Received for publication October 3, 2006.
Revised November 22, 2006.
Accepted for publication November 29, 2006.

CLASSIFICATION OF METABOLITES WITH KERNEL PARTIAL LEAST SQUARES (K-PLS)

Abstract

Numerous experimental and computational approaches have been developed to predict human drug metabolism. As databases of human drug metabolism information are widely available these can be used to train computational algorithms and generate predictive approaches. In turn these may be used to assist in the identification of possible metabolites from a large number of molecules in drug discovery based on molecular structure alone. In the current study we have used a commercially available database (MetaDrugTM) and extracted a fraction of the human drug metabolism data. This data was used along with augmented atom descriptors in a predictive machine learning model, Kernel Partial Least Squares (K-PLS). 317 molecules including parent drugs and their primary and secondary (sequential) metabolites were used to build these models corresponding to individual metabolism rules, representing the formation of discrete metabolites e.g. N-dealkylation. Each model was internally validated to assess the capability to classify other molecules that were left out. Using receiver operator curve statistics models for N-dealkylation, O-dealkylation, aromatic hydroxylation, aliphatic hydroxylation, O-glucuronidation and O-sulfation had area under the curve values from 0.75-0.84 and were able to predict between 61-79 % active molecules upon leave-one-out testing. This preliminary study indicates that K-PLS and possibly other similar machine learning methods (such as support vector machines) can be applied to predicting human drug metabolite formation in a classification manner. Improvements can be achieved using considerably larger datasets that contain more positive examples for the less frequently occurring metabolite rules as well as the external evaluation of novel molecules.

Key words: computational models, computer modeling and simulation, metabolite identification, structure-activity relationships

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