Silico Methodologies Modelling of Aquatic Toxicity in Tetrahymena Pyriformis Via Aromatic Amines

EU Directive for the Protection of Laboratory Animals mandates and encourages the use of alternative methods that could substitute, cut down on, and generally improve animal testing. Quantitative structure-activity relationship models (QSAR) as well as in vitro toxicity testing are among the most notable of such. QSARs are defined as computerized mathematical models that can utilize a compound’s (aromatic amine) biological activity—aquatic toxicity—to calculate or provide the experimental descriptors of the chemical structure of this compound. Multiple Linear Regression (MLR) and Artificial Neural Networks (ANN) are the approaches we use for the aim of predicting aquatic toxicity. The best models for two descriptors are the electrotopological descriptors derived from E-calc, and the partition coefficient derived by the Hyperchem software, applying a genetic algorithm—variable subset selection procedure. The important values of the statistical parameters obtained by the two approaches were as follows: By MLR: R2= 92.18, Q2 = 90.51, Q2ext= 95.26, F=188.5466, S = 0.1995. By ANN were: Q2 = 94.79, RMSE= 0.16, Q2ext= 91.71, RMSEext=0.18.

• aquatic toxicity
• Tetrahymena pyriformis
• aromatic amines
• QSAR
• alternatives to animal testing
• partition coefficient
• variable subset selection
• artificial neural networks
• multiple linear regression
• toxicity prediction

• 6 — المياه النظيفة والنظافة الصحية

• Computational Drug Discovery Methods
• Environmental Toxicology and Ecotoxicology
• Animal testing and alternatives
• aquatic toxicity
• Tetrahymena pyriformis
• aromatic amines
• QSAR
• alternatives to animal testing
• partition coefficient
• variable subset selection
• artificial neural networks
• Computational Theory and Mathematics
• Health, Toxicology and Mutagenesis
• Small Animals
• Computer Science
• Environmental Science
• Veterinary