IDBS Blog | 6th June 2019
Implementing an Effective Data Management Strategy for Your Biopharma Lab
Organizations in the biologics landscape face several challenges in the race to get their product to market, including handling their data efficiently. We have a solution…
Industry growth presents challenges
The continued growth in the biologics sector has led to a boom in outsourcing, increased pressures of time to market, balancing profit versus operational costs to get an effective drug, and regulatory scrutiny.
These challenges are well-documented, and possible solutions have been proposed and their implementation carefully tracked. To achieve competitive parity with growing markets, such as India and China, companies are looking to partner up with technology organizations for their expertise in supporting the digital therapeutic market. One such partnership is that of Biogen and Google’s life science branch, Verily. These two companies are collaborating to study the biological and environmental factors contributing to multiple sclerosis with sensors and software.
But there’s one pain point that is usually considered secondary – data management.
Data impacts all aspect of lab life
Everything from the science and tech, to resources and project planning, and outsourcing and collaboration to compliance policies and procedures, all center around data and information. For instance, data is used to perform data analysis and understand the impact of a particular instrument on the experiment as well as to make informed decisions on the right formulation and efficacy.
Data needs to be consumable – extracted, compiled, accessed, used, and shared, to add that layer of context labs need to get the most out of their research.
Ineffective data management takes its toll
As it currently stands, many labs’ approach to data management is simplistic or traditional. Innovating in this area is often not a priority. But this tactic can have negative consequences for a business.
Say a balance fails its calibration but this data point is not captured. Other experiments will use the same balance, completely unaware that it’s not calibrated. Down the line, this lost information means the experiments will need to be investigated and then reworked, wasting both time and resources.
In addition, as personnel focus on fixing the error, other projects will be delayed, reagents stock exhausted, and the compliance issue will have to be logged.
Granted, with the introduction of high-throughput systems, there is a lot of data to sift through. Scientists must decide which data is the most relevant. For example, in screening analysis, a single well in a 96-well plate can generate an image of 2MB, and across five time points, this comes up to 1GB of data. And that’s just one well.
Calculating the amount of data generated from all 96 wells in a plate over five time points, and even more plates from a single experiment, all becomes very difficult to manage.
And then there’s the numerical calculations that can go along with the wells – their concentrations, absorbance values etc., and their format. A scientist will need to link these values to the well in question and store the information where it can be accessed and used in the future.
What an effective data management system can do for your lab
You need a solution that serves both scientists at the bench and the organization as a whole, and an integrated platform checks all the boxes. A seamlessly integrated system understands and considers:
- All the lab and organizational dependencies
- Human interventions
- Data collection
- Handoff points
Scientists require a system that automatically captures and records all data associated with the experiment, thereby removing the burdensome need to transfer data between different platforms and duplicate it manually. Along with streamlining data management, this approach also reduces the chance of accidental human error, enhances reliability of the data, and enforces compliance.
With all the relevant data in one place, researchers can easily map out the journey of that data: what is its purpose, what question does it need to answer, how will this data be used, and is the quality sufficient to validate a biologic through its development?
An integrated platform can significantly impact everyday workflows and it’s vital that companies are aware of this impact if they want to achieve their goals. Understanding the personnel and instrumentation involved in the experiment is key to bringing their biologics to patients faster.
An enterprise-ready platform can support firms in their endeavor by streamlining the bioprocess workflows and shortening reporting timelines. It would form the core of an effective data management strategy for improved product quality and insight.
With this challenge addressed, scientists can focus on their science and organizations can meet their business goals and return on investment. It’s a win-win situation.