In Vitro and Predictive Computational Toxicology Methods for the Neurotoxic Pesticide Amitraz and Its Metabolites

Abstract

The Varroa destructor parasite is responsible for varroasis in honeybees worldwide, the most destructive disease among parasitic diseases. Thus, different insecticides/acaricides have been widely used within beehives to control these parasitic diseases. Namely, amitraz is the most used acaricide due to its high efficacy shown against Varroa destructor. However, pesticides used for beehive treatments could be incorporated into the honey and accumulate in other hive products. Hence, honeybee health and the impairment of the quality of honey caused by pesticides have gained more attention. Amitraz and its main metabolites, N-(2,4-dimethylphenyl) formamide (2,4-DMF) and 2,4-dimethylaniline (2,4-DMA), are known to be potent neurotoxicants. In this research, the cytotoxicity of amitraz and its metabolites has been assessed by MTT and PC assays in HepG2 cells. In addition, possible target receptors by in silico strategies have been surveyed. Results showed that amitraz was more cytotoxic than its metabolites. According to the in silico ADMEt assays, amitraz and its metabolites were predicted to be compounds that are able to pass the blood–brain barrier (BBB) and induce toxicity in the central and peripheral nervous systems. The main target class predicted for amitraz was the family of A G protein-coupled receptors that comprises responses to hormones and neurotransmitters. This affects, among other things, reproduction, development, locomotion, and feeding. Furthermore, amitraz and its metabolites were predicted as active compounds interacting with diverse receptors of the Tox21-nuclear receptor signaling and stress response pathways.

Amitraz and Its Metabolites: Oxidative Stress-Mediated Cytotoxicity in HepG2 Cells and Study of Their Stability and Characterization in Honey

“…For cell culture, a variety of in vitro cytotoxicity tests with various outcomes are available. The cytotoxic effect of amitraz (from 46.88 to 625 µM) and its metabolites 2,4-DMF and 2,4-DMA (from 93.75 to 1500 µM) was evaluated by the MTT assay and PC assay after 24 h of exposure in a previous study carried out in our laboratory [ 9 ]. Amitraz significantly decreased cell viability from 31% to 100% at 156.25 μM by MTT assay and from 69% to 100% at 312.5 μM by PC assay.…”

“…According to earlier research by the Environmental Protection Agency (EPA) and the Joint Meeting of Pesticide Residues (JMPR), amitraz toxicity has been linked to genotoxicity, cell death, neurotoxicity, immunotoxicity, endocrine disruption, and other developmental toxicity. However, the dose–response relationship for a number of mechanisms and toxic effects linked to amitraz and its metabolites remain scarcely studied [ 9 ]. Therefore, more scientific evidence based on toxicity studies are required.…”

ProTox 3.0: a webserver for the prediction of toxicity of chemicals

“…Drug-induced neurotoxicity can result in severe complications in the normal functioning of the brain, leading to various neurological symptoms or disorders ( 12 ). The central nervous system, including the brain and spinal cord, is particularly vulnerable to the effects of neurotoxic substances.…”

“…ProTox platform has been used as lecture module in several universities. The results and analysis obtained using the ProTox server are also regularly reported on the regulatory platform like European Chemicals Agency (ECHA) as additional reports on alternative methods to animal trials ( 12 ).…”

Artificial Intelligence in Predictive Toxicology: Identifying influential topics based on science mapping

“…The rise of contemporary technologies is interesting since terms like "artificial intelligence," "algorithms," and "machine learning" become more prominent throughout time. The intricate interplay between frequency and quartile years adds dimension, illustrating a dynamic landscape where highly talked issues tend to be connected with earlier times (9). The themes' temporal growth, such as the rise of "machine learning" and the advent of "models, chemical," adds a level of curiosity and reflects how scientific research is always changing.…”