The Cambridge-MIT Institute launches an initiative to accelerate next-generation drug discovery

The Cambridge-MIT Institute (CMI) is today launching a new initiative to unite biologists and medical researchers with physicists, engineers, computer scientists and mathematicians to work on an innovative approach to discovering the next generation of drugs.

CMI is funding a transatlantic Next-Generation Drug Discovery Community that will bring together researchers at Cambridge University and the Massachusetts Institute of Technology (MIT) with partners from the IT, pharmaceutical and biotechnology industries. They will be working on new methods of tackling the urgent and severe bottlenecks in the discovery and development of new drugs – particularly drugs for diseases with complex causes such as cancers, arthritis, multiple sclerosis, and diabetes.

Drug discovery is currently an extremely lengthy and costly process. On average, it takes new treatments $800m and twelve years to reach the market – and those are just the ones that succeed. But the sequencing of the human genome has made available a vast array of information about the many, very complex ways in which the human body works.

Since then, says Professor Doug Lauffenburger from MIT, one of the leaders of the Next-Generation Drug Discovery Community, “Everyone has thought that now, surely, we should be able to find those genes that cause disease, and which ones to correct. But actually, it is far harder than this because organisms are very complex, and there can be multiple reactions and causes involved in a disease”.

CMI is therefore setting up this Community to move away from the ‘one gene, one protein, one drug’ approach of old and instead adopt a multi-disciplinary new approach to drugs discovery: the Systems Biology approach. The Community is working towards developing the sophisticated skills and technologies needed in order to be able to:

• conduct rapid, quantitative experimental measurement of many gene- and protein-level properties of cells and tissues simultaneously (vital to understanding diseases that have multiple factors and causes), and
• offer the computer-aided analysis of the meaning of these data for disease mechanisms, and treatment. The resulting computational models will not only be vital to speed up drug discovery research, but will also allow us to predict which drugs will be most efficient, and at what dose and time point to treat individual patients, thereby contributing to the development of “personalised medicine”.

“Our aims are to develop safer and more effective new drugs, faster and cheaper,” says Professor Lauffenburger from MIT. “Another aim of this Community is reduce the current reliance on animal experiments to predict effects on humans.”

As part of its work, the Community is conducting two research projects. One is studying adult, blood stem cells with the aim of using them to establish new experimental systems to test the efficacy and toxicity of drugs on human physiology. The other project aims to establish new, computational methods by which drug targets can be identified from human gene- and protein-level data.

“There are major computational challenges involved,” says Dr Gos Micklem, who is part of the Cambridge team, “if we are going to make sense of all the data, and use it to start building systems-level views of life and disease processes. As we start to do this, and take into account the genetic variation between individuals, this opens up new possibilities in evaluating disease susceptibility, improved diagnosis and the ability to offer therapy tailored to each individual patient.”

The Next-Generation Drug Discovery Community is one of several new Knowledge Integration Communities (KICs) that the Cambridge-MIT Institute is setting up. These KICs aim to find new ways in which academia and industry can work together and exchange knowledge to push forward research in areas where UK industry has a demonstrable competitive position – like biotechnology and information technology . So alongside the research work, there are other strands to the Community. New educational programmes are being created. These include a new Masters degree programme at the University of Cambridge, an MPhil in Computational Biology, to teach biology to mathematicians and others, and make biologists more familiar with computer science.

Meanwhile, industry is also being informed about this work, and encouraged to join the Community.

Dr Adriano Henney, Director in Global Sciences & Information at AstraZeneca, says, “AstraZeneca, through its collaboration with MIT, already recognises the potential value of systems biology and mathematical modelling. During this collaboration, the joint project teams have prototyped the use of these approaches in drug discovery and this has helped to influence investment in the creation of a new, multidisciplinary capability in this area within the Company. We are looking forward to continuing our close collaboration with Professor Lauffenburger, and to the possibility of extending this to include Cambridge in the near future.”