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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 -

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33. Antignac M HJ, Boleslawski E, Hannoun L, Touitou Y, Farinotti R,

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K, Chan SY, Ho PC. Population pharmacokinetics of tacrolimus in

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- 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