QSAR Studies on Neuraminidase Inhibitors as Anti-influenza Agents

Molecular Descriptors and QSAR Models for Sedative Activity of Sesquiterpenes Administered to Mice via Inhalation

Essential oils are often utilized for therapeutic purposes and are composed of complex structural molecules, including sesquiterpenes, with high molecular weight and potential for stereochemistry. A detailed study on the properties of selected sesquiterpenes was conducted as part of a broader investigation on the effects of sesquiterpenes on the central nervous system. A set of 18 sesquiterpenes, rigorously selected from an original list of 114, was divided into 2 groups i.e., the training and test sets, with each containing 9 compounds. The training set was evaluated for the sedative activity in mice through inhalation, and all compounds were sedatives at any dose in the range of 4 × 10-4-4 × 10-2 mg/cage, except for curzerene. Molecular determinants of the sedative activities of sesquiterpenes were evaluated using quantitative structure-activity relationship (QSAR) and structure-activity relationship (SAR) analyses. An additional test set of six compounds obtained from the literature was utilized for validating the QSAR model. The parental carbonyl cation and an oxygen-containing groups are possible determinants of sedative activity. The QSAR study using multiple regression models could reasonably predict the sedative activity of sesquiterpenes with statistical parameters such as the correlation coefficient r2 = 0.82 > 0.6 and q2 LOO = 0.71 > 0.5 obtained using the leave-one-out cross-validation technique. Molar refractivity and the number of hydrogen bond acceptors were statistically important in predicting the activities. The present study could help predict the sedative activity of additional sesquiterpenes, thus accelerating the process of drug development.

Double focus in the modelling of anti-influenza properties of 2-iminobenzimidazolines: pharmacology and toxicology

Influenza affects millions of people globally and the appearance of drug-resistant strains is an ongoing problem. Therefore, this work reports the development of quantitative structure-activity relationship (QSAR) models to predict some biological properties of new 2-iminobenzimidazoline candidates for the treatment of the flu. A series of 2-iminobenzimidazoline derivatives with experimentally available values for cytotoxicity (pCC₅₀) and anti-influenza activity (pIC₅₀) was used for multivariate image analysis applied to QSAR (MIA-QSAR). The models were vigorously validated according to the best practices in QSAR and the chemical features responsible for the response variables were analysed based on MIA-plots, which assess the PLS regression coefficients and variable importance in projection scores. MIA descriptors encoding atomic properties (van der Waals radius and electronegativity) were capable of properly modelling the pCC₅₀ and pIC₅₀ data. The internally and externally validated models were used to predict the selectivity indexes (SI = pCC₅₀/pIC₅₀) of unprecedented analogues, which were designed upon analysis of the MIA-plots that show the substituent groups most affecting the biological data and by the combination of substructures of selected molecules. At least three promising anti-influenza candidates could be proposed from the predictive MIA-QSAR models.