TargetTox: A Feature Selection Pipeline for Identifying Predictive Targets Associated with Drug Toxicity

In silico assessment
of drug toxicity is becoming
a critical step in drug development. Conventional ligand-based models
are limited by low accuracy and lack of interpretability. Further,
they often fail to explain cellular mechanisms underlying structure–toxicity
associations. We addressed these limitations by incorporating target
profile as an intermediate connecting structure to toxicity. To accommodate
for high-dimensional feature space, we developed a pipeline named
TargetTox that can identity a subset of predictive features. We implemented
TargetTox to study 569 targets and 815 adverse events. The features
identified by TargetTox comprise less than 10% of the original feature
space; nevertheless, they accurately predicted binding outcomes for
377 targets and toxicity outcomes for 36 adverse events. We demonstrated
that predictive targets tend to be differentially expressed in the
tissue of toxicity. We also rediscovered key cellular functions associated
with cardiotoxicity from the predictive targets, as well as markers
of skin and liver diseases. Furthermore, we found evidence supporting
diagnostic and therapeutic applications of some predictive targets
in hepatotoxicity and nephrotoxicity. Our findings highlighted the
critical role of predictive targets in cellular mechanisms leading
to toxicity. In general, our study improved the interpretability of
toxicity prediction without sacrificing accuracy. Our novel pipeline
may benefit future studies of high-dimensional data sets.