Improving the Ecotoxicological Hazard Assessment of Chemicals by Pairwise Learning

This study demonstrates
how machine learning techniques can bridge
data gaps in the ecotoxicological hazard assessment of chemical pollutants
and illustrates how the results can be used in practice. The innovation
herein consists of the prediction of the sensitivity of all species
that were tested for at least one chemical for all chemicals based
on all available data. As proof of concept, pairwise learning was
applied to 3295 × 1267 (chemical,species) pairs of Observed LC50
data, where only 0.5% of the pairs have experimental data. This yielded
more than four million Predicted LC50s for separate exposure durations.
These were used to create (1) a novel Hazard Heatmap of Predicted
LC50s, (2) Species Sensitivity Distributions (SSD) for all chemicals
based on 1267 species each, as well as (3) for taxonomic groups separately,
and (4) newly defined Chemical Hazard Distributions (CHD) for all
species based on 3295 chemicals each. Validation results and graphical
examples illustrate the utility of the results and highlight species
and compound selection biases in the input data. The results are broadly
applicable, ranging from Safe and Sustainable by Design (SSbD) assessments and setting protective standards to Life Cycle
Assessment of products and assessing and mitigating impacts of chemical
pollution on biodiversity.