Digital Disruption: Now Coming to the Life Science Industry

Digital Disruption: Now Coming to the Life Science Industry

No industry is exempt from the market impact of digital disruption.  Analyst firms are starting to see business risk for existing Life Science vendors. They see nimble, non-traditional, data centric companies disrupting existing market participants.

An example of a data-centric disruptor is McLaren Technology Group. McLaren describes itself as a high-technology company, and is best known for its F1 racing team.  McLaren’s success in this industry was based on its advanced capabilities in streaming and analyzing real time sensor data.  McLaren is now taking this data-centric disruption capability to other industries, including Life Sciences through partnerships with GlaxoSmithKline and KPMG.

Analysts believe that Life Science industry winners will adopt data-centric approaches extending to patient-centric and outcome-focused solutions.  Having strong data capabilities should not be seen as threatening those that are saddled with legacy systems.  As McLaren and other Life Science leaders are showing, there is a real opportunity to leverage data to transform the drug discovery and therapy processes.

What is the opportunity for disrupter and incumbent vendors?

The first opportunity is based on speed: bring targeted drugs to market and create new medicines or new uses for existing medicines faster. By decreasing the timeline to develop and get a new drug to market, a significant revenue opportunity for Life Science companies emerges.

A critical element to decreasing time-to-market is increasing researcher productivity and collaboration across organizations. This step can significantly lower the price per scientific data point while hastening focus on high value molecules and opportunistic therapeutics.

Clearly, the increasing digitization of data means that the value of pharmaceutical R&D and trial data has increased significantly. As Klaus Schwab points out in his new book on the 4th Industrial Revolution:  “With advances in computing power, scientists no longer go by trial and error, rather they test the way specific genetic variations generate particular traits and diseases”. [i]Part of delivering on this promise is sharing data between partner ecosystems. Better internal and external collaboration increases researcher productivity. This can accelerate time to market and increase the patent protected life for a compound, drug, or therapy.

Time to market of new medicines can be improved through a focus on patient recruitment & retention and risk-based monitoring.  High quality data can aid site selection and trial setup by curating a single source of data for research partners, providing guidance on past & current performance of trial sites, and faster ramp-up per site.

These seemingly minor changes matter because of the length of time a new medicine spends in the clinical trial phases.

The second opportunity for data disruptors lies ‘beyond the pill’.  As medicines are increasingly required to prove their value, complete treatment solutions become more attractive to innovative Life Science companies.  For example clinical trials can be enhanced by additional data coming from the increased use of wearables mobile apps , smart medication and patient self-recruitment.  Real world evidence (including wearables, apps, patient forums, social networks and electronic health records) are opening up new data sets for the data savvy to leverage.  This data should contribute both to improved patient care, and as evidence to show the benefits of individual treatments in order to maintain premium pricing in the face of shrinking health care budgets.

What is holding Life Science incumbents back

For most Life Science organizations, data and knowledge sharing is really a tribal phenomenon. This makes it challenging to share data on drug discovery and even to reduce the cost from discovery to production.  Additionally, the Life Science industry is risk-averse, and reluctant to innovate in the face of regulatory oversight and social responsibility.  However, both data sharing and innovation (allowing for a percentage of failures) is required in order to survive in the face of digital disruption.

For example many Life Science companies still rely on only traditional clinical test data for clinical trials even though real world data contains many more pivotal data points. With the Internet of Things, it becomes possible to bring in much more varied data points into the clinical trial process. This includes the ability to connect to clinical data patient health records and patient activity data during the trial process. This can include social data, clinical system data, and various biometric data collected from sensors. By including these data sources can we improve clinical trial productivity, especially in Phase IV trials to demonstrate the value of medicines and treatments.

Data held in organizational silos not only limits productivity, but can also negatively impact revenue.  The industry has many instances where a drug developed for one purpose, showed positive results in other fields of study. Capturing and studying those results can provide new avenues for research and ultimately revenues. Cataloging research data is essential to enabling better collaboration and quickening the discovery of efficacies for drugs applied to an unintended purpose.

Incumbent vendors need to overcome the problems described above to stay relevant in a Life Science and pharmaceutical world that increasingly includes new data-driven entrants that make non-digital products much better. Why existing vendors have larger amounts of cash than up starts, the opportunity going forward requires that existing players sharpen their data skills to stay relevant.  Doing so is a requirement for future competitive advantage.

Do you want to learn more the role of data in clinical trials or how to blunt a digital disruptor? Please attend a free webinar for the pharmaceutical industry hosted by Business Review on June 7th at 1p EDT. We’ll be discussing how to automate the application of CDISC standards, support risk based monitoring, and preparing for the future of alternate data sources. For more information and to register, click here.

[i] The Fourth Industrial Revolution, Klaus Schwab, 2016