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

We saw this behavior with doxorubicin infused over 20 min to parrots and
it seems to be quite reproducible (Gilbert et al. Aust Vet J 2004;
82:769-772) . We put it down to mid-infusion fluctuations in serum
levels as a result of altered cardiac output from the concentrated
infused drug, based on the papers by Richard Upton (Br J Anaesth 2004;
92:475-484 ; Intensive Care Med 2001; 27: 276-282).

 

Cheers

BC

 

Bruce CHARLES, PhD

Associate Professor
School of Pharmacy
The University of Queensland, 4072 Australia
[University Provider Number: 00025B]

 

TEL: +61 7 336 53194
FAX: +61 7 336 51688

 

B.Charles_at_pharmacy.uq.edu.au
http://www.uq.edu.au/pharmacy/brucecharles/charles.html

 

From: owner-nmusers_at_globomaxnm.com [mailto:owner-nmusers_at_globomaxnm.com]
On Behalf Of Bonate, Peter
Sent: Wednesday, July 11, 2007 3:01 AM
To: nmusers_at_globomaxnm.com; PharmPK_at_boomer.org
Subject: [NMusers] mid-infusion higher than end of infusion

 

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_at_genzyme.com

phone: 210-949-8662

fax: 210-949-8219

blackberry cell: 210-315-2713

 


Received on Tue Jul 10 2007 - 20:05:57 EDT