How Can Pharma Benefit from Big Data?

The pharmaceutical industry collects and holds large amounts of data. That's not surprising as, nowadays data is collected almost everywhere. While many innovations make it possible to record data anywhere and anytime, the more important question is: How can the pharmaceutical industry benefit from it?

How do nutrition, lifestyle and medication affect people's lives over a period of time? The answer to this question not only helps the pharmaceutical industry to develop new medicines, but also has a major impact on us, the people who benefit from clinical studies in general. An international health resource called UK Biobank aims to tackle this question in a long-term data analysis project. The study includes more than 500,000 participants who have not only answered questions about their lifestyle and health history, but also submitted blood, saliva and urine samples. Within the next 30 years, they will be regularly examined medically. Based on the data obtained from this study, it will be possible to understand how natural environment impacts can affect health. This knowledge will help in the development of medicines in a more effective way. But the pharma industry can do more with big data.

What does the digitalization of data mean for the pharmaceutical industry?

Over recent years it has become easier for pharmaceutical companies to exchange data with each other anonymously. This gives them access to a large amount of information that they can use for their studies to develop drugs and treatment options. It's believed that the use of big data will speed up the drug development process.

Control groups are always important when it comes to drug testing. Due to big data, those groups can now be displayed virtually. Normally, there is always a control group receiving a placebo drug in clinical trials. This is no longer necessary for a virtual control group, meaning that all study participants can receive the real drug.

A larger data pool also helps to make studies cheaper and more efficient. Machine learning can access high quality data, helping to further optimize drug development. In addition, it becomes possible to use data from subject groups that are living long distances from each other. Artificial intelligence also makes it easier to research rare diseases.

Is data privacy ensured?

The general data protection regulation (GDPR) aims to better protect personal data. Therefore, when data is being shared, the information is usually anonymized. However, the pharmaceutical sector is playing a special role. Not all information is shared anonymously because it is important for studies to localize all data. In addition, study participants agree that their data cannot be deleted.

Big data or smart data?

It is not always the case that a huge amount of data provides the ultimate results. Many participants in long-term studies live a healthier lifestyle than the average person. Many of them drink less alcohol, do not smoke and generally pay attention to their diet. Although this is not an optimal prerequisite, it doesn't matter for a long-term study as long as it is taken into account in the analysis. Therefore, it is not about "big data", but more about "smart data".

It is now easier to access data than ever before. The challenge is to use this data purposefully to draw the greatest possible learning from it.


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