Computational in Vitro Toxicology Uncovers Chemical Structures Impairing Mitochondrial Membrane Potential

Abstract

Technological advances in molecular biology have enabled high-throughput screening (HTS) of large chemical libraries. These approaches have provided valuable toxicity data for many physiological responses, including mitochondrial dysfunction. While several quantitative structure-activity relationship (QSAR) models have been developed for mitochondrial dysfunction, there remains a need to identify specific chemical features associated with this response. Thus, the objective of this study was to identify chemical structures associated with altered mitochondrial membrane potential (MMP). To achieve this, we developed computational models to examine the relationship between specific chemotypes (e.g., ToxPrints) and bioactivity in ToxCast/Tox21 HTS assays for altered MMP. The analysis revealed that the bond:COH_alcohol_aromatic , bond:COH_alcohol_aromatic_phenol , and ring:aromatic_benzene ToxPrints had the highest average correlations (phi coefficient) with ToxCast/Tox21 assay component endpoints for decreased MMP. These structures also constituted a core group of ToxPrints for decreased MMP in a force-directed network model and were the most important chemotypes in a random forest (RF) classification model for the TOX21_MMP_ratio_down assay component endpoint. Based on\xa0multiple lines of evidence, these structures, which are present in numerous chemicals (e.g., aromatic hydrocarbons, pesticides, and industrial chemicals) are likely involved in mitochondrial dysfunction. Because of the hierarchical structure of ToxPrints, these chemotypes were highly convergent and, when excluded from training data, had limited effects on the classification performance as related structures compensated for predictor loss. These results highlight the flexibility of the RF algorithm and ToxPrints for QSAR modeling, which is useful to identify chemicals affecting mitochondrial function.