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I have a data set that contains the time-concentration profile after oral
administration. The compound follows a two-compartment model. Estimation of ka
value with only oral c-t data available seems tricky, as the Wagner-Nelson
method did not work well with two-compartment model, and Loo-Riegelman method
requires i.v. profile. In addition, Phoenix WinNonlin did not give much useful
information except for mentioning that they used deconvolution method. That
being said, could anybody give some suggestions, or references about this
situation? Thanks a million.
[I didn't think the W-N method was appropriate for a two compartment model although I have seen it used without iv data. How does WinNonlin do a deconvolution with only oral data. If you have enough data to state
'The compound follows a two-compartment model.' why can't you try to model the data with a two compartment model with absorption? db]
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The following message was posted to: PharmPK
You are approaching a potentially very complex problem with the weakest
possible tools. Without knowing more about your compound, its
and your data, it's not possible to say how to best build an adequate PK
model for it. You could have absorption that is limited by solubility,
dissolution, or permeability (including transporter effects) - or a
combination of all of them. You could have first-pass metabolism in the
gut wall and in the liver that affect your C-t data. You could have
complex distribution among various tissues.
One thing you will never have is a constant ka. The value of ka varies
with time, from zero at time zero, to some maximal value, and back to zero
when there is no more drug left in the lumen. It can even become negative
if drug is exsorbed/secreted back into the lumen.
Sometimes a constant ka model can provide an acceptable model, but there
is no guarantee. If you have a compound that is not solubility-limited or
dissolution-limited and has high permeability so that it is absorbed very
quickly in the upper small intestine, it might work. For low solubility,
slow-dissolving formulations, and/or low permeability, assuming a constant
ka can mean that you will adjust other parameters to compensate, and the
PK model will be wrong. If you have only one dose, you can make a model
that looks good because it goes through the data points, but you will be
fitting PK parameters incorrectly to offset the constant ka assumption.
Chairman and CEO
Simulations Plus, Inc. (NASDAQ: SLP)
42505 10th Street West
Lancaster, CA 93534
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