Structure–Activity Relationship (SAR) Modeling of Mosquito Repellents: Deciphering the Importance of the 1-Octanol/Water Partition Coefficient on the Prediction Results
2024
Devillers, James | Devillers, Hugo | CTIS FRA ; Partenaires IRSTEA ; Institut national de recherche en sciences et technologies pour l'environnement et l'agriculture (IRSTEA)-Institut national de recherche en sciences et technologies pour l'environnement et l'agriculture (IRSTEA) | Sciences Pour l'Oenologie (SPO) ; Institut National de Recherche pour l’Agriculture, l’Alimentation et l’Environnement (INRAE)-Institut Agro Montpellier ; Institut national d'enseignement supérieur pour l'agriculture, l'alimentation et l'environnement (Institut Agro)-Institut national d'enseignement supérieur pour l'agriculture, l'alimentation et l'environnement (Institut Agro)-Université de Montpellier (UM) | This research project was funded by the French National Research Program for Environmental and Occupational Health of Anses; PNREST 2019/1/041.
International audience
Show more [+] Less [-]English. Repellents play a fundamental role in vector control and prevention to keep mosquitoes away from humans. Available in limited numbers, it is absolutely necessary to find new repellents for preventing problems of resistance. QSAR (Quantitative Structure–Activity Relationship) methods are particularly suited for designing molecules with potential repellent activity. These models require that the molecules be described by physicochemical properties, topological indices, and/or structural indicators. In the former situation, QSPR (Quantitative Structure–Property Relationship) models are used for calculating physicochemical descriptors. Use of different QSPR models for the same property can lead to different values for the same molecule. In this context, the influence of the 1-octanol/water partition coefficient (log P) calculated according to two different methodologies was statistically evaluated in the modeling of 2171 molecules for which their skin repellent activity against Aedes aegypti was available. The two series of supervised artificial neural networks differed only by their input neuron coding for log P. Although both categories of classification models led to overall good statistics, we clearly showed that differences in log P values calculated for a molecule could result in very different prediction results. This was especially true for repellents. The practical implication of these differences was discussed.
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