Hi:Back to the Top
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
Dear Dr Mahomed,Back to the Top
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:
OsamaBack to the Top
"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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