Insights into Drug Cardiotoxicity from Biological and Chemical Data: The First Public Classifiers for FDA Drug-Induced Cardiotoxicity Rank

Drug-induced cardiotoxicity
(DICT) is a major concern in drug development,
accounting for 10–14% of postmarket withdrawals. In this study,
we explored the capabilities of chemical and biological data to predict
cardiotoxicity, using the recently released DICTrank data set from
the United States FDA. We found that such data, including protein
targets, especially those related to ion channels (e.g., hERG), physicochemical
properties (e.g., electrotopological state), and peak concentration
in plasma offer strong predictive ability for DICT. Compounds annotated
with mechanisms of action such as cyclooxygenase inhibition could
distinguish between most-concern and no-concern DICT. Cell Painting
features for ER stress discerned most-concern cardiotoxic from nontoxic
compounds. Models based on physicochemical properties provided substantial
predictive accuracy (AUCPR = 0.93). With the availability of omics
data in the future, using biological data promises enhanced predictability
and deeper mechanistic insights, paving the way for safer drug development.
All models from this study are available at https://broad.io/DICTrank_Predictor.