Predicting Fraction Unbound in Human Plasma from Chemical Structure: Improved Accuracy in the Low Value Ranges

Predicting
the fraction unbound in plasma provides a good understanding
of the pharmacokinetic properties of a drug to assist candidate selection
in the early stages of drug discovery. It is also an effective tool
to mitigate the risk of late-stage attrition and to optimize further
screening. In this study, we built in silico prediction models of
fraction unbound in human plasma with freely available software, aiming
specifically to improve the accuracy in the low value ranges. We employed
several machine learning techniques and built prediction models trained
on the largest ever data set of 2738 experimental values. The classification
model showed a high true positive rate of 0.826 for the low fraction
unbound class on the test set. The strongly biased distribution of
the fraction unbound in plasma was mitigated by a logarithmic transformation
in the regression model, leading to improved accuracy at lower values.
Overall, our models showed better performance than those of previously
published methods, including commercial software. Our prediction tool
can be used on its own or integrated into other pharmacokinetic modeling
systems.