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14 Pharmaceutical Technology ® Regulating Innovation, Quality, and Risk eBook January 2024 PharmTech.com Regul ations anticipation of these benefits, FDA is proactively prepar- ing for the arrival of AI technologies in pharmaceutical manufacturing. The AI revolution AI is an enabler of Industry 4.0, the fourth industrial revolution characterized by integrated, autonomous, and self-organizing production systems (5). The Indus- try 4.0 paradigm brings the hope of a well-controlled, hyper-connected, digitized ecosystem and supply chain. The COVID-19 public health emergency seems to have accelerated the development of Industry 4.0 technolo- gies, such as AI, that might respond to rapidly changing supply and demand and reduce dependence on human intervention. So how might manufacturers deploy AI in process design and control? Process and scale-up optimization. AI models employing ma- chine learning might use process development data to more quickly design and identify optimal process pa- rameters or scale-up strategies, reducing development time and waste. Process control. Advanced process control might allow for dynamic control of a manufacturing process which, in combination with real-time sensor data, might be used to develop process controls that can precisely predict the trajectory of a process. Pharmaceutical manufac- turers are expecting to adopt advanced process control approaches that combine AI techniques with chemistry and physics knowledge to improve manufacturing effi- ciency and output. Process monitoring and fault detection. AI methods might be used to better monitor equipment and detect changes from optimal performance. AI-detected deviations might trigger maintenance activities in a manner that minimizes process downtime. Trend monitoring. AI methods integrated with process performance metrics might offer better trend monitor- ing, even across products or locations, and consequently allow for proactive corrective and preventive actions to address manufacturing discrepancies before they im- pact the supply chain, or worse, cause drug shortage. The potential use of AI to monitor quality doesn't even end at the process. AI might also be used to monitor a product's quality after manufacturing, for example, for the integrity of its packaging or presence of particulates. A vision-based quality control system might use AI to analyze images of packaging, labels, or glass vials to de- tect deviations (6). Beyond the product itself, AI might be used to identify cluster problems from consumer com- plaints and deviation reports that might contain large volumes of unstructured text. Though the potential ap- plications of AI in pharmaceutical manufacturing will continue expanding, whether a manufacturer employs AI or not, high-quality drugs should be consistently available to patients. Regulating AI CDER established a Framework for Regulatory Ad- vanced Manufacturing Evaluation (FRAME) initiative to prepare a regulatory framework to support the adop- tion of advanced manufacturing technologies that could benefit patients, such as AI. FDA recognizes potential benefits, and risks, of AI throughout the drug product lifecycle. As some manufacturers look to employ AI in their manufacturing processes, the regulatory frame- work will need to enable the timely adoption of these technologies while also keeping patients safe. CDER recognizes the need to better understand how this new manufacturing paradigm could impact pharma- ceutical operations and regulation. The regulatory strategies of the past might not work as effectively in an AI-enabled Industry 4.0. However, regulators do not develop and implement manufacturing technologies. There is a need for the stakeholders developing these technologies to provide FDA a better understanding of where and how AI innovations might be used in drug manufacturing. Industry and regulators will not be able to address the issues related to AI by working separately; true innovation requires science-based collaboration. In March 2023, CDER released a discussion paper to solicit public and stakeholder input on AI in drug manu- facturing to better identify areas of policy consideration for AI technologies (2). This discussion paper proposed areas of consideration based on CDER's evaluation of the existing regulatory framework: standards for develop- ing and validating AI models, clarification on regula- tory oversight for AI in pharmaceutical manufacturing, maintenance of cloud applications and continuously learning AI systems that adapt to real-time data, and data management practices commensurate with the volume of data generated. FDA's public workshop on the use of AI in Drug Manu- facturing on September 26–27, 2023, in conjunction with the Product Quality Research Institute, was designed to provide another opportunity for stakeholders to discuss key topics with regulators, such as: • Artificial Intelligence in Process Development, Process Monitoring, and Commercial Batch Trend Monitoring • Artificial Intelligence and the Use of Big Data and Data Management within the Pharmaceutical Quality System • Lifecycle Approaches to Management of Artificial Intelligence. Discussions on topics like these will help further in- form the evaluation, development, and implementation of a regulatory framework that considers the benefits and risks of AI. The future and AI Like industry, regulators can employ AI to improve pro- cesses. In fact, FDA currently uses AI for translating docu-