Data Gap Filling for the Micronucleus Assay and External Validation of the MNQSTR model

Background: Humans and animals are exposed daily to a mixture of pesticides, either directly or indirectly. Most of these pesticides have a direct effect on their genomes. Testing the genotoxicity of pesticides before production is crucial for preventing genotoxic hazards and establishing appropriate usage guidelines or updating existing methods. However, genotoxicity testing is costly. Therefore, it is preferable to predict their toxicity before manufacturing and marketing. Validation of in silico models helps to confirm their accuracy and increase experts' confidence. We aim to address the gap in genotoxicity test results (in vitro micronucleus assay) for ۶۰ pesticides using a validated in vitro method (OECD guideline no ۴۸۷). Subsequently, we will validate the predictive performance of a QSAR model (MNQSTR) developed to predict in vitro micronuclei induction. Methods: In the in vitro micronucleus assay, Chinese Hamster Ovary (CHO) cells were exposed to ۲۰۰ µM of each pesticide for ۲۴ hours. After exposure, an aliquot was removed, and the cells were washed, fixed, and stained. Each slide was then examined under an optical microscope. Subsequently, the SMILES representations of each pesticide were input into the MNQSTR model, and the model's predictions were compared with the in vitro results to calculate its accuracy. Results: Among ۶۰ slides for each pesticide, if the number of micronuclei was significantly different from the negative control (T-test, p < ۰.۰۵), the result was considered positive. The accuracy of the MNQSTR model was ۷۰%. Conclusion: Genotoxicity tests are conducted to assess the effects of chemicals on DNA and chromosomes. In silico methods serve as valuable complementary approaches to in vitro and in vivo tests. In this study, we address the gap in the toxicity profiles of ۶۰ pesticides and evaluate the accuracy of the MNQSTR model, which demonstrated acceptable performance.

کلیدواژه ها:

Micronucleus Assays ، QSAR ، Pesticides ، Validation Study