Tracking Samples in Virtual Organizations
“Often precompetitive research, focused on disease understanding, uses a consortium, a virtual organization,” explained Anthony Rowe, Ph.D., principal research scientist, external innovation R&D IT, Janssen. “Data-collection and results-management technologies must be diffused across all of the organizations.”
There are currently several hundred ongoing collaborative studies worldwide and little existing technology that can be deployed across multiple institutions to track samples. Typically, the classical LIMS biobanking software is tied to a specific enterprise and does not support consortium-based research.
The quality management of a collaborative scientific research environment is challenging enough without highly ineffective sample-tracking methods intervening. Different institutions are tackling different parts of the scientific problem, and samples can be at a variety of places. Without global visibility of the entire consortium, identification of quality problems and bottlenecks is difficult.
“Biosamples are critical elements of precompetitive research,” said Dr. Rowe. “Sample management should be viewed similar to supply chain management. Consortia need infrastructure to manage samples across the virtual enterprise just like they need a research database to collate all of their scientific results. LIMS work fantastically well within a single enterprise, but you want to provide sample tracking across multiple enterprises.
“If there are systematic problems, you need to identify and rectify them, and monitor samples as they go through the study network. Up-front sample issues affect downstream data analysis. The entire project can be jeopardized by small differences in how samples are handled and processed. We use barcodes to track sample movement along with a paper trail. This gives us the capability to track even if the samples are sent to a third group,” he continued.
“Transmart, an open-source translational data warehouse, developed as a Janssen in-house solution, is often deployed as the results’ data warehouse. We realized early on that if we made Transmart open source we could enable better-quality science. There would be a single research database. Funding would go to the sciences, and not IT,” concluded Dr. Rowe.
Developing Biobank Networks
In Europe, the BBMRI (Biobanking and Biomolecular Resources Research Infrastructure) was funded as a preparatory project to develop plans for a pan-European network of biobanks.
“A network of biobanks is just one component of a biobanking network. Future research needs require distinct networks to work together. A biobanking network is a network of networks,” discussed Martin Yuille, Ph.D., joint director, Centre for Integrated Genomic Medical Research, University of Manchester.
“These networks include experts in managing all the varied data types—clinical, environmental, experimental, and so on. Then there are groups of public and private funders, technology providers, and governance experts. All this expertise is essential to a biobanking network.
“It is complicated. Existing medical information classification systems, such as HL7 and SNOMED, are useful and allow flexibility, but not everyone follows them. In addition, samples are processed in varied ways using different instrumentation. Standards have to be consensual between all stakeholders, and those stakeholders need to include instrument manufacturers.
“Networking processes—sharing standards and allowing each network to focus on its strengths, and coordinating all this—will produce the biobanking network needed for future competitive research and cost efficiency.”
An example is the University of Manchester hub-and-spoke pilot project. Accrual and initial sample stabilization takes place in the spokes, such as hospitals, while the final processing, storage, and retrieval is undertaken centrally in the hub. This organizational set-up provides the opportunity for large-scale sample management and allows division of labor and role differentiation. Each stakeholder does what they do best.
“Running a biobank requires high-quality, consistent sample management, cost effectiveness, and associated data management. Running a biobanking network requires a new combination of skills—lab methods research, informatics, and organizational research. There is no Big Bang solution to move from the current fragmented landscape toward a coordinated one.
“Change management is essential to remove barriers. The biomedical research community needs to reach out to experts in the humanities for help, to political scientists, sociologists, and economists, and we need to engage the public in research around their health,” concluded Dr. Yuille.