Predicting Fraction Unbound in Human Plasma from Chemical Structure: Improved Accuracy in the Low Value Ranges.
Mol Pharm
; 15(11): 5302-5311, 2018 11 05.
Article
de En
| MEDLINE
| ID: mdl-30259749
ABSTRACT
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.
Mots clés
Texte intégral:
1
Collection:
01-internacional
Base de données:
MEDLINE
Sujet principal:
Plasma sanguin
/
Pharmacocinétique
/
Découverte de médicament
/
Modèles biologiques
Type d'étude:
Prognostic_studies
/
Risk_factors_studies
Limites:
Humans
Langue:
En
Journal:
Mol Pharm
Sujet du journal:
BIOLOGIA MOLECULAR
/
FARMACIA
/
FARMACOLOGIA
Année:
2018
Type de document:
Article