Design your digital strategy early to avoid the new biotech bottleneck
In the quest to grow science and raise funds, developing a digital strategy in general is an afterthought for fledgling biotech companies – if it is even considered at all. However, not paying attention to it early can hamper their ability to advance their science and limit their income-generating options.
With data siled with various researchers and departments, it is largely inaccessible and ultimately forgotten. Digital management has become the new bottleneck in the development of biotechnologies.
“Data is intrinsic to a company’s value and how it differentiates itself commercially,” Matthew Clifford, director of research and innovation strategy at IDBS, told BioSpace. It is as important overall as it is for the development of a particular program, from research and clinical trials to regulatory approval and commercialization. “If the data is an afterthought, you have to tinker with it, which slows down decisions and therefore time to market.”
Innovative companies and multiple industries know this. It’s one of the main things they have in common, Clifford said, whether the company is Amazon Go, which monitors real-time demographic trends among its grocery customers, Tesla, which relies on live telemetry for self-driving cars, or number of biopharmaceutical companies integrating data from research through to commercialization. “They each see data as the foundation of what they do and tie it into decision-making…and they have a strategy to get that data quickly.”
“(Biopharmaceutical) companies need a clear picture of their IT landscape and systems and where this can impede transformation efforts,” McKinsey and company mentioned Last year. Until now, however, digital transformations have rarely been fully realized.
Ideally, a company will develop its data strategy even before it is funded. “Data is the asset you seek to present to investors, so you need to explain how you capture your intellectual property,” Clifford pointed out.
“At this point, it is natural to focus on notebooks, as they are critical to protecting intellectual property and providing data to meet regulatory compliance requirements,” he continued. . Scientific observations and storytelling are part of it.
However, many companies use Excel spreadsheets in the beginning. If they are sufficient at the start, they quickly become insufficient. Specifically, data becomes siled and difficult to locate, access, and integrate once the business grows. Excel spreadsheets also lack audit trails and don’t meet most US Food and Drug Administration (FDA) compliance requirements. “They’re a bad base,” Clifford said.
As a company moves beyond the early stages of discovery research, it may consider adding tools such as a laboratory information management system (LIMS) that supports routine laboratory testing. Although LIMS are designed to meet regulatory compliance requirements, they are generally not agile or flexible enough to meet the needs – such as capturing scientific observations – of a biotech startup.
More robust tools are emerging that combine some of the functionality of electronic lab notebooks (ELNs) and LIMS. As a result, some ELNs can now manage sample workflows, sample and lab inventory (such as reagents).
A new category of software – BioPharma Lifecycle Management (BPLM) – is emerging to bridge the data gap between research, development and manufacturing. Solutions that support BPLM capture both the context and scientific intelligence of an experiment as well as sample information and test results, creating a rich database that supports analysis data throughout the lifecycle of biopharmaceutical development.
As these tools evolve, they become integral to the management and growth of biotechnology companies of all sizes. As Clifford recounted, “I spoke to a major venture capital organization before the pandemic. He has several biotech companies in his portfolio, and he asked them to invest in a digital management system to manage their intellectual property.
The rationale is that digital data systems help companies protect intellectual property and, more importantly, reuse data in higher-level experiences or package it for licensing. “So they have a line of sight to the next phase, as part of their data lifecycle management system,” he explained. The question is whether companies can access and use all their data and have the tools to easily and quickly interpret, visualize and report this data.
When companies consider data, they need to understand that data strategies are iterative, Jonathan MacQuitty, Ph.D., life sciences group leader at Lightspeed Venture Partners, told BioSpace. “Planning a data strategy is like planning a manufacturing strategy. Today you are manufacturing lab quantities, but eventually you will need to use GMP manufacturing to manufacture clinical quantities,” so plan for this transition now. “Often what you do today has an impact on tomorrow.”
As Clifford advised, “Start small. Collect IP, but think about the next level of needs. The burden of quality assurance is huge for good laboratory practice (GLP), so design your digital strategy to reduce your current and future regulatory and administrative burdens by using technology to minimize data errors. When considering specific data tools, identify which ones integrate well with the others. “It’s a good mindset that will help you screen many options in the market.”
The key, he said, “is choosing a digital strategy that meets your current needs and can evolve. Evaluate systems carefully to avoid rework and minimize future costs. By designing your digital strategy early, you can avoid duplicate work later.”