Predicting volume of distribution with decision tree-based regression methods using predicted tissue:plasma partition coefficients

Freitas, Alex A., Limbu, Kriti, Ghafourian, Taravat (2015) Predicting volume of distribution with decision tree-based regression methods using predicted tissue:plasma partition coefficients. Journal of Cheminformatics, 7 (6). ISSN 1758-2946. (doi:10.1186/s13321-015-0054-x)

Abstract

Background Volume of distribution is an important pharmacokinetic property that indicates the extent of a drug’s distribution in the body tissues. This paper addresses the problem of how to estimate the apparent volume of distribution at steady state (Vss) of chemical compounds in the human body using decision tree-based regression methods from the area of data mining (or machine learning). Hence, the pros and cons of several different types of decision tree-based regression methods have been discussed. The regression methods predict Vss using, as predictive features, both the compounds’ molecular descriptors and the compounds’ tissue:plasma partition coefficients (Kt:p) – often used in physiologically-based pharmacokinetics. Therefore, this work has assessed whether the data mining-based prediction of Vss can be made more accurate by using as input not only the compounds’ molecular descriptors but also (a subset of) their predicted Kt:p values. Results Comparison of the models that used only molecular descriptors, in particular, the Bagging decision tree (mean fold error of 2.33), with those employing predicted Kt:p values in addition to the molecular descriptors, such as the Bagging decision tree using adipose Kt:p (mean fold error of 2.29), indicated that the use of predicted Kt:p values as descriptors may be beneficial for accurate prediction of Vss using decision trees if prior feature selection is applied. Conclusions Decision tree based models presented in this work have an accuracy that is reasonable and similar to the accuracy of reported Vss inter-species extrapolations in the literature. The estimation of Vss for new compounds in drug discovery will benefit from methods that are able to integrate large and varied sources of data and flexible non-linear data mining methods such as decision trees, which can produce interpretable models.

Item Type:	Article
DOI/Identification number:	10.1186/s13321-015-0054-x
Uncontrolled keywords:	data mining, machine learning, volume of distribution, pharmacokinetics, cheminformatics, pharmaceutical sciences
Subjects:	Q Science > Q Science (General) > Q335 Artificial intelligence
Divisions:	Faculties > Sciences > School of Computing > Computational Intelligence Group
Depositing User:	Alex Freitas
Date Deposited:	27 Mar 2015 17:28 UTC
Last Modified:	29 May 2019 14:23 UTC
Resource URI:	https://kar.kent.ac.uk/id/eprint/47803 (The current URI for this page, for reference purposes)