Re: [NMusers] mid-infusion higher than end of infusion

From: Paul Hutson <prhutson_at_pharmacy.wisc.edu>
Date: Tue, 10 Jul 2007 14:11:26 -0500

Peter:
The low hanging fruit answer might be that your drug is binding to neutrophils, and that over the course of the infusion there is enhanced margination of these PMNs.  Marginated to the endothelium, they and their adsorbed drug would not be measured.

PS. Your book is great.
Paul

Bonate, Peter wrote:

Dear all,

 

I have a very unusual situation and wanted to see about getting the collective opinion on the group regarding the best way to handle this modeling problem.  I have a drug that is given by 30 minute infusion.  Samples were collected at predose, mid-infusion, end of infusion, and serial thereafter for 8 halflives.  In about a third of the samples the mid-infusion sample had a considerably higher concentration (25 to 50%)than the end of infusion concentration.  This phenomenon occurred across multiple studies, on multiple days (although not always in the same subject twice), and across multiple analytical runs.  I have ruled out switched tubes and analytical error.  For a variety of reasons this appears to be a valid phenomenon.

 

Now, how best to model it or even explain it.  The best I have been able to come up with is it is a distribution phenomenon.  In discussions with another modeler I was informed that he just reviewed a paper having the same phenomenon and in that paper the authors discarded the midinfusion data.  I have tried using time-dependent volumes using continuous and change-point functions.  I get modest improvements in goodness of fit compared to completely ignoring the phenomenon which has a residual variability of about 30% using a 3-C model.

 

As a company we have decided to pursue an oral formulation of this drug so it seems to me that modeling the iv data to the point of completely capturing the phenomenon may be a modeling exercise and not of any real value any longer.

 

Any opinions on the validity of throwing out the data, just running with the model that ignores the phenomenon and has high residual variability, or something else I haven't been able to think of would be appreciated.

 

Thanks,

 

pete bonate

 

Peter L. Bonate, PhD, FCP

Genzyme Corporation

Senior Director, Pharmacokinetics

4545 Horizon Hill Blvd

San Antonio, TX  78229   USA

peter.bonate@genzyme.com

phone: 210-949-8662

fax: 210-949-8219

blackberry cell: 210-315-2713

 

Received on Tue Jul 10 2007 - 15:11:26 EDT