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22 Pharmaceutical Technology QUALITY AND REGULATORY SOURCEBOOK EBOOK MARCH 2022 P h a r mTe c h . c o m means of the algorithm qualification perspective, proposing a set of standard procedures to establish the acceptance criteria of its implementation based on a set of expected conditions (4). Now, it's up to the industry to leverage and optimize this capability. The following are four key points to consider when abstracting this generalized qualification method to qualify other AI algorithms. AI is only as good as the data you feed into it. AI sys- tems designed to interpret multivariable scenarios subject to safety, quality, and efficacy regulatory requirements must be trained with high quality data. Data must be prepared, including the suffi- cient variety of use cases to cover process experts, regulators, and patient expectations. If data does not properly cover all the potential scenarios, an incomplete AI model will be generated that is un- able to provide the expected accuracy. Conversely, when data contain all the potential situations, the model will present robust outputs. Innovation is just beginning. Global regulator y bodies are increasingly evaluating the benefits of AI as related to pharmaceutical environment processes. In 2019, FDA published a discussion paper to request feedback regarding AI/ML usage for medical devices (5). The European Medicines Agency (EMA) and Heads of Medicines Agencies (HMA) expanded the scope of the Joint Big Data Task Force to include AI, ref lected in the Big Data Steering Group Plan. They published a report in 2020 on Evolving Data-Driven Regulation (6), and in 2021 held a workshop to gather opinions from main pharmaceutical stakeholders regarding big data and AI-related technologies and tools. It's clear that regulatory bodies are seeing the benefits of AI/ML and are fast-tracking the acceptance of these valuable tools in a safe and responsible way. AI will improve response to future health threats. Re- search and development are only part of the equa- tion for discovering and developing new therapies, such as the COVID-19 vaccines. Manufacturing and t he abilit y to sca le up production and delivery of these life-saving prod- ucts was key. AI and ML have already helped manufacturers increase yield, identify issues be- fore they occur, and speed the identification of root causes of deviations. These examples demonstrate how AI accelerates current operations by means of a multivariate approach. While speed is critical, all tools, including AI/ML, must be appropriately vetted and proven. It's time to accelerate Pharma 4.0. The promise of Pharma 4.0 has never been greater. Powered by qualified AI/ML, pharmaceutical and life sci- ences companies will be able to fully digitize their manufacturing processes through data-driven decision-ma k ing. The smart factor y, robotic manufacturing, and the Industrial Internet of Things, for example, will graduate from concepts on a white board to fully realized business tools. Most importantly, these companies will be able to unleash the power of their data to optimize the Quality: Validation If data does not properly cover all the potential scenarios, an incomplete AI model will be generated that is unable to provide the expected accuracy. The promise of Pharma 4.0 has never been greater.