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Ambitious plans versus on-the-ground realities
It is true that the vast majority of pharma and biotech companies are working on crafting a strategy for AI tools. Only 1% of the participants in CRB’s Horizons Life Sciences 2023 report said they did not have such a strategy. Three-quarters, 76%, said such a strategy would be implemented within the next two years while 21% said they planned on doing so in the next three to five years. (The survey, which probed the state of digital pharma and biotech in 2023, included responses from over 500 leaders from various-sized companies across North America and Europe.)
Peter Walters
The most popular areas for AI investments were drug discovery and R&D (48%) followed by quality and regulatory (40%) and clinical manufacturing (34%). Rounding out the top five were supply chain and logistics (27%) and engineering (22%).
For all of the talk about Pharma 4.0 (an ISPE trademark), which refers to the integration of advanced digital technologies, such as AI and IoT, in the pharma industry, executing on those ambitions is rarely straightforward. “Sometimes, the cost of fully implementing these initiatives can be a hard pill to swallow,” said Peter Walters, fellow, advanced therapies at CRB. There is also a staffing burden dimension. “You’ve got the implementation of the systems — the coding, the hardware, and then there’s the massive task of changing workflow practices, training staff, and altering how people work,” Walters continued. While the benefits can be considerable for mature digital initiatives, “the initial lift to get there can be substantial,” Walters said. Resource-strapped companies in particular are more likely to postpone ambitious digital plans.
Challenges in implementing AI and digital strategies in pharma
While nearly all companies have some sort of strategy for AI and digital technologies, many struggle to know how to execute it. “They might be implementing small pieces of their digital transformation plans, laying some groundwork, but the bigger leaps, the major steps in that process, are likely being postponed a bit,” Walters said.
The Horizons report also captured notable skepticism, especially in North America, regarding the return on investment for Industry 4.0 initiatives. Common perceived barriers included cybersecurity (cited by 41%), regulatory concerns (33%), lack of data connectivity (29%) and market confusion (28%).
Digital dreams
Divergent perspectives on the barriers to data and AI implementation, comparing global, North American, and European corporate sentiments. Image courtesy of CRB.
In the positive column for digital strategies, most sites have a budget in the next two years of at least $1 million for data and AI projects, with 20% having a budget of $10–50 million. In addition, Industry 4.0 initiatives are likely to have C-suite backing.
While the financial commitment to digital initiatives highlights their perceived importance, the potential value of the data that these projects yield stands out. “People are beginning to realize that in some instances, the data can actually be more valuable than the products themselves, depending on the context,” Walters said. That doesn’t necessarily mean that data is worth more than novel therapeutics, but “for companies building technological platforms, especially in emerging areas like cell therapy and branching into stem cells, the collection of all your research data becomes immensely powerful,” Walters added. Such data are crucial for building predictive models, for instance, understanding how stem cells differentiate, or assessing their use and success rates at the patient level. “Data can be extremely valuable to the industry and as a company asset, potentially influencing acquisitions by larger pharma or in sales.”
Embracing digital data strategies in pharma and biotech in 2023
In terms of how pharma and biotech companies use data internally, there is significant potential for organizations to grow more sophisticated in, say, identifying critical process parameters and justifying their recipe parameters, the processes they use, and the endpoints in their label claims for drug products. “Historically, all this data has been confined to notebooks, very paper-bound, making it challenging to synthesize it holistically in a way that’s truly helpful,” Walters said. “It’s all been managed by people, and you’re limited by the intelligence and capability of your staff to devise robust solutions.”
But now, with companies adopting more data strategies and moving away from paper to digital, there’s an opportunity to leverage this data more effectively. Drug developers “can look into the data, pull trends from it in a way that’s more comprehensive, leading to more accurate, better, and faster solutions for these kinds of challenges,” Walters said. “This is a shift from the past, where it was just people sifting through notebooks, and a lot of the knowledge was just dormant on the shelf.”
Filed Under: Data science, Drug Discovery and Development, Industry 4.0, machine learning and AI, Uncategorized
