Extensive use of pharmaceuticals as human and veterinary medication raises concerns for their adverse effects on non-target organisms. The purpose of this study was to employ multiple linear regression (MLR) to predict the toxicities of a diverse set of pharmaceuticals to fish. The descriptor pool consisted of about 1500 descriptors calculated using Dragon 5.4, Spartan 06 and Codessa 2.2 software. Descriptor selection was made by the heuristic method available in Codessa 2.2. The data set was divided into training and test sets using Kohonen networks. The training set contained approximately 65% of the compounds of the full data set (99 compounds). The training set model contained eight descriptors from all dimensions, all of which were obtained from Dragon 5.4. The statistical parameters of the model for the training set are R ²= 0.664, F = 13.588, and (LOO) = 0.542 while it achieves R ²= 0.605 for the test set. The training, test and external sets have no response outliers considering the standardized residual greater than three. The external validation of the model was made with a set of pharmaceuticals obtained from several databases. The is 0.777, reflecting a relatively good predictive power for the external set.

Fullerene derivatives (FDs) are widely used in nanomaterials production, the pharmaceutical industry and biomedicine. In the present study, we focused on the potential toxic effects of FDs on the aquatic environment. First, we analyzed the binding affinity of 169 FDs to 10 human proteins (1D6U, 1E3K, 1GOS, 1GS4, 1H82, 1OG5, 1UOM, 2F9Q, 2J0D, 3ERT) obtained from the Protein Data Bank (PDB) and showing high similarity to proteins from aquatic species. Then, the binding activity of 169 FDs to the enzyme acetylcholinesterase (AChE)—as a known target of toxins in fathead minnows and Daphnia magna, causing the inhibition of AChE—was analyzed. Finally, the structural aquatic toxicity alerts obtained from ToxAlert were used to confirm the possible mechanism of action. Machine learning and cheminformatics tools were used to analyze the data. Counter-propagation artificial neural network (CPANN) models were used to determine key binding properties of FDs to proteins associated with aquatic toxicity. Predicting the binding affinity of unknown FDs using quantitative structure–activity relationship (QSAR) models eliminates the need for complex and time-consuming calculations. The results of the study show which structural features of FDs have the greatest impact on aquatic organisms and help prioritize FDs and make manufacturing decisions.

Acyl glucuronidation is an important Phase II biotransformation, which is an efficient detoxification mechanism for the metabolism of carboxylic acid group-containing drugs. However, the reactivity of acyl glucuronide (AG) metabolites associated with short half-lives may be an indication of idiosyncratic drug toxicity.The degradation half-lives of AGs elucidate several important reactions such as hydrolysis, acyl migration and covalent binding to proteins. Prediction of degradation half-life using computational methods is a promising alternative approach to costly and time-consuming experiments, enabling a priori evaluation of the properties of drug candidates during the drug design process.The main objective of the present study was to develop a linear model for the quantitative prediction of half-lives of acyl glucuronidated drug-like compounds. The proposed model revealed that the number of total quaternary carbons, the complexity of the ring in the compound, Sanderson electronegativities, and dipole moment of the compound are important molecular features in predicting the half-life of an AG.The rigorously validated model can contribute to a better understanding of molecular features of these drugs to predict degradation half-lives.