Chemometrics-driven prediction and prioritization of diverse pesticides on chickens for addressing hazardous effects on public health

The extensive use of various pesticides in the agriculture field badly affects both chickens and humans, primarily through residues in food products and environmental exposure. This study offers the first quantitative structure-toxicity relationship (QSTR) and quantitative read-across-structure toxi...

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Bibliographic Details
Published in:Journal of hazardous materials 2024-06, Vol.471, p.134326-134326, Article 134326
Main Authors: Das, Shubha, Samal, Abhisek, Ojha, Probir Kumar
Format: Article
Language:English
Subjects:
Acute toxicity
Algorithms
Animals
Chickens
Machine Learning
Pesticides
Pesticides - toxicity
PPDB
Public Health
Quantitative Structure-Activity Relationship
RASTR
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Summary:The extensive use of various pesticides in the agriculture field badly affects both chickens and humans, primarily through residues in food products and environmental exposure. This study offers the first quantitative structure-toxicity relationship (QSTR) and quantitative read-across-structure toxicity relationship (q-RASTR) models encompassing the LOEL and NOEL endpoints for acute toxicity in chicken, a widely consumed protein. The study's significance lies in the direct link between chemical toxicity in chicken, human intake, and environmental damage. Both the QSTR and the similarity-based read-across algorithms are applied concurrently to improve the predictability of the models. The q-RASTR models were generated by combining read-across derived similarity and error-based parameters, alongside structural and physicochemical descriptors. Machine Learning approaches (SVM and RR) were also employed with the optimization of relevant hyperparameters based on the cross-validation approach, and the final test set prediction results were compared. The PLS-based q-RASTR models for NOEL and LOEL endpoints showed good statistical performance, as traced from the external validation metrics Q2F1: 0.762–0.844; Q2F2: 0.759–0.831 and MAEtest: 0.195–0.214. The developed models were further used to screen the Pesticide Properties DataBase (PPDB) for potential toxicants in chickens. Thus, established models can address eco-toxicological data gaps and development of novel and safe eco-friendly pesticides. [Display omitted] •This work reports the first PLS q-RASTR model for acute toxicity in chicken, the widely consumed source of animal protein.•The developed q-RASTR model is robust and practical for toxicity & risk assessment.•The models identify the essential features of chemicals associated with toxicity against chicken.•The compliance between the predicted acute toxicity by the PLS q-RASTR model with real-world data demonstrates its feasibility for screening acute toxicants in chickens.
ISSN:0304-3894
1873-3336
DOI:10.1016/j.jhazmat.2024.134326