Data Agility in Biopharma – Strategies for Success

In the rapidly evolving landscape of biopharmaceutical product development, effective data strategies are essential for driving innovation, ensuring regulatory compliance, facilitating efficient decision-making, improving research outcomes, and accelerating development timelines.

In this on-demand webinar, Tom Brohan, BioPharma Leader at LabWare, and Eugene Olkhov, Data Scientist at LabWare, detail the common and distinctive challenges encountered in biopharmaceutical laboratories and provides practical strategies for overcoming them. Topics they cover include data integration, volume, and complexity; data quality and standardization; data security and compliance; and access and collaboration challenges. Furthermore, the webinar highlights the specific data management challenges faced by R&D, bioanalysis, and QA/QC bioprocessing laboratories, offering solutions to address these intricacies.

Key learning objectives

• Explore the common data challenges scientists face in biopharmaceutical research and development
• Discover practical strategies on how to overcome specific data challenges
• Learn new ways to unlock and interpret information from different data sources

Who should attend?

• Quality managers in biopharma operations
• R&D scientists in biopharma
• Lab managers
• Bioprocess engineers

Find highlights from the live Q&A session below or register to watch the webinar at any time that suits you.

Given the challenges managing unstructured data from early cell line development, what features does LabWare offer to enhance data structuring and flexibility?

TB: So we mentioned earlier that LabWare has several products. We have LIMS, we have ELN, and we have data analytics. The biggest strength is that they all sit together on the same database. We have a lot of different relations in our database, which are reusable and which are very common in the industry. So our strategy really is to make sure that we reuse parts of the system where possible. So if you're doing a cell line development, you may have to do the same transfection steps more than once. So in order to do that, we reuse our ELNs more than once. That gives the same structure to the data for every time you do that step. So it's very easy for us to apply and learn or to apply analytics techniques to mine that data.

What is LabWare's capability in managing method validation data? And how does it assist in establishing method robustness and reliability?

BK: We collect the whole set of data from facilitation from start to end. That begins in planning the samples, through to sample management, collecting all the data during result entry, and evaluation of the data until the end to have a final report for submission. So to check all the data, we use analytics for the acceptance criteria checks so scientists have also the confidence later to basically do the documentation and prove the robustness and reliability of the method.

How scalable is LabWare's system in handling increasing data volumes and complex biopharmaceutical development projects?

TB: Biopharma development is a huge task for any company. I guess our proven track record in handling large data volumes across many different industries is up here. So we're not just a biopharma system. We have experience in many other industries, like clinical and food, for example, where data volumes are probably even bigger than this. We have long-standing customers for 20 plus years still using their same database with absolutely no issues. So I think LabWare's structure really lends itself to being able to manage this data quite well.

How can LabWare help labs that are currently relying on having to use multiple technologies to conduct advanced statistical analysis?

EO: So we're aware of labs that conduct these advanced statistical analyses where their LIMS might not actually have the capability to support some of these methods. So the lab is then forced to rely on some of the other software, which also requires taking the data out of LIMS and into other software. Because LabWare allows running custom statistical scripts inside of LIMS, this can remove the need of relying on additional statistical software, as well as maintaining the data inside of LIMS, which is pretty important.

To explore the common and distinctive challenges encountered in biopharmaceutical laboratories watch this webinar on demand.

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