From:"Bonate, Peter"
Subject: [NMusers] signficant covariate question
Date: Tue, 8 Oct 2002 13:48:29 -0500
Dear all,
Not to distract from the discussion on Omega matrices and correlation, but I
have another question to the group. Does anyone know of a drug where a single
covariate "explains" all the between-subject variability in a parameter or acts to
reduce residual variability by a very large degree. I'm thinking maybe a drug
with little metabolism and excreted entirely by the kidney. Hence, creatinine
clearance may be the covariate. I'm looking for a "silver bullet" covariate
here as a teaching example.
Thanks,
pete bonate
Peter L. Bonate, PhD
Director, Pharmacokinetics
ILEX Oncology, Inc
4545 Horizon Hill Blvd
San Antonio, TX 78229
phone: 210-949-8662
fax: 210-949-8487
email: pbonate@ilexonc.com
signficant covariate question
From:Nick Holford
Subject:Re: [NMusers] signficant covariate question
Date:Wed, 09 Oct 2002 08:24:43 +1300
Peter,
In collaboration with Ivan Mathews and Carl Kirkpatrick I have recently examined
this issue with a large aminoglycoside PK dataset. 56% of overall variability in
clearance was predictable from serum creatinine, age, sex and weight (Cockcroft
& Gault model), 36% was unexplained between subject variability and 8% was within
subject (between occasion) variability. A study of topotecan in cancer patients was
able to assign 47% of overall variability in clearance using the same covariates to
predict renal clearance (plus a clinical performance index (ECOG) on non-renal
clearance) (Mould DR, Holford NHG, Schellens JHM, et al. Population pharmacokinetic
and adverse event analysis of topotecan in patients with solid tumors. Clinical
Pharmacology & Therapeutics 2002;71:(5)334-348 ).
So I think this about as good as you can get. Aminoglycosides are excreted ~90%
in the urine. Prediction of renal clearance from creatinine clearance is an unusually
strong mechanistic covariate model. But its still not one "silver bullet" covariate
because you need serum creatinine, age, weight and sex in most cases to predict
creatinine clearance.
Nick
Nick Holford, Divn Pharmacology & Clinical Pharmacology
University of Auckland, 85 Park Rd, Private Bag 92019, Auckland, New Zealand
email:n.holford@auckland.ac.nz tel:+64(9)373-7599x6730 fax:373-7556
http://www.health.auckland.ac.nz/pharmacology/staff/nholford/
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