Leveraging biomolecular simulations to understand and predict the Blood-Brain-Barrier permeability of drugs

利用生物分子模拟来了解和预测药物的血脑屏障渗透性

• 批准号: 2596627
• 金额: --
• 依托单位: University of Warwick
• 依托单位国家: 英国
• 项目类别: Studentship
• 财政年份: 2021
• 资助国家: 英国
• 起止时间: 2021 至 无数据
• 项目状态: 未结题
• 来源: https://gtr.ukri.org/projects?ref=studentship-2596627
• 关键词: Leveraging; biomolecular; simulations; understand; predict

The context of the researchThe Blood-Brain-Barrier (BBB) is a collection of endothelial cells that selectively regulates the influx of any given substance from our blood into the central nervous system (CNS). Crucial to medical treatments relying on the delivery of medicinal drugs in to CNS is the capability of siad drugs to actually cross the BBB.In recent years, there has been an increase in pibicly available data on the BBB permeability of drugs. This has led to novel research in Machine Learning (ML) methods of predicting BBB permeability. However, current ML methods of predicting the permeability of the BBB to a given drug seem to have reached its limit in accuracy and lack explainability.The aim of the project will be twofold, to both improve the accuracy and explanability of ML models of BBB permeability. We ultimately seek to understand the structure-function relation at the heart of a given drug (in-ability) to cross the BBB. The primary method for this will ne through combining machine learning with biomolecular simulations of the BBB. Through leveraging the high-performance computational resources at Warwick, this project hopes to fully simulate dynamics for hundreds for drug molecules potentially aided by enhanced sampling techniques. This is far a greater number than any exsiting technique as present techniques are limited to a small handful of molecules at a time. This project will yield unparallel novel insight into kinetics of BBBoermeability, as well revolutionise the current ML moldelling paradigm.The blood brain barrier (BBB) is a thin layer of cells which seperates brain cells from the bloodstream. In order to effectively develop drugs whihc treat ailments of the brain, these drugs need to be able to cross the BBB. In the recent years, as availabilty of data has improved data driven techniques to predict whether a drug can cross the BBB. However, these models seemed to have reached a limit performance. They can also can't explain why a molecule can or cannot pass through the BBB.The aims and objectives of the researchThis project aims to use large scale simulations of the BBB along with new data driven techniques to develop new paradigms of BBB modelling. These techniques should allow better accuracy in predicting whather a drug can pass through the BBB. along with providing explanations as to the reasoning behind is predictions.The novelty of the research methodologyCurrently, sumulations of drugs-BBB exist. but they can only be applied to a small handful of drug molecules. This project aims to use new methods of simulation with high-performance computing to simulate hundreds of drug molecules. this can also be used to inform new data driven approaches and develop more accurate methods.The potential impact, applications and benefitsAn improved model and understanding of BBB permeability can greatly reduce the cost of and accelerate drug development. This would be of great interest to pharmacetical companies such as Astrazeneca, with whom Dr Sosso's Group is actively collaborating.How the research relates to the remitThe research falls into both the EPSRC remit of biological chemistry and biological informatics as well as computational chemistryExternal Partner - AtstraZenecaAs one of the major players in the context of Pharmceuticals, AstraZeneca has obvious interest in improving on the current capabilities of Machine Learning on terms of predicting the ability of drugs to permeate tje Blood-Brain-Barrier. This project specifically seeks to go beyond the state of the art leveraging large scale molecular dynamic simulations to both enhance the datasets available to us at the moment and to understand tje mechanism(s) at the heart of the Blood-Brain-Barrier permeation by drug-like molecules. thus, the outcomes of this project layout a very concrete path toward real-world impact.

血脑屏障（BBB）是一组内皮细胞，它们选择性地调节血液中任何给定物质流入中枢神经系统（CNS）。对于依赖于将药物递送到CNS的医学治疗来说，关键是药物实际穿过BBB的能力。近年来，关于药物的BBB渗透性的可利用数据增加。这导致了对预测BBB渗透性的机器学习（ML）方法的新研究。然而，目前预测血脑屏障对特定药物的渗透性的ML方法似乎已经达到了其准确性的极限，并且缺乏可解释性。该项目的目的是双重的，以提高血脑屏障渗透性的ML模型的准确性和可解释性。我们最终寻求了解给定药物（不能）穿过血脑屏障的核心结构-功能关系。主要的方法是将机器学习与BBB的生物分子模拟相结合。通过利用沃里克的高性能计算资源，该项目希望通过增强的采样技术来充分模拟数百种药物分子的动力学。这比任何现有技术都要多得多，因为目前的技术每次仅限于少数分子。该项目将产生对血脑屏障的动力学的独特见解，并彻底改变当前的ML建模范式。血脑屏障（BBB）是将脑细胞与血流分离的薄层细胞。为了有效地开发治疗大脑疾病的药物，这些药物需要能够穿过BBB。近年来，随着数据的可用性已经改进了数据驱动技术来预测药物是否可以穿过BBB。然而，这些模型似乎已经达到了极限性能。他们也不能解释为什么一个分子可以或不能通过血脑屏障。研究的目的和目标这个项目的目的是使用大规模的血脑屏障模拟沿着新的数据驱动技术，以开发血脑屏障建模的新范式。这些技术可以更准确地预测药物可以通过BBB。沿着的是提供解释背后的推理是预测。研究方法的新奇目前，药物的模拟-血脑屏障存在。但它们只能应用于少数药物分子。该项目旨在使用高性能计算的新模拟方法来模拟数百种药物分子。这也可以用来通知新的数据驱动的方法，并开发更准确的方法。潜在的影响，应用和好处一个改进的模型和对BBB渗透性的理解可以大大降低药物开发的成本并加速药物开发。这对阿斯利康这样的制药公司来说将是非常有兴趣的，Sosso博士的团队正在与之积极合作。研究与汇款的关系研究福尔斯既属于EPSRC的生物化学和生物信息学的职权范围，也属于计算化学的职权范围外部合作伙伴-AtstraZeneca作为制药领域的主要参与者之一，阿斯利康显然有兴趣提高机器学习的当前能力，预测药物渗透血脑屏障的能力。该项目旨在超越现有技术，利用大规模分子动力学模拟来增强我们目前可用的数据集，并了解药物样分子渗透血脑屏障的核心机制。因此，该项目的成果为实现现实世界的影响规划了一条非常具体的道路。