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The Future of Regulatory Automation: Implementation Considerations for AI in Regulatory Operations

This blog is part of our ongoing series, “The Future of Regulatory Automation in the Biopharma Industry.” Read the full series and explore how our experts can help you streamline your regulatory operations

New tools using new artificial intelligence (AI) are almost ready for prime time and despite the industry’s initial reluctance to embrace it, regulatory teams are being inundated with new, AI-assisted tools for improving regulatory compliance. But with a machine-first mindset comes the development team’s commitment to real-time evaluation of data. AI and machine learning tools are poised to bring better outcomes to patients, payers and stakeholders.

Despite an initially low uptake, some major pharmaceutical companies have already invested in AI tools to automate the analysis of regulatory documents. These programs range from simple automation tasks to the use of AI technology leading a fully supervised review system. These applications can streamline various processes, improve efficiency and accuracy, and provide valuable insight and analysis, including:

  • Automated Study Protocols: The system can automatically populate the study protocols with text from the Investigator's Brochure and protocol concept documents.
  • Hyperlinked New Drug Applications: Automation can create hyperlinks throughout the document, making it easier to navigate and access relevant information.
  • Clinical Study Report (CSR) Safety Narratives: A time-consuming process can be simplified through automation, especially when the data is large or commonly populated. A hybrid approach with medical writer oversight can provide both efficiency and medical usefulness.
  • Structured Authoring: This approach allows writers to focus on content and creates a set of rules to standardize document organization.
  • Clinical and Scientific Perspective: Automated systems can generate clinical and scientific information, but expert assessment is necessary to ensure correct scientific judgment is applied.
  • Automated Review Tools: These tools streamline the tedious process of sending out sequential versions for review and allow for simultaneous commenting and tracking.
  • Translation and Localization: The use of AI/ML for writing in another language is especially useful for lay summaries required by regulatory agencies such as the EMA.
  • Robotic Process Automation (RPA) Cases: Automation can assist with tasks such as data extraction from Excel, data upload to servers, archiving, document import, data consistency and quality checks, bookmarking, and data arrangement.

Key Considerations for Implementing Regulatory Automation

Implementing regulatory automation requires careful consideration and planning to ensure success. It is important to understand the risks and potential for failure and to develop appropriate mitigation plans. Return-on-investment (ROI) must be carefully understood, and expectations managed. Motivating and engaging the team is also crucial, as is obtaining sponsorship from senior leaders. Working with a partner can help with co-investment and offer different approaches to implementation with their own benefits and risks. Overall, the successful implementation of regulatory automation requires a comprehensive and thoughtful approach.

  • Data Management: It is important to have a robust data management system in place to ensure the accuracy and security of the data being used for automation. This may include data validation, data quality checks and data security measures.
  • Technology Requirements: Organizations need to carefully evaluate their technology requirements, including hardware, software, and networking infrastructure, to ensure the successful implementation of automation. This may also include the evaluation of potential vendor solutions and the selection of the most appropriate technology.
  • Resources and Skills: The implementation of automation requires specialized skills and resources, including data management, programming, and project management expertise. Organizations need to ensure they have the right resources and skills in place to support the implementation and ongoing maintenance of automation initiatives.
  • Change Management: Implementing automation involves significant change, both in terms of processes and technology. Organizations need to have a well-defined change management plan in place to ensure a smooth transition and minimize disruption to ongoing operations. This may include communication and training plans, as well as the development of clear policies and procedures.

These key considerations are important for organizations to take into account when implementing regulatory automation. Technology transformations are significant and complicated endeavors. They can't be too large and overwhelming to handle, nor too small to have a noticeable impact. Finding the right balance is key.

Is your regulatory operations team looking for more insight into automation processes for regulatory processes? Contact the GRAS team to explore new options developed specifically by experts in regulatory operations for the life sciences industry.


Tapasya Bhardwaj
Associate Director, Regulatory Affairs | Consulting at Syneos Health

Keira Ledger
Senior Director, Regulatory Operations | Consulting at Syneos Health

Mark Bailey
Associate Director, Regulatory Affairs | Consulting at Syneos Health

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