SMART Practices for Responding to FDA Observational Warnings Using Generative AI

Observational warnings, such as those issued after Current Good Manufacturing Practice (CGMP) inspections by FDA investigators, are critical to ensuring pharmaceutical products' safety, efficacy, and quality. These warnings, including Form 483 observations and Warning Letters, highlight deficiencies that, if left unaddressed, can lead to systemic failures. Yet, organizations often adopt a reactive approach—focusing on immediate fixes while neglecting root causes. This approach risks eroding trust, tarnishing brand reputation, and increasing the likelihood of repeated violations.  

This article explores how leveraging tools like SmartSearch+ and FDA AI/ML Copilot within the eSTARHelper app can transform a company’s response to FDA observational warnings. By facilitating the adoption of a SMART (Specific, Measurable, Achievable, Relevant, Time-bound) framework, these tools help organizations foster proactive quality management and an operational culture of excellence. For a more detailed discussion on these tools, refer to our companion paper, "SMART Tools for FDA Regulatory Excellence: Integrating SmartSearch+ with FDA AI/ML Copilot for Compliance".

Bridging the Gap with New Prior Knowledge 

Typical Form 483 observations can pertain to various aspects of 21 CFR 211. They often highlight failures such as: 

• Absent or inadequate written procedures for production and process control (21 CFR 211.100(a)).  
• Failure to investigate unexplained discrepancies or batch failures, regardless of whether the batch has already been distributed (21 CFR 211.192).  
• One of the most significant findings was that the company's Quality Unit did not fulfil its responsibility for Quality Oversight (21 CFR 211.22).  

Many of these observations tend to recur a few years after previous CGMP inspections. For legacy products—particularly those submitted before the 2011 FDA guidance on Process Validation—submissions may lack a systems approach to development, often called Quality by Design and objective means for risk assessment. It is essential to take a product lifecycle approach to comprehensively evaluate the company's quality system and create a detailed action plan with a timeline for remediation. Furthermore, to address deficiencies in the state of control, as in statistical process control and objective risk management identified in FDA observations, we often require technical solutions and a shift in mindset, usually called "immunity to change.” This is illustrated in the case study "How to Break The Pharmaceutical 2-3 Sigma Barrier (Like Amgen)." 

Moving beyond the mindset of “it is FDA approved” and “the process was validated,” breaking free from the comfort zone of believing that “if I don’t look, there is no problem” often requires a cultural change, and introducing generative AI can break the impasse.  

SMART Shepherding Practices: A Transformative Approach 

Open-mindedness and a commitment to understanding root causes, even when challenging long standing assumptions and previous claims, require a dedication to generating new knowledge and correcting errors in existing understanding. AI tools like SmartSearch+ enable organizations to access comprehensive insights into FDA guidance, historical Warning Letters, and regulatory trends. By combining historical data, advanced analytical methods, and insights from various disciplines, this framework facilitates a deeper understanding of root causes and the development of effective corrective measures. Additionally, it can inform organizations about state-of-the-art analytical tools and expertise that can be employed to address issues at their root. 



Regulatory excellence demands a strong commitment to accuracy, precision, and continuous improvement. The well-known SMART criteria for structured business processes—specific, measurable, achievable, relevant, and time-bound objectives—can be more valuable than we often realize. In a broader context, the SMART Shepherding Practice can guide cross-functional teams in identifying root causes and developing effective remediation plans that adhere to SMART principles. 

• Specific: Address identified deficiencies and uncover additional issues during internal audits. 
• Measurable: Set quantifiable objectives for corrective actions, focusing on root causes.
• Achievable: Provide evidence to justify the feasibility of proposed remediation plans. 
• Relevant: Align actions with FDA guidelines and organizational goals.
• Time-Bound: Commit to actionable timelines with precise reporting mechanisms. 

Moreover, the self-monitoring, analyzing, and reporting technology meaning of SMART emphasizes that a pharmaceutical quality management system should operate effectively without relying on external auditors, as in the case of the FDA CGMP inspections. Additionally, the acronym SMART reminds us that "Stupid Managers Always Resort to Tyranny," as in blaming others, especially frontline workers, in concluding investigations with phrases like "root cause unknown" or "operator errors," and subsequently resorting to remedial retraining or worse. SMART also needs to be a Skeptic Managing Anger Reflectively and Temporally. SMART Shepherding Practice can help prevent trading future blessings for temporary anger or pleasures. Each step in SMART emphasizes adherence to first principles and the transformation of organizational quality culture, which can be operationalized more effectively and efficiently using AI tools.  

Using Generative AI for Proactive Responsibility  

Generative AI, when integrated with tools like FDA Copilot, offers transformative capabilities: 

• Enhanced Searchability: SmartSearch+ facilitates precision searches across FDA guidance documents, 21 CFR regulations, and historical Warning Letters, enabling teams to retrieve critical insights without downloading extensive files. 

• Automated Drafting: FDA Copilot via ChatGPT generates initial response drafts aligned with the Code of Federal Regulations and enforcement trends. These drafts are starting points for cross-functional discussions, shifting the focus from blame to critical analysis. 

• Root Cause Analysis: Retrieval-augmented generation (RAG) capabilities enable integrating proprietary enterprise data into secure AI systems and access to research and scholarly publications. This integration helps expand awareness and obtain new knowledge needed to address gaps in legacy submissions and overcome barriers to effective corrective and preventive action (CAPA). Ensuring this remediation plan puts the company on a path to continuous improvement is essential. This facilitates comprehensive gap and risk analyses, enabling customized responses that address systemic and specific issues. 

• Mobile Accessibility: Teams can access these AI tools from laptops, tablets, or phones, fostering collaboration and real-time problem-solving across geographically dispersed teams. 

Ethical Considerations and Data Integrity    

The integration of AI in regulatory processes necessitates a commitment to ethical practices. The ALCOA principles (Attributable, Legible, Contemporaneous, Original, Accurate) must guide the use of AI tools to prevent breaches in the assurance of data integrity (BAD-I). Generative AI can also help detect and mitigate such breaches by identifying inconsistencies and anomalies in datasets. 

Addressing ethical concerns requires transparency in AI operations, ensuring that tools enhance, not replace, human judgment. Organizations can build trust with regulators and stakeholders by embedding these principles into their operations.  

Continuous Improvement and Professional Development 

Regulatory excellence is as much about cultivating professional maturity as it is about technical proficiency. Cross-functional teams equipped with AI tools can more effectively adopt a systems-thinking approach to problem-solving, fostering a culture of continuous learning and improvement. Training programs integrated with AI platforms can also enhance the professional development of pharmaceutical scientists, aligning with Quality by Design (QbD) considerations and appreciating the statistical rigor needed for process validation and continued process verification.  

Steps to Responding to FDA Observational Warnings Using Generative AI  

1. Preliminary Drafts: Use generative AI to create baseline drafts based on publicly available information. This shifts the team’s mindset toward collaborative refinement.

2. Analytical Alignment: Employ SmartSearch+ and FDA Copilot to align internal audits with external regulatory expectations. 

3. Secure Integration: Upload proprietary data into secure AI platforms and, when necessary, scholarly publications to enable comprehensive risk analysis and tailored responses.  

4. Expert Review: Finalize responses through multidisciplinary reviews, ensuring alignment with evidence and regulatory standards.  

Conclusion  

The FDA's CGMP observational warnings present a challenging yet invaluable opportunity for improvement. By integrating both natural and artificial intelligence within a SMART framework, organizations can turn these challenges into opportunities for growth. Tools such as SmartSearch+ and FDA Copilot help cross-functional teams align their efforts, better understand regulatory expectations, and adopt a product life-cycle approach. These nuances foster trust and assurance and promote a culture of continuous improvement, setting the stage for sustainable success in the pharmaceutical industry. 

Adopting SMART practices ensures organizations move beyond reactive fixes to proactive quality management. This approach is not merely about regulatory compliance but about securing the trust and well-being of patients worldwide.