Pharmaceutical technologies with potential application to insecticide discovery†

Environmental effects of harvesting some Mexican wild edible insects: An overview

Most traditional edible insects are collected from the forest and agricultural fields, where they are considered pests. However, their importance goes beyond this. They also have an ecological role and potential to be an emerging alternative source of high-quality nutrients that can help satisfy the growing food demand for the human population. Agricultural insect pests are a healthy food source during the harvesting season in many tropical countries. In Mexico, wild insects such as chicatana (queen of flying leaf-cutter ant, Atta mexicana Smith, 1,858; Hymenoptera: Formicidae), chapulín (grasshopper, Pyrgomorphidae), chinicuil (agave red worm, Comadia redtenbacheri Hammerschmidt, 1,848: Lepidoptera, Cossidae), and meocuil (agave white worm, Aegiale hesperiaris Walker 1,856, Lepidoptera, Hesperiidae) are seasonally collected from the agricultural land and forest for food and medicine. Thus, their consumption might be regarded as support for biological plague control. However, in most countries (Mexico included), there is a lack of legislation about edible insects from harvesting to sacrifice and even their main safety aspects. So then, this research aims to provide an updated assessment of the potential use of agricultural pest insects as a sustainable alternative for food, considering current international legislative and ethical concerns about harvesting and consuming wild edible insects, focusing on some of the wild edible pest insects in Mexico.

Innovation in insecticide discovery: Approaches to the discovery of new classes of insecticides

The continuing demand for agrochemical insecticides that can meet increasing grower, environmental, consumer and regulatory requirements creates the need for the development of new solutions for managing crop pest insects. The development of resistance to the currently available insecticidal products adds another critical driver for new insecticidal active ingredients (AIs). One avenue to meeting these challenges is the creation of new classes of insecticidal molecules to act as starting points and prototypes stimulating further spectrum, efficacy and environmental impact refinements. A new class of insecticides is foreshadowed by the first molecule exemplifying that class (first-in-class, FIC) and offers one measure of innovation within the agrochemical industry. Most insecticides owe their discovery to competitor-inspired (i.e. competitor patents/products) or next-generation (follow-on to a company's pre-existing product) strategies. In contrast, FIC insecticides primarily emerge from a bioactive hypothesis approach, with the largest segment resulting from the exploration of new areas of chemistry/heterocycles and underexploited motifs. Natural products also play an important role in the discovery of FIC insecticides. Understanding the origins of these FIC compounds and the approaches used in their discovery can provide insights into successful strategies for future FIC insecticides. This review analyses information on historic and recently introduced FIC insecticides. Its main objective has been to identify the most successful discovery strategies for identifying new agrochemical solutions to meet the challenge of minimizing crop losses resulting from insects. © 2022 Society of Chemical Industry.

Behavioral fingerprints predict insecticide and anthelmintic mode of action

Novel invertebrate-killing compounds are required in agriculture and medicine to overcome resistance to existing treatments. Because insecticides and anthelmintics are discovered in phenotypic screens, a crucial step in the discovery process is determining the mode of action of hits. Visible whole-organism symptoms are combined with molecular and physiological data to determine mode of action. However, manual symptomology is laborious and requires symptoms that are strong enough to see by eye. Here, we use high-throughput imaging and quantitative phenotyping to measure Caenorhabditis elegans behavioral responses to compounds and train a classifier that predicts mode of action with an accuracy of 88% for a set of ten common modes of action. We also classify compounds within each mode of action to discover substructure that is not captured in broad mode-of-action labels. High-throughput imaging and automated phenotyping could therefore accelerate mode-of-action discovery in invertebrate-targeting compound development and help to refine mode-of-action categories.