Clearance Mechanism Assignment
and Total Clearance
Prediction in Human Based upon in Silico Models
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Abstract
We
introduce a two-tier model based on an exhaustive data set,
where discriminant models based on principal component analysis (PCA)
and partial least squares (PLS) are used separately and in conjunction,
and we show that PCA is highly discriminant approaching 95% accuracy
in the assignment of the primary clearance mechanism. Furthermore,
the PLS model achieved a quantitative predictive performance comparable
to methods based on scaling of animal data while not requiring the
use of either in vivo or in vitro data,
thus sparing the use of animal. This is likely the highest performance
that can be expected from a computational approach, and further improvements
may be difficult to reach. We further offer the medicinal scientist
a PCA model to guide in vitro and/or in vivo studies to help limit
the use of resources via very rapid computations