Ecotoxicological QSTR and QSTTR Modeling for the Prediction of Acute Oral Toxicity of Pesticides against Multiple Avian Species

Abstract

The ever-increasing use of pesticides in response to the rising agricultural demand has threatened the existence of nontarget organisms like avian species, disrupting the global ecological integrity. Therefore, it is critical to protect and restore different endangered bird species from the perspective of ecosystem safety. In the present work, we have developed regression-based two-dimensional quantitative structure toxicity relationship (2D QSTR) and quantitative structure toxicity–toxicity relationship (QSTTR) models to estimate the toxicity of pesticides on five different avian species following the Organization for Economic Co-operation and Development (OECD) guidelines. Rigorous validation has been performed using different statistical internal and external validation parameters to ensure the robustness and interpretability of the developed models. From the developed models, it can be stated that the presence of electronegative and lipophilic features greatly enhance pesticide toxicity, whereas the hydrophilic characters are shown to have a detrimental impact on the toxicity of pesticides. Moreover, the developed QSTTR models have been employed to the in silico toxicity prediction of 124, 154, and 250 pesticides against bobwhite quail, ring-necked pheasant, and mallard duck species, respectively, extracted from the Office of Pesticides Program (OPP) Pesticide Ecotoxicity Database. The information obtained from the modeled descriptors might be used for pesticide risk assessment in the future, with the added benefit of providing an early caution of their possible negative impact on birds for regulatory purposes.

On Some Novel Similarity-Based Functions Used in the ML-Based q-RASAR Approach for Efficient Quantitative Predictions of Selected Toxicity End Points

“…A discussion on the quality of the present q-RASAR models with the previously developed QSAR models ,− is presented here.…”

“…We have used here 5 different toxicity data sets for which toxicity predictions were already reported by us in previous QSAR studies. ,− Data set 1 consists of rat androgen receptor binding affinity data of 147 compounds and 8 different structural and physicochemical descriptors, while data set 2 contains acute oral toxicity data of 128 pesticides against bobwhite quail ( C. virginianus ) with 10 different structural and physicochemical descriptors . Data set 3 contains acute contact toxicity data of plant 113 protection products against honey bees ( A. mellifera ) along with 10 different structural and physicochemical descriptors .…”

“…13,20−22 Data set 1 consists of rat androgen receptor binding affinity data of 147 compounds and 8 different structural and physicochemical descriptors, 13 while data set 2 contains acute oral toxicity data of 128 pesticides against bobwhite quail (C. virginianus) with 10 different structural and physicochemical descriptors. 20 Data set 3 contains acute contact toxicity data of plant 113 protection products against honey bees (A. mellifera) along with 10 different structural and physicochemical descriptors. 21 Data set 4 involves acute oral toxicity data of 62 pesticides against mallard duck (A. platyrhynchos) along with 7 different structural and physicochemical descriptors.…”

“…In the present work, we have performed an extensive study to understand the potential of the novel q-RASAR approach. We have taken five toxicity end point-related data sets for which the QSAR analysis has already been published ,− and then applied the q-RASAR approach using the same descriptor combinations as reported previously (Table ). In this work, we have also tried to explore the importance of the RA function, the Banerjee–Roy coefficient ( g m ) and average similarity as RASAR descriptors and how they influence the quality of the q-RASAR models.…”

Quantitative predictions from chemical read-across and their confidence measures

“…Bobwhite Quail ecotoxicity data [20], and androgen receptor binding affinity [21]. We have used the same physicochemical features as reported in the original QSAR reports for the present study.…”