Weintroduce 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% accuracyin the assignment of the primary clearance mechanism. Furthermore,the PLS model achieved a quantitative predictive performance comparableto methods based on scaling of animal data while not requiring theuse of either in vivo or in vitro data,thus sparing the use of animal. This is likely the highest performancethat can be expected from a computational approach, and further improvementsmay be difficult to reach. We further offer the medicinal scientista PCA model to guide in vitro and/or in vivo studies to help limitthe use of resources via very rapid computations. [ABSTRACT FROM AUTHOR]