Data sharing, an activity of great relevance to a company’s overall efficiency and effectiveness, runs the gamut from ad hoc to institutional. In other words, you’d be hard pressed to find a life sciences company that doesn’t share data, but the efficiency with which it’s done varies widely.
Why does that efficiency range so widely, what are the benefits of data sharing, and how can a company, and even the whole industry, establish data sharing without compromising proprietary information and privacy?
According to the Tufts Center for the Study of Drug Development, the average cost to reach drug approval is $2.6 billion USD. For each day a clinical trial exceeds its enrollment timeline, a company loses $600,000 to $8 million in potential revenue. The motivation to find and employ any tool or process that makes development easier and faster is greater than ever. Data sharing fits the bill.
According to a study by the National Institutes of Health, “Patient-level data sharing has the potential to significantly impact the lives of patients by optimizing and improving the medical product development process.” Another article, in Datawatch, maintains “a number of major pharmaceutical players, academics, and government bodies have begun to share their data, making insights more readily available and reducing the costs and time required to bring drugs to market.”
Why is data sharing inconsistent?
All companies compete, of course. That’s the nature of business. They hope to create and retain an advantage over their peers, including research and scientific conclusions.
“Life sciences is a very acquisitive industry in the sense that people are always gobbling up either competitors or smaller players to improve their pipeline,” says Jesse Cugliotta, Global Industry GTM Lead for Snowflake’s Healthcare & Life Sciences division.
“Large pharmaceutical companies, for instance, have the financial muscle and regulatory experience to accelerate time to market for some of the innovative new therapies being designed by smaller biotechs. Then the bigger companies often buy or partner with those smaller companies,” he says. But costs and timelines are significant—for the company developing the drug, that means “a billion dollars and a decade before you can make any money.”
These start-ups that have been merged into the purchasing company can retain a sense of separateness that acts as an obstacle to sharing. Post-acquisition, the companies are now one, but they’ve possibly been in competition for years.
There’s more to it than that, according to Bennett Klusas, Senior Manufacturing Specialist for Novavax.
“People will blame the lack of data sharing on data integrity requirements or on not having the bandwidth to share the data,” says Klusas. “But it’s not usually an active siloing so much as it is something passive that comes up in the course of system setup. On the clinical trial side there are very rigorous HIPAA protections in place. On the manufacturing side, where I work, we need to keep people from messing with our data, which is a very real concern.”
“If someone accesses your database and deletes data, it’s not the end of the world. You can just roll back your server and get your data back, but that’s 45 minutes out of your day.”
The challenges of data sharing are extensive, but so are the reasons to make it happen.
Why should life sciences companies share data?
The motivation for sharing has increased significantly because of where we are in the progress of modern medicine.
“25% of the world’s diseases have now been solved thanks to existing therapies and medicines,” says Cugliotta. “But those were the easy 25%.” If drug companies wish to continue developing drugs, solving problems, and making money, they are going to have to develop more efficient methods, including for data sharing, and spread the risk and the cost across more stakeholders.
“It used to take up to 12 years and cost around 1.2 billion to discover and commercialize a drug,” says Loic Giraud, the Global Head of Digital Delivery for the drug company Novartis, in a conversation with The Cube, a technology-focused video platform. “We have been able to reduce that by 2 to 3 years, which ultimately is a benefit for our patients.”
In short, data sharing allows any life sciences company embracing it to make progress in the following ways:
- Faster, more agile development cycles, faster time to market
- Reduced costs due to reduced time and effort
- Increased innovation, including faster and more complete access to data that was siloed before and which can lead to breakthroughs
- More lives saved and other patient benefits, resulting in better company
“Data sharing can add a lot of value because it makes it easy for folks to communicate across boundaries,” says Cugliotta. But currently, the data tools that have captured so much of the market do not easily communicate with each other.
John Pastor at Pfizer outlined the benefits of data sharing in life sciences, especially for the supply chain, which suffered terribly during both the pandemic and recent natural disasters, such as last year’s storms in Texas that cost much of the state its energy access.
“If you’re sharing things like your inventory positions, warehouse withdrawals, consumption at a pharmacy—there’s lots of benefit to sharing all that with your manufacturer,” says Pastor. “You get advanced information as to demand fluctuations. Now we’ve all lived, as consumers anyway, through a year and a half of empty shelves, shortages, inflation, and not being able to always get what we want. So clearly, there is a ton of mutual benefit to collaborating up and down the supply chain. And clearly the more we can share and collaborate on data, the smarter we’re all going to get about getting a product to where it needs to be when people need it.”
When companies don’t share, the result is a landscape of castles with the drawbridges up—with insights left on the other side of the moat.
“Without data sharing, you have all of these walled gardens that grow around the technologies specific to different departments,” says Cugliotta. “On the commercial side, the supply chain side, the clinical side … and they don’t integrate with one another because they each want to become the dominant player.”
“Our goal is to eliminate conversations that go something like this: ‘I’d love to answer that question, start this project, etc., but I’m still waiting on access to the data.’”
So how can life sciences companies get on with the task?
- Make sure data sharing comes from the top. It can be rather challenging, and those employing it need to know leadership believes in the strategy.
- Identify where you may have gaps in data—for example, environmental and economic data—that could help accelerate drug discovery, and work with partners and/or a data marketplace to bring that missing data in.
- Use AI, which is improved by data sharing; the more information used to train AI, the less likely it will be to make mistakes.
- Anonymize IP addresses and provide contextual data to help enable privacy and compliance with governmental regulations.
- Make interoperability an IT priority.
From a very practical point of view, Klusas advises this set of measures:
Scope your systems properly: “System scope creep leads to these data clusters, where it’s a 12-car pileup on the highway, and now somebody’s got to come in and fix all of this stuff to make sure it’s operating the way it’s meant to operate. So, proper scoping of systems would be a policy I’d be very rigorous about implementing.”
Train your users: “If your users aren’t properly trained and there’s nothing driving the action to ensure that data gets into that system where it can be distributed publicly and actually be usable, you’ve hit another huge roadblock.”
The problem of data sharing is a solvable problem
The inconsistency of data sharing can be the result of inherited corporate issues as well as the result of work demands. To make data sharing consistent can speed up and smooth product development as well as reduce aggravation and inefficiency.
This is more important than ever, because the most difficult drug development challenges remain ahead of us. Whoever solves those development challenges is going to grow, creating and releasing new products that solve increasingly tough problems, while those who can’t will find progress harder.
These are practical, simple, and enactable measures that any organization that wishes to engage in effective data sharing can take.