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.