Background

Insecticides are an essential component of the fight against vector-borne infectious diseases, from Zika to dengue fever to malaria, the last of these being responsible for 400,000 deaths annually. Contact insecticides are substances that work when the cuticular parts of insect feet(tarsi) contact particles of their crystalline forms, resulting in uptake through the legs and transport to the target site of the organism. In 2019, NYU investigators discovered that various contact insecticides readily form polymorphs, which are solid forms with identical compositions but different three-dimensional structures. The investigators then found that insecticidal activities were inversely correlated with their thermodynamic stability (jump to these reports here). This behavior, which has been observed for a wide range of contact insecticides, indicates that removal of insecticide molecules from their crystal surfaces is the critical step for insect knockdown. The utility of metastable polymorphs in the field, however, requires that they remain stable against transformation to less active, thermodynamically more stable forms. By integrating computations and experiment, this DMREF project aims to accelerate the discovery of high free energy crystal polymorphs that are also kinetically stable against conversion to more stable forms, reducing the amount insecticide needed, thereby lessening environmental impact and meeting key sustainability goals on multiple fronts.

Thirty years ago, the solid-state structure of pharmaceutical compounds was an afterthought among medicinal scientists. Today polymorphism is recognized as critical for regulating the bioavailability of active pharmaceutical ingredients. Similarly, the role of polymorphism in insect knockdown was unknown to entomologists until the recent discoveries at NYU. Our project employs an interdisciplinary team of computational and experimental scientists working in an iterative loop to leverage machine learning and other computational tools to determine (i) stability rankings of insecticide crystal polymorphs, (ii) kinetic pathways for the interconversion of these crystal forms via a variety of mechanisms that incorporate molecular-level defects and molecular mobility at crystal interfaces, and (iii) surface energies and the work needed to extract molecules from accessible crystal surfaces. The research plan aims to create new capabilities for optimizing insecticide crystals by identifying high free energy, persistent amorphous and crystalline forms while generating new tools for mapping energy landscapes of phase transformations.

The goal of the project is to accelerate the discovery of metastable insecticide polymorphs exhibiting more rapid activity that also are stable against transformation to lower energy forms. The resulting materials are expected to reduce disease transmission and mitigate resistance acquisition, thereby increasing the useful lifetime of insecticides and ultimately reducing the number of annual deaths from insect-borne maladies. More active crystal forms require less toxic compound, which reduces the environmental footprint, thereby meeting a key sustainability goal. The use of polymorphs rather than new compounds circumvents risks from unanticipated chemistry and toxicology. The project will produce and disseminate computational tools for the rapid development and deployment of active insecticide crystal forms that can address the changing needs for vector control in different regions of the world. Moreover, the methodologies devised can be transferred to other technologically important areas, such as pharmaceuticals, molecular electronics and energetic materials, where polymorph transformations impact materials properties and performance. The research plan will prepare graduate students and postdoctoral associates for work force development in a marketplace that increasingly relies on computational science skills. Additionally, the DMREF team will build on the strong relationships with NYC schools to introduce students to crystals and crystal growth, as well as age-appropriate workshops related to concepts of machine learning and ‘bigdata’ in the science of molecular materials.