Experimental evaluation and structure-activity relationship analysis of bridged-ring terpenoid derivatives as novel Blattella germanica repellent

Development of terpenoid repellents against Aedes albopictus: a combined study of biological activity evaluation and computational modelling

To explore novel terpenoid repellents, 22 candidate terpenoid derivatives were synthesized and tested for their electroantennogram (EAG) responses and repellent activities against Aedes albopictus. The results from the EAG experiments revealed that 5-(2-hydroxypropan-2-yl)-2-methylcyclohex-2-en-1-yl formate (compound 1) induced distinct EAG responses in female Aedes albopictus. At concentrations of 0.1, 1, 10, 100, and 1000 mg/L, the EAG response values for compound 1 were 179.59, 183.99, 190.38, 193.80, and 196.66 mV, demonstrating comparable or superior effectiveness to DEET. Repellent activity analysis indicated significant repellent activity for compound 1, closest to the positive control DEET. The in silico assessment of the ADMET profile of compound 1 indicates that it successfully passed the ADMET evaluation. Molecular docking studies exhibited favourable binding of compound 1 to the active site of the odorant binding protein (OBP) of Aedes albopictus, involving hydrophobic forces and hydrogen bond interactions with residues in the OBP pocket. The QSAR model highlighted the influential role of hydrogen-bonding receptors, positively charged surface area of weighted atoms, polarity parameters of molecules, and maximum nuclear-nuclear repulsion force of carbon-carbon bonds on the relative EAG response values of the tested compounds. This study holds substantial significance for the advancement of new terpenoid repellents.

Nonlinear SAR Modelling of Mosquito Repellents for Skin Application

Finding new marketable mosquito repellents is a complex and time-consuming process that can be optimized via modelling. In this context, a SAR (Structure-Activity Relationship) model was designed from a set of 2171 molecules whose actual repellent activity against Aedes aegypti was available. Information-rich descriptors were used as input neurons of a three-layer perceptron (TLP) to compute the models. The most interesting classification model was a 20/6/2 TLP showing 94% and 89% accuracy on the training set and test set, respectively. A total of 57 other artificial neural network models based on the same architecture were also computed. This allowed us to consider all chemicals both as training and test set members in order to better interpret the results obtained with the selected model. Most of the wrong predictions were explainable. The 20/6/2 TLP model was then used for predicting the potential repellent activity of new molecules. Among them, two were successfully evaluated in vivo.