AI capable of refining content and analysing data offers incremental gains in life sciences, argues Bryan Hill.
It takes approximately seven years to bring a new drug to market. However, this time can be reduced by months or even years if life science companies leverage generative AI.
This timesaving is crucial in clinical development, expediting the availability of treatments and improving or even saving lives. It also presents a revenue opportunity, with industry sources suggesting bringing new treatments to market early can lead to a daily increase in value between £0.5 and £6.5 million [1]. Nevertheless, the pharmaceutical industry faces an uncertain regulatory environment.
Compiling a clinical trial protocol document can take between a few months to over a year. Generative AI capabilities can bring it down to days or even hours by automating parts of the writing process
As such, some companies are adopting a more cautious approach to generative AI tools, postponing investments until the path becomes clearer. Although this may seem a prudent approach, it could become a source of regret in the long term. By delaying adoption, they risk missing out on the opportunities presented by generative AI, including advancements in drug discovery and an accelerated speed to market that their competitors may benefit from.
For enterprises who want to hasten their time to market, they should prioritise digitally transforming specific areas of the clinical development lifecycle:
Streamlining the research pipeline
Research and development is often the most time consuming part of drug development, but AI can accelerate this process by up to 50%. Life sciences can implement generative AI at the beginning of the R&D cycle, aiding searching and synthesising available literature on a specific potential drug. Instead of beginning with a manual keyword search and sifting through hundreds of articles, teams could prompt a generative AI-enabled tool, providing context and intent, to rapidly search, gather and distil relevant articles – or even suggest unanticipated information pathways to explore. This saves time while broadening the research horizon.
Speeding up clinical trial protocol creation
Compiling a clinical trial protocol document can take between a few months to over a year. Generative AI capabilities can bring it down to days or even hours by automating parts of the writing process.
Generative AI can be trained on thousands of existing protocols in industry databases and companies’ own research data to identify the patterns relevant to investigational products, certain conditions, specific patient populations, or other factors. As the tool identifies relevant patterns, it can combine the insights to design a baseline study, with a defined narrative that determines eligibility, drafts exclusionary criteria and provides other necessary details. It can also create draft options that would later be evaluated and refined by a human.
Quicker secondary market launches
Once a new therapy has been approved to launch in one market, companies will be looking to expand into others. This process takes a tremendous amount of time and resources, from strategy development and market research to agency engagement, and content creation. However, many steps in this process could be automated with generative AI.
For instance, when the drug is close to approval, generative AI could support commercial teams’ research and compile strategy documents for secondary markets, taking into account specific regulations the therapy will need to adhere to in the new country. Similarly, generative AI can adapt existing content – including website copy, brochures and other promotional materials – to the language of the secondary market. This could take a year off the go-to-market timeline in new countries and reduce marketing and design costs.
Setting the groundwork
Introducing generative AI into a business should be done one step at a time. Beginning with fostering a culture of AI literacy, where employees understand how the technology can empower their role. It is also important to build an ecosystem of partners, including academic institutions, data providers and specialty generative AI vendors that will support the business’ knowledge growth and internal capabilities.
Once generative AI is introduced, firms must establish an internal body to supervise how the organisation uses the technology and manage the upskilling of employees engaging with the tech. It should also establish best practices and frameworks that guide the deployment of generative AI across the business.
Using generative AI in a life sciences company is a significant undertaking and not something to rush. Nevertheless, it is crucial for companies aspiring to stay ahead of their competitors. Equally vital is providing comprehensive training to employees so they can maximise the benefits of this technology. Establishing an internal governing body to oversee responsible deployment of generative AI is also imperative to prevent any potential misuse.
Companies are establishing the groundwork required to harness the full potential of these technologies. Through ongoing experimentation, companies can accelerate the discovery, testing and market release of drugs. This advancement improves patient outcomes through safer, more effective and affordable drug development, while amplifying revenue opportunities in a competitive market.
Bryan Hill is chief technology officer, life sciences at Cognizant, responsible for digital solutions and technology innovation
