Did you know that it takes approximately seven years to develop and bring a new drug to market? However, this time can be significantly reduced by months or even years if life sciences companies leverage generative AI to accelerate insight and content generation. This timesaving is crucial in clinical development, as it can expedite the availability of treatments, improving or even saving lives. It also presents a substantial revenue opportunity, with some industry sources suggesting that bringing new treatments to market ahead of schedule can translate to a daily value ranging from £500,000 to £6.5 million.
Nevertheless, the pharmaceutical industry faces an uncertain regulatory environment, despite the application of generative AI rapidly advancing. In the face of this, some companies are adopting a more cautious approach to adopting generative AI tools, postponing investments until the path forward becomes clearer. Although this may seem like a prudent approach, it could become a source of regret in the long term for these organisations. By delaying adoption, they risk missing out on the numerous opportunities presented by generative AI, including advancements in drug discovery and an accelerated speed to market that their more forward-thinking competitors may benefit from.
For life sciences enterprises who want to stay ahead of the game and hasten their time to market, they should prioritise digitally transforming specific areas of the clinical development lifecycle.
Streamlining the Research Pipeline
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 is applied.
Life sciences can implement generative AI at the very beginning of the R&D cycle, to aid in searching and synthesising available literature on a specific potential drug. Instead of beginning with a manual keyword search and sifting through hundreds of articles across various sources, teams could prompt a generative AI-enabled tool to rapidly search, gather and distil relevant articles – or even suggest unanticipated information pathways to explore.
Generative AI also has the potential to change how researchers find existing literature. Usually, researchers simply type keywords into the search box. But with a generative AI tool, they could state their goal into the 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.
Speeding Up Clinical Trial Protocol Creation
Compiling a clinical trial protocol document is a lengthy process that can take anywhere from a few months to over a year. Generative AI technology’s capabilities can automate a substantial proportion of the protocol writing process, bringing it down to days or even mere hours.
Generative AI can be trained on thousands of existing protocols in industry databases and each company’s own research data so that it can identify the patterns relevant to investigational products, certain conditions, specific patient populations, or other factors. As the generative AI tool identifies relevant patterns, it can combine all the insights to design a baseline study, with a defined narrative that determines eligibility, drafts exclusionary criteria, and provides other necessary details. It can generate several draft options that would later be evaluated and refined by a human.
