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Publication Detail
Automated phenotyping of mosquito larvae enables high-throughput screening for novel larvicides and offers potential for smartphone-based detection of larval insecticide resistance
  • Publication Type:
    Journal article
  • Publication Sub Type:
    Article
  • Authors:
    Buckingham SD, Partridge FA, Poulton BC, Miller BS, McKendry RA, Lycett GJ, Sattelle DB
  • Publisher:
    Public Library of Science (PLoS)
  • Publication date:
    03/06/2021
  • Journal:
    PLOS Neglected Tropical Diseases
  • Volume:
    15
  • Issue:
    6
  • Article number:
    e0008639
  • Status:
    Published
  • Language:
    en
Abstract
Pyrethroid-impregnated nets have contributed significantly to halving the burden of malaria but resistance threatens their future efficacy and the pipeline of new insecticides is short. Here we report that an invertebrate automated phenotyping platform (INVAPP), combined with the algorithm Paragon, provides a robust system for measuring larval motility in Anopheles gambiae (and An. coluzzi) as well as Aedes aegypti with the capacity for high-throughput screening for new larvicides. By this means, we reliably quantified both time- and concentration-dependent actions of chemical insecticides faster than using the WHO standard larval assay. We illustrate the effectiveness of the system using an established larvicide (temephos) and demonstrate its capacity for library-scale chemical screening using the Medicines for Malaria Venture (MMV) Pathogen Box library. As a proof-of-principle, this library screen identified a compound, subsequently confirmed to be tolfenpyrad, as an effective larvicide. We have also used the INVAPP / Paragon system to compare responses in larvae derived from WHO classified deltamethrin resistant and sensitive mosquitoes. We show how this approach to monitoring larval response to insecticides can be adapted for use with a smartphone camera application and therefore has potential for further development as a simple portable field-assay with associated real-time, geo-located information to identify hotspots.
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