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Hi there, I have two questions about PK modeling, one question about
the learning chance and one suggestion for this discussion group.
Thank you very much in advance for the information you=EDll give.
1. I am doing simultaneous curve fitting for drug concentration-time
data in several tissues using compartmental models with PCNonlin.
Since drug concentration magnitude is very different, say, one tissue
concentration is 700 mg/g and another is 0.5 mg/g. It was suggested to
transform the data into dimensionless value to get the concentrations at the
same scale or even use the logarithmic transformation to decrease the data
distance. My questions are (1) is there any reference about such data
transformation in PK, (2) how to transform the data into the same magnitude and
(3) if I transform the data by dividing them by their averages, which way I
should go, transforming the data first and then doing the modeling or using the
averages as weighting factors-but how to write the weighting command for the
average weighting option.
2. Is there any "tricky" part about fitting infusion data from both infusion
and postinfusion periods? I can use following differential equations to
simultaneously fit the data of both periods.
If t>240, then
The problem is I can fit the infusion period well but the results of
simultaneous fit were not satisfying, specially the postinfusion period. Is
this problem model-related or drug disposition different between the infusion
and postinfusion periods?
3. Is there any cheap way for a graduate student to learn how to use the
program, Nonmem? Any online course about the PK programs?
4. I have found the discussions in this PK and PD group are very informative.
But how about an idea that there is a monthly important PK/PD topic discussion
brought up by some experts besides the individual Q&A pattern.
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Response to Tony Lee:
Your problem concerning what you call a large "data distance" is very
physiological modeling because biological concentrations can range from
1E-2 M down to
1E-15 M or even less. A standard solution to this large dynamic range
using relative weighting instead of absolute weighting of the data to be
not familiar with the way PCNonlin handles this situation, but I'm quite
could solve this problem easily in SAAM II.
I believe you could also reproduce your infusion-postinfusion protocol
quite easily in
SAAM II, and all without writing code or having to own a compiler.
Last I knew, the SAAM Institute was still offering deep student discounts
purchase of SAAM II. You can reach them at 1-800-421-SAAM or
I've used the SAAM II software for several years, and have taught it to
more than 100
undergraduate and graduate students. It is *very* easy to use.
Good luck and let me know how you make out.
Robert D Phair PhD: rphair.-at-.ix.netcom.com
BioInformatics Services: http://www.webcom.com/rphair
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