Nonlinear SAR Modelling of Mosquito Repellents for Skin Application

Abstract

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.

Structure–Activity Relationship (SAR) Modeling of Mosquito Repellents: Deciphering the Importance of the 1-Octanol/Water Partition Coefficient on the Prediction Results

“…The repellent activity of structurally diverse compounds against Aedes aegypti was measured in cage tests under controlled conditions. The test protocol is fully described in Knipling et al [26] and summarized in Devillers et al [25]. The endpoint was the protection time defined as the time in minutes between the treatment with the tested molecule and the first mosquito bite.…”

“…To facilitate the search for structure-repellency relationships, compounds belonging to classes 2 and 3 were discarded and only classes 1 and 4 were selected for SAR modeling. Data cleaning was performed as previously described [25]. This leads to a total of 2171 chemicals.…”

“…Recently, the 2171 compounds and their repellent activity (0 or 1) listed in Table S1 were successfully modeled from a three-layer perceptron (TLP) [27] including 20 molecular descriptors as input neurons [25]. These descriptors encoded topological information (e.g., size, branching), the presence of specific atoms, functional groups, structural features, and physicochemical properties.…”

“…Last, the atom/fragment contribution method of Meylan and Howard [30] is widely used to compute the 1-octanol/water partition coefficients of pesticides and chemicals of environmental concern [23,[32][33][34][35]. Besides MlogP or MHlogP descriptors, 13 topological indices [36] and descriptors encoding specific functional groups and structural fragments in the molecules were also considered in addition to the 19 other descriptors selected in Devillers et al [25]. Selected on the basis of our previous results [25,31], their goal was to find the same optimal set of descriptors for the classification models built either with MlogP or MHlogP descriptors.…”

“…Recently, Devillers et al [25] have proposed a SAR model for predicting the skin repellent activity of molecules against the yellow fever mosquito (Aedes aegypti). The model was computed from a large set of 2171 molecules and among its constitutive parameters, the 1-octanol/water partition coefficient (log P) was found to be a key physicochemical descriptor.…”