Ecotoxicological risk assessment of pesticides against different aquatic and terrestrial species: using mechanistic QSTR and iQSTTR modelling approaches to fill the toxicity data gap

Abstract

The toxicity prediction for newly designed or untested pesticides will reduce unnecessary chemical synthesis and animal testing, and contribute to the design of “greener and safer” pesticide chemicals.

The first report on the assessment of maximum acceptable daily intake (MADI) of pesticides for humans using intelligent consensus predictions

“…Kumar et al 27 reported on pesticide sub-chronic and chronic toxicity prediction against dogs using QSAR and chemical read-across. Li et al 28 reported an ecotoxicological risk assessment of pesticides against different aquatic and terrestrial species using mechanistic QSTR (quantitative structure–toxicity relationship) and i-QSTTR (interspecies quantitative structure toxicity–toxicity relationship) modeling approaches to fill the toxicity data gap. Yang et al 29 reported the QSAR modeling of the toxicity of pesticides against Americamysis Bahia .…”

“…[16][17][18][19][20] There has been no QSAR work reported previously on toxicity assessment of pesticides (prediction of maximum acceptable daily intake) specically against humans but some similar studies focusing on pesticide risk assessment embodying similar objectivesexploring less toxic and safer pesticides, though without specically targeting their MADIwere previously reported. 15,[21][22][23][24][25][26][27][28][29][30][31][32] These studies include, for example, several diverse indicator speciescrucial for better understanding the toxicity of the pesticide in different ecosystems, pesticidal mechanisms, and so forth, utilizing either linear or machine learning (ML) non-linear models. However, some of the previous studies reported neither different internal and external validation metrics nor mechanistic interpretations.…”

Identification of structural features of surface modifiers in engineered nanostructured metal oxides regarding cell uptake through ML-based classification

“…Descriptor ndssC is recognized as the most contributing descriptor in the developed model and it denotes the total number of double bonded carbons present in the structure [ 60 ]. The positive contribution of the descriptor is confirmed by the presence of maximum double-bonded carbons in the structures (e.g., 39 , 43 , and 46 ), which actively contribute to a higher cellular uptake of ENMOs in the case of the HUVEC cell line.…”