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16 January 2023

Artificial Intelligence in Regulatory Affairs?

More and more people are discussing artificial intelligence (AI) these days. It is only a matter of time before AI is used in every aspect of our lives. Do we know what lies ahead of us? Are we prepared?


AI is a broad term that encompasses many different technologies that use computers to mimic aspects of human decision-making. This can include things like recognizing patterns in data, making predictions, and learning from previous experiences.


AI has immense potential but also poses risks. Regulating its development and use to ensure ethical standards are maintained, without stifling innovation, is a huge challenge. In April 2021, the European Commission published the draft regulation of AI. The regulation aims to ensure that AI systems are safe, reliable, and trustworthy and to provide a legal framework for the use of AI in the EU.


The pharmaceutical industry has been utilizing AI for many years. In 2000, the FDA began using AI in their review process for new drugs. The FDA’s computer-based systems are now able to review more data than ever before.


Regulatory affairs is a complex field that encompasses CMC, data evaluation, and understanding guidelines, as well as the interpersonal skills needed for meetings with health authorities, making it difficult to fully automate with AI. The development of AI is having a similar impact on the industry as the introduction of the internet, and professionals are working to understand how these changes can help them in their work. However, AI can be used as a tool to assist regulatory affairs professionals in decision making by utilizing prediction and trend analysis based on previous scenarios and a large pool of data.


Let’s look at some of the ways AI can be used:


Many companies and consultants already collect the questions received from health authorities in various Excel tables to improve the dossier quality. Now, assume that AI uses its ability to analyze and recognize patterns in collected data to detect deficiencies in the newly created documentation for a drug. It then suggests including the information based on regulatory inquiries, potentially improving the overall quality of the documentation. Moreover, AI can automatically generate documents from templates or extracting data from another regulatory drug dossiers. AI will be able to automatically interpret sensitive data when developing output reports and censor sensitive data in publications. AI will be able to automatically interpret sensitive data when creating output reports and censor sensitive data in publications.


Do we dare to link this data even further? Imagine that other people in the company or manufacturers who have contracts with you have access to this pool of data. They could organize and predict their work more efficiently, build a scenario from start to finish and save time in the long run.


This large pool of data can also be accessed by everyone in regulatory affairs, providing increased confidence in their decision-making. Actions informed by this data can be more accurate and faster, enabling the prediction of future moves. AI could also prepare trainings based on available data, increasing the experience and knowledge of regulatory affairs professionals.


Furthermore, the health agencies already increasingly utilizing AI in Labeling department. AI machine learning algorithms can read, parse, and understand label text, detect potential errors, and make recommendations for corrections. This improved understanding of a drug’s content can provide companies with the knowledge they need to create more effective marketing materials. Additionally, AI technology can help companies gain valuable insights into how their claims are perceived by the public.


Moreover, the use of AI in regulatory publishing is already transforming the industry. Regulatory publishing involves submitting XML-formatted data about a drug to regulatory authorities, which is a tedious and labor-intensive task. By incorporating AI-based machine learning concepts such as hyperlinking and bookmarking, this process can become much faster and more efficient.


Finally, AI can manage and identify potential risks through predictive analytics. AI can also be used to automatically generate reports and provide insights into current and future trends. Additionally, AI can be used to help compare different regulatory frameworks, policies and documents to identify any discrepancies.


AI will play a significant role in driving innovation in the development of new drugs. By analyzing large datasets, AI can identify patterns that would be difficult for humans to do. In regulatory affairs, AI technologies can help to speed up the review process, enabling new medicines to reach the market more quickly. Although the use of AI in drug regulatory affairs is still relatively new, many companies are beginning to embrace this technology, which will likely lead to increased adoption in the future.


If you have more ideas for how AI can be used in Regulatory Affairs, please let us know in the comments section of this post on LinkedIn. We would love to hear your thoughts!