- On 10 Jul 2004 at 13:14:41, "Osama Mohamed" (mohamedo.-at-.ohsu.edu) sent the message
Hi:

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I have 5 hemodialysis( HD) patients in my pilot clinical study about

administering drugs to HD population. Patients received the drug through

IV infusion via two different methods. We then collected blood samples

at different time points (only four samples from each patient). Now I

have 4 samples from each of the 5 patients (20 points total) for the

first method of drug administration and the same (20 points form 5

patients) for the second method of drug administration.

My Question is, How can I plot the best fitting curve for each method?

In other words, I want to present the 20 points of each method (5 points

at each sampling time) and draw the best fitting curve for them. I can

always draw the best fitting curve or line for each separate patient

data points but this is not what I want to do. I want to draw the best

fitting curve for the whole data family (20 points) for the first method

and another curve for the second method. I think that is called global

curve fitting.

The main problem that my model can not be expressed by a single

equation as the Kel should be changing depending on the time of dialysis

session relative to drug administration time.

Can anyone guide me how to do this ? what is the software I should use?

I am familiar with winnonlin, Kinetica, S-plus and SPSS but I am not

expert in using them. I am doing this study as a part of my PhD in

clinical Pharmacokinetics.

Any suggestions will be highly appreciated.

Thanks,

Osama

Osama H. Mohamed

Ph.D. Candidate in Clinical Pharmacokinetics

College of Pharmacy, Oregon State University - On 11 Jul 2004 at 14:32:57, "Kazimierz H.Kozlowski" (khkoz.aaa.czd.waw.pl) sent the message
Dear Dr Mahomed,

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I propose following:

1) Consider multiple infusions PK model for each patient (2 doseing

occasions)

2) Parametrize model (for 1 kompartment model):

V1=THETA(1)*(1+ETA(1)) ; eta -

population error with mean=0 and wariance fitted

K=THETA(3)**TYPE*THETA(2)*(1+ETA(2)) ; TYPE=0 for standard type of

infusion

; TYPE=1 for modyfied type of infusion

; THETA(3) = parameter for testing infusion types

; if THETA(3)<>1 then K depends on infusions type

2a) Alternatively parametrize model (for 2 kompartment model):

V1=THETA(1)*(1+ETA(1)) ; eta -

population error with mean=0 and wariance fitted

ALPHA=THETA(2)*(1+ETA(2))

BETA=THETA(3)*(1+ETA(3))

K10=THETA(5)**TYPE*THETA(4)*(1+ETA(4)) ; TYPE=0 for standard type

of infusion

; TYPE=1 for modyfied type of infusion

; THETA(5) = parameter for testing infusion types

; if THETA(5)<>1 then K10 depends on infusion type

K21=ALPHA*BETA/K K12=ALPHA+BETA-K21-K10

CLE=V1*K10 ;

total elimination clearance to be also tested

CLD=V1*K12 ;

distribution clearance to be also tested

3) I suggest increasing N until 7 patients (2 extra patients) and

performing population pharmacokinetics comput. 4)

I can doing computations using NONMEM and Figures (without any

obligation)- I working on similar problem

in childrens (but without hemodialysed)

sincerely

Kazimierz H. Kozlowski

Laboratory of Pharmacokinetics

The Childrens Memorial Health Institute

04-736 Warsaw, Poland

E-mail: khkoz.at.czd.waw.pl Osama Mohamed wrote: - On 12 Jul 2004 at 09:36:58, "Steve Duffull" (sduffull.-a-.pharmacy.uq.edu.au) sent the message
Osama

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"My Question is, How can I plot the best fitting curve for

each method?"

I am assuming that you have used a cross-over design where 5 patients

are assigned at random to each infusion method, and then you have 4

blood samples per patient per occasion. It is unclear in what manner

you wish to compare the methods? What are your criteria/criterion for

comparison?

Any form of population approach that you take in modelling the data

(e.g. NONMEM or a 2-stage approach) will be fairly limited due to the

amount of data that you have.

Regards

Steve

Stephen Duffull

School of Pharmacy

University of Queensland

Brisbane 4072

Australia

Tel +61 7 3365 8808

Fax +61 7 3365 1688

University Provider Number: 00025B

Email: sduffull.-a-.pharmacy.uq.edu.au

www: http://www.uq.edu.au/pharmacy/sduffull/duffull.htm

PFIM: http://www.uq.edu.au/pharmacy/sduffull/pfim.htm

MCMC PK example: http://www.uq.edu.au/pharmacy/sduffull/MCMC_eg.htm

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