The longevity we enjoy today has brought new problems: a rise in chronic conditions such as diabetes, dementia and heart failure, made more complicated by many patients suffering from multiple conditions. At the same time, while the need for new medicines continues to grow unabated, our costly and inefficient drug discovery systems see less than one per cent of efforts reach the market.
The Oxford Martin Programme on Deep Medicine will apply techniques such as machine learning and data mining to some of the largest and most complex biomedical datasets ever collected, with the aim of generating insights into complex disease patterns, risk trajectories and treatment effects.
Combining expertise in healthcare, biomedical data and advanced machine learning, the research team will take advantage of the phenomenon of ‘Big Data’, accessing vast datasets including the UK Biobank.
“Big Data gives us new opportunities to learn about the interaction of chronic conditions and what the risks are for patients,” said programme Co-Director Professor Kazem Rahimi. “At the moment we have a limited understanding of complex disease patterns and risks, and this is further compounded by current methods of research, which tend to oversimplify complexity and often underrepresent patients with complicated disease history or concurrent treatments.
“Our aim is to develop new and innovative methods of analysis so that we can fully harness the potential of the biomedical data now available to us.”
Professor Rahimi’s Co-Directors are Professor Simon Lovestone, Professor of Translational Neuroscience at Oxford University, Professor Stephen Smith, Professor of Biomedical Engineering and head of the Analysis Group at The Oxford University Centre for Functional Magnetic Resonance Imaging of the Brain (FMRIB), and Andrea Vedaldi, Associate Professor of Engineering Science at the University of Oxford.
The Oxford Martin Programme on Affordable Medicines will investigate new models of drug discovery, where approaches and results are no longer kept secret but are shared openly, as alternatives to traditional ‘closed’ models of research and development, which have high failure rates.
“We need drug discovery systems for the future that are more efficient and sustainable,” said programme Co-Director Professor Chas Bountra, Professor of Translational Medicine and Chief Scientist at Oxford’s Structural Genomics Consortium (SGC). “Currently it is estimated that less than 10% of drug discovery efforts pass the phase II clinical efficacy stage, and less than one per cent reach the market.”
Bringing together both academic and pharmaceutical industry expertise, the programme is a collaboration between the SGC, the Nuffield Department of Population Health, the Oxford Academic Health Science Network (AHSN), the Nuffield Department of Medicine and the UK Office for Health Economics (OHE).
The team will study new ‘open innovation’ models that are being trialled, and analyse the economic and efficiency benefits of these, versus traditional systems. They will also look at alternatives to conventional intellectual property protection and develop tools to help different parties model the economic benefits of new systems.
“Alternative systems are being developed but the benefits of these are not yet clear,” added Professor Bountra. “Our mission with this new research programme is to generate solid, unbiased evidence for policy makers seeking to drive change in the way new drugs are developed.”