Generative AI and its Impact on Speed to Market for Pharmaceuticals

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 in order to 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 a number of draft options that would later be evaluated and refined by a human.

Facilitating quicker secondary market launches

Once a new therapy has been approved to launch in one market, many companies will be looking to expand the launch 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 the steps in this part of the pharmaceuticals process could be automated with generative AI.

For instance, when the drug is close to gaining 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 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.

Setting the groundwork

Introducing generative AI into a business should be done one step at a time. It starts with fostering a culture of AI literacy, where 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 is a good idea to establish a body within the pharmaceuticals 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 across the business.

Transforming patient outcomes

Beginning to use generative AI in a life sciences company is a significant undertaking and understandably not something to rush. Nevertheless, it is a must-have for companies aspiring to stay ahead of their competitors and a changing market. Equally vital is the commitment to providing comprehensive training to employees so they feel comfortable with and can maximise the benefits of using this technology. Establishing an internal governing body to oversee responsible deployment of generative AI is also imperative to prevent any potential misuse.

Companies are already gradually establishing the groundwork required to harness the full potential of generative AI technologies in pharmaceuticals. Through ongoing experimentation, companies can accelerate the discovery, testing, and market release of their drugs. This advancement improves patient outcomes through safer, more effective and affordable drug development, whilst amplifying revenue opportunities in a fiercely competitive market.

About the author

Bryan Hill is Chief Technology Officer for Cognizant Life Sciences, responsible for digital solutions and technology innovation. His focus is how emerging tech can help clients increase innovation to bring new therapies to market faster.

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