Big Data Holds Promise For Pharma R&D Efforts, But Many Challenges Remain

Big Data is Pharma's key to a successful future, opening doors to more efficient drug production and greater insight from research.

As with many industries, big data is transforming pharmaceuticals. The use of large datasets can give drug companies insights that may lead to faster development of drugs as well as improve their effectiveness.

That’s the promise of having the ability to collect billions of records and then analyze them in mere seconds.

The promise, however, is somewhat tempered by challenges. The data infrastructure and skilled data scientists needed to leverage the potential of data collection and analysis are currently not in place.

Steve Labkoff, executive director of medical affairs strategy at Purdue Pharma, told Pharm Exec that “there really aren’t enough well-trained people who understand how to deal with big data problems.”

That’s just one of the many barriers standing between Big Pharma and the successful use of data.

Data Challenges For Pharma

Labkoff pointed to a need for a change in strategy at the executive level of pharmaceutical companies, as many executives view data as an issue to be handled entirely through the IT department.

He said too many believe “if you can throw enough servers” at the issue, then the company will automatically get better insights through data, adding that this attitude is “about as naive a perspective as you can get.”

The sheer volume of data also presents an issue. Much of what comes into pharmaceutical companies is unstructured data that is difficult to combine with other data to form useful conclusions.

This is to say nothing of security concerns, especially with medical records, which Reuters reported could prove more valuable than stolen credit card information on the black market. With confidential medical information being transmitted through web applications to cloud-based storage – the typical scenario for most pharmaceutical companies – the potential for security breaches is high.

A Three-Phase Approach

The way forward, as suggested by Labkoff and others, involves three key issues.

IT people– Companies need people who understand how to set up frameworks for the analysis of large datasets, as well as the ability to combine data from many different sources.

Data scientists Skilled data scientists are needed to take complicated sets of data and combine and analyze them in such a way that it provides meaningful insights that help drive strategic decisions.

Education– Business executives need to understand the use of data, the work data scientists produce and how it can be leveraged. They also need to understand the importance of security and how it can be achieved.

Collaboration is seen as key. Drug companies typically have not shared information with outside agencies, partly because of the highly competitive nature of the pharmaceutical industry. Consideration should be given to working with insurance companies, data analytics agencies and healthcare professionals.

Uses of Big Data In Pharma

From an economic standpoint, the use of big data in the pharmaceutical industry holds promise because of the simple fact the industry fails almost every time it tries to develop a new medication. Only about one out of every 10,000 potential new drugs is developed, and then only three out of 20 developed drugs make enough money to turn a profit.

The cost of developing just one new drug runs into the hundreds of millions of dollars and can take up to a decade.

That’s a lot of risk for drug companies to take on. The analysis of large datasets such as patient records, drug trial results and other records could make a big difference. For example:

  • Large sets of clinical and molecular data could help companies focus on potentially effective drug treatments to provide safer and more effective treatment than what is currently available
  • Databases allow companies to be more selective at picking people for drug trials, increasing the amount of insight as well as avoiding medical issues during the trials. This could involve looking at the genetic makeup of potential participants.
  • Real-time data from wearable medical devices can instantly transmit information about patients and those in clinical tests, making for better, faster treatment

McKinsey & Company estimates that big data could result in $100 billion in savings across the healthcare industry.

Examples of Big Data In Pharma

Some companies already have put data analysis to work.

The vast amount of global data on Alzheimer’s patients already is being put to use by the The Global CEO Initiative on Alzheimer’s disease to find associations between patients. The goal is to identify data points that can lead to a better understanding of who gets the disease, the most effective treatment and enhancing the ability to prevent the disease altogether.

An organization called Project Data Sphere is also working to collect all historical cancer patient information into one large database that can be used by researchers to work on both prevention and cure of the disease, as well as drug treatment plans that are most effective.


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