Comparative Chemical Space Analysis of Pesticides and Substances with Genotoxicity Data.

Experimental genotoxicity data are required for pesticidal and biocidal active substances prior to regulatory approval, while for their metabolites and impurities, in silico predictions are often accepted. Nonetheless, the extent to which these compounds are represented in publicly available genotoxicity databases remains unclear. Herein, we utilize chemical space methods to define the overlap between pesticide substances (active substances, metabolites, and impurities) and activity data for six genotoxicity test types commonly employed in regulatory toxicology: the Ames test, the in vitro mammalian cell gene mutation test, the in vitro micronucleus test, the in vitro chromosomal aberration test, the in vivo micronucleus test, and the in vivo chromosomal aberration test. After merging and performing structure standardization on 18 public pesticide/biocide databases, we identified 4826 unique substances. Within 19 public genotoxicity databases, 19,897 substances had at least one data point in at least one genotoxicity test. The chemical space overlap between the pesticide substances and each genotoxicity set was evaluated by calculating physicochemical descriptors and molecular fingerprints, which were visualized by using dimensionality reduction methods. The chemical space of pesticide substances is well represented by substances with Ames test data and, to varying degrees, by substances with data from the other genotoxicity tests, with particularly low coverage for in vivo chromosomal aberration. The major scaffolds identified in pesticide substances were present in all of the genotoxicity data sets. Compared to pesticide substances, the genotoxicity data sets were enriched in functional groups characteristic of genotoxic compounds, such as annulated rings, but depleted in pesticide-typical structural motifs like halogens. Chemical space methods can assist regulatory toxicologists in understanding regions of pesticide substance chemical space that are well- or poorly characterized by genotoxicity data. This understanding is important for the accurate and targeted use of databases and data-based nontesting methods in line with regulatory requirements.