Computational identification of structural factors affecting the mutagenic potential of aromatic amines: study design and experimental validation.

A grid-based, alignment-independent 3D-SDAR (three-dimensional spectral data-activity relationship) approach based on simulated 13C and 15N NMR chemical shifts augmented with through-space interatomic distances was used to model the mutagenicity of 554 primary and 419 secondary aromatic amines. A robust modeling strategy supported by extensive validation including randomized training/hold-out test set pairs, validation sets, "blind" external test sets as well as experimental validation was applied to avoid over-parameterization and build Organization for Economic Cooperation and Development (OECD 2004) compliant models. Based on an experimental validation set of 23 chemicals tested in a two-strain Salmonella typhimurium Ames assay, 3D-SDAR was able to achieve performance comparable to 5-strain (Ames) predictions by Lhasa Limited's Derek and Sarah Nexus for the same set. Furthermore, mapping of the most frequently occurring bins on the primary and secondary aromatic amine structures allowed the identification of molecular features that were associated either positively or negatively with mutagenicity. Prominent structural features found to enhance the mutagenic potential included: nitrobenzene moieties, conjugated π-systems, nitrothiophene groups, and aromatic hydroxylamine moieties. 3D-SDAR was also able to capture "true" negative contributions that are particularly difficult to detect through alternative methods. These include sulphonamide, acetamide, and other functional groups, which not only lack contributions to the overall mutagenic potential, but are known to actively lower it, if present in the chemical structures of what otherwise would be potential mutagens.

Why are most phospholipidosis inducers also hERG blockers?

Recent reports have noted that a number of compounds that block the human Ether-à-go-go related gene (hERG) ion channel also induce phospholipidosis (PLD). To explore a hypothesis explaining why most PLD inducers are also hERG inhibitors, a modeling approach was undertaken with data sets comprised of 4096 compounds assayed for hERG inhibition and 5490 compounds assayed for PLD induction. To eliminate the chemical domain effect, a filtered data set of 567 compounds tested in quantitative high-throughput screening (qHTS) format for both hERG inhibition and PLD induction was constructed. Partial least squares (PLS) modeling followed by 3D-SDAR mapping of the most frequently occurring bins and projection on to the chemical structure suggested that both adverse effects are driven by similar structural features, namely two aromatic rings and an amino group forming a three-center toxicophore. Non-parametric U-tests performed on the original 3D-SDAR bins indicated that the distance between the two aromatic rings is the main factor determining the differences in activity; at distances of up to about 5.5 Å, a phospholipidotic compound would also inhibit hERG, while at longer distances, a sharp reduction of the PLD-inducing potential leaves only a well-pronounced hERG blocking effect. The hERG activity itself diminishes after the distance between the centroids of the two aromatic rings exceeds 12.5 Å. Further comparison of the two toxicophores revealed that the almost identical aromatic rings to amino group distances play no significant role in distinguishing between PLD and hERG activity. The hypothesis that the PLD toxicophore appears to be a subset of the hERG toxicophore explains why about 80% of all phospholipidotic chemicals (the remaining 20% are thought to act via a different mechanism) also inhibit the hERG ion channel. These models were further validated in large-scale qHTS assays testing 1085 chemicals for their PLD-inducing potential and 1570 compounds for hERG inhibition. After removal of the modeling and experimental inconclusive compounds, the area under the receiver-operating characteristic (ROC) curve was 0.92 for the PLD model and 0.87 for the hERG model. Due to the exceptional ability of these models to recognize safe compounds (negative predictive values of 0.99 for PLD and 0.94 for hERG were achieved), their use in regulatory settings might be particularly useful.