Nick Armstrong, Senior Director of Digital Enablement at CAI and co-chair of the ISPE Community of Practice on AI, specializes in the intersection of artificial intelligence and regulated industries. With a passion for driving innovation while maintaining compliance, Nick brings valuable insights into how cutting-edge technologies can transform pharmaceutical processes. In the following blog, Nick delves into the FDA’s new framework for AI credibility, providing a thoughtful analysis of its implications for building trust in AI and ensuring its responsible adoption in the pharmaceutical industry.
If there’s one takeaway from the FDA’s recent guidance on artificial intelligence (AI) in drug and biologics development, it’s this: the agency doesn’t just want to regulate AI; it wants to collaborate on it. For those of us working in the pharmaceutical manufacturing space, this represents a unique opportunity. By engaging early and proactively with the FDA, organizations can align their AI strategies with regulatory expectations while accelerating innovation.
Early engagement isn’t just about checking a compliance box—it’s about creating a partnership that helps you navigate the complexities of applying AI in a highly regulated environment. Whether you’re deploying AI for clinical trial design, manufacturing optimization, or post-marketing safety monitoring, the FDA’s guidance lays out several collaborative pathways to streamline the journey.
Why Early Engagement Matters
AI applications in pharma are diverse, ranging from predictive analytics in drug development to quality assurance in manufacturing. But no matter the use case, one thing remains constant: the need for trust. Regulators, industry stakeholders, and patients all need confidence that AI models are reliable, transparent, and fit for purpose. Early engagement with the FDA is a powerful tool for building that trust.
Through early dialogue, sponsors can clarify the context of use for their AI models, understand risk-based credibility requirements, and identify potential challenges before they become roadblocks. The FDA, in turn, gains insight into the nuances of emerging AI applications, enabling it to adapt its regulatory approach to support innovation. This back-and-forth not only de-risks the regulatory submission process but also helps establish AI as a credible tool in the drug development lifecycle.
Exploring Engagement Options
The FDA offers multiple pathways for early engagement, tailored to the specific use of AI in your program. For example:
- The Emerging Technology Program (ETP): Ideal for sponsors deploying AI in pharmaceutical manufacturing, the ETP provides a forum to discuss novel technologies before they’re implemented in regulatory applications.
- The Real-World Evidence (RWE) Program: For AI models analyzing real-world data, this program offers guidance on how to generate evidence that supports regulatory decisions.
- The Model-Informed Drug Development (MIDD) Program: This pathway is designed for sponsors using AI to create predictive models that inform drug development strategies, offering paired meetings with FDA experts to refine your approach.
Each of these options emphasizes collaboration over oversight, encouraging sponsors to bring AI technologies into the fold earlier in the process.
Tailoring Engagement to Your AI Use Case
One of the most valuable aspects of these pathways is their flexibility. Whether you’re working with an AI model to optimize clinical trial recruitment or developing an AI-enabled digital health technology, there’s a specific avenue for you. For instance, if your AI application involves real-world data integration, the FDA can provide input on data relevance, bias mitigation, and validation strategies. If your focus is on manufacturing, discussions might center on life cycle maintenance, model drift, and the integration of AI into your quality systems.
The key is to be proactive. The FDA strongly encourages sponsors to engage early and iteratively, starting with high-level discussions and refining the details as the program evolves. This approach ensures that both sponsors and the agency have a shared understanding of expectations and challenges, reducing uncertainty down the line.
Moving From Dialogue to Execution
Engaging with the FDA isn’t just about getting feedback—it’s about building a roadmap for successful implementation. By leveraging the agency’s insights, sponsors can refine their AI models, streamline regulatory submissions, and ultimately bring innovations to market faster.
For those of us in the pharmaceutical manufacturing industry, this is a chance to lead the charge in responsible AI adoption. By embracing early engagement, we can ensure our AI solutions not only meet compliance standards but also set new benchmarks for quality, efficiency, and safety.
Why It’s a Game-Changer
The FDA’s openness to early collaboration signals a shift in how regulatory agencies approach AI. This is more than a box-ticking exercise—it’s a recognition that AI is too dynamic and impactful to fit neatly into traditional regulatory frameworks. By fostering early and ongoing dialogue, the FDA is creating an environment where innovation and compliance can coexist.
For sponsors, this isn’t just about gaining regulatory approval; it’s about shaping the future of AI in the pharmaceutical space. Engaging early allows you to align your AI strategies with evolving regulatory expectations, mitigate risks proactively, and bring transformative technologies to market with confidence.
Final Thoughts
The path to regulatory acceptance for AI may be complex, but it doesn’t have to be daunting. The FDA’s collaborative pathways provide a structured, supportive approach to navigating this landscape. As an AI strategy consultant, I see early engagement as one of the most powerful tools sponsors have to ensure the success of their AI initiatives.
AI in pharma isn’t just a trend—it’s the future. By embracing early dialogue with the FDA, we can ensure this future is one that prioritizes safety, efficacy, and innovation. The message is clear: don’t wait to engage. The earlier we start the conversation, the stronger our AI solutions will be.
Related Links
- Part 1: A Framework for Trust: Understanding the FDA’s AI Risk-Based Credibility Assessment
- Learn how the FDA’s framework builds trust in AI for pharma.
- Part 2: Sustaining Reliability: Life Cycle Maintenance of AI Models in Drug Development
- Dive deeper into strategies for ensuring AI reliability throughout its lifecycle, including performance monitoring and risk-based approaches.