For artificial intelligence to truly support GxP operations, understanding key concepts is essential. However, the rapid evolution of AI has led to varying interpretations of key terms, making it crucial to establish a common language.
To address this, the Parenteral Drug Association (PDA) is developing an AI Glossary to standardize terminology across drug manufacturing. Once finalized, this resource will provide a shared framework for understanding AI in GxP environments. In the meantime, industry professionals can build their knowledge by familiarizing themselves with essential AI concepts.
Below, we break down some of the most critical terms shaping AI’s role in pharmaceutical manufacturing:
Explainability
- What It Is: The ability of an AI system to provide clear, human-understandable reasons for its decisions and predictions.
- Why It Matters: Regulatory agencies require transparency to ensure decisions made by AI systems are traceable and justifiable, particularly in critical processes like batch release or deviation management.
- Example: A model predicting equipment failure explains that temperature trends and vibration patterns are the leading indicators.
Data Integrity
- What It Is: Ensuring the accuracy, completeness, and consistency of data throughout its lifecycle.
- Why It Matters: GxP regulations demand that manufacturing decisions rely on trustworthy data. Any compromise can lead to compliance violations or product recalls.
- Example: AI systems should operate on ALCOA+ principles (Attributable, Legible, Contemporaneous, Original, Accurate, and more).
Validation
- What It Is: The documented process of proving that an AI system consistently produces reliable, intended outcomes under GxP conditions. In pharma, validation focuses on compliance with strict regulatory requirements, differing from validation in the AI field, which emphasizes algorithm performance.
- Why It Matters: AI solutions must meet stringent Pharma regulations, including performance qualification and risk assessment, to be deemed safe and effective.
- Example: Validating an AI-driven predictive maintenance model ensures it operates accurately within its defined parameters as outlined in the problem statement and the User Requirements Specification (URS).
Governance
- What It Is: The framework for managing AI systems responsibly, ensuring compliance with industry regulations, ethical standards, and organizational goals.
- Why It Matters: Effective governance prevents misuse, ensures data privacy, and aligns AI operations with business and compliance requirements. It also ensures proper model retirement when objectives are achieved or the system becomes obsolete.
- Example: Establishing an AI oversight committee to monitor system performance and compliance risks and planning model retirement as necessary.
Digital Twins
- What It Is: Virtual replicas of physical manufacturing processes or equipment, integrated with AI to simulate real-world scenarios and optimize performance.
- Why It Matters: They allow for predictive analytics and testing changes without impacting actual production, ensuring efficiency and compliance.
- Example: Using a digital twin to simulate how a process deviation affects product quality.
In-Process Deployment
- What It Is: Implementing AI models within active manufacturing workflows to enhance real-time decision-making and process control.
- Why It Matters: It enables continuous monitoring and adjustments, reducing deviations and improving efficiency during production runs.
- Example: AI algorithms dynamically adjusting mixing speeds to maintain product consistency.
Monitorization
- What It Is: The continuous monitoring of AI systems to ensure their outputs remain accurate, reliable, and compliant over time.
- Why It Matters: As processes and datasets evolve, regular monitoring ensures AI performance doesn’t drift or lead to non-compliance.
Example: Tracking an AI model’s prediction accuracy for yield optimization over multiple production cycles.
