899 1895 1896 893 1733 1902 1902 1902 256
PearsonCorrelation
.017 -.025 .033 .319 .608 .614 -.735 -.644 .535 -.720 .231 -.727 .358 .049 .656 -.231 -.539 1 .407 .a
Sig. (2-tailed)
.455 .306 .177 .000 .000 .000 0.000 .000 .000 0.000 .000 0.000 .000 .030 .000 .000 .000 .000 0.000
N 1938 1622 1634 1882 1882 1882 1930 1936 1935 1932 1932 1935 1931 1932 894 1769 1902 4557 4557 256
PearsonCorrelation
.015 -.043 -.100 .190 .352 .264 -.409 -.363 .255 -.411 .202 -.437 .290 .109 .472 -.234 -.285 .407 1 .081
Sig. (2-tailed)
.515 .085 .000 .000 .000 .000 .000 .000 .000 .000 .000 .000 .000 .000 .000 .000 .000 .000 .195
N 1938 1622 1634 1882 1882 1882 1930 1936 1935 1932 1932 1935 1931 1932 894 1769 1902 4557 4557 256
PearsonCorrelation
-.115 .a .a .011 -.305 -.274 .023 .274 .322 -.192 .109 .201 -.412 .026 .060 -.085 -.352 .a .081 1
Sig. (2-tailed)
.066 .867 .000 .000 .713 .000 .000 .002 .086 .001 .000 .677 .344 .432 .000 0.000 .195
N 256 0 0 248 248 248 248 254 254 251 251 254 250 251 251 87 256 256 256 256
Red text in red cell means strong correlation (r≥0.7), Red text in white cell means moderate correlation (0.3≤r<0.7)
TPRO
Correlations
DV
PCRA
WBCC
HEMO
HCTT
PLTT
BUNN
SCRR
CHOL
a. Cannot be computed because at least one of the variables is constant.
ALBU
URIC
TBIL
ASTT
ALTT
BWTT
AGE
MDV
SEX
DIAG
- 36 -
Table 6. Correlations between variables (2)
DV
PC
RA
WB
CC
HE
MO
HC
TT
PL
TT
BU
NN
SC
RR
CH
OL
TP
RO
AL
BU
UR
ICT
BIL
AS
TT
AL
TT
BW
TT
AG
EM
DV
SE
XD
IAG
Pe
ars
on
Co
rre
latio
n1
.02
8.1
26
.14
6.1
32
.03
4-.
08
9-.
07
5.0
52
-.1
04
.15
8-.
07
4.0
27
.02
6.1
50
-.0
10
-.0
37
.01
7.0
15
-.1
15
Sig
. (2
-ta
iled
).2
56
.00
0.0
00
.00
0.1
36
.00
0.0
01
.02
3.0
00
.00
0.0
01
.22
9.2
53
.00
0.6
72
.10
8.4
55
.51
5.0
66
N1
93
81
62
21
63
41
88
21
88
21
88
21
93
01
93
61
93
51
93
21
93
21
93
51
93
11
93
28
94
17
69
19
02
19
38
19
38
25
6
Pe
ars
on
Co
rre
latio
n.0
28
1.0
56
-.0
67
-.0
66
.15
6.1
86
.10
9.3
53
-.4
98
.05
5-.
27
2.0
01
.03
1.0
28
.05
5.1
91
-.0
25
-.0
43
.a
Sig
. (2
-ta
iled
).2
56
.02
6.0
08
.00
9.0
00
.00
0.0
00
.00
0.0
00
.02
7.0
00
.96
3.2
07
.48
8.0
27
.00
0.3
06
.08
5
N1
62
21
62
21
57
41
57
41
57
41
57
41
62
21
62
21
62
11
62
11
62
11
62
11
62
11
62
16
25
16
22
16
11
16
22
16
22
0
Pe
ars
on
Co
rre
latio
n.1
26
.05
61
.05
3.0
85
.35
7.1
27
.09
5.2
02
-.1
37
.11
0-.
05
8-.
03
8.1
00
-.0
93
-.1
65
.03
7.0
33
-.1
00
.a
Sig
. (2
-ta
iled
).0
00
.02
6.0
31
.00
1.0
00
.00
0.0
00
.00
0.0
00
.00
0.0
18
.12
6.0
00
.01
9.0
00
.13
9.1
77
.00
0
N1
63
41
57
41
63
41
63
41
63
41
63
41
63
41
63
41
63
31
63
31
63
31
63
31
63
31
63
36
36
16
34
15
98
16
34
16
34
0
Pe
ars
on
Co
rre
latio
n.1
46
-.0
67
.05
31
.84
0.4
13
-.5
95
-.5
16
.53
2-.
57
4.2
05
-.5
18
.26
2.0
54
.68
7-.
32
6-.
50
7.3
19
.19
0.0
11
Sig
. (2
-ta
iled
).0
00
.00
8.0
31
0.0
00
.00
0.0
00
.00
0.0
00
.00
0.0
00
.00
0.0
00
.02
0.0
00
.00
0.0
00
.00
0.0
00
.86
7
N1
88
21
57
41
63
41
88
21
88
21
88
21
88
21
88
21
88
11
87
81
87
81
88
11
87
71
87
88
81
17
20
18
46
18
82
18
82
24
8
Pe
ars
on
Co
rre
latio
n.1
32
-.0
66
.08
5.8
40
1.6
50
-.8
76
-.7
65
.71
2-.
84
0.3
62
-.8
11
.39
5.0
75
.82
0-.
40
6-.
69
1.6
08
.35
2-.
30
5
Sig
. (2
-ta
iled
).0
00
.00
9.0
01
0.0
00
.00
00
.00
00
.00
0.0
00
0.0
00
.00
00
.00
0.0
00
.00
1.0
00
.00
0.0
00
.00
0.0
00
.00
0
N1
88
21
57
41
63
41
88
21
88
21
88
21
88
21
88
21
88
11
87
81
87
81
88
11
87
71
87
88
81
17
20
18
46
18
82
18
82
24
8
Pe
ars
on
Co
rre
latio
n.0
34
.15
6.3
57
.41
3.6
50
1-.
72
5-.
63
2.6
43
-.7
07
.31
4-.
71
8.3
07
.04
6.6
96
-.3
19
-.5
36
.61
4.2
64
-.2
74
Sig
. (2
-ta
iled
).1
36
.00
0.0
00
.00
0.0
00
.00
0.0
00
.00
0.0
00
.00
0.0
00
.00
0.0
47
.00
0.0
00
.00
0.0
00
.00
0.0
00
N1
88
21
57
41
63
41
88
21
88
21
88
21
88
21
88
21
88
11
87
81
87
81
88
11
87
71
87
88
81
17
20
18
46
18
82
18
82
24
8
Pe
ars
on
Co
rre
latio
n-.
08
9.1
86
.12
7-.
59
5-.
87
6-.
72
51
.77
0-.
70
5.9
08
-.3
77
.87
5-.
42
3-.
07
4-.
79
5.4
01
.66
5-.
73
5-.
40
9.0
23
Sig
. (2
-ta
iled
).0
00
.00
0.0
00
.00
00
.00
0.0
00
0.0
00
.00
00
.00
0.0
00
0.0
00
.00
0.0
01
.00
0.0
00
.00
00
.00
0.0
00
.71
3
N1
93
01
62
21
63
41
88
21
88
21
88
21
93
01
93
01
92
91
92
61
92
61
92
91
92
51
92
68
88
17
68
18
94
19
30
19
30
24
8
DV
PC
RA
WB
CC
HE
MO
HC
TT
PL
TT
BU
NN
- 37 -
Table 7. Correlations between variables (3)
DV
PC
RA
WB
CC
HE
MO
HC
TT
PL
TT
BU
NN
SC
RR
CH
OL
TP
RO
AL
BU
UR
ICT
BIL
AS
TT
AL
TT
BW
TT
AG
EM
DV
SE
XD
IAG
Pe
ars
on
Co
rre
latio
n-.
07
5.1
09
.09
5-.
51
6-.
76
5-.
63
2.7
70
1-.
62
0.7
64
-.3
48
.86
8-.
37
2-.
07
8-.
66
4.3
48
.65
8-.
64
4-.
36
3.2
74
Sig
. (2
-ta
iled
).0
01
.00
0.0
00
.00
00
.00
0.0
00
0.0
00
.00
00
.00
0.0
00
0.0
00
.00
0.0
01
.00
0.0
00
.00
0.0
00
.00
0.0
00
N1
93
61
62
21
63
41
88
21
88
21
88
21
93
01
93
61
93
51
93
21
93
21
93
51
93
11
93
28
94
17
69
19
00
19
36
19
36
25
4
Pe
ars
on
Co
rre
latio
n.0
52
.35
3.2
02
.53
2.7
12
.64
3-.
70
5-.
62
01
-.7
02
.34
4-.
70
9.3
11
.09
3.7
50
-.2
15
-.5
50
.53
5.2
55
.32
2
Sig
. (2
-ta
iled
).0
23
.00
0.0
00
.00
0.0
00
.00
0.0
00
.00
0.0
00
.00
0.0
00
.00
0.0
00
.00
0.0
00
.00
0.0
00
.00
0.0
00
N1
93
51
62
11
63
31
88
11
88
11
88
11
92
91
93
51
93
51
93
21
93
11
93
51
93
11
93
28
93
17
68
18
99
19
35
19
35
25
4
Pe
ars
on
Co
rre
latio
n-.
10
4-.
49
8-.
13
7-.
57
4-.
84
0-.
70
7.9
08
.76
4-.
70
21
-.4
45
.85
6-.
41
8-.
07
6-.
74
9.3
16
.69
3-.
72
0-.
41
1-.
19
2
Sig
. (2
-ta
iled
).0
00
.00
0.0
00
.00
00
.00
0.0
00
0.0
00
0.0
00
.00
0.0
00
0.0
00
.00
0.0
01
.00
0.0
00
.00
00
.00
0.0
00
.00
2
N1
93
21
62
11
63
31
87
81
87
81
87
81
92
61
93
21
93
21
93
21
93
11
93
21
93
11
93
28
93
17
66
18
96
19
32
19
32
25
1
Pe
ars
on
Co
rre
latio
n.1
58
.05
5.1
10
.20
5.3
62
.31
4-.
37
7-.
34
8.3
44
-.4
45
1-.
43
3.2
57
.11
9.5
29
-.1
22
-.3
41
.23
1.2
02
.10
9
Sig
. (2
-ta
iled
).0
00
.02
7.0
00
.00
0.0
00
.00
0.0
00
.00
0.0
00
.00
0.0
00
.00
0.0
00
.00
0.0
00
.00
0.0
00
.00
0.0
86
N1
93
21
62
11
63
31
87
81
87
81
87
81
92
61
93
21
93
11
93
11
93
21
93
11
93
01
93
18
93
17
66
18
96
19
32
19
32
25
1
Pe
ars
on
Co
rre
latio
n-.
07
4-.
27
2-.
05
8-.
51
8-.
81
1-.
71
8.8
75
.86
8-.
70
9.8
56
-.4
33
1-.
41
3-.
07
1-.
74
0.3
39
.66
8-.
72
7-.
43
7.2
01
Sig
. (2
-ta
iled
).0
01
.00
0.0
18
.00
00
.00
0.0
00
0.0
00
0.0
00
.00
00
.00
0.0
00
.00
0.0
02
.00
0.0
00
.00
00
.00
0.0
00
.00
1
N1
93
51
62
11
63
31
88
11
88
11
88
11
92
91
93
51
93
51
93
21
93
11
93
51
93
11
93
28
93
17
68
18
99
19
35
19
35
25
4
Pe
ars
on
Co
rre
latio
n.0
27
.00
1-.
03
8.2
62
.39
5.3
07
-.4
23
-.3
72
.31
1-.
41
8.2
57
-.4
13
1.8
11
.52
6-.
03
1-.
28
8.3
58
.29
0-.
41
2
Sig
. (2
-ta
iled
).2
29
.96
3.1
26
.00
0.0
00
.00
0.0
00
.00
0.0
00
.00
0.0
00
.00
00
.00
0.0
00
.19
8.0
00
.00
0.0
00
.00
0
N1
93
11
62
11
63
31
87
71
87
71
87
71
92
51
93
11
93
11
93
11
93
01
93
11
93
11
93
18
92
17
65
18
95
19
31
19
31
25
0
Pe
ars
on
Co
rre
latio
n.0
26
.03
1.1
00
.05
4.0
75
.04
6-.
07
4-.
07
8.0
93
-.0
76
.11
9-.
07
1.8
11
1.0
98
-.0
02
-.0
15
.04
9.1
09
.02
6
Sig
. (2
-ta
iled
).2
53
.20
7.0
00
.02
0.0
01
.04
7.0
01
.00
1.0
00
.00
1.0
00
.00
20
.00
0.0
03
.94
6.5
12
.03
0.0
00
.67
7
N1
93
21
62
11
63
31
87
81
87
81
87
81
92
61
93
21
93
21
93
21
93
11
93
21
93
11
93
28
93
17
66
18
96
19
32
19
32
25
1
TP
RO
SC
RR
CH
OL
AL
BU
UR
IC
TB
IL
AS
TT
- 38 -
Table 8. Correlations between variables (4)
DV
PC
RA
WB
CC
HE
MO
HC
TT
PL
TT
BU
NN
SC
RR
CH
OL
TP
RO
AL
BU
UR
ICT
BIL
AS
TT
AL
TT
BW
TT
AG
EM
DV
SE
XD
IAG
Pe
ars
on
Co
rre
latio
n.1
50
.02
8-.
09
3.6
87
.82
0.6
96
-.7
95
-.6
64
.75
0-.
74
9.5
29
-.7
40
.52
6.0
98
1-.
35
1-.
67
8.6
56
.47
2.0
60
Sig
. (2
-ta
iled
).0
00
.48
8.0
19
.00
0.0
00
.00
0.0
00
.00
0.0
00
.00
0.0
00
.00
0.0
00
.00
3.0
00
.00
0.0
00
.00
0.3
44
N8
94
62
56
36
88
18
81
88
18
88
89
48
93
89
38
93
89
38
92
89
38
94
72
88
93
89
48
94
25
1
Pe
ars
on
Co
rre
latio
n-.
01
0.0
55
-.1
65
-.3
26
-.4
06
-.3
19
.40
1.3
48
-.2
15
.31
6-.
12
2.3
39
-.0
31
-.0
02
-.3
51
1.3
20
-.2
31
-.2
34
-.0
85
Sig
. (2
-ta
iled
).6
72
.02
7.0
00
.00
0.0
00
.00
0.0
00
.00
0.0
00
.00
0.0
00
.00
0.1
98
.94
6.0
00
.00
0.0
00
.00
0.4
32
N1
76
91
62
21
63
41
72
01
72
01
72
01
76
81
76
91
76
81
76
61
76
61
76
81
76
51
76
67
28
17
69
17
33
17
69
17
69
87
Pe
ars
on
Co
rre
latio
n-.
03
7.1
91
.03
7-.
50
7-.
69
1-.
53
6.6
65
.65
8-.
55
0.6
93
-.3
41
.66
8-.
28
8-.
01
5-.
67
8.3
20
1-.
53
9-.
28
5-.
35
2
Sig
. (2
-ta
iled
).1
08
.00
0.1
39
.00
0.0
00
.00
0.0
00
.00
0.0
00
.00
0.0
00
.00
0.0
00
.51
2.0
00
.00
0.0
00
.00
0.0
00
N1
90
21
61
11
59
81
84
61
84
61
84
61
89
41
90
01
89
91
89
61
89
61
89
91
89
51
89
68
93
17
33
19
02
19
02
19
02
25
6
Pe
ars
on
Co
rre
latio
n.0
17
-.0
25
.03
3.3
19
.60
8.6
14
-.7
35
-.6
44
.53
5-.
72
0.2
31
-.7
27
.35
8.0
49
.65
6-.
23
1-.
53
91
.40
7.a
Sig
. (2
-ta
iled
).4
55
.30
6.1
77
.00
0.0
00
.00
00
.00
0.0
00
.00
00
.00
0.0
00
0.0
00
.00
0.0
30
.00
0.0
00
.00
0.0
00
0.0
00
N1
93
81
62
21
63
41
88
21
88
21
88
21
93
01
93
61
93
51
93
21
93
21
93
51
93
11
93
28
94
17
69
19
02
45
57
45
57
25
6
Pe
ars
on
Co
rre
latio
n.0
15
-.0
43
-.1
00
.19
0.3
52
.26
4-.
40
9-.
36
3.2
55
-.4
11
.20
2-.
43
7.2
90
.10
9.4
72
-.2
34
-.2
85
.40
71
.08
1
Sig
. (2
-ta
iled
).5
15
.08
5.0
00
.00
0.0
00
.00
0.0
00
.00
0.0
00
.00
0.0
00
.00
0.0
00
.00
0.0
00
.00
0.0
00
.00
0.1
95
N1
93
81
62
21
63
41
88
21
88
21
88
21
93
01
93
61
93
51
93
21
93
21
93
51
93
11
93
28
94
17
69
19
02
45
57
45
57
25
6
Pe
ars
on
Co
rre
latio
n-.
11
5.a
.a.0
11
-.3
05
-.2
74
.02
3.2
74
.32
2-.
19
2.1
09
.20
1-.
41
2.0
26
.06
0-.
08
5-.
35
2.a
.08
11
Sig
. (2
-ta
iled
).0
66
.86
7.0
00
.00
0.7
13
.00
0.0
00
.00
2.0
86
.00
1.0
00
.67
7.3
44
.43
2.0
00
0.0
00
.19
5
N2
56
00
24
82
48
24
82
48
25
42
54
25
12
51
25
42
50
25
12
51
87
25
62
56
25
62
56
Red
text
in red
cel
l mea
ns s
trong
cor
rela
tion
(r≥
0.7)
, Red
tex
t in
whi
te c
ell m
eans
mod
erat
e co
rrel
atio
n (0.
3≤r<
0.7
)
a. C
an
no
t be
co
mp
ute
d b
eca
us
e a
t le
as
t on
e o
f th
e v
ari
ab
les
is c
on
sta
nt.
AL
TT
BW
TT
AG
E
MD
V
SE
X
DIA
G
- 39 -
Table 9. Summary of Model Development Process
Covariates OFV P value
Base Model Kel/F = 28.8
V/F = 454
5319.58
Forward Selection
<Effect on Kel/F>
PCR 5293.604
(-25.976)
<0.05
WBC 5274.883
(-44.679)
<0.05
HEMO 5266.796
(-52.784)
<0.05
HCT 5265.874
(-53.706)
<0.05
PLT 5277.641
(-41.939)
<0.05
BUN 5271.635
(-47.945)
<0.05
S.Cr 5262.028
(-57.552)
<0.05
Chol 5255.376
(-64.204)
<0.05
T.Pro 5255.606
(-63.974)
<0.05
Albumin 5258.275
(-61.305)
<0.05
Uric acid 5265.628
(-53.952)
<0.05
T.Bil 5274.536
(-45.044)
<0.05
AST 5267.858
(-51.722)
<0.05
ALT 5266.297 <0.05
- 40 -
(-53.283)
Body weight 5310.675
(-8.905)
<0.05
Age 5266.225
(-53.355)
<0.05
Rosuvastatin 5276.105
(-43.475)
<0.05
Omega-3-
fatty aicd
5309.434
(-10.146)
<0.05
Steroid 5259.178
(-60.402)
<0.05
Omeprazole 5312.954
(-6.626)
<0.05
Nifedipine 5312.82
(-6.76)
<0.05
Rifampin 5286.096
(-33.484)
<0.05
Etiology of
Gn
5287.491
(-32.089)
<0.05
<Effect on V/F>
PCR 5316.599
(-2.981)
>0.05
WBC 5316.482
(-3.098)
>0.05
HEMO 5305.615
(-13.965)
<0.05
HCT 5301.615
(-17.965)
<0.05
PLT 5277.641
(-41.939)
<0.05
BUN 5309.436
(-10.144)
<0.05
S.Cr 5313.934
(-5.646)
<0.05
- 41 -
Chol 5314.623
(-4.957)
<0.05
T.Pro 5259.492
(-60.088)
<0.05
Albumin 5256.723
(-62.857)
<0.05
Uric acid 5284.304
(-35.276)
<0.05
T.Bil 5296.504
(-23.076)
<0.05
AST 5318.854
(-0.726)
>0.05
ALT 5319.578
(-0.002)
>0.05
Body weight 5319.579
(-0.001)
>0.05
Age 5293.813
(-25.767)
<0.05
Rosuvastatin 5316.143
(-3.437)
>0.05
Omega-3-
fatty aicd
5310.891
(-8.689)
<0.05
Steroid 5305.833
(-13.747)
<0.05
Omeprazole 5311.507
(-8.073)
<0.05
Nifedipine 5311.35
(-8.23)
<0.05
Rifampin 5305.833
(-13.747)
<0.05
Etiology of
Gn
5287.491
(-32.089)
<0.05
Backward elimination
<Full Model 1> 5218.892
- 42 -
Cholesterol
in V/F
5220.969
(2.077)
>0.005
S.Cr in V/F 5230.839
(11.947)
<0.005
BUN in V/F 5221.067
(0.098)
>0.005
Hemoglobin
in V/F
5226.146
(5.079)
>0.005
Hct in V/F 5234.42
(8.274)
<0.005
PLT in V/F 5235.581
(1.161)
>0.005
P/Cr in Cl/F 5258.366
(9.638)
<0.005
WBC in Cl/F 5234.77
(3.548)
>0.005
HEMO in
Cl/F
5236.843
(2.073)
>0.005
HCT in Cl/F 5255.631
(18.788)
<0.005
PLT in Cl/F 5244.506
(7.663)
>0.005
BUN in Cl/F 5259.236
(14.73)
<0.005
<Full Model 2> 5260.552
T.Bil in V/F 5234.324
(0.282)
>0.005
AGE in V/F 5235.157
(0.833)
>0.005
URIC in V/F 5235.181
(0.024)
>0.005
ALB in V/F 5243.639
(8.458)
<0.005
T.PRO in V/F 5239.847 >0.005
- 43 -
(4.666)
T.BIL in Cl/F 5240.461
(0.614)
>0.005
Cholesterol
in Cl/F
5241.002
(0.541)
>0.005
URIC in Cl/F 5241.804
(0.802)
>0.005
T.Pro in Cl/F 5250.723
(8.919)
<0.005
ALB in Cl/F 5249.937
(8.133)
<0.005
<Full Model 3> 5260.552
AGE in Cl/F 5264.22
(3.668)
>0.005
ALT in Cl/F
Bwt in Cl/F
5267.858
(3.638)
>0.005
AST in CL/F 5319.58
(51.722)
<0.005
<Full Model 1,2,3 combined> 5201.612
AST 5202.169
(0.557)
>0.005
BUN in Cl/F 5203.486
(1.317)
>0.005
ALB in Cl/F 5212.916
(9.43)
<0.005
ALB in V/F 5216.773
(13.287)
<0.005
HCT in V/F 5212.229
(8.743)
<0.005
T.Pro in Cl/F 5212.17
(8.684)
<0.005
HCT in Cl/F 5204.739
(1.253)
>0.005
PCR in Cl/F 5209.201 >0.005
- 44 -
(4.462)
S.Cr in Cl/F 5219.445
(10.244)
<0.005
T.Pro in Cl/F 5219.689
(3.171)
>0.005
S.Cr in Cl/F 5246.733
(27.044)
<0.005
ALB in V/F 5242.056
(22.367)
<0.005
HCT in V/F 5226.283
(6.594)
>0.005
- 45 -
Table 10. Final Population Pharmacokinetic Parameters of Tacrolimus and
bootstrap validation
ParameterFinal data estimate Bootstrp
Population mean
SE Interindividual variability (%)
Median
2.5th percentile
97.5th percentile
θCL (L/h) = θ1
19.5 2.15 11 19.352 14.997 23.944
θV (L) = θ2 380 4.51 12 385.093
237.517 522.484
θSCR in V/F = θ4
- 0.22 0.135 61 -0.2167
-0.467 0.0280
θSCR in CL/F = θ5
- 0.341 0.141 41 -0.3489
-0.6258 -0.0569
θALB in V/F = θ6
0.0821 0.0451 55 0.04845
-0.01088 0.17511
ωCL 0.3382 0.056 58.1
ωVol -
Residual propotional variance (σ)
0.266
- 46 -
8. Figures
Figure 1. Scatterplot matrix of clinical data (1)
- 47 -
Figure 2. Scatterplot matrix of clinical data (2)
- 48 -
Figure 3. Scatterplot matrix in clinical data (3)
- 49 -
Figure 4. Base model - Population and individual prediction vs.
observation
- 50 -
Figure 5. Final Model - Population and individual predictions vs. observation
(1)
Figure 6. Observations/Individual prediction/ Population predictions vs
observation
- 51 -
Figure 7. Final Model – Observation vs. population prediction
Figure 8. Final Model – Observation vs. individual prediction
- 52 -
Figure 9. Individual observation vs. prediction
- 53 -
Figure 10. Individual Plots (1)
- 54 -
Figure 11. Individual Plots (2)
- 55 -
Figure 12. Individual Plots (3)
- 56 -
Figure 13. Individual Plots (4)
- 57 -
Figure 14. Individual Plots (5)
- 58 -
Figure 15. Individual Plots (6)
- 59 -
Figure 16. Individual Plots (7)
- 60 -
Figure 17. Weighted residuals vs. Population predictions (1)
- 61 -
Figure 18. Population prediction vs. weighted residuals
- 62 -
Figure 19. Conditional weighted residuals vs. population prediction
- 63 -
Figure 20. Conditional weighted residuals vs. Covariates
- 64 -
Figure 21. Observed concentration over time
Tim
e (
Hr)
- 65 -
Figure 22. Population predictions vs. Observation over time (1)
- 66 -
Figure 23. Population predictions vs. Observation over time (2)
- 67 -
Figure 24. Population predictions vs. Observation over time (3)
- 68 -
Figure 25. Visual Prediction Check
- 69 -
초록
사구체신염 환자에서 Tacrolimus의
집단약동학 연구
서울대학교 약학대학원
약학과 예방임상약학 전공
심 미 경
연구배경 및 목적: 사구체 신염의 약물치료는 종종 면역 체계를 목표로
하고 있으며, Tacrolimus는 면역억제제로서 일부 사구체신염의 약물치
료에 사용되고 있다. 그러나 tacrolimus는 좁은 약물치료영역과 개인간
격차가 큰 약물로 약물농도를 예측하기가 쉽지 않다. 따라서 본 연구를
통해 사구체 신염환자에게서 집단약동학 모델을 수립하여 tacrolimus
약동학에 영향을 미치는 인자를 규명하고자 하였다.
연구방법: 연구기간인 2000년 1월 1일과 2013년 10월 30일 사이에
서울대학교병원 외래를 방문한 환자로서 사구체 신염으로 진단을 받고
경구용 tacrolimus 약물을 투약받은 환자가 본 연구에 포함되었다. 임
상적, 인구학적, 약물학적 정보를 수집하기위하여 전자의무기록을 후향
적으로 조사하였다. Tacrolimus의 집단약동학 모델을 수립하기 위하여
non-linear mixed effect 모델을 수립하였으며 first-order
conditional estimation with interaction 알고리즘을 사용하였다.
- 70 -
결과: 총 145명의 환자가 본 연구에 포함되었으며, 채혈된 약물 농도
샘플은 1938개 이었다. 본 연구에서 공변량 사이의 관계를 검토하였으
며, urine protein creatinine ratio 는 상승된 콜레스테롤수치와 감소된
총 단백량과 관계가 있었으나 강하지는 않았다. 집단 약동학 분석에서
one compartment model with linear absorption 를 채택하였으며 최종
모델에서 혈청 알부민 수치와 혈청 크레아티는 수치는 tacrolimus V/F
에 영향을 미쳤으며, 혈청 크레아티닌 수치는 tacrolimus의 CL/F 에
유의한 영향을 미쳤다. 본 연구를 통하여 도출된 최종모델의 식은 아래
와 같다.
TypicalValue θ .
TypicalValue θ . .
결론: 본 연구를 통하여 Tacrolimus 약동학에 영향을 미치는 다양한
인자들을 탐색하였다. 연구결과는 tacrolimus의 약동학에 혈청 크레아
티닌 수치와 혈청 알부민 수치가 영향을 미치고 있음을 보여주었다. 도
출된 집단약동학 모델은 이러한 사구체 신염환자에게서 개인 맞춤 약
물요법의 임상적 가이드를 제공할 수 있을 것이며 이 환자군에서의 약
물치료를 향상시킬 수 있을 것이다.
주요어: 사구체신염, tacrolimus, 타크로리무스, 집단약동학
- 71 -
학 번 : 2012-21596