Characterising novel insecticide transport to maximise efficiency and minimize environmental impact

全球最大的优先事项之一是找到战略，为世界快速增长的人口提供高质量、有营养的食物。支持这一点的一个重要因素是开发对环境影响小的新型高效、安全的杀虫剂。杀虫剂的开发在靶向特定的活性蛋白位点方面取得了巨大的成功，但在杀虫剂分子的应用部位和活性部位之间存在大量的物理和分子障碍。目前对活性分子和这些生物屏障之间发生的相互作用缺乏了解，事实上，通过这些屏障的传输效率并不是简单地基于活性杀虫剂本身，而是强烈依赖于所应用的配方和其物理状态。虽然在预测杀虫剂的传输和药效的建模和机器学习方法方面已经取得了进展，但这些方法也由于缺乏与膜传输相关的输入表征数据而严重受阻。在这个项目中，我们的目标是开发一个模块化的、基于实验室的毫流控测量平台，它将允许快速表征杀虫剂通过串联模型膜和体外物理屏障的传输。这些数据将允许直接快速筛选和预测候选分子的效率，并优化配方，我们将输入机器学习方法，使我们能够基于分子结构预测膜传输。这里使用的方法将与巴斯夫内部使用的方法保持一致，因此将直接输入他们的监督和非监督预测模型，以显著提高杀虫剂发现和开发的效率和针对性。通过将发育参数空间显著扩展到靶点识别和相互作用之外，我们预计，在未来，这将导致开发出应用浓度更低、特异性更高和环境性能更好的杀虫剂配方。

One of the greatest global priorities is finding strategies that will support the supply of high quality, nutritious food for the worlds fast-growing population. One vital strand that will support this is the development of novel efficient, safe insecticides with low environmental impact.The development of insecticides has been highly successful at targeting specific active protein sites, however there are a host of physical and molecular barriers between the application site and active site of insecticide molecules. There is currently a lack of understanding of the interactions that take place between the active molecules and these biological barriers and indeed, the efficiency of transport through these barriers is not simply based on the active insecticide its self, but depends strongly on the applied formulation and its physical state.While there has been progress in both modeling and machine learning approaches to predicting the transport and efficacy of insecticides, these approaches are also severely hampered by lack of input characterization data relating to membrane transport.In this project we aim to develop a modular, lab based, milli-fluidic measurement platform that will allow rapid characterisation the transport of insecticides across serial model membranes and in-vitro physical barriers. This data will allow direct rapid screening and prediction of efficiency of candidate molecules, and optimisation of formulation, and we will feed into machine learning approaches that will allow us to predict membrane transport based on molecular structure. The approaches employed here will be aligned with those used within BASF and so will feed directly into their supervised and unsupervised predictive models to significantly enhance the efficiency and targeting of insecticide discovery and development. By significantly expanding the developmental parameter space beyond target site identification and interactions, we anticipate that, in the future this will lead to development of insecticide formulations with lower applied concentrations, increased specificity and improved environmental performance.