Pharma R&D Today
Ideas and Insight supporting all stages of Drug Discovery & Development
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.
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