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Biological big data and the future of pharmaceuticals

24 Nov 2016

Dr Emma Sceats, CEO of CNBio Innovations, looks at how big data and cutting edged biotech are coming together to change the future of medicine

Imagine if scientists discovered a drug that cured 9 in 10 patients within 12 weeks. Patients who might otherwise require an organ transplant to live.  Now imagine that 150 million people in the world currently suffering from that disease demanded access to the drug which cost $86,000 per patient.  Who would pay the $12.6 trillion in drug costs?

It is because of the hard work of literally thousands of researchers and doctors that wonder medicines do exist. For instance, sofosbuvir, which is a curative treatment for Hepatitis C.  

But these fantastic breakthroughs have come at a price. The tremendously difficult and increasingly expensive process of discovering new drugs has caused drug prices to rise. Soaring development costs are placing an enormous burden on healthcare providers around the world. Politicians on both sides of the Atlantic have flagged this issue during recent election campaigns.

The great news is that in Britain alone, thousands of scientists, engineers and clinicians working in large companies, start-ups, universities and hospitals are hard at work to improve the process of discovering and developing new drugs.  This work will, in time, translate to better and more affordable drugs. It will transform the lives of patients in both developed and developing countries.

Many of us remember the headlines when the human genome was sequenced for the first time in 2001. This was supposed to unlock the secrets of human biology and lead to a flood of new medicines to treat all manner of diseases.  However, a new challenge emerged. How would researchers integrate and interpret the explosion of scientific data and information?  How could these vast data sets be turned into an advantage for more efficient drug discovery?  This remains an open challenge.  Bioengineers have a central role in this mission.

Will “Big Data” approaches which use algorithms or machine learning tools to make sense of vast databases of scientific or clinical data speed up drug discovery and development? Large pharmaceutical companies are already mining their historic data to uncover new insights into biology and disease.

New players such as the UK’s own Deep Mind (acquired in 2014 by Google) and enterprising start-ups such as BenevolentBio are pioneering computational methods which should lead to better diagnostics and therapeutics.  We already know that Big Data is boosting our ability to identify protein targets for important diseases. The UK’s 100,000 genomes project has the potential to reveal hitherto unmapped links between genetics and disease.  

But Big Data does not remove the need for pharmaceutical sciences.  We have known the identity of the key genes responsible for Huntingdon’s disease and cystic fibrosis for over 20 years. Yet the task of developing molecules that tackle rogue proteins in a therapeutically beneficial way has proven extremely difficult.  

At CN Bio Innovations, we’re aiming to provide pharma and biotech companies with another piece of the puzzle needed to improve the drug discovery process.  Our engineers have developed a smartphone sized organ-on-a-chip device for drug discovery testing. It is an array of tiny microfluidic devices which contain small numbers of human cells. These can recreate the function of the entire organ.  

Why is this type of tool important? When trying to predict if a new medicine will work, being able to model complex biological phenomenon such as circadian variations in hormone levels gives you a real advantage. This is because hormone levels can alter the effectiveness of some cancer treatments. And that is just one example of why using real human cells is a better way to test and choose successful drug candidates.

Our organ-on-a-chip technology also generates large amounts of high quality, human relevant, well curated biological data – biological “Big Data”. The potential for Organ-on-a-Chip technologies to disrupt the multi-billion dollar per year animal testing industry is significant.

I believe that there are more lifesaving drugs, just like sofosbuvir, waiting on the shelves of every major pharmaceutical company.  And soon the practice of testing new medicines on animals such as mice could become obsolete. Instead an organ-on-a-chip testing process will provide detailed and enormously useful intelligence about how a new drug interacts with actual human cells and tissues. This will allow us to automate and speed up the essential process of discovering new medicines. I and my team are a small part of a much a wider community of scientists dedicated to ensuring that this happens as soon as possible.

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