Synthesis and bioassay of improved mosquito repellents predicted from chemical structure
ALAN ROY KATRITZKYa✉, Zuoquan Wanga, Svetoslav H. Slavova, Maia Tsikoliaa, Dimitar A. Dobcheva, Novruz G. Akhmedova, C. Dennis Halla, Ulrich R. Bernierb, Gary G. Clarkb, Kenneth J. Linthicumb
- aCenter for Heterocyclic Compounds, Department of Chemistry, 127 Chemistry Research Building, University of Florida, Gainesville, FL 32611-7200; and
- bUnited States Department of Agriculture, Agricultural Research Service, Center for Medical, Agricultural and Veterinary Entomology, Gainesville, FL 32608
- ✉Corresponding author
Papers open inside SciRah from the original publisher or repository; SciRah never hosts them.
Abstract
Mosquito repellency data on acylpiperidines derived from the U.S. Department of Agriculture archives were modeled by using molecular descriptors calculated by CODESSA PRO software. An artificial neural network model was developed for the correlation of these archival results and used to predict the repellent activity of novel compounds of similar structures. A series of 34 promising N-acylpiperidine mosquito repellent candidates (4a-4q') were synthesized by reactions of acylbenzotriazoles 2a-2p with piperidines 3a-3f. Compounds (4a-4q') were screened as topically applied mosquito repellents by measuring the duration of repellency after application to cloth patches worn on the arms of human volunteers. Some compounds that were evaluated repelled mosquitoes as much as three times longer than N,N-diethyl-m-toluamide (DEET), the most widely used repellent throughout the world. The newly measured durations of repellency were used to obtain a superior correlation equation relating mosquito repellency to molecular structure.
TL;DRMosquito repellency data on acylpiperidines derived from the U.S. Department of Agriculture archives were modeled by using molecular descriptors calculated by CODESSA PRO software and a artificial neural network model was developed to predict the repellent activity of novel compounds of similar structures. (Semantic Scholar)