Synthesis and Bioassay of Improved Mosquito Repellents Predicted From Chemical Structure

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.

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Document Details

Document Type
Technical Report
Publication Date
May 27, 2008
Accession Number
ADA495027

Entities

People

  • Alan R. Katritzky
  • C. D. Hall
  • Dimitar Dobchev
  • Gary G. Clark
  • Kenneth J. Linthicum
  • Maia Tsikolia
  • Novruz G. Akhmedov
  • Svetoslav Slavov
  • Ulrich R. Bernier
  • Zuoquan Wang

Organizations

  • University of Florida

Tags

Communities of Interest

  • Ground and Sea Platforms

DTIC Thesaurus Topics

  • Agriculture
  • Amides
  • Assays
  • Bioassay
  • Carboxylic Acids
  • Chemical Synthesis
  • Chemistry
  • Chlorides
  • Data Sets
  • Dengue
  • Insect Repellents
  • Neural Networks
  • Organic Chemistry
  • Pest Control
  • Test Methods
  • Three Dimensional
  • Yellow Fever

Fields of Study

  • Chemistry

Readers

  • Vector-Borne Disease and Entomology

Technology Areas

  • AI & ML
  • AI & ML - Bayesian Inference