Predicting mosquito repellents for clothing application from molecular fingerprint-based artificial neural network SAR models

A critical review of current laboratory methods used to evaluate mosquito repellents

Pathogens transmitted by mosquitoes threaten human health around the globe. The use of effective mosquito repellents can protect individuals from contracting mosquito-borne diseases. Collecting evidence to confirm and quantify the effectiveness of a mosquito repellent is crucial and requires thorough standardized testing. There are multitudes of methods to test repellents that each have their own strengths and weaknesses. Determining which type of test to conduct can be challenging and the collection of currently used and standardized methods has changed over time. Some of these methods can be powerful to rapidly screen numerous putative repellent treatments. Other methods can test mosquito responses to specific treatments and measure either spatial or contact repellency. A subset of these methods uses live animals or human volunteers to test the repellency of treatments. Assays can greatly vary in their affordability and accessibility for researchers and/or may require additional methods to confirm results. Here I present a critical review that covers some of the most frequently used laboratory assays from the last two decades. I discuss the experimental designs and highlight some of the strengths and weaknesses of each type of method covered.

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.

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

Cockroaches are urban pests that are very difficult to control. Using repellents is a green, safe and effective strategy for their control. In order to find novel cockroach repellents, the repellent activity of 45 bridged-ring terpenoid derivatives synthesized from β-pinene against Blattella germanica was tested. The relationship between the molecular structure of these bridged-ring terpenoid derivatives and their repellent activity against Blattella germanica was also analysed. The results show that some of the bridged-ring terpenoid derivatives exhibit good repellent activity against Blattella germanica, and six compounds (RR = 60.44-87.32%) show higher repellent activity against Blattella germanica than DEET (RR = 54.77%), making them promising for development as new cockroach repellents. Quantitative structure-activity relationship (QSAR) analysis revealed that the HOMO-1 energy, Kier and Hall index (order 2), Balaban index, and relative positive charged surface area of bridged-ring terpenoid derivatives have effects on repellent activity against Blattella germanica. The present study may provide a theoretical basis for the high-value use of β-pinene and can be helpful to the development of novel repellents against Blattella germanica.