Computationally Assessing the Bioactivation of Drugs by N‑Dealkylation

Cytochromes P450
(CYPs) oxidize alkylated amines commonly found
in drugs and other biologically active molecules, cleaving them into
an amine and an aldehyde. Metabolic studies usually neglect to report
or investigate aldehydes, even though they can be toxic. It is assumed
that they are efficiently detoxified into carboxylic acids and alcohols.
Nevertheless, some aldehydes are reactive and escape detoxification
pathways to cause adverse events by forming DNA and protein adducts.
Herein, we modeled N-dealkylations that produce both amine and aldehyde
metabolites and then predicted the reactivity of the aldehyde. This
model used a deep learning approach previously developed by our group
to predict other types of drug metabolism. In this study, we trained
the model to predict N-dealkylation by human liver microsomes (HLM),
finding that including isozyme-specific metabolism data alongside
HLM data significantly improved results. The final HLM model accurately
predicted the site of N-dealkylation within metabolized substrates
(97% top-two and 94% area under the ROC curve). Next, we combined
the metabolism, metabolite structure prediction, and previously published
reactivity models into a bioactivation model. This combined model
predicted the structure of the most likely reactive metabolite of
a small validation set of drug-like molecules known to be bioactivated
by N-dealkylation. Applying this model to approved and withdrawn medicines,
we found that aldehyde metabolites produced from N-dealkylation may
explain the hepatotoxicity of several drugs: indinavir, piperacillin,
verapamil, and ziprasidone. Our results suggest that N-dealkylation
may be an under-appreciated bioactivation pathway, especially in clinical
contexts where aldehyde detoxification pathways are inhibited. Moreover,
this is the first report of a bioactivation model constructed by combining
a metabolism and reactivity model. These results raise hope that more
comprehensive models of bioactivation are possible. The model developed
in this study is available at http://swami.wustl.edu/xenosite/.