Regulatory
Next-generation AI Process Automation Essential to Cope with Soaring Regulatory Workloads, Survey Finds A new survey of senior US regulatory professionals has confirmed a direct correlation between unsustainable regulatory workloads and planned AI investment. That’s as long as the function can accept advances in the technology and its demonstrated applicability in a pharma regulatory context, says ArisGlobal’s Renato Rjavec. For the pharma/biopharma industry to ensure affordable access for patients as well as commercial viability as products grow ever more sophisticated, companies must become smarter in how they allocate resources to routine late-stage R&D processes, including regulatory workloads. It is in this context that organisations are turning towards artificial intelligence (AI), and in particular next-generation technologies such as Generative AI (GenAI) powered by large language models (LLMs), even though confidence in the technology is taking time to catch up. To understand the evolving balance between AI appetite and barriers to adoption, ArisGlobal recently commissioned a Censuswide survey, with 100 senior regulatory professionals in US pharma and biopharma organisations. Regulatory Inefficiency is Intensifying The poll, conducted in September, found that almost all (97% of) respondents had seen their regulatory obligations swell over the last five years, with three in five (60%) citing the increase as beyond what might be expected as the result of company growth. The trend is almost unanimously expected to continue over the next five years, with 41% saying next increases will be significant. Particular process challenges include excessive time spent producing submissions/dossiers; maintaining labelling compliance; inputting data/documents into IT systems; verifying submission correctness/completeness; performing regulatory impact assessments; and locating data or documents in existing IT systems. Further
18 Journal for Clinical Studies
barriers to efficiency include responding to agency queries; inadequacy of current IT systems; and time lost to data quality checks, assessing submission readiness, and other administrativelydemanding preoccupations. A lack of qualified people was not identified to be a great concern; preferred strategies do not involve allocating more people to processing regulatory workloads. Rather, pharma and biopharma regulatory functions are looking to smarter use of technology to ease the impact of their rising workloads. Determining AI’s Value in a Regulatory Context There is general acceptance of AI’s potential usefulness in solving information or process bottlenecks in a regulatory context, with 96% of survey respondents citing its current or potential value here, and almost half (45%) describing AI as “very useful”. Almost all respondents could see direct potential for AI in transforming labelling compliance and deviations maintenance; capturing, searching, filtering the latest regulatory requirements; automating the intake of Health Authority interactions; automating regulated content translations for different markets; automating the authoring of responses to Health Authority queries; suggesting improvements to submissions/dossiers; performing regulatory impact assessments; authoring submission documents; automating document summarisation; and generating entire regulatory submissions. Over a third (35%) of respondents claimed to be using AI for regulatory purposes in some form already, while 42% plan to invest in the next 18 months. A further 15% are looking at a timeframe beyond that, but do also have plans to roll out AI within the regulatory function. Bases for AI Hesitancy When asked what might be holding back initial or further investment in AI for Regulatory purposes, respondents most commonly cited outdated existing IT landscapes (45%); a belief that risks currently
Volume 16 Issue 4