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Vol. 283, Issue 1, 46-58, 1997
Departments of
Drug Metabolism, Pfizer Central Research, Groton,
Connecticut (R.S.O., J.G.B., T.E.L., B.M.S.), and
Sandwich, Kent
(B.C.J., F.M., D.J.R., P.W.), UK
We describe a comprehensive retrospective analysis in which the
abilities of several methods by which human pharmacokinetic parameters
are predicted from preclinical pharmacokinetic data and/or in
vitro metabolism data were assessed. The prediction methods
examined included both methods from the scientific literature as well
as some described in this report for the first time. Four methods were
examined for their ability to predict human volume of distribution.
Three were highly predictive, yielding, on average, predictions that
were within 60% to 90% of actual values. Twelve methods were assessed
for their utility in predicting clearance. The most successful
allometric scaling method yielded clearance predictions that were, on
average, within 80% of actual values. The best methods in which
in vitro metabolism data from human liver microsomes
were scaled to in vivo clearance values yielded predicted clearance values that were, on average, within 70% to 80%
of actual values. Human t1/2 was predicted
by combining predictions of human volume of distribution and clearance.
The best t1/2 prediction methods
successfully assigned compounds to appropriate dosing regimen
categories (e.g., once daily, twice daily and so forth) 70% to 80% of the time. In addition, correlations between human t1/2 and t1/2
values from preclinical species were also generally successful
(72-87%) when used to predict human dosing regimens. In summary, this
retrospective analysis has identified several approaches by which human
pharmacokinetic data can be predicted from preclinical data. Such
approaches should find utility in the drug discovery and development
processes in the identification and selection of compounds that will
possess appropriate pharmacokinetic characteristics in humans for
progression to clinical trials.
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