Predicting Prenatal Developmental Toxicity Based On the Combination of Chemical Structures and Biological Data

For
hazard identification, classification, and labeling purposes,
animal testing guidelines are required by law to evaluate the developmental
toxicity potential of new and existing chemical products. However,
guideline developmental toxicity studies are costly, time-consuming,
and require many laboratory animals. Computational modeling has emerged
as a promising, animal-sparing, and cost-effective method for evaluating
the developmental toxicity potential of chemicals, such as endocrine
disruptors, without the use of animals. We aimed to develop a predictive
and explainable computational model for developmental toxicants. To
this end, a comprehensive dataset of 1244 chemicals with developmental
toxicity classifications was curated from public repositories and
literature sources. Data from 2140 toxicological high-throughput screening
assays were extracted from PubChem and the ToxCast program for this
dataset and combined with information about 834 chemical fragments
to group assays based on their chemical–mechanistic relationships.
This effort revealed two assay clusters containing 83 and 76 assays,
respectively, with high positive predictive rates for developmental
toxicants identified with animal testing guidelines (PPV = 72.4 and
77.3% during cross-validation). These two assay clusters can be used
as developmental toxicity models and were applied to predict new chemicals
for external validation. This study provides a new strategy for constructing
alternative chemical developmental toxicity evaluations that can be
replicated for other toxicity modeling studies.