When selecting life sciences research management software, both discovery labs and clinical teams should start thinking about the solutions to address the challenges of collaboration from the very beginning. Whether the two teams plan to use the same data management software or to use separate infrastructure for clinical data and early discovery data, it’s essential to pick a system that supports this collaboration.
Understanding how and when discovery labs and clinical trial teams collaborate
When thinking about the interaction between discovery labs and clinical trial teams, most lab members will focus on the early discovery data they will contribute to an Investigational New Drug (IND) filing for clinical trials and approval in the US or a Clinical Trial Application (CTA) in the EU. This requires a clear data quality trail to ensure patient safety during clinical trials. But it’s only the first of multiple touchpoints between the two teams.
Even before filing an IND or CTA, discovery teams may help with biomarker development to identify target patient populations. This may involve a different set of assays and a new suite of analysis tools.
During and after the clinical trial, if it fails or returns unexpected results, the discovery team may need to evaluate patient samples to determine the issue. And even if the trial is successful, the discovery team may be able to use patient samples from the clinical trial to explore new biology or begin work on a follow-up drug.
So discovery labs and clinical trial teams need to create a foundation for more productive and better collaboration across all these touch points.
Three kinds of functionality are particularly important for ensuring discovery and clinical teams can collaborate effectively:
- GxP compatibility to ensure regulatory compliance
- Metadata support for in vitro, in vivo and clinical samples
- APIs and integrations to enable collaboration across systems
Choose GxP compatible software to ensure regulatory compliance
Good Laboratory Practice (GLP) is a set of internationally recognized principles designed to ensure the quality, integrity, and reliability of laboratory data. It falls under a broader set of data practices called GxP that applies to any kind of data. These standards include measures like audit trails and minimal access policies.
Note that no software is automatically compliant with GLP or any of the other similar regulatory frameworks. To be compliant, a system must be configured correctly and the organization must implement processes that ensure it is used correctly. Some software is more compatible than others with being configured appropriately and supporting these processes.
Clinical teams typically work within a GLP setting, while discovery labs are typically more flexible in how they manage the data that isn’t shared with the clinical team. This means that the two teams need very different technology with very different requirements, from data tracking to user access.
Discovery teams may need GxP compliant software for the data that they share with clinical teams. Or they may want to use the same software to collaborate rather than integrating separate systems. But the restrictions that are necessary for GxP compliance make it harder to do the more flexible work that’s typical of a discovery team. So compliance isn’t just unnecessary—it can get in the way of this early stage work.
The ideal approach is for both teams to use software that can be configured for compliant workflows where necessary while enabling flexible workflows where compliance isn’t as important.
For more information on making this work, check out Bridging Non-GLP Research into GLP/CLIA-Compliant Operations.
Ensure metadata support for in vitro, in vivo and clinical samples
In addition to the different compliance requirements, each of the sample types involved in the collaboration between discovery and clinical teams has its own metadata requirements.
Early discovery primarily involves in vitro samples used for target discovery and early screening through hit-to-lead. These are typically derived from cell lines and only require basic metadata and batch tracking. More of the metadata at this stage is related to the range of treatments that are being screened. So teams should ensure their system includes or can be integrated with a compound or sequence registry.
During the pre-clinical and translational stages that immediately precede filing an IND/CTA, in vivo models require tracking individual animals, including their genetic background. These studies are often carried out in a different facility from the in vitro testing, so discovery labs should ensure that their systems can import data from these studies without excess manual work.
After collaborating with the clinical team on an IND/CTA, discovery labs may also have the opportunity to analyze clinical samples from trial participants, either to evaluate unexpected results or to begin working on the next drug. Again, these samples have very different metadata requirements, covering demographic and other data for individual patients.
Discovery labs that plan to work closely with clinical trial teams need to choose data management software that can either manage all three kinds of metadata, or integrate with other systems that do.
Enabling collaboration across systems with APIs and integration
Regardless of how flexible a discovery lab’s research management software is, it will still likely need to cooperate with additional specialized software systems. These may include Electronic Data Capture (EDC) systems for clinical study data at research sites, metadata systems at external CGMP manufacturing sites, and external LIMS used in animal study facilities.
To automate communication between these systems without resorting to manual copying and pasting that saps productivity and introduces errors, modern lab software platforms provide Application Programming Interfaces (APIs) that allow the systems to communicate directly with each other. There’s still additional work required to fully integrate them, but the APIs are what make it possible.
Note that not all APIs are built the same. Some APIs provide only limited functionality with minimal benefits. So companies should always rely on employees with technical expertise to evaluate APIs to determine if they meet the organization’s needs.
Giving life science investigators the tools they need
Both discovery and clinical teams should begin developing a collaboration plan as early in the process as possible. And that includes adopting and configuring clinical research management software that will allow them to efficiently share data. By choosing software that flexibly enables GxP compliance, supports a wide range of sample types, and provides a deep API, life science organizations can ensure that the business of science continues uninterrupted.
For broader insights on upholding data quality and compliance, see our article Real-Time Audits and Validation: Strategies for Ensuring Accurate and Compliant R&D Data.