A systematic review of computational simulation methods for predicting the toxicity of chemical compounds - Akram Tabrizi; Fatemeh Paridokht; Yaser Khorshidi Behzadi; Rezvan Zendehdel (بهداشت و ایمنی کار)
Introduction: With the rapid development of new chemicals across various industries and the growing need for efficient and accurate toxicity assessments, in silico methods have emerged as a screening tool due to their cost-effectiveness, time efficiency, and reduction in animal testing. The aim of this review is to examine the existing studies on the application of in silico methods in predicting the toxicity of chemical compounds in occupational and industrial settings. Material and Methods: This systematic review follows established protocols and is based on data extracted from reputable scientific databases such as PubMed, Scopus, and Web of Science. The review analyzes articles published between 2000 and 2024 that utilized in silico methods for toxicity prediction in occupational toxicology. Inclusion criteria focused on studies that applied modeling, simulation, and prediction methods primarily to chemical toxicity in workplace environments. Also, the quality assessment of the articles was done using the STROBE form. Results: This study surveyed 13 articles on computer simulation of chemical compounds from 2000 to 2024. The majority of research was conducted between 2020 and 2024. The reviewed articles, based on the STROBE form, had a moderate to high quality. Various methods, including Quantitative Structure-Activity Relationship (QSAR), machine learning, and molecular dynamics, were widely used to predict the toxicity of chemical compounds, with the predictive accuracy of these models generally being high. The results also indicated that QSAR methods had the most application in studies predicting the toxicity of chemical compounds used in industries. Conclusion: In silico methods, using molecular descriptors and structural data, have shown high accuracy in predicting toxicity. However, challenges such as limitations in reliable data, the need for model improvement, lack of experimental data, and the complexity of chemical interactions exist. The results indicated that the use of computational methods can significantly reduce the need for animal testing and improve risk assessment. These studies also emphasize the importance of improving and developing predictive models to enhance their accuracy and applicability. Overall, it can be said that modeling can serve as an effective tool in reducing costs and improving safety in workplace environments.