Clearance mechanism assignment and total clearance prediction in human based upon in silico models.
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.
Metabolism and Pharmacokinetics, Novartis Institutes for Biomedical Research , 250 Massachusetts Avenue, Cambridge, Massachusetts 02139, United States.
Franco
F
Lombardo
R Scott
RS
Obach
Manthena V
MV
Varma
Rowan
R
Stringer
Giuliano
G
Berellini
D000818:Animals; D003198:Computer Simulation; D000882:Haplorhini; D006801:Humans; D016018:Least-Squares Analysis; D008657:Metabolic Clearance Rate; D025341:Principal Component Analysis