Machine Learning-Based Quantitative Structure Activity Relationship Modeling of Repeated Dose Toxicity: A Data-Driven Approach Following Organisation for Economic Co-operation and Development Test Guidelines 407, 408, and 422 Supported by Experimental Validation

In
recent years, the rapid increase in the production and environmental
release of synthetic organic chemicals has raised serious concerns
about their potential adverse effects on human health and the environment.
Repeated exposure to such substances can lead to significant toxicological
effects, underscoring the importance of early and reliable hazard
assessment. However, experimental determination of repeated-dose toxicity
(RDT) is costly, time-consuming, and constrained by ethical considerations.
In this study, we developed various classification-based predictive
models to evaluate the subchronic RDT potential of chemicals after
oral exposure. We compiled data from eChemPortal and J-CHECK databases.
The data set contains two study-derived effect levels: NOAEL (no observed
adverse effect level) and LOAEL (lowest observed adverse effect level),
for which separate models have been developed. A key strength of this
data set is that all studies followed standardized OECD test guidelines
(407, 408, and 422) and were conducted under good laboratory practice
(GLP) conditions, ensuring regulatory relevance and high data reliability.
Multiple machine learning algorithms were systematically evaluated,
and the best models were selected using a multicriteria analysis based
on the sum of ranking differences (SRD) technique. The final selected
models achieved accuracies on the training sets ranging from 0.665
to 0.902, while the test sets showed accuracies ranging from 0.642
to 0.682. We also conducted a substructure analysis to identify the
key substructures involved in the toxicity. This analysis revealed
eight structural motifs, with chlorine- and amine-group-containing
aromatic systems being particularly significant. The final developed
models were experimentally validated using chemical substances provided
by Global Product Compliance (GPC) Europe AB. Additionally, the models
were applied to the Pesticides Properties DataBase (PPDB) to screen
for pesticides with potential toxicity upon repeated exposure. To
facilitate accessibility and regulatory application, the final models
have been implemented in both a Python-based tool and a web application. Scientific contribution: this study
presents predictive models as alternatives to traditional animal testing
for assessing the subchronic oral repeated-dose toxicity (RDT) of
chemicals. Our models demonstrate strong statistical performance,
indicating their suitability for further application, as supported
by experimental validation. These models could be used for preliminary
hazard screening or weight-of-evidence evaluations. An additional
advantage is that these models were developed using data that were
tested in accordance with internationally harmonized test protocols,
thereby enhancing their acceptance for regulatory decision-making.