Finding Answers in R & D Data is a Team Sport

In 2009 the i2b2 project was adapted for use in pharmaceutical organizations by Jannsen to produce an internal system called tranSMART. This system was transitioned into an open source project in 2012 to allow for collaboration between academic researchers and life sciences organizations. The tranSMART software allows analysis of clinical trial data and queries molecular markers in mRNA and sequencing data. The open model has allowed groups to leverage the software and add to it across multiple institutions. Jannsen, Pfizer, Takeda and Sanofi have adopted and extended the tools. Recent extension contributions have included new ways to search GWAS (Genome Wide Association Studies), cross trial analysis and an interactive genome browser.

The OMOP (Observational Medical Outcomes Project) standards were being established as a parallel effort at the FDA. OMOP structures data from health plans and EMRs for standardized analysis methods. Shared methods reduce the effort for analyzing observational data to identify signals such as statistically relevant adverse events. Most major life sciences organizations have chosen OMOP as an approach to tame the beast of data acquired from health systems to convert licensed data sets from a variety of sources.

Handling the Challenge of Open Source and Open Standards

The challenge of these open innovation projects are that—unlike commercial software they are not delivered with a commercial support model and road map from a single vendor with an eye towards regular investment to improve and support the product following adoption. So for an IT organization in pharma that is used to a conservative mind-set driven by tight compliance constraints they represent a risky investment. Furthermore, many organizations already own commercial analytics tools and commercially licensed data sets that need to be analyzed in order to establish a fully functioning system that meets end-user demands.

Commercial products and ser­vices build upon the concept of open innovation by adding with reliable support contracts and extended features offered through famil­iar enterprise analytics tools like Spotfire, Busi­ness Objects, Qlikview and Netezza. The ecosystem of commercial open source services and packaged software that extends tools provides the advantage that buyers can mandate compatibil­ity with open standards and open tools then choose among the service pro­viders based on spe­cific expertise, project requirements or prior experience with the vendor. The open frame­works of i2b2, tranSMART and OMOP combined are the equiva­lent of mobile phone app stores but instead the app store focuses on solving key use cases for epidemiology, bioinformatics and health economics.

For example—Qlikview provides a set of capabilities for data visualization that are not open source but provide an easy way for scientists and bio-statisticians to visualize patient data. As a visualization tool with slice and dice viewing of large data sets packaged commercial Qlikview dashboards can wow end-users in comparison to the limited capabilities in the open source free visualization utilities. ConvergeHEALTH by Deloitte, this year, has used Qlikview to produce a commercial software package called PrecisionMiner that offers interactive visualizations to highlight variations that drive outcomes. PrecisionMiner leverages data in supported i2b2, tranSMART and OMOP open source tools. The offering is provided in a cloud subscription model through a web based interface to enable life sciences teams to access the rich information from participating provider organizations without exposing underlying data in raw forms. The tools are also offered as on premise installations to analyze local data sets. Having this full set of tools integrated will allow the next generation of data scientists to ask new questions regarding whether signals identified in clinical trials are also true in real world data sets and to validate findings located in messy observational data sets within the more precise data collected during clinical studies.

What’s Next?

R&D IT initiatives are echoing life science organizations working to externalize innovation processes and engage more closely with collaborators. The resulting open source software options are rapidly maturing and moving towards standards to reduce interoperability challenges. Commercial offerings provide a sustainable model with continuous investment into extension and performance that is needed to accomplish advanced uses. Pharma teams that master this new model will spend less time and resources building and integrating data and more time answering difficult questions about medicine.