title: Predicting volume of distribution with decision tree-based regression methods using predicted tissue:plasma partition coefficients.
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. Graphical AbstractDecision trees for the prediction of tissue partition coefficient and volume of distribution of drugs.
authors.affiliation: School of Computing, University of Kent, Canterbury, CT2 7NF UK.
authors.forename: Alex A
authors.initials: AA
authors.lastname: Freitas
authors.affiliation: Medway School of Pharmacy, Universities of Kent and Greenwich, Chatham, Kent, ME4 4TB UK.
authors.forename: Kriti
authors.initials: K
authors.lastname: Limbu
authors.affiliation: Medway School of Pharmacy, Universities of Kent and Greenwich, Chatham, Kent, ME4 4TB UK ; Drug Applied Research Centre and Faculty of Pharmacy, Tabriz University of Medical Sciences, Tabriz, Iran.
authors.forename: Taravat
authors.initials: T
authors.lastname: Ghafourian
keywords: ADME; Data mining; Decision tree; Machine learning; Pharmacokinetics; QSAR; QSPkR; Tissue partition; Volume of distribution