Computational Approach to Structural Alerts: Furans, Phenols, Nitroaromatics, and Thiophenes

Structural alerts
are commonly used in drug discovery to identify
molecules likely to form reactive metabolites and thereby become toxic.
Unfortunately, as useful as structural alerts are, they do not effectively
model if, when, and why metabolism renders safe molecules toxic. Toxicity
due to a specific structural alert is highly conditional, depending
on the metabolism of the alert, the reactivity of its metabolites,
dosage, and competing detoxification pathways. A systems approach,
which explicitly models these pathways, could more effectively assess
the toxicity risk of drug candidates. In this study, we demonstrated
that mathematical models of P450 metabolism can predict the context-specific
probability that a structural alert will be bioactivated in a given
molecule. This study focuses on the furan, phenol, nitroaromatic,
and thiophene alerts. Each of these structural alerts can produce
reactive metabolites through certain metabolic pathways but not always.
We tested whether our metabolism modeling approach, XenoSite, can
predict when a given molecule’s alerts will be bioactivated.
Specifically, we used models of epoxidation, quinone formation, reduction,
and sulfur-oxidation to predict the bioactivation of furan-, phenol-,
nitroaromatic-, and thiophene-containing drugs. Our models separated
bioactivated and not-bioactivated furan-, phenol-, nitroaromatic-,
and thiophene-containing drugs with AUC performances of 100%, 73%,
93%, and 88%, respectively. Metabolism models accurately predict whether
alerts are bioactivated and thus serve as a practical approach to
improve the interpretability and usefulness of structural alerts.
We expect that this same computational approach can be extended to
most other structural alerts and later integrated into toxicity risk
models. This advance is one necessary step toward our long-term goal
of building comprehensive metabolic models of bioactivation and detoxification
to guide assessment and design of new therapeutic molecules.