Harnessing the Power of Biopharma Research and Development Data
Transform your organization to compete in a digital economy
These are exciting times for the pharmaceutical industry. Innovation is leading the way, with new technologies and new approaches to disease emerging at a rapid pace. Global collaborations—partnerships between organizations, as well as outsourcing of development and manufacturing—are becoming the norm.
The biologics sector, in particular, is taking off. Currently, biologics account for more than half of all drugs in development, and they are expected to continue to dominate the market.
Innovation and growth, of course, can also give rise to new challenges. Global partnerships and clinical trials, new technologies and other innovations can all lead to massive amounts of data being generated.
Is your company ready to take full advantage of the data you generate to compete in the global digital economy? Are your laboratories able to efficiently generate high-quality data and easily store, manage and process that data? Can your scientists easily communicate and collaborate with partners around the globe?
Many companies, unfortunately, are still operating with legacy data management systems that bog down their efforts at innovation; some still capture data on paper. The scientists at these companies want to use their data to explore new cures for disease, but all too often they spend much of their time just trying to find that data.
An advanced, electronic data management system could enable your researchers to take full advantage of their data to gain insights, make discoveries, make better, faster decisions, and be even more productive. It can also ensure that regulatory hurdles and mis-steps are avoided, by making that data searchable, findable and, most importantly, trackable.
Read on to explore some of the challenges and opportunities of transforming your lab into a truly digital lab of the future.
“We’ve always done it that way”
The stakes are high for companies involved in drug development. Bringing a new drug to the market is a very, very expensive venture that typically takes a decade or longer.
Many drugs fail along the way, never making it to the market. And with the significant changes we’ve seen in the pharmaceutical business model in recent years, more than ever before large and small companies alike are now collaborating with numerous partners, both internal and external, all around the world.
Just the biologics outsourcing market alone is expected to reach $32 billion by 2024.
So, why are so many companies lagging so far behind when it comes to their data management systems? Why are they failing to capture and store their data using the best-possible methods available, potentially putting entire projects, years of work and millions of dollars at risk?
Organizational inertia is a major barrier to what would seem a common-sense move.“People have a lot of history with paper,”says Will Gray, Lead Solutions Consultant, IDBS. “They’ll say,‘We’ve always done it this way, therefore we will continue to do it this way.’ It’s in their comfort zone.”
A large number of diverse “legacy systems,” already in-situ in many businesses, is another major barrier to the move to an advanced enterprise data management system. Many companies and labs invested in what are now legacy data management systems years ago. At the time, those systems probably were adequate, in large part because labs were self-contained and did not need to frequently communicate with others. Those companies are not able to easily share information now, in this era of global collaboration, but still these legacy systems are rarely updated.
Outdated systems are no match for today’s data deluge
Outdated data management systems, such as humans recording experimental data on paper or the legacy system of ‘yesteryear,’ naturally can lead to data errors. Those older systems also mean that researchers spend far too much time on tasks such as tracking procedures and cell cultures, flagging expired reagents and tracking supplies. But the implications go far beyond that.
Older systems simply can’t keep up with the current flow of data.
Newer lab equipment can produce millions of data points. Modern clinical trials, too, can produce enormous amounts of data, especially when enrolled patients are using products such as real-time blood sugar or blood pressure monitors. Humans simply can’t record those volumes of data by hand, rendering some new technologies less than useful. More often than not, scientists are unable to make the best use of the data, even if it is captured automatically on a spreadsheet.
It’s hard to keep track of what has been done, and sometimes just as hard to get access to previously recorded data.
Employees leave, taking institutional knowledge with them. Others inadvertently lock up spreadsheets, causing access delays. Data gets stored in disparate locations, or data silos, so there is no “central source of truth.” Consequently, researchers spend valuable time re-searching for the information they need. They don’t have a clear vision of what work has been done, so work gets repeated. Errors in process often go unnoticed for long periods of time, requiring even more work to be repeated.
Past failures are forgotten.
“People always remember the things that worked, because those get reported,” says Abhay Kini, Director of Product Management, IDBS. “What gets lost is what didn’t work. If someone does a series of experiments and the results are negative, chances are they won’t share that information. So others end up repeating those mistakes, and they are not able to learn from those mistakes.” Again, researchers waste valuable time and resources repeating work.
Data integrity and global collaborations are imperative
Two other potential consequences of an outdated data management system can be crippling to a company: a lack of data integrity, and the lack of ability to use that data and to collaborate.
A scientific company’s data, and the integrity of that data, is its heart and soul. If researchers do not all follow the same procedures and collect data in the same way, and there are no systems in place to avoid inaccurate or incomplete data, all of the company’s data can be called into question. And that can lead to questions about the validity and effectiveness of an entire research program, blocking further progress and resulting in poor business outcomes, compliance issues, and questions about everyone’s integrity and credibility.
Most importantly, when scientists are held back by a legacy data management system, they will never be able to make the best possible use of their data. They spend an inordinate amount of time manually processing, analyzing and managing data. They don’t have the most up-to-date tools that will help them to extract valuable insights from their data or make the best possible decisions on next steps.
Furthermore, they are unable to take advantage of emerging technologies, such as artificial intelligence algorithms, that could help them to pull innovative insights out of their data.
Not to mention the inability to collaborate efficiently and effectively with partners across campus and around the globe. The list can go on…
“The ability to collaborate has really taken center stage in the industry,” says Kini. “It’s incredibly important today.” But, he notes, far too many labs and companies are self-contained, dependent on legacy data systems, and unable to connect with others effectively. That’s often true even within a single organization, due to the many
mergers of recent years.
What often happens, he says, is that many different individuals, teams and companies will be working on various components of a project. Those at the upper level will be responsible for analyzing all of their input and making decisions on how to move forward. Decision makers depend on getting good insight from their partners, but what they get is a jumble of disparate information: emails, spreadsheets, PowerPoints, pdf reports. “Your ability to track that over time, and make sense of it all later on, tends to get challenging,” says Kini.
“Email collaboration goes slowly,” agrees Will Gray. “It’s hard to innovate in that way.”
All these factors can lead to vastly diminished productivity, efficiency and data quality, and ultimately, a failure to launch new products.
An advanced data management system opens doors
When data is recorded and stored with precision in a standardized, comprehensive, digital knowledge management system, opportunities abound.
The IDBS E-WorkBook Cloud is a complete end-to-end, enterprise research and development scientific informatics platform, delivered in a secure, SaaS environment.
It goes beyond traditional lab management software, providing cutting-edge data capture and analysis tools, job requesting and management, inventory management, and biology and chemistry functionality, at the heart of which sits the world’s best and most successful electronic lab notebook. It’s flexible, scalable, and powerful enterprise software that enables companies to vision and build their own digital lab of the future.
A single, integrated solution such as this allows laboratories to assess and optimize their processes and workflows, leading to dramatic improvements in quality and productivity. It provides a solid foundation for researchers to get far more out of their data, and also facilitates collaboration.
For example, this type of system can register samples, track their location, assign them to a project, enable schedules and notifications, pull raw data off instruments, and much more.
“An advanced, enterprise data management system converts paper capture processes into a digital system, but that’s just the starting point,” says Gray. “It can apply validation rules to your data; it can let you know that something you took off a shelf is expired, or incorrectly labeled. So rather than finding out weeks later that you shouldn’t have run experiments with it, you know right away.
“It can enable a lab manager to see how much a project is going to cost, and it can tell them if a set of experiments was done before. It can point out that a protein they are looking at is similar to something else in the system.”
It also enables you to take full advantage of emerging technologies, says Gray. “For example, a current trend is to implement more automation in the lab, using robots to handle more of the processes. That doesn’t work well in the paper world. If you want to move in that direction, you’ve got to have a system in place to support it.”
Better data also means that researchers will be able to make better use of artificial intelligence tools to identify trends and glean insights from their work. “It can help us to learn from the data,” says Gray.
A good data management system, adds Kini, gives you the ability to pull up information on demand, and visualize it in the way that matters most to you. It helps inspire the more forward-looking questions. And it helps you to collaborate with your partners, in the cloud, and make the best decisions for moving forward.
An advanced data management system also greatly facilitates one very crucial step in the drug development process: complying with FDA requests or audits.
With high-quality data that is properly stored and easily accessible, complying with an FDA request becomes a matter of simply “pull and present.” The FDA itself is moving toward an advanced data management system for the massive amounts of scientific knowledge it maintains and, says Gray, “It’s becoming clear that the FDA and other authorities really want private companies to have advanced electronic systems.”
A few basic questions about your current lab procedures can help you start to envision a new data management system and a new, smart lab of the future. How are you getting data off your instruments? How are you storing that data? How are you categorizing and tagging the data? How do your researchers and decision makers access that data? Are they able to easily share that data with colleagues around the globe and collaborate on high-level decisions?
Also consider how extensive you would need your system to be right now, and what you might need to scale up to in the future. Where will your company be in the next five or ten years?
A major key to implementing an advanced data management system successfully, is securing the buy-in of all staff, from bench scientists to senior decision makers.
“The bench scientists and technicians need to understand what it will do for them,” says Gray. “But it’s not about telling them what to do; it’s about helping them do their jobs. So it has to be a system that’s accessible where, and when, you need it in the lab.
“The managers and directors need to understand what it will do for them, as well,” says Gray. “We need to show them how it will make their lives easier.”