Abstract
Research on compound toxicity is a crucial step in introducing biologically active substances as potential medicinal agents. Studying the toxic effects of a compound upon single administration provides detailed information on the dose-toxicity relationship. These data are crucial for establishing effective doses during repeated exposure toxicity studies. To introduce this promising compound as a potential antifungal agent, additional toxicity studies are necessary. Such an approach will determine the safety of this compound before its further use in medical practice.
The aim of our study is to investigate the acute toxicity of 2-(((3-(2-fluorophenyl)-5-mercapto-4H-1,2,4-triazol-4-yl)imino)methyl)phenol using both computer prediction methods and in vivo experimental studies.
For the prediction of 2-(((3-(2-fluorophenyl)-5-mercapto-4H-1,2,4-triazol-4-yl)imino)methyl)phenol toxicity in silico, computer programs GUSAR (Germany), ProTox 3.0 (Germany), TEST (USA), and pkCSM (Australia) were utilized. This approach significantly reduces the number of studies required to determine substance toxicity. The acute toxicity study of 2-(((3-(2-fluorophenyl)-5-mercapto-4H-1,2,4-triazol-4-yl)imino)methyl)phenol was conducted in vivo using the V. B. Prozorovsky express-method on white nonlinear rats of both sexes. The Student's parametric criterion was used for statistical data processing.
As a result of applying toxicity prediction models, it was established that the investigated compound belongs to substances of low toxicity. Such an approach to toxicity assessment allows for a quick and effective conclusion regarding the risks associated with the use of this substance and determining potential safety and risks. According to K. K. Sidorov's classification, 2-(((3-(2-fluorophenyl)-5-mercapto-4H-1,2,4-triazol-4-yl)imino)methyl)phenol belongs to the IV toxicity class. This is important information for assessing the risks associated with the use of this compound.
According to the results of computer prediction, acute toxicity indicators with high values of cross-validation and correlation coefficients were established. This indicates the potential feasibility of using the QSAR analysis computer method in further research.
Publisher
The State Expert Center of the Ministry of Health of Ukraine
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