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What happens when big pharma and big data collide?

Ian Guerit
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Few drug discoveries - if any - happen by chance anymore. Gone are the days when an Alexander Fleming could ‘accidentally’ happen upon antibiotics.

Nowadays the future is hidden under huge piles of data and the many complex, analytical processes needed to make sense of it all.

The truth is that many pharmaceutical companies have found it hard to bring enough successful new drugs to market. However, data analytics potentially enables them increase R&D productivity. Processing their big data in better ways can help big pharma to:

More accurately identify commercially viable research areas

Pharmaceutical companies need to pick winners in their research activities, and can’t afford to waste time on bad investments. Data analytics helps at the pre-research stage to assess the market for viable gaps, and throughout the research process itself to help managers make real-time decisions about additional investment or - at the other end of the spectrum - mothballing the project.

Locate larger sample groups of trial patients with the right characteristics

Finding the right patients with the right symptoms can delay clinical trials for months or even years, especially for rare conditions. Data analytics makes the process more efficient by identifying patterns of where the most applicable patients are found, often by processing different stores of information. Finding patients with the precise characteristics required also means that trial samples can be smaller than would otherwise be necessary.

Correlate and analyse trial data alongside historic data and other sources to speed up time-to-market of new drugs

Applying analytics to your own data sets is essential but the value of this is significantly enhanced when added to existing data from earlier research studies and those conducted by external parties.

More easily present comprehensive, compelling clinical evidence to regulatory bodies such as the FDA

One of the by-products of big analytics is the infinite degree to which data can be processed and interpreted to reveal specific insights. These are then presented in numerous visual formats. All of this is helpful to peer review processes as well as the culmination of the entire research - the highly document driven, protocol-led drug submission to the relevant administration authorities.

Reduce post-research clinical risks

Nobody really knows how a drug will perform until it is approved and on the market, and there are plenty of cases where drugs have been withdrawn because of unforeseen side effects. Big data analytics can be applied to evaluate early warning signals so that the process can be as fast as possible, where necessary.

Sadly, accomplishing all these benefits (and more) is not straightforward. It relies upon critical data integrations between previously siloed information sources, collaboration with external data processes and frameworks, and the ability to manage rapid change as well as huge volumes.

However there are opportunities to do just that in the shape of:

  • Collaborative Open Space Initiatives

Pharmaceutical R&D labs are becoming less secretive and more open to collaboration through a series of initiatives that protect their own intellectual property while opening up access to more data to complement live and planned research projects. Great examples of this include Eli Lilly’s Open Innovation Drug Discovery Program, designed to stimulate collaboration with academia in exchange for access to EL’s proprietary tools, and InnoCentive - a kind of X-Prize project for life sciences.

  • Support for New Devices and the Medical Internet of Things

Patients are producing vastly more data than ever before, thanks to a new breed of wearable devices and smart companion applications that collect information on everything from heart rate to mood. The software that drives these is being developed with one purpose in mind: to be analysed! This ultimately supports the evolution of data analytics in pharmaceutical R&D, although the constant and rapid nature of this evolution poses its own challenges.

  • Integration of Gene-Age Technology

The revolution of genetic medicine has led to new gene sequencing technologies and other research breakthroughs that produce unprecedented quantities of information. Data storage and analytic systems are also evolving in order to scaling to these levels sustainably, and keep pace with future development.

Everyone has a stake in the future of pharmaceutical R&D. It’s what we hope for when a loved one is gravely ill. Crunching all that data is, ultimately, why millions have ‘Raced for Life’.

Pharmaceutical R&D in 2016 is what Thomas Edison could have been describing when he called genius 1% inspiration and 99% perspiration.

Faster, more accurate big data analysis, using wider information sources from a greater range collaborators, will be a blessing for patients with chronic conditions and incurable diseases.

It could be the saviour of a few pharmaceutical companies too.

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Topics: healthcare

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