Computer-based technologies are becoming one of the most promising novel approaches due to continuously accelerated growth of both hardware processing power and software algorithm efficiency. One recent example includes machine learning algorithms that revolutionised data analysis in computer science, and lead to new computer games, visual recognition, and other applications that overtake human performance in many cases. Here, we propose to perform atomistic molecular simulations using novel enhanced sampling algorithms. Most biologically important processes take place on significantly longer timescales than those accessible to current computer simulations. Therefore, to obtain meaningful and accurate results regarding the kinetics and conformational dynamics of complex molecular systems, we use algorithms that enhance the sampling using parallel calculations with different biases. Developing more optimal biasing algorithms will allow us to model faster and more accurately the key biological processes of interest, including ligand binding, protein conformations, etc. Here we aim to use statistical algorithms inspired by machine learning to develop novel enhanced sampling methods for molecular simulations. Novel algorithms can be applied to a wide range of molecular modeling problems. We will focus on phosphate catalytic enzymes, and study key DNA processing enzymes to reveal the catalytic mechanism in these systems. Due to the essential nature of phosphate catalytic enzymes in most biological processes, a large number of drugs in current clinical practice also target phosphate-processing enzymes treating a wide range of diseases. Examples include reverse transcriptase and integrase inhibitors used against HIV and hepatitis B, proton pump inhibitors used in gastric diseases, kinase, PARP and topoisomerase inhibitors used against a large number of cancers. Studying phosphate catalytic systems with modern molecular modeling methods will enable fundamental advances in our current knowledge of the molecular basis of life. It will also create opportunities for rational development of better drugs to fight diseases.

Background Health systems face a time of unprecedented change, with spiraling costs, increasing cultural disparity in access to healthcare and research, and an infrastructure that is decades old. Today, telehealth is a realistic alternative making care and research more accessible and personalised with less burden to better support the most vulnerable and under-served in our society. The ability to test and monitor for illnesses using Patient Centric micro-Sampling (PCmS) is at the centre of this reform. Aim and main objectives This project is designed to build upon existing pilots and knowledge, then collaborate cross-sectorially to co-create and test the logistics, infrastructure and tools required to make PCmS a core healthcare tool and an acceptable alternative to venous blood-draw across Europe. This project aligns with many IHI’s objectives focusing on cross-sectorial collaboration, emphasizing patient and end-user- centric co-design of outputs, harmonised regulatory and data generation approaches enhancing the potential of digital innovations in healthcare, while aiming to reduce the environmental footprint during the project and in final outputs to ensure that the expected long-term impact is a reachable reality that will deliver significant benefit to the community and address unmet public health needs at scale. To achieve our objectives, we bring together a broad group of required expertise, know-how and end-users (i.e., public and patients) to form a public-private-partnership specifically equipped to tackle this challenge. This collaborative approach where the relevant stakeholders such as healthcare professionals, regulatory agencies and patients are involved and integrated to deliver solutions and innovation across healthcare systems and ensure the best chances for success and long-term positive impact from this project. Key deliverables include: 1) An optimized, tested and validated ‘Gold Standard’ infrastructure and workflow for PCmS across Europe as a proven and reliable alternative to venipuncture 2) Harmonised and clear regulatory and HTA pathways, standards and acceptability, measures and cost-benefit models across Europe 3) Documented evidence to draw a citable ‘line in the sand’ for future research to support decisions to integrate PCmS into decentralised trials and care pathways 4) Stakeholder engagement and patient involvement models and research on preferences and acceptability for PCmS 5) Foundation for future: Enable access to the developed PCmS scientific findings, tools and assessment measures for rapid uptake and integration of PCmS approaches into decentralised clinical studies and healthcare Expected impact: - Patient-centric microsampling becomes an accepted alternative to the current standard of care venipuncture and the data gathered can be leveraged in healthcare planning. - Lowered patient burden and lowered barrier to access in situations where blood samples need to be collected, whether as part of diagnosis, care plan, health monitoring etc. - A solution to leverage high amounts of data gathered from increased testing can be explored already in this project so that it can pave the way for future research that can improve health outcomes.