The Impact of Artificial Intelligence on Pharmaceutical Manufacturing

This article discusses the potential areas for FDA guidance regarding the incorporation of Artificial Intelligence (AI) in pharmaceutical manufacturing.

The Impact of Artificial Intelligence on Pharmaceutical Manufacturing

Artificial intelligence (AI) is reshaping the pharmaceutical industry, particularly in the realm of drug manufacturing. As AI continues to evolve and integrate with industry practices, it is imperative to consider the accompanying challenges and potential solutions. In this essay, we will review a recent paper published by the FDA, title Artificial Intelligence in Drug Manufacturing, and explore the implications of AI applications in pharmaceutical manufacturing, its influence on data management and oversight, the potential regulatory hurdles, and the need for industry standards for AI models.

AI and Cloud Applications: A New Horizon for Data Oversight

AI has emerged as a key player in the pharmaceutical manufacturing process, potentially altering the course of data management in the industry. Advances in cloud and edge computing could lead to changes in software location involved in pharmaceutical manufacturing. The potential use of third-party data management systems, such as AI-supported cloud applications for process monitoring, poses new challenges in terms of data integrity and security, particularly in an era where cybersecurity vulnerabilities are a prominent concern.

These new applications may necessitate an expansion in the FDA's inspection approaches for evidence gathering of records management, due to the intricacies of managing third-party cloud data and models. The existence of quality agreements between the manufacturer and a third party may also need to be reevaluated to address the potential risks of AI applications in the manufacturing monitoring and control context.

The Internet of Things: Catalyzing a Data Revolution in Pharmaceutical Manufacturing

The integration of the Internet of Things (IoT) in pharmaceutical manufacturing could increase the amount and types of data generated during the manufacturing process. This digitization might affect existing data management practices, prompting a reevaluation of regulatory compliance for generated data.

As the volume of raw data increases, there may be a need to balance data integrity and retention with the logistics of data management. Clarity regarding data sampling rates, data compression, or other data management approaches might be necessary to maintain an accurate record of the drug manufacturing process.

Moreover, as manufacturing equipment becomes interconnected, maintaining the stewardship, privacy, and security of such data may pose challenges to preserving product quality or maintaining pharmaceutical manufacturing.

Regulatory Oversight: The Need for Clarity

Given the rapidly evolving landscape of AI applications in pharmaceutical manufacturing, there may be a need for clarity regarding the regulatory oversight these operations warrant. As AI is increasingly used in manufacturing operations such as equipment maintenance, continuous improvement, and raw materials characterization, clarity regarding regulatory oversight and compliance is essential.

The Quest for Standards in AI Model Development and Validation

AI has the potential to revolutionize Advanced Process Control (APC) applications in pharmaceutical manufacturing. However, the lack of industry standards and FDA guidance for the development and validation of models impacting product quality can create challenges in establishing a model's credibility.

The ability of AI models to learn and adapt raises questions about the standards for validating these models and the explainability of their output. Furthermore, the potential for transferring learning from one AI model to another may require additional clarity during model development and validation.

Continuous Learning AI Systems: A Regulatory Challenge

Continuous learning AI systems that adapt to real-time data can pose challenges to regulatory assessment and oversight. These models evolve over time as new information becomes available, making it difficult to determine when a model can be considered an established condition of a process.

Takeaway

Artificial Intelligence presents numerous opportunities and challenges for pharmaceutical manufacturing. As AI continues to evolve and infiltrate various aspects of manufacturing, it is essential to consider the implications on data management, the need for new regulatory oversight, and the establishment of industry standards for AI models. The pharmaceutical industry and regulatory bodies must work hand in hand to harness the benefits of AI while ensuring product quality and safety.

References, AI Questions & Feedback

This section opens up an avenue for further questions to save space. The entire message is: AI Models for Process Control and Support Release Testing](#AI_Models_for_Process_Control_and_Support_Release_Testing) 4. Regulatory Assessment and Oversight of Continuously Learning AI Systems

Cloud Applications and Data Oversight in Pharmaceutical Manufacturing

The integration of artificial intelligence and cloud computing presents several implications for pharmaceutical manufacturing. One aspect that demands attention is how this convergence impacts data oversight and integrity, specifically with third-party data management systems that are increasingly being used to extend beyond data storage.

These systems may harness AI for analyzing data to support models for process monitoring and Advanced Process Control (APC). While the Food and Drug Administration (FDA) permits third-party involvement in CGMP functions under appropriate manufacturer oversight, it’s apparent that existing quality agreements may lack provisions that address the risks of AI in manufacturing monitoring and control.

The issue becomes more complex as we consider the FDA's inspection approaches. Current methodologies may require expansion due to the increasing intricacy of managing third-party cloud data and models. Ongoing interactions between cloud applications and process controls could make establishing data traceability more challenging, potentially leading to cybersecurity vulnerabilities. Ensuring the efficacy of procedures that monitor data integrity vulnerabilities during an inspection becomes a critical concern.

Potentially Associated Requirements and Policies

Regulations such as 21 CFR 11, 211.180, 211.184, 211.194, 600.12 and Guidance for Industry like Contract Manufacturing Arrangements for Drugs: Quality Agreements (November 2016), and ICH Q7 Good Manufacturing Practice Guidance for Active Pharmaceutical Ingredients (September 2016) could potentially guide these areas of concern.

Implications of the Internet of Things (IoT) for Data Management

As the digitization of manufacturing controls continues, the amount of data generated during pharmaceutical manufacturing could surge significantly, affecting existing data management practices. This increase could be in both the frequency and types of data recorded, which may require a delicate balance between data integrity and retention with data management logistics.

There is a pressing need for regulatory compliance clarification concerning data generated. Questions about which data needs to be stored or reviewed, and the implications of data loss on future quality decisions, including product recalls, need to be addressed. Moreover, maintaining stewardship, privacy, and security of the massive amounts of data generated by interconnected manufacturing equipment could pose a significant challenge.

Potentially Associated Requirements and Policies

Regulations such as 21 CFR 211 Subparts D and J and Guidance for Industry like ICH Q7 Good Manufacturing Practice Guidance for Active Pharmaceutical Ingredients (September 2016) could potentially guide these areas of concern.

Regulatory Oversight for AI Application in Pharmaceutical Manufacturing

AI could play a significant role in various manufacturing operations. This includes monitoring and maintaining equipment, identifying areas for continuous improvement, scheduling, and supply chain logistics, and characterizing raw materials. Applicants may need clarity about whether and how these applications of AI in manufacturing operations are subject to regulatory oversight, such as CGMP compliance, new drug, or biologics license applications.

Potentially Associated Requirements and Policies

Regulations such as 21 CFR 11, 211.68, 211.84, 211.180, 211.184, 314.50, 314.94, 601.20 and Guidance for Industry like ICH Q7 Good Manufacturing Practice Guidance for Active Pharmaceutical Ingredients (September 2016), to see next part of the conversation. Quality Systems Approach to Pharmaceutical CGMP Regulations (September 2006), and Considerations in Demonstrating Interchangeability With a Reference Product (May 2019) could potentially guide these areas of concern.

AI Models for Process Control and Support Release Testing

AI models are increasingly being applied for process control and supporting product release testing. Yet, there is a need for clarifications regarding how manufacturers can validate AI algorithms' stability and accuracy, particularly given that these algorithms can evolve over time through machine learning. Applicants may also need guidance on how the FDA will evaluate the robustness and validation of AI algorithms during inspections.

Potentially Associated Requirements and Policies

Regulations such as 21 CFR 211.160, 211.194 and Guidance for Industry like ICH Q7 Good Manufacturing Practice Guidance for Active Pharmaceutical Ingredients (September 2016), Analytical Procedures and Methods Validation for Drugs and Biologics (July 2015), and General Principles of Software Validation; Final Guidance for Industry and FDA Staff (January 2002) could potentially guide these areas of concern.

Regulatory Assessment and Oversight of Continuously Learning AI Systems

AI algorithms, especially those based on machine learning, can evolve and adapt over time, leading to continuous learning systems. This brings about questions concerning the periodicity and scope of validation, given that these algorithms are not static and could undergo significant changes as they learn from new data. This leads to the challenge of how to ensure continuous compliance in the ever-evolving landscape of machine learning and AI.

Potentially Associated Requirements and Policies

Regulations such as 21 CFR 211.68, 211.160, 211.194 and Guidance for Industry like ICH Q7 Good Manufacturing Practice Guidance for Active Pharmaceutical Ingredients (September 2016), General Principles of Software Validation; Final Guidance for Industry and FDA Staff (January 2002), and Data Integrity and Compliance With Drug CGMP: Questions and Answers (December 2018) could potentially guide these areas of concern.


Read next