Article Highlights
- A recent study introduced a novel approach integrating pesticide interactions with environmental markers into toxicity prediction models.
- Focus was on acetolactate synthase inhibitor herbicides, using spectroscopic techniques and molecular docking to investigate their interaction with human serum albumin (HSA) and superoxide dismutase (SOD).
- Predictive models constructed based on carrier protein binding information show high accuracy in predicting toxicity to humans and animals, as well as environmental toxicity.
- Extension of predictive modeling to include SOD binding information marks the first attempt to predict pesticide toxicity to the environment.
In the agriculture industry, the use of pesticides is essential to protect and preserve the crops. However, many pesticides contain chemical compounds that can harm not only the target pests but also non-target organisms, including humans. These chemicals can also emerge in the environment, contaminating water sources, soil, and the food chain, which threatens biodiversity and ecosystems. Numerous studies have commented on how the inappropriate use of pesticides has resulted in an increase of residues on plants and crops, threatening human health (1,2).
As a result, there is a need for sustainable and eco-friendly pest management strategies. A recent study conducted by researchers at Heilongjiang University explored this issue. The study, published in Spectrochimica Acta Part A: Molecular and Biomolecular Spectroscopy, introduced a novel approach that incorporates the interaction between pesticides and environmental markers into toxicity prediction models (3).
Read More: Novel LC–MS/MS Method with a Dual ESI and APCI Ion Source for Analysis of California-Regulated Pesticides and Mycotoxins in Medium-Chain Triglyceride (MCT) Oil Cannabis Tinctures
The research focuses on acetolactate synthase inhibitor herbicides (ALS inhibitor herbicides), a common type of pesticide (3). The research team wanted to investigate the interaction between ALS inhibitor herbicides and human serum albumin (HSA) as well as superoxide dismutase (SOD), an environmental marker (3). The research team accomplished this objective by using spectroscopic techniques and molecular docking, yielding crucial fluorescence parameters, and revealing changes in protein conformation upon pesticide binding.
The researchers then constructed their predictive models for ALS inhibitor herbicides’ toxicity. To do this, they used the carrier protein binding information (3). These models demonstrated high accuracy in predicting toxicity to humans and animals, with a coefficient of determination (R2) of 0.977 for human and animal toxicity (3).
However, what sets this study apart is its extension to predict environmental toxicity. By incorporating SOD binding information into the predictive modeling, the researchers established a model with an R2 value of 0.883 for predicting the potential environmental toxicity of ALS inhibitor herbicides (3). This marks the first time such an approach has been taken to predict pesticide toxicity to the environment.
Read More: A Near-Infrared Fluorescent Probe for Monitoring Butyrylcholinesterase Activity and Detecting Pesticide Residue is Developed
The implications of this research are significant. By accurately predicting the toxicity of pesticides to both living organisms and the environment, this method could revolutionize pesticide development (3). With the ability to assess environmental impact during the early stages of pesticide development, researchers can work towards creating safer, more environmentally friendly pesticides (3).
The study not only advances our understanding of pesticide toxicity but also provides a practical tool for developing low-toxicity pesticides. By incorporating environmental markers into toxicity prediction models, this research opens new avenues for sustainable pesticide development, ensuring the protection of both human health and the environment.
This article was written with the help of artificial intelligence and has been edited to ensure accuracy and clarity. You can read more about our policy for using AI here.
References
(1) Armenta, S.; Garrigues, S.; Guardia, M. Determination of Iprodione in Agrochemicals by Infrared and Raman Spectrometry. Anal. Bioanal. Chem. 2007, 387 (8), 2887–2894. DOI: 10.1007/s00216-007-1152-z
(2) Wu, J.-C.; Qiu, H.-M.; Yang, G.-Q.; et al. Effective Duration of Pesticide-induced Susceptibility of Rice to Brown Planthopper (Nilaparvata lugens Stal, Homoptera: Delphacidae), and Physiological and Biochemical Changes in Rice Plants Following Pesticide Application. Int. J. Pest Manage. 2004, 50, 55–62. DOI: 10.1080/09670870310001630397
(3) Li, X.; Gao, X.; Fu, B.; et al. Study on the Toxicity Prediction Model Ofacetolactate Synthase Inhibitor Herbicides based on Human Serum Albumin and Superoxide Dismutase Binding Information. Spectrochimica Acta Part A: Mol. Biomol. Spectrosc. 2024, 309, 123789. DOI: 10.1016/j.saa.2023.123789
