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

The context of the research
The 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 research
This 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 methodology
Currently, 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 benefits
An 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 remit
The research falls into both the EPSRC remit of biological chemistry and biological informatics as well as computational chemistry
External Partner - AtstraZeneca
As 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.

In the 2018 Government Office for Science report, 'Computational Modelling: Technological Futures', Greg Clarke, the Secretary of State for Business Energy and Industrial Strategy, wrote "Computational modelling is essential to our future productivity and competitiveness, for businesses of all sizes and across all sectors of the economy". With its focus on computational models, the mathematics that underpin them, and their integration with complex data, the MathSys II CDT will generate diverse impacts beyond academia. This includes impacts on skills, on the economy, on policy and on society.

Impacts on skills.
MathSys II will produce a minimum of 50 PhD graduates to support the growing national demand for advanced mathematical modelling and data analysis skills. The CDT will provide each of them with broad core skills in the MSc, a deep knowledge of their chosen research specialisation in the PhD and a complementary qualification in transferable skills integrated throughout. Graduates will thus acquire the profiles needed to form the next generation of leaders in business, government and academia. They will be supported by an integrated pastoral support framework, including a diverse group of accessible leadership role models. The cohort based environment of the CDT provides a multiplier effect by encouraging cohorts to forge long-lasting professional networks whose value and influence will long outlast the CDT itself. MathSys II will seek to maximise the influence of these networks by providing topical training in Responsible Research and Innovation, by maintaining a robust Equality, Diversity & Inclusion policy, and by integration with Warwick's global network of international partnerships.

Economic impacts.
The research outputs from many MathSys II PhD projects will be of direct economic value to commercial, public sector and charitable external partners. Engagement with CDT partners will facilitate these impacts. This includes co-supervision of PhD and MSc projects, co-creation of Research Study Groups, and a strong commitment to provide placements/internships for CDT students. When commercial innovations or IP are generated, we will work with Warwick Ventures, the commercial arm of the University of Warwick, to commercialise/license IP where appropriate. Economic impact may also come from the creation of new companies by CDT graduates. MathSys II will present entrepreneurship as a viable career option to students. One external partner, Spectra Analytics, was founded by graduates of the preceding Complexity Science CDT, thus providing accessible role models. We will also provide in-house entrepreneurship training via Warwick Ventures and host events by external start-up accelerator Entrepreneur First.

Impacts on policy.
The CDT will influence policy at the national and international level by working with external partners operating in policy. UK examples include Department of Health, Public Health England and DEFRA. International examples include World Health Organisation (WHO) and the European Commission for the Control of Foot-and-mouth Disease (EuFMD). MathSys students will also utilise the recently announced UKRI policy internships scheme.

Impacts on society.
Public engagement will allow CDT students to promote the value of their research to society at large. Aside from social media, suitable local events include DataBeers, Cafe Scientifique, and the Big Bang Fair. MathSys will also promote a socially-oriented ethos of technology for the common good. Concretely, this includes the creation of open-source software, integration of software and data carpentry into our computational and data driven research training and championing open-access to research. We will also contribute to the 'innovation culture and science' strand of Coventry's 2021 City of Culture programme.