Pragmatic Approaches to Using Computational Methods To Predict Xenobiotic Metabolism

In
this study the performance of a selection of computational models
for the prediction of metabolites and/or sites of metabolism was investigated.
These included models incorporated in the MetaPrint2D-React, Meteor,
and SMARTCyp software. The algorithms were assessed using two data
sets: one a homogeneous data set of 28 Non-Steroidal Anti-Inflammatory
Drugs (NSAIDs) and paracetamol (DS1) and the second a diverse data
set of 30 top-selling drugs (DS2). The prediction of metabolites for
the diverse data set (DS2) was better than for the more homogeneous
DS1 for each model, indicating that some areas of chemical space may
be better represented than others in the data used to develop and
train the models. The study also identified compounds for which none
of the packages could predict metabolites, again indicating areas
of chemical space where more information is needed. Pragmatic approaches
to using metabolism prediction software have also been proposed based
on the results described here. These approaches include using cutoff
values instead of restrictive reasoning settings in Meteor to reduce
the output with little loss of sensitivity and for directing metabolite
prediction by preselection based on likely sites of metabolism.