2025/01/02 by Saurabh Das, Arnab Bhattacharjee, Probir Kumar Ojha · 1 voice · 1 citation
Agricultural and Biological Sciences · #Insect and Pesticide Research #Pesticide Exposure and Toxicity #Pesticide Residue Analysis and Safety
paper · doi:10.1080/1062936x.2025.2462559
openalex publication_date 2025/01/02 · openalex created_date 2025/10/10 · openalex updated_date 2026/07/21
Pesticides are crucial in modern agriculture, significantly enhancing crop productivity by managing pests. It is important to evaluate their toxicity to minimize health risks to bird species and preserve ecosystem balance. Traditional parameters including lethal concentration (LC50) or median lethal dose (LD50) often underestimate hazards due to limited data and uncertainty about the most sensitive species tested. This limitation can be addressed using extrapolation factors like HD5 accounting for 50% mortality of the most sensitive 5% of bird species. In this research, a QSTR model was developed utilizing a diverse set of 480 pesticides using partial least squares (PLS) regression with 2D descriptors. Additionally, a PLS-based quantitative read-across structure-toxicity relationship (q-RASTR) and classification based models were constructed. The q-RASTR model outperformed traditional QSTR approaches, achieving robust statistical performance with internal validation metrics r2 = 0.623, Q2 = 0.569 and external validation metrics Q2F1 = 0.541, Q2F2 = 0.540. Key factors influencing avian toxicity were identified. The q-RASTR model was used to screen the Pesticide Properties Database (PPDB) to recognize the most and least toxic pesticides for avian species, aligning well with real-world data. This work provides a more economical and ethical alternative to conventional in vivo testing methods, aiding regulatory bodies and industries in developing safer, environmentally friendly pesticides.