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

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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.