Natural pesticides present in edible plants are predicted to be carcinogenic

Area of Science:

• Toxicology
• Computational chemistry
• Carcinogenesis

Background:

• Natural pesticides are widely present in edible plants.
• Assessing the carcinogenic potential of these compounds is crucial for public health.
• Existing methods for evaluating carcinogenicity can be time-consuming and resource-intensive.

Purpose of the Study:

• To evaluate the carcinogenic potential of natural pesticides using an artificial intelligence approach.
• To leverage the US National Toxicology Program (NTP) database for structure-activity relationship (SAR) analysis.

Main Methods:

• Utilized CASE, an artificial intelligence structure-activity evaluation method.
• Analyzed the US National Toxicology Program (NTP) rodent carcinogenicity database.
• Predicted the likelihood of rodent carcinogenicity for natural pesticides found in edible plants.

Main Results:

• CASE predicts that a significant proportion of natural pesticides in edible plants are rodent carcinogens.
• The findings highlight potential risks associated with dietary exposure to certain natural compounds.
• The study demonstrates the utility of AI in predictive toxicology.

Conclusions:

• Artificial intelligence-based methods like CASE can efficiently screen natural pesticides for potential carcinogenicity.
• Further research is warranted to confirm these predictions and assess human health risks.
• This approach can aid in prioritizing compounds for more detailed toxicological investigation.