Ensemble Quantitative Read-Across Structure–Activity Relationship Algorithm for Predicting Skin Cytotoxicity

Read-across (RA) and quantitative structure–activity
relationship
(QSAR) are two alternative methods commonly used to fill data gaps
in chemical registrations. These approaches use physicochemical properties
or molecular fingerprints of source substances to predict the properties
of unknown substances that have similar chemical structures or physicochemical
properties. Research on RA and QSAR is essential to minimize the time,
money, and animal testing needed to determine biological properties
that are not currently known. This study developed a stacked ensemble
quantitative read-across structure–activity relationship algorithm
(enQRASAR) for predicting skin irritation toxicity based on negative
log cell viability inhibition concentration at 50% (pIC50) against
skin keratinocytes as the end point. The goodness-of-fit and predictability
of this algorithm were validated using leave-one-out cross-validation
and external test data sets. The results obtained were statistically
reliable in terms of goodness-of-fit, robustness, and predictability
metrics. Additionally, the developed model demonstrated a low prediction
error when predicting FDA-approved drugs. These results confirm that
the enQRASAR algorithm can be used to predict skin cytotoxicity of
chemicals. Therefore, this model was publicly available to further
facilitate toxicity predictions of unknown compounds in chemical registrations.