Laboratory data management strategies for building the digital lab of the future today

In the digital age, paper data persists in pharmaceutical and biologic R&D labs, including experimental data recorded as part of cell line development, analytical results and raw instrumentation data for assays. Many organizations lack a standardized, comprehensive approach to laboratory data management and exhibit a heavy reliance on paper-based, manual processes. This isn’t because today’s tech-savvy scientists prefer traditional, hand-written lab notebooks, as the same scientists casually interact with the digital economy on smartphones and live in a world of IoT and smart, digitally connected homes of the future. So, what’s delaying the digital lab of the future?

The root cause of paper laboratory data management

Ironically, it’s the bioinformatics information landscape that forces scientists to resort to paper data. R&D teams use a wide range of different bioinformatics systems to support the daily activities within the lab. Devices such as chromatography data systems, pH meters and balances serve a specific purpose and have had a positive impact on how the everyday work of scientists is conducted. The problem is rather that these are all too often disconnected, creating data silos that lock up the free flow of R&D data. As a result, scientists manually move data between applications, using Microsoft Excel and paper records to function as the metaphorical “glue” between them.

Bioinformatics data silos have negative and costly consequences – scientists are spending a lot of time manually processing, analyzing, managing and moving data and an unnecessarily large amount of their effort is focused on data administration. More manual intervention means more errors, affecting data integrity and delaying discovery and innovation. Evidence of this data management burden, the top 10 types of FDA citations used in warning letters over the last few years involve data integrity issues. (1, 2)

Building the digital lab of the future begins with a solid laboratory data management foundation. This should include strategies for digitally capturing experimental data as well as integrating sample data from the bioinformatics information landscape.

Managing experiment data efficiently with electronic lab notebooks

Electronic lab notebook (ELN) software replaces the traditional paper lab notebook with a digital version to simplify and automate data entry related to assays and experiments. It enables scientists, engineers and technicians to document research, experiments and procedures performed in a laboratory with data ranging from the protocols and narrative of the experimental process to the write-ups and results.

A key objective of an ELN is to store and protect intellectual property (IP) in electronic format, which enhances data integrity and availability and can be vital in protecting intellectual property claims. Eliminating manual transcription from paper to data files or spreadsheets also reduces the risk of human error and improves R&D throughput. In turn, this ensures repeatability and traceability. As a result, organizations have easy access to experimental data, reducing the time needed to prepare registration documentation and enabling faster responses to questions from regulators.

The most advanced ELNs go beyond capturing and validating lab data. An ELN should also capture the context and scientific intelligence of an experiment alongside the assay details and test results as well as automate workflows to reduce the chance of human error during data input and processing.

Top tip: Learn more about laboratory data management.

Managing sample data with a laboratory information management system

Because an ELN is experiment-centric, many R&D teams also rely on laboratory information management systems (LIMS) to round out their laboratory data management strategy. LIMS software allows R&D teams to effectively manage scientific samples, test data and processes system-wide, from sample registration through to reporting of the result.

Since the primary purpose of a LIMS is to track and manage samples, it excels at summarizing test results. A LIMS can capture and manage sample-specific summaries of test results across multiple assays and protocols. It also centralizes access and storage of quality control data, tracks reagents/lots and improves the quality and reproducibility of an experiment.

Top tip! Discover the key considerations for using a LIMS to manage laboratory data.

 

R&D data, a critical first link in the bioinformatics information chain

Pharmaceutical and biologics R&D doesn’t take place in a vacuum, and neither should laboratory data management. Just as R&D is collaborative with scientists understanding that their work is part of a greater whole involving process development, clinical trials, manufacturing and compliance, laboratory data management is also part of a greater, collaborative process. However, typical ELNs and LIMS can only provide a myopic view of research data, narrowing the data management focus to a single lab and creating a fragmented information landscape.

The digital lab of the future zooms out to put laboratory data management in the broader context of an organization’s bioinformatics ecosystem spanning the entire product lifecycle. In this light, laboratory data management can be seen as the first critical link in a chain of interlocking workflows, where efficiently managed R&D data pays dividends not just for discovery, but tech transfer and auditability as well.

Forces shaping the future of laboratory data management

For the most part, what R&D organizations do has gone unchanged for many decades until a confluence of forces, including a growing reliance on external partners, advancements in precision medicine and a new possibilities ushered in by COVID-19 vaccines. When it comes to laboratory data management, the widescale shift from in-house R&D to outsourced contract research organizations (CRO) and contract development and manufacturing organizations (CDMO) will continue to add additional layers of data access and integration complexity. This presents new challenges as R&D professionals are required to analyze and aggregate results that may have been generated by someone else at a different lab halfway around the globe. This hybrid R&D model also means that pharmaceutical and biologic product sponsors must overcome issues ranging from integrating externally generated lab data to IP protection and IT security.

The digital lab of the future also extends to new modalities and rapidly emerging therapeutics as cell and gene therapies (CGT) become viable, underscoring the need for effective laboratory data management where organizations may be tempted to adopt paper notebooks until products approach commercialization. Precision medicine introduces multiple complexities where a robust laboratory data management and analytical capabilities are essential from the earliest phases of product development, including cell type variability and complex raw material supply chains.

Profoundly reducing development costs, increasing speed to market and innovative agility are matters of commercial survival and competitive edge. R&D teams can achieve these business targets with the digital lab of the future where the right choice of laboratory data management technologies eliminates the manual, repetitive and disconnected processes that hold teams and discovery back. In turn, pharmaceutical and biologic organizations are empowered to thrive despite the market headwinds, accelerating R&D throughput and delivering life-changing products to patients with increased speed and lower cost.

Interested in how advanced ELN technology can serve as your laboratory data management hub, connecting and integrating LIMS, SDMS and your entire bioinformatics information landscape?

Discover why Polar is the product for you.

 

 

References

  1. The FDA Group. FDA Warning Letter & Inspection Observation Trends. Feb. 6, 2023.
  2. FDA. Inspections. Data Dashboard. datadashboard.fda.gov/ora/cd/inspections.html (Aug. 30, 2023)