On average, notes Bryan Hill (pictured), Chief Technology Officer at Cognizant Life Sciences, it takes about 7 years to develop a new drug and bring it to market. For ambitious life science businesses, generative AI’s ability to generate insights and content in a fraction of that time means wiping months, or even years, off that average.
When it comes to clinical development, saving time translates to saving lives — or at least improving them — through the faster availability of treatments. It also translates into significant revenue opportunities.
Some industry sources estimate that bringing new treatments to market ahead of schedule can be worth between £500,000 to £6.5 million per day.
However, owing to uncertain regulatory landscapes, coupled with the rapidly evolving nature of the technology, some companies are taking a wait-and-see approach to adopting generative AI.
They’re delaying investment until the course forward is clearer. Although this approach may seem prudent, it could be short-sighted; they could miss out on the opportunities that generative AI offers, such as drug discovery and speed to market, while their peers jump ahead and take the lead.
For life sciences companies looking to seize a competitive edge and supercharge their speed to market, there are a few key areas of the clinical development lifecycle they should focus on first.
Simplifying the research process: Research and development (R&D) is often the most time-consuming part of the drug development process, but AI can accelerate this process by up to 50% as the technology has a multiplier effect wherever it’s applied.
At the very beginning of the R&D cycle, generative AI can help to search and summarise any available literature on a potential drug.
Instead of beginning with a manual keyword search and sifting through hundreds of articles from various sources, teams could prompt a generative AI-enabled tool to rapidly search, gather and distil relevant content — or even suggest unanticipated information pathways to explore.
Generative AI also has the potential to change how researchers find existing literature. Usually, they simply type keywords into a search engine.
But, with a generative AI tool, they could state their goal as a prompt — providing context and intent — for the technology to find reference materials to support that specific ask, saving significant time while broadening the research horizon.
Expediting launch processes in secondary markets: Once a new therapy has been approved for launch in one market, many companies will look to expand into others. This process takes a tremendous amount of time and resources, from strategy development and market research to agency engagement, content creation and material development. Much like in the research and protocol writing processes, a lot of these steps could be automated with generative AI.
When the drug is close to gaining approval, for instance, generative AI could help the commercial teams to research and compile strategy documents for secondary markets, considering specific regulations and country specific compliance requirements.
Similarly, generative AI can be used to adapt existing content — including website copy, brochures and other promotional materials — to the language and culture of the secondary market. This could shave up to a year off the go-to-market timeline in new countries and massively reduce marketing and design costs.
Taking the first steps
Introducing generative AI into a business should be done one step at a time. It starts with fostering a culture of AI literacy, wherein every employee understands how the technology can be used to reshape and empower their role.
It is also important to build a solid ecosystem of partners, which includes relationships with academic institutions, data providers and specialty generative AI vendors that will support the business’ knowledge growth and internal capabilities.
Once generative AI is introduced, it’s a good idea to establish a body within the business to supervise how the organisation uses the technology and manages the upskilling and development of employees engaging with the tech.
This body should also establish best practices and develop frameworks that guide the deployment of generative AI throughout the business.
A life-saving revolution
Introducing generative AI into a pharmaceutical business is no mean feat. It is, however, essential for companies that want to stay ahead of their competitors and the market to invest in generative AI.
Likewise, it is crucial to ensure that employees know how to optimise the technology and create a body that supervises how the technology is being deployed across the business to avoid any misuse.
As companies continue to experiment with generative AI across various use cases, they will begin to lay the foundations needed to harness the full potential of this transformative technology — discovering, testing and bringing their drugs to market sooner.
