The pharma industry is rapidly coming to appreciate the substantial scope of Generative AI to transform medical writing, for regulatory documentation, safety report summaries, and more. In response, some companies have begun to grow GenAI capabilities in-house, only to realize the scale of the task ahead of them to make the technology deliver. Drawing on the findings of a new survey conducted with the Regulatory Affairs Professionals Society, Punya Abbhi, Chief Operating Officer and Co-Founder of Celegence, weighs up the opportunity and companies’ best way forward.
Generative AI (GenAI) is already transforming the way organizations of all kinds search, create, and process high volumes of knowledge and content, to distill new reports and insights. The results can be impressive, but how efficient and trustworthy the output is relies on how the technology is harnessed and governed. And this is where some life sciences companies are coming unstuck, as they look to apply GenAI to their increasingly challenging medical writing workloads.
The industry is a prime target for GenAI-based process transformation, with its heavy documentation needs, yet templated repeat activities including license application and maintenance safety report writing. If GenAI-based models could even just pull together the right content and craft a first draft of these sizeable documents, with expert oversight, this would alleviate some of the strain on busy Regulatory, Quality, safety,y and Clinical professionals.
The key is that companies should neither overestimate the role the technology can play nor underestimate the work involved in optimizing its output; preparation is everything. GenAI needs a lot of molding to be of reliable use, even with the benefit of large language models (vast data sets) to build system ‘knowledge’.
However advanced and intuitive the natural language processing and deep learning capabilities, AI models will still need to be guided in what to look for, how to repurpose information and data correctly, and what ‘good’ looks like.
This requires a unique combination of both AI proficiency and hands-on industry experience.
Even the Smartest and Most Powerful Tools Need to be Guided
In pharmaceutical medical writing, AI specialists, already over-subscribed in the war on talent,1 need not just to be skilled in the latest combinations and applications of natural language processing and deep learning; they also need access to experts in the life science industry. That’s so they can get to grips with the specialist language and vocabularies, required templates, and nuanced demands of each market.
They need an intricate appreciation for what will be accepted by regulatory agencies, and how that differs from region to region, and country to country, with a strong feel for specific medical writing best practices linked to each use case. After all, to be trusted as a credible generator of first-round content, and to maximize any return on investment, GenAI must be better and faster than humans at the initial collation and interpretation of what’s critical and required in technical documentation.
At the same time, variations of successful example content will be required to coach a GenAI model in the ideal output to aim for. There needs to be a robust understanding of formal terminology (and its abbreviations and variants); of how to decipher tables, listings, and figures; and of meaningful correlations between data and a drug or treatment. Only in this honed, specialist context can AI tools be trusted to interpret complex data and point to a logical outcome.
For pharma companies, bringing their capabilities up to speed and staying ahead is a daunting prospect.
Research Reveals a Capability Gap
Pharma medical writing remains a critical area requiring support, which was confirmed in a recent survey conducted with the Regulatory Affairs Professionals Society (RAPS).2 This is largely due to the growing pressure on regulatory professionals’ time. As a result, the appetite to intelligently automate some facet of the medical writing process is significant and growing stronger.
In the research, 57 percent of surveyed companies specified planned investment in technology to improve medical writing over the year ahead, almost on a par with eCTD v4.0 spending (specified by 58 percent for the coming 12 months), the two categories dominating immediate regulatory IT spending plans. The primary medical writing needs identified by regulatory professionals, requiring additional support, were clinical study protocol/report writing, and drafting of regulatory documents.
More than half of respondents in the 2024 survey identified a need to harness AI in data extraction (56 percent) and information summarization (53 percent), where just 9-10 percent are using AI for those purposes today - specifically within the context of medical writing. Twelve percent said they were actively in the process of incorporating AI into automated report generation from multiple sources, which is the ultimate opportunity on offer.
Despite these ambitions, more than half (53 percent) of survey respondents in pharma conceded that their organizations did not possess sufficient knowledge internally to implement AI technologies themselves. This was further confirmed at May’s RAPS Euro Convergence 2024 event in Berlin, where regulatory professionals also revealed some trepidation linked to what the use of AI might mean for their roles. (In reality, the technology offers to alleviate the more burdensome and repetitive aspects of content drafting, allowing teams to focus more intently on final document quality.)
Maintaining Regulatory Compliance
Additional barriers to AI uptake revolve around confidentiality and the scope for inadvertent breaches, linked in part to how the technology is ‘prompted’ to call up information. Because the technology is so complex ‘under the hood’, there are concerns that applying public cloud-based GenAI models could compromise internal data security.
All of these concerns are readily addressable in an appropriate ‘closed’ processing environment, which has been purposefully tailored to life sciences use cases. The closed environment ensures that all data interactions remain within a secure and controlled ‘space’, for data confidentiality and integrity purposes.
Further considerations include traceability and auditability: the ability to see where extracted data has come from or from which source content summarization has been achieved (for instance, clear links in the final report to original documents). This is essential to build confidence and trust in solutions so that they are seen to add significant value and save time; and that they are not a ‘quick fix’ option that requires painstaking checks (compromising the return on investment).
Identifying Solutions
Knowing that there are solutions that can be validated for priority pharma medical writing use cases will be an important facilitator for companies with a growing need for smarter support, as medical writing workloads continue to soar rather than diminish over time.
In looking for the value from an effective (and ideally fully-managed) GenAI medical writing automation capability, pharma companies need to look not just at the efficiency gains associated with smart data extraction, information summarization, and narrative authoring - albeit that these are promising areas to start focusing on.
Further gains will come from improvements to consistency, and to leaner, tighter output once specified regulatory documents are being drafted according to training (from extensive exposure to approved documents) on what ‘good’ looks like.
Investing in R&D or partnership with external technology solution providers on a variety of use cases promises to make better use of scientific experts’ time, as use of their time shifts from initial drafting to strategic thinking in collaboration with clinical development professionals. In a more strategic context, in early clinical evaluation, Gen-AI-based tools could help summarize the vast wealth of existing information on the internet to hone the focus of planned research to avoid wasted time investment. AI-assisted search across multiple sources can also summarize headline findings, while also highlighting what emerges as the best path to follow.
Determining What’s Possible, Through Experimentation
Only by trying out the technology will companies get a true feel for what’s possible, and the scale of difference it could make to their everyday Regulatory workflow. The risk otherwise is one of falling behind as industry trailblazers like Moderna (which already has deployed more than 750 GPTs -to improve process efficiency)3 continue to push the envelope.
While pharma companies may not have sufficient R&D resources internally to test out the various possibilities, experimentation is a powerful way forward in determining where GenAI offers maximum value in transforming medical writing. Finding a way to at least co-design a pilot solution (for example with an appropriate technology or service partner), that can be tested with example documents, is one practical way forward, to determine how powerful and accurate the technology can be and how quickly it learns and adapts, to hone its output.
References
- The AI talent shortage — can companies close the skills gap? Computerworld, 10 Apr 2024: https://www.computerworld.com/article/2086920/the-ai-talent-shortage-can-companies close-the-skills-gap.html
- Pharmaceutical Industry Regulatory Readiness & Resources 2024 Survey Report, RAPS Celegence – a survey of 139 pharma companies headquartered chiefly in the US, Europe or Asia-Pac regions
- https://investors.modernatx.com/news/news-details/2024/Moderna-and-OpenAI Collaborate-To-Advance-mRNA-Medicine/default.aspx
Author Details
Punya Abbhi, Chief Operating Officer and Co-Founder - Celegence
Punya Abbhi is Chief Operating Officer & Co-Founder at Celegence, which is led by herself, and CEO Sonia Veluchamy She was previously a Life Sciences Consultant at Capgemini Consulting and before that spent time at Kates Kesler Organization Consulting and Gartner, and worked for a specialist life sciences service provider. Punya holds an MBA from INSEAD and a BA in Politics, Philosophy, and Economics (PPE) with a minor in Cognitive Science from the University of Pennsylvania. [email protected], www.celegence.com
Publication Details
This article appeared in Pharmaceutical Outsourcing: Vol. 25, No. 3 July/Aug/Sept 2024Pages: 22-23