Development of a Computational
Tool to Rival Experts
in the Prediction of Sites of Metabolism of Xenobiotics by P450s
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Abstract
The metabolism of xenobioticsand more specifically
drugsin
the liver is a critical process controlling their half-life. Although
there exist experimental methods, which measure the metabolic stability
of xenobiotics and identify their metabolites, developing higher throughput
predictive methods is an avenue of research. It is expected that predicting
the chemical nature of the metabolites would be an asset for designing
safer drugs and/or drugs with modulated half-lives. We have developed
IMPACTS (In-silico Metabolism Prediction by Activated Cytochromes
and Transition States), a computational tool combining docking to
metabolic enzymes, transition state modeling, and rule-based substrate
reactivity prediction to predict the site of metabolism (SoM) of xenobiotics.
Its application to sets of CYP1A2, CYP2C9, CYP2D6, and CYP3A4 substrates
and comparison to experts’ predictions demonstrates its accuracy
and significance. IMPACTS identified an experimentally observed SoM
in the top 2 predicted sites for 77% of the substrates, while the
accuracy of biotransformation experts’ prediction was 65%.
Application of IMPACTS to external sets and comparison of its accuracy
to those of eleven other methods further validated the method implemented
in IMPACTS