To Be a Digital Pharma Player, You Need Data – Reusable Data

Posted on July 21st, 2020 by Ani Marrs in Pharma R&D

A new paper published in Drug Discovery Today finds that the pharmaceutical industry is in an “early mature” phase of using artificial intelligence in R&D, which means that major industry players are interested in and pursuing AI, but not as actively as one might expect. For instance, they note that of the top 20 pharma companies that they looked at, only two (Novartis and Johnson & Johnson) have commercialized AI through products and services so far.

The cost
of good data

A significant challenge to
effectively utilizing AI, machine learning and deep learning in pharma is
having access to large enough, clean and connected datasets, which
sometimes only the biggest, most well-resourced companies can easily afford (and
even if money isn’t an issue, privacy and legal issues might be). Another
challenge is around data access and reuse, lack of standards and proper formatting, all of which make it
harder to effectively use and reuse data for AI.

While the Drug Discovery Today paper notes that tech firms have gotten much further along in their use of AI than pharma players, they are not necessarily dealing with the same obstacles. As a recent assessment on Artificial Intelligence in Health Care, published by the U.S. Government Accountability Office (GAO), explained: “According to one industry representative, collecting data from the early drug discovery phase can be cost prohibitive. This representative said that certain health-related data may cost tens of thousands of dollars, as compared to just cents for other consumer related data that many technology companies use.”

Working together
towards AI adoption

What
pharmaceutical companies are discovering is that there is strength in numbers
when it comes to overcoming data challenges. Forming partnerships and
initiating collaborative projects to tackle some of the logistical
difficulties, as well as developing consortia to promote data sharing and the
creation of data standards, are among the ways that organizations in the
industry can join together for the common purpose of making data AI-ready.

Companies can also turn to Elsevier for assistance with some of their data challenges. Platforms like Entellect enable data reuse and provide linked datasets for AI use across a number of discovery applications (e.g. drug repurposing). Furthermore, Elsevier’s Professional Services Group is able to work with organizations on custom deliverables in specific therapeutic areas based on clean datasets.

While the application of AI in pharma R&D may not be easy, the authors of the Drug Discovery Today piece conclude that “it is worthwhile to invest to become a ‘digital pharma player’.” And creating partnerships, sharing information and seeking out expertise are all critical steps on the path to widespread adoption of AI and machine learning in the pharmaceutical industry.

Please enable JavaScript to view the comments powered by Disqus.

R&D Solutions for Pharma & Life Sciences

We’re happy to discuss your needs and show you how Elsevier’s Solution can help.

Contact Sales