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Regulatory Framework for Artificial Intelligence (AI) in Pharmaceutical Manufacturing: Ensuring Compliance and Reliability

Writer's picture: Ángel BenitezÁngel Benitez
Jan 176 min read


Integrating Artificial Intelligence (AI) into Pharmaceutical Manufacturing

Artificial Intelligence is transforming pharmaceutical manufacturing by enhancing efficiency, reducing costs, and improving quality control. However, due to the stringent regulatory environment, understanding the current regulatory framework for AI systems is essential to ensure reliability, safety, and compliance.


This article explores key considerations and guidelines for implementing compliant AI systems, ensuring organizations meet regulatory expectations while unlocking AI’s full potential.


The Need for a Strong Regulatory Framework

Technological advances often outpace risk management controls like regulations, industry guidance, and organizational policies. As AI adoption surges in pharmaceutical manufacturing, collaboration between health authorities, businesses, and professionals is essential to:


  • Protect patient safety.

  • Maintain product quality and process integrity.

  • Support quality decision-making through validated systems.


AI tools in pharmaceutical processes must align with global and regional regulatory frameworks such as FDA guidelines, EU GMP, and GAMP 5. These frameworks ensure structured and compliant lifecycle management of AI systems.


Key Regulatory Frameworks

To successfully implement AI in pharmaceutical manufacturing, organizations must adhere to these regulatory frameworks and guidelines:


Note: Medical Device references are included in the table below to provide a comprehensive picture of current regulatory guidance and expectations.  While medical device-related guidance and regulations were developed for medical device applications, the key concepts can be leveraged to address decision-making when it comes to AI implementation for drug product manufacturing.

Framework/Guideline

Key Focus

Examples in AI Implementation

DRAFT FDA Guidance

Considerations for the use of artificial intelligence to support regulatory decision making for drug and biological products

Establishes proactive methods to manage changes in AI models, ensuring compliance and consistency.

Using PCCPs to modify AI system algorithms post-deployment without disrupting compliance.

21 CFR Part 11

Ensuring data integrity, traceability, and security for electronic records and signatures.

AI systems for automated data handling and secure audit trails in production monitoring.

FDA Software Validation

Emphasizes risk-based validation across system lifecycle stages (design, testing, deployment).

Validating AI algorithms for quality control in pharmaceutical batch optimization.

Good Machine Learning Practice (GMLP) FDA Guidance

Provides principles for transparency, robust training data, and periodic revalidation of AI systems.

Training AI models for predictive maintenance or process optimization.

Predetermined Change Control Plan (PCCP) FDA Guidance

Establishes proactive methods to manage changes in AI models, ensuring compliance and consistency.

Using PCCPs to modify AI system algorithms post-deployment without disrupting compliance.

EU AI Act

Comprehensive requirements for risk classification, data governance, and transparency.

AI tools for automated quality checks or compliance audits in high-risk pharmaceutical sectors.

Annex 11 (EU GMP)

System validation, audit trails, and consistent performance under operational conditions.

Validating AI-driven supply chain tracking systems or real-time monitoring systems.

GAMP 5 (2nd Edition)

Risk-based lifecycle management aligned with system impact on product quality.

Implementation of AI models for precision quality checks during drug production.

ICH Q9 (Quality Risk Management)

Identifies, evaluates, and mitigates risks throughout the product lifecycle.

AI tools for risk assessment in batch release and predictive quality maintenance.

ICH Q10 (Pharmaceutical Quality System)

Focuses on a comprehensive QMS, integrating AI-driven systems into quality oversight.

AI-driven systems for continuous process verification or real-time quality control.

ISO 13485 (Medical Device Standards)

Provides quality management requirements for medical devices, including AI-enabled tools.

AI tools used in drug delivery systems or digital therapeutics adhering to device standards.

ISO/IEC 27001 (Information Security)

Establishes controls to ensure confidentiality, integrity, and availability of AI system data.

Securing AI-based manufacturing systems against cyber threats or data breaches.

Data Privacy Regulations (e.g., GDPR)

Addresses the ethical use of data, especially sensitive information in AI training.

Ensuring compliance when using patient data in AI model development and validation.

21 CFR 211 (cGMP for Finished Pharmaceuticals)

Core GMP requirements for drug products.

Use of ML and predictive analytics to enhance drug product manufacturing.

21 CFR 820 (Quality System Regulation)

Core GMP requirements for medical device products.

AI algorithms used in medical device design.

**ProQuality Network is excited to continue a mini-series on AI in pharmaceuticals, providing insights into compliant systems, addressing challenges, and practice al integration solutions. The next article will focus on AI and the proposed FDA Risk-Based Credibility Assessment Framework that delves into leveraging your current QMS framework to ensure validated and compliant AI systems. Here’s a sneak peek. 


Simplified 7 Steps of the FDA Risk-Based Credibility Assessment Framework for AI

Define the Question of Interest: Identify the specific decision or problem the AI model will address, guiding its purpose and the evidence needed.

  1. Define the Context of Use (COU): Clearly describe the AI model's role, scope, and how its outputs will be used, including any additional supporting evidence.

  2. Assess Model Risk: Evaluate the AI model’s influence on decision-making and the consequences of incorrect decisions to determine its risk level.

  3. Develop a Credibility Assessment Plan: Create a plan to verify/validate the AI model's reliability, tailored to its risk and COU.

  4. Execute the Plan: Implement the credibility assessment plan and engage with the FDA to address potential challenges and refine the approach.

  5. Document the Results: Record the outcomes of the credibility assessment, including any deviations from the plan and findings from earlier steps.

  6. Determine Model Adequacy: Based on results, decide if the AI model is appropriate for its COU; adjust its use, assessment rigor, or approach if necessary, or reject/revise the model.


Why ProQuality Network?

ProQuality Network serves as a reliable ally for pharmaceutical companies in their pursuit of solid, compliant AI implementation. With a deep understanding of validation requirements, we help organizations:


  • Develop and implement validation strategies tailored to their unique AI systems.

  • Ensure compliance with evolving regulations and industry best practices.

  • Maintain operational focus while meeting the highest standards of quality and safety.


Our consulting expertise bridges the gap between compliance and innovation, empowering pharmaceutical manufacturers to harness AI’s full potential confidently and responsibly. With ProQuality Network, your AI systems won’t just meet compliance standards—they’ll drive sustainable innovation in pharmaceutical manufacturing.


By aligning with established frameworks and keeping pace with emerging guidelines, organizations can build robust, compliant AI systems that uphold safety and integrity while fostering innovation.


References:


About the Authors


Rose Mary Aversa

A dynamic leader in healthcare quality, compliance, and operational excellence. With deep expertise in cGMP, regulatory standards, and validation, she develops and implements science- and risk-based quality systems that ensure compliance and optimize performance. Passionate about advancing regulatory readiness and fostering continuous improvement, Ms. Aversa specializes in FDA inspection preparedness and modernization of pharmaceutical operations. Currently serving as Vice President of Compliance Services at ProQuality Network, she is dedicated to empowering businesses with strategic solutions that drive quality, safety, and compliance.


Angel Benitez, MD, MBA, MEng, MSHS

A visionary leader in integrating artificial intelligence within regulated healthcare environments. With his extensive expertise in medicine, business, and engineering, he leads a multidisciplinary team of data scientists and engineers to design AI-driven workflows that optimize operational efficiency, ensure regulatory compliance, and elevate patient outcomes. Passionate about aligning AI with frameworks such as the FDA’s Good Machine Learning Practices (GMLP), 21 CFR Part 11, and EU AI Act. Dr. Benitez pioneers innovative solutions for healthcare systems and pharmaceutical operations. Currently advancing his expertise with a Master’s in Artificial Intelligence, he remains committed to leveraging cutting-edge technology to drive impactful advancements in healthcare quality, safety, and compliance.

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