Predicting Chemical-Induced Liver Toxicity Using High-Content Imaging Phenotypes and Chemical Descriptors: A Random Forest Approach

Hepatotoxicity
is a major reason for the withdrawal or discontinuation
of drugs from clinical trials. Thus, better tools are needed to filter
potential hepatotoxic drugs early in drug discovery. Our study demonstrates
utilization of HCI phenotypes, chemical descriptors, and both combined
(hybrid) descriptors to construct random forest classifiers (RFCs)
for the prediction of hepatotoxicity. HCI data published by Broad
Institute provided HCI phenotypes for about 30 000 samples
in multiple replicates. Phenotypes belonging to 346 chemicals, which
were tested in up to eight replicates, were chosen as a basis for
our analysis. We then constructed individual RFC models for HCI phenotypes,
chemical descriptors, and hybrid (chemical and HCI) descriptors. The
model that was constructed using selective hybrid descriptors showed
high predictive performance with 5-fold cross validation (CV) balanced
accuracy (BA) at 0.71, whereas within the given applicability domain
(AD), independent test set and external test set prediction BAs were
equal to 0.61 and 0.60, respectively. The model constructed using
chemical descriptors showed a similar predictive performance with
a 5-fold CV BA equal to 0.66, a test set prediction BA within the
AD equal to 0.56, and an external test set prediction BA within the
AD equal to 0.50. In conclusion, the hybrid and chemical descriptor-based
models presented here should be considered as a new tool for filtering
hepatotoxic molecules during compound prioritization in drug discovery.