The Application of Artificial Neural Networks to the Identification of New Spinosoids with Improved Biological Activity toward Larvae of Heliothis virescens

The spinosyns are a new class of fermentation-derived insect control agents that are effective against a variety of lepidopteran insect pests, including the tobacco budworm Heliothis virescens. Efforts to improve the efficacy of the spinosyns led to synthesis of a variety of spinosoids, synthetic or semisynthetic analogs of the naturally occurring spinosyns. One approach used to help define synthetic directions for the spinosoids includes the application of artificial neural networks. Artificial neural network-based analysis of the naturally occurring spinosyns suggested a number of synthetic directions that could potentially result in spinosoids with improved biological activity compared to spinosyn A. One of the artificial neural network-derived synthetic improvements involved increasing the alkyl chain length of methoxy groups of the 2ʹ,3ʹ,4ʹ-tri-0methyirhamnosyl moiety. The 2\3ʹ,4ʹ-tri-0-ethyl analog was predicted to be more active than spinosyn A, and upon synthesis and testing in a variety of bioassays, this was indeed found to be the case. Depending on the bioassay, the 2ʹ,3ʹ,4ʹ-tri-0-ethyl analog was between 3.8 and 13-fold more active than spinosyn A. Further artificial neural network-based analysis suggested that the 3ʹ position would be responsible for most of the observed improvement in activity. Again, as predicted by the neural network models, the 3ʹ-(9-ethyl analog was about as active as the 2ʹ,3ʹ,4ʹ-tri-0-ethyl analog, while the 2ʹ- or 4ʹ-0-ethyl analogs were much less active. Thus, the use of a novel approach to quantitative structure-activity relationships has allowed the identification of new spinosoids with biological activity against larvae of H. virescens superior to that observed in the naturally occurring spinosyns.