Cell Therapy Data Management
This client, a global pharmaceutical company that makes and sells a number of branded prescription drugs, was looking for a partner to conduct a cell therapy data management assessment of their existing processes in preparation for a phase II study. The company also makes over-the-counter medications, such as cold remedies and vitamins. Cell therapy development presents significant data challenges which include managing large amounts of data generated by bioprocesses, the need for reliable data throughout the lifecycle, increasing lack of connectivity among supporting systems and the need for speed to market. Adding to the data challenge, companies are faced with increased regulations on data integrity, scrutinized data systems and continuous process verification.
At the time of engagement with our client, they were experiencing all of the above and were in the process of embarking on a transformation within the cell therapy group toward Data Collective Intelligence where high-quality data is widely available for organizational use. The client’s present need was to understand their current data processes to address the gaps and challenges in the cell therapy data management process to prepare for an upcoming phase II study.
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The Clarkston cell therapy consulting team worked with every functional group within research, clinical operations, translational medicine, as well as clinical and commercial manufacturing to map the current data process. Nine broad themes emerged as challenges related to data:
- Data Collection – Users from every function have trouble getting to the data they need in the format they need it.
- Data Analysis – Data is difficult to consume and analyze – including aggregation, segregation, visualization, etc.
- Data Quality – The lack of standards, naming conventions, verification, validation, and automation contributes to data quality issues. This leads to significant manual intervention that won’t scale well. Additionally, employees spend their time getting the data to a workable state instead of on data analysis. This frustrates employees and could lead to turnover.
- Data Governance – There is not a commonly understood governance process for cell therapy data. There is a sense data will be misused without proper processes.
- Resources – There is a shared belief that resources are required to manage data and those resources do not exist or are not available.
- Dynamic Process – The cell therapy science is cutting-edge and creates a dynamic environment. The need for data is great. The lack of formal, repeatable processes (outside of manufacturing) and reliance on tribal knowledge contributes to significant gaps in the quality of data
- Organizational Structure – Silos and the team does not always know who does what.
- Culture – There is a shared belief that collaboration is key to success, but the team recognizes significant cultural challenges with execution. The company needs to close the gap between the vision and present capability
- Technology – The perception is that IT spend, capacity, and speed to delivery does not support cell therapy data objectives.
In summary, three key elements are needed to enable progress in cell therapy data maturity:
- Structure and Stability are needed to create repeatable processes, data capture, and nomenclature for functional groups integrated across functions.
- Dedicated Organization Roles and Responsibilities are needed to create ownership and accountability
- Cross-Functional Communication are needed to break down silo’s
