Contributed Commentary by Guy Rachmuth

September 10, 2019 | Effective management of the large volume of data is essential for both the efficiency and the quality of clinical trials. In the current ecosystem, not everyone has access to all the data. Piecing the data together (samples, consent, results etc.) is a complex process with the potential for errors which can be painful and expensive to reconcile. 

65% of clinical operations leaders within pharmaceutical companies report relying on manually compiled spreadsheets of data derived from multiple Clinical Trial Management Systems (CTMS), Electronic Data Capture (EDC) systems, and laboratory systems, to assemble a complete picture of information related to trial participants. 

The technology exists to alleviate these challenges. It’s called blockchain or Distributed Ledger Technology (DLT). But the inner workings and potential of DLT remain a mystery for many people. It can be a difficult concept to visualize. Further, because implementation involves multiple stakeholders across different parts of the ecosystem, e.g. investigators, couriers, laboratories, storage facilities etc., widespread adoption is unlikely until each party understands the mechanisms and agrees on the potential of the technology to solve those challenges. 

In order to get the ball rolling, we decided to do a proof of concept implementation so all interested parties could have a common view of the components of a DLT and the benefits it could bring to clinical research sample management. Our use case focused specifically on the management of biological samples in a clinical trial. We chose a subset of stakeholders for our implementation: a courier, lab operations, the laboratory itself, and a storage facility. An actual trial could include more stakeholders, investigators, trial sites, patients etc. 

Blockchain brings with it some terminology that may be unfamiliar. Our case study includes a glossary of terms along with a more detailed description of the proof of concept. Nodes, for example, are connection points in a network. (Think of airports serving as Southwest Airlines hubs.) In this case, nodes simply refer to stakeholder entities.  

Smart contracts manage the exchange of data between the nodes (stakeholders). These are business rules or protocols set up in advance and agreed to by the stakeholders. They can describe what data are required at each point in the chain of custody. When the terms of the contract are fulfilled by completion of the data, the transaction is recorded automatically. This ensures that the data are complete, permanent, and transparent to anyone with permission to access, use, or audit (but not alter) the data. 

Further, transactions from stakeholders are recorded on the ledger, a single source of truth. All nodes have a copy of the ledger.  This ensures that all participants can be confident they are looking at the same information which is unalterable. 

In our case study focused on biological samples, visibility into the chain of custody is critical for these samples, which in some cases are of extremely high value (T cells used in CAR-T therapies, for example.) To address the chain of custody problem at the time of sample collection, most large central laboratories offer e-requisition or investigator site accessioning tools. However, while these tools enable query reduction by eliminating manual data entry, uptake by investigator sites has been slow due to a perceived increase in workload. 

Our blockchain pilot focused on a distributed ledger technology approach to the Chain of Custody process. Some of the benefits of DLT include: 

•Longitudinal immutability of records 

•Automation of processes using smart contracts 

•Audit and validation (visibility for audit and compliance) 

•Security – data in transit and at rest is encrypted; identity management is handled cryptographically 

The proof of concept was not “plug and play”. Adopting DLT did require the development of technology to enable communication with the blockchain and enable the nodes via trigger-based calls to REST APIs. 

In the proof of concept, each stakeholder was able to share data at a granular level in real time and on a permissioned basis. A subset of sample tracking data was shared with one or more participants based on the configuration and logic in the applicable smart contract. Scaling the various smart contracts (adding requirements) was found to be straightforward, a promising attribute for building DLTs for various sponsor needs. 

Looking ahead, a broader implementation of a DLT would allow patients to control and permission access to their medical records and edge data from devices, for example. 

The proof of concept demonstrated the ability to create a functioning DLT system for biological sample management during the clinical trial process. While many of the benefits may be accomplished using a centralized database, the possibility of immutable data, enabled by DTS, is a clear and unique differentiator. 

Guy Rachmuth, Ph.D., is the global head of strategy and corporate development, where he leads a focused market-based insights team to drive strategy that supports Q2 Solutions’ goal of improving human health through innovation. Previously, Dr. Rachmuth was a director of corporate strategic services for IQVIA, leading the clinical development strategic planning process. Prior to joining IQVIA, he led corporate development and strategy for M*Modal, a $500M healthcare IT company. Dr. Rachmuth is an experienced entrepreneur, founding and leading several companies including HEALTHeME INC in 2009, a pioneering mobile health company leveraging AI technology, and NeuroAnalogics Inc. an innovative hardware-based brain-machine interface company spun out of MIT. He can be reached at guy.rachmuth@q2labsolutions.com.