Digital Transformation: Execs Weigh in on the New Future for Pharma

By Allison Proffitt 

September 21, 2021 | The Bio-IT World Conference and Expo kicked off last night both in Boston and online. The hybrid event welcomed attendees in person for the first time since April 2019, starting with an expansive plenary panel on digital transformation—a process that has been kicked into high gear for many companies during the pandemic.

In many ways the digital transformation that pharma companies and others in the life sciences are going through is really a reconnecting of the various siloes and business groups that have grown large and distinct in the past years. Again and again members of the panel emphasized more communication, closer collaborations, and better alignment of both science and IT goals.

Our digital transformation is thinking deeply about what we’re trying to achieve in R&D, said Lihua Yu, who just joined FogPharma as the Chief Data Officer after her tenure at H3 Biomedicine. For instance, in drug discovery target identification is relatively easy, Yu said. Now we have to find the right patients, she said. That’s the challenge.

COVID is changing culture and mindsets, said Holly Soares, VP and Head of Precision Medicine at Pfizer Research Labs. The pandemic has given all of us an opportunity to develop our change or growth mindsets, she said. Pfizer’s focus has been on speed: using digital technologies to speed up discovery and making research faster and easier by using automation. 

Across both pharma and diagnostics, Roche is advocating for data citizenship, said Bryn Roberts, global head of data services. Data citizenship is a mindset that considers the rights, responsibilities, and capabilities that individuals within the organization have toward the data they create and use. 

At GlaxoSmithKline, Mike Montello, SVP, is also prioritizing speed. The whole organization, he said, has shifted to a product-centric organization built on agile principles, and is encouraging a “minimal viable product” approach that aims to deliver products every quarter—must faster than in the past. It’s working, he reports. Teams are moving faster and managing their priorities and backlogs better.

Ramesh Durvasula, VP and Information Officer at Eli Lilly, highlighted the need for digital transformations that focus on people and processes as well as data. Digital transformation is a one-way door that we’re all going through, he said. 

Playing the Team Sport

Digital transformation does not just refer to changes in how we use data, it impacts how people work, how they view their jobs, and how career paths take shape. The panel members all mentioned ways that their teams are seeking to become more tightly knit to better serve the company’s goals.

Data science is a team sport and it’s a multidisciplinary sport, Durvasula said. It’s not just about building great apps. Data science depends on a deepening collaboration between bench scientists and data scientists. That’s going to be the killer app, he predicted: collaboration. Durvasula predicted that collaborative nature of the team will directly impact when and if AI and machine learning make a real difference in health. 

If Durvasula is right—and the panel agreed—our success will be impacted by the makeup of the team, which has big implications for the people we choose for our organizations. 

The most valuable skill today is learning agility, Durvasula said, the ability to learn new skills rapidly and deploy them. We are seeing how quickly things change and how quickly new skills become useful and then essential. For instance, Durvasula quipped, a few months ago most of us didn’t think about understanding how proteins fold. Now—with the release of AlphaFold—there’s so much we can do! 

He suggested deputizing more IT people to do data science and training more data scientists to write better code. The formality of different roles is less and less critical, he said.

Yu agreed. We have to move away from technical specialists, she said. I want to hire Ph.D.s who are well-rounded: they can write the code, they can solve the problems. I want my data scientists on equal footing with the biologists and chemists. But she also warned against too much generalities. We need both, she said, people who are well-rounded enough to lead a project but also some who are specialized.

Soares also highlighted the value of partnerships and clear communication on the team. Can your science teams formulate clear questions so they get valuable data in return? She said that decentralized IT models are now more the norm than the exception. The better—and more often—researchers and data scientists speak with one another, but more quickly research can progress. 

Computational biologists used to work under kindergarten constraints: “you get what you get, and you don’t get upset,” Durvasula said. But now they are working more closely on the design of the experiments so the data they need are generated. They are close to the bench; they are tuning the algorithm, he said. That feedback loop between in vitro, in vivo, and in silico is being better tuned so predictions are more powerful, he said. 

Tight feedback loops apply not only within the organization, argued Yu, but with outside partnerships as well. You really need to understand the core business for a pre-commercial biotech, Yu said, using her new role at FogPharma as an example. Companies should prioritize their vendor relationships based on the most critical business need of the company. Soares agreed, advising vendors to spend time really understanding the unmet needs of their partners of choice.

In fact, Roberts took the call for understanding one step further. Rather than seeing individuals sporadically—only when they become “patients”—he called for pharma to seek to understand the health journey from end to end. He elevated the challenge by highlighting underserved populations globally—not just optimizing healthcare for those of us with reasonably good access to care already. He was particularly excited about ways to leapfrog standard healthcare for underserved populations and move straight to digital delivery of care models. 

Taking Stock

One of the essential changes in this digital transformation is the metrics by which we gauge our success or progress. Here, companies gauge their progress differently.

The only metric that matters is the speed and robustness of the pipeline, Durvasula argued. How quickly are we getting medications to patients that need them? To that end, he said, Eli Lilly is moving away from IT or computational goals and aligning the whole business around R&D goals.

Montello agreed that the R&D goals are the ultimate targets for the company, but underneath those metrics is still the flow of data and decision making and Glaxo-Smith Kline is working to deliberately improve each of those steps. For instance, Montello said, How long does it take to prepare a dataset? Can we speed that up? Those steps matter in the whole cycle.