In silico Prediction of
Total Human Plasma Clearance
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Abstract
The prediction of the total human plasma clearance of
novel chemical
entities continues to be of paramount importance in drug design and
optimization, because it impacts both dose size and dose regimen.
Although many in vivo and in vitro methods have been proposed, a well-constructed,
well-validated, and less resource-intensive computational tool would
still be very useful in an iterative compound design cycle. A new
completely in silico linear PLS (partial least-squares) model to predict
the human plasma clearance was built on the basis of a large data
set of 754 compounds using physicochemical descriptors and structural
fragments, the latter able to better represent biotransformation processes.
The model has been validated using the “ELASTICO” approach
(Enhanced Leave Analog-Structural, Therapeutic, Ionization Class Out)
based on ten therapeutic/structural analog classes. The model yields
a geometric mean fold error (GMFE) of 2.1 and a percentage of compounds
predicted within 2- and 3-fold error of 59% and 80%, respectively,
showing an improved performance when compared with previous published
works in predicting clearance of neutral compounds, and a very good
performance with ionized molecules at pH 7.5, able to compare favorably
with fairly accurate in vivo methods