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

Freitas, Alex A, Limbu, Kriti and 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

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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. Figure not available: see fulltext. © 2015 Freitas et al.; licensee Springer.

Item Type: Article
Schools and Departments: School of Life Sciences > Biochemistry
Related URLs:
Depositing User: Taravat Ghafourian
Date Deposited: 30 Nov 2017 09:36
Last Modified: 30 Nov 2017 09:36
URI: http://sro.sussex.ac.uk/id/eprint/64126

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