QSAR modelling of synergists to increase the efficacy of deltamethrin against pyrethroid-resistant Aedes aegypti mosquitoes$. Issue 8 (3rd August 2018)

• Record Type: Journal Article
• Authors: Devillers, J.; Larghi, A.; Lagneau, C.
• Is Part Of: SAR and QSAR in environmental research. Volume 29:Issue 8(2018)
• Page Start: 613
• Page End: 629
• Publication Date: 2018-08-03
• Subjects: Aedes aegypti -- resistance -- pyrethroids -- synergist -- three-layer perceptron -- nonlinear QSAR; Structure-activity relationships (Biochemistry) -- Periodicals; QSAR (Biochemistry) -- Periodicals; 572.4
• DOI: 10.1080/1062936X.2018.1503846
• Languages: English
• ISSNs: 1062-936X

Abstract

ABSTRACT: Space spraying of deltamethrin allows the control of adult Aedes ( Stegomyia ) aegypti mosquitoes. Unfortunately, many vector control programs are threatened by the development of resistances that decrease the efficacy of this adulticide. Faced with this situation, we can either try to use another insecticide presenting a different mechanism of action or find a strategy that brings back the efficacy of the insecticide at a satisfying level to pursue its use in vector control. Restoration of the efficacy of an insecticide can be obtained by means of a synergist. In this context, QSAR modelling was used to find synergists to combine with deltamethrin for increasing its efficacy against resistant strains of Ae. aegypti . Seventy-four structurally diverse chemicals with their 24-hour LD50 values, obtained under the same experimental conditions on Ae. aegypti females, were used. Molecules were described by means of autocorrelation vectors encoding lipophilicity, molar refractivity, H-bonding acceptor and donor ability. A three-layer perceptron (TLP) was employed as statistical tool. The performances of the models were evaluated through the analysis of the prediction results obtained on the different training and test sets (80%/20%) as well as from an out-sample test set. A 6/4/1 TLP computed with the Broyden–Fletcher–Goldfarb–Shanno second-order training algorithm led to the best prediction results. The convergence was obtained in 132 cycles. The sum of squares was used as error function. The hidden and output activation functions were tanh and exponential, respectively. Various chemical structures were identified as potential synergists and searched for their commercial availability. Molecules of interest were tested in vivo on Ae. aegypti by using the susceptible reference Bora Bora strain and two resistant strains from Martinique island. This led to the identification of the PSM-05 molecule that shows interesting synergistic activity.