Understanding Cytotoxicity and Cytostaticity in a High-Throughput Screening Collection

While mechanisms of cytotoxicity
and cytostaticity have been studied
extensively from the biological side, relatively little is currently
understood regarding areas of chemical space leading to cytotoxicity
and cytostasis in large compound collections. Predicting and rationalizing
potential adverse mechanism-of-actions (MoAs) of small molecules is
however crucial for screening library design, given the link of even
low level cytotoxicity and adverse events observed in man. In this
study, we analyzed results from a cell-based cytotoxicity screening
cascade, comprising 296 970 nontoxic, 5784 cytotoxic and cytostatic,
and 2327 cytostatic-only compounds evaluated on the THP-1 cell-line.
We employed an in silico MoA analysis protocol, utilizing
9.5 million active and 602 million inactive bioactivity points to
generate target predictions, annotate predicted targets with pathways,
and calculate enrichment metrics to highlight targets and pathways.
Predictions identify known mechanisms for the top ranking targets
and pathways for both phenotypes after review and indicate that while
processes involved in cytotoxicity versus cytostaticity seem to overlap,
differences between both phenotypes seem to exist to some extent.
Cytotoxic predictions highlight many kinases, including the potentially
novel cytotoxicity-related target STK32C, while cytostatic predictions
outline targets linked with response to DNA damage, metabolism, and
cytoskeletal machinery. Fragment analysis was also employed to generate
a library of toxicophores to improve general understanding of the
chemical features driving toxicity. We highlight substructures with
potential kinase-dependent and kinase-independent mechanisms of toxicity.
We also trained a cytotoxic classification model on proprietary and
public compound readouts, and prospectively validated these on 988
novel compounds comprising difficult and trivial testing instances,
to establish the applicability domain of models. The proprietary model
performed with precision and recall scores of 77.9% and 83.8%, respectively.
The MoA results and top ranking substructures with accompanying MoA
predictions are available as a platform to assess screening collections.