The pharmaceutical industry should be one of the most data driven, but there are a number of significant roadblocks preventing it from reaching its full potential. Given that it already lives and dies by the analysis of the data it creates, a more joined up and collaborative use analytics program is going to create significant benefits. However, as yet, something has managed to stay relatively elusive.
So what is holding the industry back?
Although most pharmaceutical companies already use platforms that allow them to store and manipulate data, there is a growing trend for the vendors who create these systems to use 'data blocking'. The idea behind this is that they charge companies to export the data from their systems, meaning that the sharing of this data with other companies can become a significant expense.
There is a movement to stop vendors using this practice, and a few have already agreed to stop, but given the huge variety of companies in the space, it could take a while before they all agree. The transfer of data between companies is essential to creating an industry that operates effectively through data, rather than just having some companies using it effectively whilst others are lag behind.
Challenges in format
As with every industry, the use of standardized data formatting is key to easy shareability and usability. The issue that the pharmaceutical industry is currently facing is that this is not always the case.
Every single clinical trial will be looking at specific, often unique, data. Therefore, the more data that is created for this in a particular format, the harder it is to then use in a unified system, especially if a number of companies are looking at the same data but recording it in different formats. The creation of standardized and universal data formatting for this is going to be incredibly important for the industry and, unfortunately, the longer it takes, the harder the job is eventually going to be.
Currently most applications for the collection and storage of data are created with specific questions in mind before the data is collected. For instance, if you are collecting data to look at the potential side effects of a new drug or the reduction in signs of a specific disease, the data collected is going to be significantly different and specific to those use cases.
New apps need to be created to allow companies to approach this in a data lake format, where all data is stored in a useable way in a central repository and called upon when needed for whatever task, rather than simply being used to answer a single query set by the application.
One of the biggest obstacles that the industry needs to get over is going to be the natural secrecy that is inherent in the system. There is no surprise that this exists, given that according to Tufts Center for the Study of Drug Development the current price of bringing a prescription drug to market is $2.6 billion. Why would companies want to share the data they have collected with others when they need to put out such a huge outlay?
This is having a big impact on the industry as it represents a 145% increase in costs since 2003. Some of the biggest names in the industry are feeling the heat, with AstraZeneca seeing operating profit decrease by 12% in the past year. It has put significant pressure on them, so giving information to competitors shouldn't be their top priority.
However, this being said, they are utilizing data to attempt to overcome this.
They are partnering with Human Longevity to look at 2 million mapped human genomes from their own database, combined with those from the Wellcome Trust Sanger Institute and Finland’s Institute for Molecular Medicine. Through machine learning they are hoping to find new drugs through this exploration to turn their revenues around, only made possible through this data collaboration.
However, this secrecy is likely to damage other less open companies as the margins become tighter, especially with potential new laws coming into place regarding drug pricing in the next year. Having an open data policy may be one of the only ways for companies to reduce their R&D spend, it may take a change in mindset to achieve, but ultimately it may be needed to simply survive.