Part 2: How Generative AI is Transforming Regulatory Submissions in Pharma R&D

Posted on: December 06th 2024

In our first blog, we explored the complex landscape of regulatory submissions in pharmaceutical R&D, discussing challenges in data management, compliance, and collaboration. In Part 2, we focus on how Generative AI can change the way the industry approaches these submissions while acknowledging the challenges that come with this transformation.

The Role of Generative AI in Regulatory Submissions

Generative AI is beginning to make an impact in regulatory submissions in the pharmaceutical industry, but its use is not without complexities. Regulatory submissions require the creation of numerous documents that pull information from various data sources, including clinical trial data, safety reports, and manufacturing details. The content must be consistent, accurate, and meet various regulatory requirements—tasks that traditionally require significant human effort and time. Generative AI presents opportunities to automate content generation, integrate data more efficiently, and reduce manual work, but it also introduces challenges that require careful consideration. Working with an experienced partner can help navigate these challenges and ensure that AI solutions are implemented effectively.

Streamlining Document Drafting and Iteration: Opportunities and Limitations

Agentic workflows can be particularly helpful in managing the drafting, review, and revision steps in regulatory submissions. By enabling AI systems to autonomously handle repetitive tasks while interacting with human stakeholders at key decision points, agentic workflows can enhance the efficiency of the submission process.

These workflows can streamline the iterative drafting process, facilitate reviews by automatically routing drafts to the right stakeholders, and manage revisions by incorporating feedback in a structured way. This approach not only saves time but also ensures that the focus remains on high-value activities that require human expertise.

One of the most time-consuming parts of regulatory submissions is drafting and refining multiple content versions until they are ready for submission. Generative AI can help create first drafts for several key submission documents—including Clinical Study Reports (CSRs), Integrated Summaries of Safety (ISS), and Integrated Summaries of Efficacy (ISE)—by analyzing both structured and unstructured data.

Generative AI-based tools can generate a preliminary draft within minutes, using information from the protocol, statistical analysis plan, and tables, listings, and figures. This allows for faster iteration by cross-functional stakeholders through conversational prompting, which can significantly speed up the overall process.

Partnering with experts who understand both AI technology and regulatory requirements can further streamline this process, ensuring that the drafts are both compliant and ready for regulatory scrutiny. Using agentic workflows in conjunction with experienced partners can help automate routine interactions, further accelerating the drafting and review process while maintaining compliance.

However, these drafts are not always perfect and require human oversight to ensure they meet the specific requirements of regulatory bodies. Regulatory writers and subject matter experts still need to validate the AI-generated content, which can sometimes lead to additional rounds of editing if the AI-generated text fails to capture complex regulatory language.

Agentic workflows can assist in managing these rounds of edits by automatically assigning tasks and tracking progress, reducing the administrative burden on human reviewers. The potential impact of generative AI includes faster submission timelines and fewer quality issues—but only if the human-in-the-loop process is strong enough to handle the limitations of AI.

Moreover, while AI-generated content can be customized to match regulatory requirements for different regions, variations in regulatory expectations across countries mean that human expertise is crucial for ensuring compliance. AI can speed up the drafting process, but it cannot replace the need for expert review and interpretation.

Ensuring Consistency and Addressing Compliance Risks

Pharmaceutical R&D involves a large amount of data that must be presented consistently across multiple documents, from clinical protocols to safety narratives. Ensuring consistency while also meeting stringent regulatory requirements is often a significant challenge.

Generative AI tools can help maintain a single source of truth by pulling data from centralized repositories and generating content that remains consistent across documents. However, ensuring that AI correctly interprets and presents the data consistently is not always straightforward.

Misinterpretations by AI models can lead to inconsistencies or errors that require manual correction, and regulatory authorities are not yet fully comfortable with AI-generated submissions without thorough human oversight.

There is also a risk that relying on AI could introduce compliance issues if the data sources or algorithms are not properly validated. Companies need to ensure that their AI tools are rigorously tested and that the generated content can be traced back to the original data.

This requires strong processes and governance to mitigate risks and ensure compliance. Collaborating with a knowledgeable team can help establish these processes and governance frameworks, reducing the risk of compliance issues.

The potential impact includes improved cost efficiency across regulatory organizations, but this depends on the reliability and transparency of AI processes.

A Realistic Approach: Integration, Flexibility, and Human Oversight

Generative AI tools are most effective when integrated into the broader technology ecosystem that supports pharma R&D. These tools can interact with existing Regulatory Information Management (RIM) systems, ensuring that submissions are informed by up-to-date and accurate information, without the need for manual updates.

However, integration is not always straightforward, and many pharma companies face challenges in aligning AI tools with their existing infrastructure, which may be outdated or siloed. Working with a technology partner who has experience integrating AI solutions can help overcome these challenges, ensuring that the tools are effectively aligned with existing systems.

Additionally, while the flexibility of these AI systems allows them to work with different Large Language Models (LLMs), making them adaptable, this flexibility also brings challenges in maintaining consistency as new models are introduced.

Frequent updates to LLMs can lead to variability in outputs, which can be problematic when consistency is critical. Therefore, pharma organizations need to balance the adoption of new AI technology with the need for stability and reliability in their regulatory submissions.

Technology and Data Engineering Perspectives

1. Data Integration and Infrastructure Requirements

Generative AI tools rely heavily on access to high-quality, well-structured data, which means that the data integration infrastructure in pharma R&D must be robust. Implementing these tools often requires extensive data engineering to connect disparate data sources, such as clinical trial databases, safety repositories, and document management systems.

This involves setting up secure data pipelines, ensuring data integrity, and applying data transformation processes to prepare the data for AI models. Companies can significantly benefit from partnering with technology providers who have experience integrating AI tools into legacy systems and ensuring that data flows smoothly across different platforms.

2. Data Security and Compliance in AI Systems

Regulatory submissions involve sensitive and highly confidential data, including patient information and proprietary drug research. From a data engineering standpoint, implementing generative AI systems for regulatory submissions requires a strong focus on data security and compliance.

This includes employing secure data storage solutions, encryption, and access controls, as well as ensuring compliance with global regulations such as GDPR and HIPAA.

Working with partners who understand these regulatory requirements and have experience in secure data management is crucial to mitigating risks and ensuring compliance throughout the AI adoption process.

3. Model Training and Performance Optimization

For generative AI tools to be effective in regulatory submissions, they need to be well-trained on a diverse range of data, including historical submissions, clinical data, and regulatory guidelines. This requires robust data engineering capabilities to curate and preprocess large volumes of both structured and unstructured data for training purposes.

Additionally, regular updates and retraining are necessary to keep the models accurate and relevant. Performance optimization of these models is also critical, as they need to be efficient enough to generate content quickly without compromising accuracy.

Engaging with experts in AI model development and optimization can help ensure that the models are fine-tuned to meet specific regulatory requirements and deliver high-quality outputs.

A Look Ahead: Efficiency, Scalability, and the Need for Collaboration

The use of generative AI in regulatory submissions is not just about saving time—it’s about improving the overall efficiency, scalability, and collaboration of pharma teams. By handling the more mechanical aspects of submission preparation, AI enables regulatory professionals to focus on decision-making and quality review.

However, it is important to recognize that the efficiency gains from AI are not always straightforward. AI-generated content often requires careful review and adjustment, and training AI models to understand complex regulatory language is resource-intensive.

Generative AI also supports scalability, enabling companies to manage an increasing volume of regulatory submissions as product portfolios grow. However, scaling AI solutions across different therapeutic areas and regulatory jurisdictions requires careful planning and significant customization.

Teams must collaborate to ensure that AI-generated content meets the specific requirements of each submission, which can be challenging given the diverse regulatory standards worldwide. Partnering with specialists who have a deep understanding of global regulatory landscapes can help teams navigate these complexities more effectively.

Conclusion

The adoption of Generative AI in regulatory submissions has the potential to be an important development for the pharmaceutical industry, but comes with challenges. While it reduces manual workload and can help ensure consistency, it also requires human oversight to address the complexities of regulatory compliance.

As the regulatory landscape evolves, companies that adopt AI-driven solutions with a realistic understanding of their limitations—and invest in the necessary governance and human expertise—will be best positioned to meet the growing demands of global and regional authorities.

Companies that collaborate with experienced partners to adopt AI-driven solutions—while investing in the necessary governance and human expertise—will be best positioned to meet the growing demands of both global and regional authorities. The path forward involves using AI to complement, not replace, human expertise, ensuring that submissions are both efficient and high-quality.

Let’s continue the conversation if you want to learn more about how AI can enhance the regulatory submission process or have questions about implementing these technologies in your organization.

About the Author

Santosh Shevade

Leader in healthcare strategy, operations and innovation practices with expertise in digital health/mHealth strategy, outcome-oriented program design with proven track record of excellent program delivery