Integrating Concentration-Dependent Toxicity Data and Toxicokinetics To Inform Hepatotoxicity Response Pathways

Failure of animal
models to predict hepatotoxicity in humans has
created a push to develop biological pathway-based alternatives, such
as those that use in vitro assays. Public screening programs (e.g.,
ToxCast/Tox21 programs) have tested thousands of chemicals using in
vitro high-throughput screening (HTS) assays. Developing pathway-based
models for simple biological pathways, such as endocrine disruption,
has proven successful, but development remains a challenge for complex
toxicities like hepatotoxicity, due to the many biological events
involved. To this goal, we aimed to develop a computational strategy
for developing pathway-based models for complex toxicities. Using
a database of 2171 chemicals with human hepatotoxicity classifications,
we identified 157 out of 1600+ ToxCast/Tox21 HTS assays to be associated
with human hepatotoxicity. Then, a computational framework was used
to group these assays by biological target or mechanisms into 52 key
event (KE) models of hepatotoxicity. KE model output is a KE score
summarizing chemical potency against a hepatotoxicity-relevant biological
target or mechanism. Grouping hepatotoxic chemicals based on the chemical
structure revealed chemical classes with high KE scores plausibly
informing their hepatotoxicity mechanisms. Using KE scores and supervised
learning to predict in vivo hepatotoxicity, including toxicokinetic
information, improved the predictive performance. This new approach
can be a universal computational toxicology strategy for various chemical
toxicity evaluations.