COMMENT
Data for better drugs
B
lockbuster drugs, formerly the main focus of the pharma industry, are becoming ever rarer, as the industry shifts to more niche therapeutics. As a result, R&D teams
are now expected to work across a range of disciplines, while also looking outside their own organisations for collaborative projects. There is growing pressure to work alongside other industries – technology partners – for example. We have seen an increase in mobile or mHealth devices and internet of things (IoT) technologies and greater accessibility of genome sequencing, to the public and researchers alike, This increasing availability of data should
potentially allow researchers to make more informed decisions when developing new drugs. However the big problem for R&D teams is that, while data are abundant, they are often in a format that isn’t usable by scientists. Given the investment in generating these data
in the first place, it is critical they are usable by the researchers who will extract value from them. However, this information is often discarded as researchers move onto the next stage of drug discovery, and data that could be invaluable in other research is often lost, inaccessible or unnecessarily duplicated. This has a huge impact on productivity and consequently the costs of drug development. Increasingly, we are seeing more links
between the technology and scientific communities, with reports of major players in the life science industry looking to hire from top tech firms like LinkedIn and Facebook. Each industry has its own unique set of skills and expertise, while having scientists working for tech companies and tech pioneers working in the science industry goes some way to bridge the skills gap between tech and science. However, the two industries must collaborate further or risk losing the life sciences technology expertise and skills necessary to advance R&D. Examples of the tech industry dipping its
toes into pharma’s wheelhouse include Google- owned 23andMe, a genetic testing kit available to the public. Given genomic data is one of the fastest growing datasets in the world, it’s no surprise a tech titan like Google has seen the potential. While 23andMe has previously come under press scrutiny for selling the genomic data it collects, it recently announced a joint venture with the Michael J Fox Foundation. In a great example of collaboration between the tech and science industries, the two companies will
be undertaking joint research into Parkinson’s disease. As tech and life sciences move closer together, the pharma industry now has a greater range of potential data sources. However, data are often stored in variable formats, and even when shared, are often in a format that is unreadable by the scientists who want to use them. This means either taking valuable time and resources to convert these data into a usable format, or discarding them regardless of their potential value. Much of these data are classified as pre- competitive, and hold little value to the company that owns them, but can be of huge worth to the whole industry if they are pooled and accessible. The Pistoia Alliance is working hard to
promote pre-competitive collaboration, and to provide the industry with the tools to support a collaborative approach. We are currently working with our members to develop a Unified Data Model (UDM), with the ultimate aim of publishing an open and freely available format for the storage and exchange of drug discovery data. The UDM will become a common model allowing data to be easily shared and integrated between parties, making research data discoverable across different industries and geographies. And making the sharing of data from different sources easier, in a readable and usable format for everyone. The pharma industry is becoming more
used to the idea of collaborating with other life science organisations. But it must start looking at other potential collaborators. These could be anything from technology companies to start- ups, where increasingly innovative technology is being used in surprising ways. For example, Transformative AI, winner of
the 2017 Pistoia Alliance’s Presidents Challenge, is a UK start-up using the cutting-edge artificial intelligence (AI) and novel analysis techniques also employed at CERN, the European Organization for Nuclear Research. The team’s mission is to transform the treatment of serious medical conditions by collecting and translating clinical data into real-time, predictive assessments that will guide action. Given 45% of Big Pharma’s forecast revenue
came from external sources in 2016, the industry has already seen the importance of sharing information. With collaboration growing, the industry needs to implement these practices to allow collaboration to thrive. The first step is to define a data standard to ensure information is accessible across the industry.
Steve Arlington president of The Pistoia Alliance
This increasing availability of data should potentially allow researchers to make more informed decisions when developing new drugs. While data are abundant, they are often in a format that isn’t usable by scientists
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