Sanjay Dalwani

In the area of drug discovery, open data sharing reigns as a top driver for new development; however, issues relating to the security of protected health information during data research partnerships—as well as the ability to protect intellectual property (IP) and make data ready for analysis—remain primary concerns for pharmaceutical and life sciences organizations as information moves beyond their firewalls.

For these reasons, decentralized approaches to data collaboration have become a strategic trend in technological innovation. Specifically, the development of technology that can provide a “neutral zone” for data sharing is fast becoming a top trend in health care technological development.

A neutral zone empowers pharmaceutical companies and life sciences organizations to harness the power of identified patient data at scale—their own and those of data research collaborators. Organizations partner with a third-party algorithm provider to create a “secure sandbox” where artificial intelligence (AI), machine learning, and other advanced algorithms can be applied to the data for analysis.

 In this neutral zone, data are essentially shared but remain sealed to each organization.

 The raw data are never disclosed to the parties involved, and data owners can control both which portions of a data set are used and how they will allow those data sets to be used.

The potential impact of this technology on drug discovery is significant. With the use of a neutral zone for data sharing, innovation no longer depends solely on the ability of one company to capture and analyze data. Instead, companies can collaborate with each other as well as other organizations, such as academic medical centers, to uncover insights using real-world data. This deepens the well of data to which pharmaceutical companies have access without sacrificing the security of that data or putting their intellectual properties (IP) at risk. Following the release of the results to each data-sharing party, the data sets and the algorithms used within the neutral zone are destroyed, maintaining the safety of the sealed data for all parties.

Currently, several organizations have begun to use a neutral zone for the purpose of secure data collaboration, such as St. Jude Children’s Research Hospital, Adaptive Biotechnologies, and Answer ALS, who are working to develop targeted care interventions that could reduce the cost of AI-powered analysis over time.

Additionally, 10 pharmaceutical companies have come together to advance drug discovery in MELLODDY (Machine Learning Ledger Orchestration for Drug Discovery), which is a coopetition model using a decentralized, data-private, machine learning approach to collaborative research.

 Further, Roche and Bristol Myers Squibb have begun to collaborate on AI and digital pathology to develop highly personalized cancer treatments, including for non-small cell lung cancer.

In these collaborations, these organizations will not have access to each other’s data directly, but will instead have access to insights gathered by pooling their data together for analysis by AI technology. In light of this development in data sharing, Deloitte predicts that a significant number of health care organizations will be exploring opportunities to accomplish business goals using AI-based analysis of other organizations’ data by the end of 2023.

For pharmaceutical and life sciences companies, the ability to support secure data collaboration for research and analysis is quickly becoming a competitive differentiator. As COVID-19 has accelerated the decentralization of virtual trials—with more trials performed remotely or with the help of other parties to cut down on travel times and expand the pool of participants—neutral zones can also enable pharmaceutical and biomedical companies to securely access and analyze this data for research. This has the potential to support and strengthen the validity of the data accumulated for populations that have historically been underrepresented in clinical trials due to issues pertaining to health care access and travel limitations to in-person clinical trial sites.

Now, 76% of executives across industries believe new ways of collaborating with ecosystem partners and third-party organizations—including competitors—are key to innovation.

 Experts also predict that secure data-sharing capabilities could better position organizations to monetize their own data as well.

The success of a neutral zone approach starts with selecting a third-party organization that can bring parties together for collaborative research and analysis in a neutral environment. Three attributes of a neutral zone partner are helpful to consider.

Expertise in preparing data for AI analysis.

 AI analyses are only as good the data that go into the algorithms, but preparing data for analysis can be time-consuming and complex work. This is especially true when data come from numerous sources and in multiple forms. Look for a partner who has demonstrated expertise in aggregating, ingesting, and extracting data for rapid analysis.

Prebaked security, GxP, HITRUST, and performance controls.

 This ensures that AI algorithms are deployed in a baseline secure and compliant environment, allowing organizations to focus on leveraging the neutral zone for research rather than worrying about the risks involved.

 After each analysis, the neutral zone should disseminate the results to each party. It should also create a record of the analysis so that data owners can see how their data was used.

A neutral zone for collaborative research is one that is HIPAA-compliant, prevents data leaks, and ensures robust data access control and monitoring. It’s a capability that can broaden health care researchers access to data for analysis and strengthen the agility and performance of research endeavors across health care, potentially leading to new breakthroughs in drug discovery for disease targets with high unmet need.

 is chief revenue officer for Healthcare Triangle.

Ceulemans, H, et al. From competition to collaboration: How secure data sharing can enable innovation. World Economic Forum. June 27, 2021. Accessed June 7, 2022. 

https://www.weforum.org/agenda/2021/06/collaboration-data-sharing-enable-innovation/

Goasduff, L. Data Sharing Is a Business Necessity to Accelerate Digital Business. Gartner. May 20, 2021. Accessed June 7, 2022. 

https://www.gartner.com/smarterwithgartner/data-sharing-is-a-business-necessity-to-accelerate-digital-business

New Data Stations Project Creates Architecture, Marketplace for Sharing Data. The University of Chicago Department of Computer Science. Sept. 18, 2020. Accessed June 7, 2022. 

https://cs.uchicago.edu/news/data-stations/

Rooney K. A doctor, a researcher and an activist: Perspectives on how sharing data is advancing health care. Microsoft On the Issues. June 30, 2021. Accessed June 7, 2022. 

https://news.microsoft.com/on-the-issues/2021/06/30/open-data-health-care-ai-covid-19/

Ceulemans H, et al. From competition to collaboration: How secure data sharing can enable innovation. World Economic Forum. June 27, 2021. Accessed June 7, 2022. 

Park A. Roche, Bristol Myers partner to develop digital pathology AI to analyze clinical trial assays. Fierce BioPharma. March 25, 2022. Accessed June 7, 2022. 

https://www.fiercebiotech.com/medtech/roche-bristol-myers-partner-develop-digital-pathology-ai-analyze-clinical-trial-assays

Farrall F, et al. Data-sharing made easy. Deloitte. Dec. 7, 2021. Accessed June 7, 2022. 

https://www2.deloitte.com/us/en/insights/focus/tech-trends/2022/data-sharing-technologies.html

No place like home? Stepping up the decentralization of clinical trials. McKinsey & Company. June 10, 2021. Accessed June 7, 2022. 

https://www.mckinsey.com/industries/life-sciences/our-insights/no-place-like-home-stepping-up-the-decentralization-of-clinical-trials

Belissent J. Chief Data Officers: Invest In Your Data Sharing Programs Now. Forrester. March 11, 2021. Accessed June 7, 2022. 

https://www.forrester.com/report/chief-data-officers-invest-in-your-data-sharing-programs-now/RES164496

Articles

In a collaboration using a neutral zone, organizations do not have access to each other’s data, but will instead have access to insights gathered by pooling their data together with other organizations for analysis by artificial intelligence technology. 