Population PharmacokineticPopulation Pharmacokinetic Ch Characteristics aracteristics of of LevosulpirideLevosulpiride

and Terbinafine in and Terbinafine in Healthy Male Healthy Male Korean VolunteersKorean Volunteers

Yong-Bok Lee

College of Pharmacy and Institute of Bioequivalence and

Bridging Study, Chonnam National University, Gwangju 500-757, Korea.

INTRODUCTION

The purposes of this study were to assess the population pharmacokinetics of levosulpiride and terbinafine in healthy male Korean subjects using a population approach and to investigate the influence of characteristics of subjects such as age, body weight, height and serum creatinine concentration on that of levosulpiride and

terbinafine, respectively.

SUBJECTS AND DATA COLLECTION (1)

Serum Data from 192 healthy male Korean subjects were used for levosulpiride analysis.

After overnight fast, each subject received a single 25 ㎎ oral dose of levosulpiride. Blood samples were collected for 0~36 hours.

Informed consent was obtained from the subjects after explaining the nature and purpose details of the study.

Levosulpiride serum concentrations were measured using HPLC with fluorescence detection.

SUBJECTS AND DATA COLLECTION (2)

Serum Data from 73 healthy male Korean subjects were used for terbinafine analysis.

After overnight fast, each subject received a single 125 ㎎ oral dose of terbinafine. Blood samples were collected for 0~60 hours.

Informed consent was obtained from the subjects after explaining the nature and purpose details of the study.

Terbinafine serum concentrations were measured using HPLC with UV detection.

PHARMACOKINETIC MODEL EVALUATION

Two pharmacokinetic (PK) models were evaluated and compared for population approach using WinNonlin.

A. 1-compartment model without lag time

B. 1-compartment model with lag time

C. 2-compartment model without lag time

D. 2-compartment model with lag time

Afterwards, Akaike’s Information Criteria (AIC), condition number, frequenc

y of changing +/– signs (runs) and CV% were mainly used to determine the

model that most adequately described the levosulpiride and terbinafine da

ta.

AKAIKE’S INFORMATION CRITERIA

A measure of goodness of fit based on maximum likelihood. When comparing several models for a given set of data, the model associated with the smallest value of AIC is regarded as giving the best fit out of that set of models. AIC is only appropriate for use when comparing models using the same weighting scheme.

pRNAIC 2ln

N : number of observations.R : residual sum of squares.P : number of parameters.

STANDARD TWO STAGE (STS) METHOD

First stage

Total 192 and 73 subjects were analyzed individually

- according to optimal model

- by non-compartmental methods

WinNonlin was used for STS method.

Second stage

Individual PK parameters

- mean ± standard deviation

Intersubject and intrasubject variability were modeled as follows:

ij

j

j

j

j

eCC

eTT

eKK

eFVFV

eFClFCl

ij

laglagj

aaj

ccj

j

6

3

2

1

//

//

NONLINEAR MIXED EFFECTS MODELING

Cl/F, Vc/F, Ka and Tlag are the population mean estimate

s for the apparent systemic clearance, the apparent volume of distribution, the absorption rate constant and the lag time, respectively. Each left-hand parameter is the corresponding true parameter for the jth subject. 1 throug

h 6 are normally distributed random variables with mean

zero and whose variance are being estimated, C is predicted drug concentration at the ith time for the jth subject, Cij is the observed concentration for that subject at tha

t time and ij is the normally distributed residual intrasubj

ect error with mean zero and whose variance is being estimated.

NONLINEAR MIXED EFFECTS MODELING

The first-order estimation method was used to estimate- population PK parameters- intersubject variability (in population PK parameters)- intrasubject variability (between observations and predictions)

The pharmacostatistical model-developed by initially 1- or 2-compartment model with first-order absorption and elimination. -parameterized in terms of clearance and volume of distribution using the NONMEM subroutine ADVAN2 TRANS2 and ADVAN4 TRANS4.

NONMEM was used for nonlinear mixed effects modeling.

NONLINEAR MIXED EFFECTS MODELING

Model building step

Parameters were added to the model mainly based on the decrease in the minimum objective function (MOF). The change in the MOF between reduced model and full model is approximately 2 distributed with degrees of freedom equal to the number of parameters which are set to a fixed value in the reduced model. Thus, a decrease of 6 units in the MOF was considered statistically significant (p<0.01) for addition of one parameter during the development of the model. The importance of different potential covariates (age, weight, height and serum creatinine concentration) was evaluated based on changes in the MOF and inspection of scatter plots of each covariate versus the individual pharmacokinetic parameters. In addition, a decrease in unexplained variability and an improvement in plots were investigated, also.

Population Analysis Concept

F: PRED

F: IPRE

Y

Typical value of CL( No interindividual

random variability )

Fig. 1. Levosulpiride serum concentration-time plot after oral administration of a 25 ㎎ dose.

○: mean concentration at each time. ＋ : individual serum concentrations.

Time (hr)

0 4 8 12 16 20 24 28 32 36

Levo

sulp

iride

conc

entra

tion

(ng/

ml)

0.1

1

10

100

1000

Table 1. Comparison of AIC value for pharmacokinetic model evaluation for levosulpiride.

ModelAIC

No weight1/

obsa) 1/predb) 1/(obs)2 1/(pred)2

A 60.78 59.56 24.54 51.47 -7.56

B 40.05 32.50 1.47 19.70 -34.76

C 61.40 60.75 25.58 53.68 -5.38

D 42.91 31.98 1.35 17.17 -36.35

a) obs :observed concentration

b) pred : predicted concentration

Table 2. Coefficient of variation (CV) of parameters from fitting 1-compartment and 2-compartment model with lag time to weighted (1/(pred)2) levosulpiride data.

1-compartment

Parameter

CV%

Volume/F 2.75

Ka 8.90

Kel 2.40

Tlag 3.99

2-compartment

Parameter

CV%

A 51.88

B 38.65

Ka 21.47

Alpha 104.46

Beta 16.31

Tlag 5

Fig. 2. Levosulpiride serum concentration-time plot. ━: predicted concentration from fitting 1-compartment model with lag time to weighted mean data. ○: observed mean concentration at each time. ‥: individual concentration-time curve.

Time (hr)

0 4 8 12 16 20 24 28 32 36

Levo

sulp

iride

conc

entra

tion

(ng/

ml)

0.1

1

10

100

1000

Table 3. Population pharmacokinetic parameters of levosulpiride by STS method in Korean healthy subjects.

Non-compartmental parameters

AUC0∼∞

(ng hr/∙㎖ )

Cl/F( ㎖ /hr)

Vc/F

( ㎖ )

Cmax(ng/㎖ )

Tmax(hr)

t1/2λz

(hr)

Mean 763.0036295.

31541478.

34 55.31 3.51 10.33

S.D. 233.38 12619.91

256070.40

20.27 1.62 2.96

Compartmental parameters

Ka

(hr-1)

Kel

(hr-1)

Vc/F

( ㎖ )Tlag(hr)

Mean 2.10 0.08 503217.85 0.43

S.D. 2.67 0.02 229706.88 0.37

Fig. 3. Observed concentration versus predicted concentration plot for levosulpiride.

Observed concentration (ng/ml)

0 20 40 60 80 100 120

Pred

icte

d con

cent

ratio

n (ng

/ml)

0

20

40

60

80

100

120

Fig. 4. Terbinafine serum concentration-time plot after oral administration of a 125 ㎎ dose.

○: mean concentration at each time. ＋ : individual serum concentrations.

Time (hr)

0 10 20 30 40 50 60

Terb

inafi

ne c

once

ntra

tion

(ng/

ml)

1

10

100

1000

Table 4. Comparison of AIC value for pharmacokinetic model evaluation.

Model

AIC

No weight

1/obsa)

1/predb) 1/(obs)2 1/(pred)2

C 125.02 86.61 58.65 36.42 -4.29

D 106.05 70.09 42.46 27.07 -14.37

a) obs :observed concentration

b) pred : predicted concentration

Fig. 5. Terbinafine serum concentration-time plot. ━: predicted concentration from fitting 2-compartment model with lag time to weighted mean data. ○: observed mean concentration at each time. ‥: individual concentration-time curve.

Time (hr)

0 10 20 30 40 50 60

Terb

inafi

ne c

once

ntra

tion

(ng/

ml)

0.1

1

10

100

1000

10000

Table 5. Population pharmacokinetic parameters of terbinafine obtained by STS method in Korean healthy subjects.

Non-compartmental parameter

AUC0~∞ (ngㆍ hr/ ㎖ )

Cl/F (㎖/hr)

Vz/F (㎖)

Cmax (ng/㎖)

Tmax (hr)

t1/2λz (hr)

Mean 2509.38 52941.25 1340187.50 715.11 1.24 17.86

S.D. 642.84 12816.07 1128979.42 198.63 0.28 14.74

Compartmental parameters

Ka

(hr-1) Kel

(hr-1) Vc/F (㎖)

Tlag (hr)

Mean 0.61 0.05 139170.34 0.43

S.D. 0.20 0.02 110874.02 0.14

Fig. 6. Observed concentration versus predicted concentration plot for terbinafine.

Observed concentration (ng/ml)

0 200 400 600 800 1000 1200

Pred

icte

d co

ncen

trat

ion

(ng/

ml)

0

200

400

600

800

1000

1200

Y=0.8770X+12.1603r =0.9508

Table 6. Correlation coefficients between PK parameters and subject’s characteristics of levosulpiride.

Parameters Age Weight Height Creatinine

Ka 0.135 -0.024 -0.060 0.010

Kel 0.045 -0.098 -0.161* -0.122

Tlag 0.113 -0.043 -0.064 0.046

Vc/F -0.036 0.276** 0.122 -0.014

Cl/F -0.114 0.294** 0.075 -0.091

* Correlation is significant at the 0.05 level (2-tailed).** Correlation is significant at the 0.01 level (2-tailed).

Table 7. Model building steps for levosulpiride.Model

number Model

Compared with

Change in MOFa)

Inclusion In model

1 1-compartment model with intersubject variability on Cl/F

- - -

2 Model 1 with intersubject variability on Vc/F

1 -474.626 Yes

3 Model 2 with intersubject variability on Ka

2 -161.833 Yes

4 Model 3 with lag time

3 -421.386 Yes

5 Model 4 with intersubject variability on lag time

4 -237.986 Yes

6 Model 5 with weight as covariate for Cl/F

5 34.565 No

7 Model 5 with weight as covariate for Vc/F

5 -27.802 Yes

a) A reduction of the MOF (minimum objective function) of more than 6 units was considered

significant (p<0.01).

Table 8. Population pharmacokinetic parameter estimates of levosulpiride using NONMEM.

Population parameter Estimate

Intersubject

variability

(CV%)

Intrasubject

variability

(CV%)

Cl/F (㎖/hr) 32100 27.60

Vc/F (㎖) 7290weight 35.07

Ka (hr-1) 1.05 67.01

Tlag (hr) 0.39 35.78

24.31

Fig. 7. The plots for the goodness of fit for levosulpiride.(A) Observed concentrations versus predicted concentrations scatter pl

ot.(B) Predicted concentrations versus weighted residuals scatter plot.

<A>

Observed concentration (ng/ml)

1 10 100

Pred

icte

d con

cent

ratio

n (ng

/ml)

1

10

100

<B>

Predicted concentration (ng/ml)

0 10 20 30 40 50 60

Wei

ghte

d res

idua

ls

-20

-15

-10

-5

0

5

10

15

20

Fig. 8. The plot showing the comparison of predictied levosulpiride concentrations between STS method and NONMEM. ＋ : individual levosulpiride observations. ━: predicted concentration obtained by NONMEM. ○: predicted mean concentration at each time by STS method.

Time (hr)

0 4 8 12 16 20 24 28 32 36

Levo

sulp

iride

conc

entra

tion (

ng/m

l)

0.1

1

10

100

1000

Table 8. Correlation coefficients between PK parameters and subject’s characteristics for terbinafine.

Parameters Age Weight Height Creatinine

Ka -0.115 0.014 0.098 -0.058

Kel -0.026 0.160 0.024 0.131

Tlag 0.158 -0.035 -0.003 -0.034

Vc/F 0.079 0.139 0.111 0.170

Cl/F 0.041 0.071 0.012 -0.044

Table 9. Model building steps for terbinafine.

Model number

Model Compared

with Change in MOFa)

Inclusion In model

1 2-compartment model with intersubject variability on Cl/F

- - -

2 Model 1 with intersubject variability on Vc/F

1 12.58 No

3 Model 1 with intersubject variability on Ka

2 -23.977 Yes

4 Model 3 with intersubject variability on Vp/F

3 -67.349 Yes

5 Model 4 with Intersubject variability on Q/F

4 0.003 No

6 Model 4 with lag time

4 -58.674 Yes

7 Model 6 with intersubject variability on lag time

6 -35.738 Yes

a) A reduction of the MOF (minimum objective function) of more than 6 units was considered significant

(p<0.01).

Table 10. Population pharmacokinetic parameter estimates of terbinafine using NONMEM.

Population parameter estimate

Intersubject

variability

(CV%)

Intrasubject

variability

(CV%)

Cl/F (㎖/hr) 52000 21.43

Vc/F (㎖) 12200 -

Ka (hr-1) 0.50 24.00

Vp/F (㎖) 439000 41.37

Q/F (㎖/hr) 25500 -

Tlag (hr) 0.43 13.25

34.43

Fig. 9. The plots for the goodness of fit for terbinafine.(A) Observed concentrations versus predicted concentrations scatter pl

ot.(B) Predicted concentrations versus weighted residuals scatter plot.

<A>

Observed concentration (ng/ml)

1 10 100 1000

Pred

icte

d co

ncen

trat

ion

(ng/

ml)

1

10

100

1000

<B>

Predicted concentration (ng/ml)

0 100 200 300 400 500 600 700

Wei

ghte

d re

sidu

als

-4

-2

0

2

4

6

Fig. 10. The plot showing the comparison of predictied terbinafine concentrations between STS method and NONMEM. ＋ : individual terbinafine observations. ━: predicted concentration obtained by NONMEM. ○: predicted mean concentration at each time by STS method.

Time (hr)

0 6 12 18 24 30 36 42 48 54 60

Terb

inafi

ne c

once

ntra

tion

(ng/

ml)

1

10

100

1000

RESULTS AND CONCLUSIONS (1)

Nonlinear mixed effects modeling was used to fit an one-compartment model with lag time to the pooled levosulpiride data.

There were relationships between body weight and th

e apparent systemic clearance (r=0.294, p<0.01), body weight and the apparent volume of distribution (r=0.276 ,p<0.01).

In an one-compartment covariate model as built by NONMEM, population mean Cl/F, Vc/F, Ka and Tlag were 32100 ㎖ /hr, 7290weight ㎖ , 1.05 hr-1 and 0.39 hr.

RESULTS AND CONCLUSIONS (2)

Nonlinear mixed effects modeling was used to fit a two-compartment model with lag time to the pooled terbinafine data.

There were no relationship between the ph

armacokinetic parameters and subject's weight, age, height and serum creatinine concentration.