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20 Pharmaceutical Technology QUALITY AND REGULATORY SOURCEBOOK EBOOK MARCH 2022 P h a r mTe c h . c o m Quality: Validation A rtificial intelligence (AI) and machine learning (ML) are transforming industries as diverse as retail and energ y, using powerful algorithms and rich data analytics to improve business agility in a constantly changing, global marketplace. But some highly-regulated indus- tries—such as pharmaceutical manufacturing and life sciences— are missing out by not fully using AI and ML. While there is cur- rently no specific guidance describing how AI must be used, it is generally accepted that AI algorithms fall into the same bucket of regulation as multivariate tools as an analytical math system. FDA and the European Pharmacopoeia have clearly indicated their support of AI as a tool to increase quality and efficiency and have published some content backing this technology. For example, the ninth edition of the European Pharmacopoeia published in 2017 announced two AI algorithms as valid chemometric methods in chapter 5.21 (1). In 2021, FDA published an action plan regarding the use of AI and ML as software for medical devices (2). Ad- ditionally, the process validation guidance published by FDA in 2011 described standard methods to calculate the validity of any analytic model (including AI as a multivariate tool) (3). Another roadblock to AI adoption is the perception that machine learning models are developed in a black box. These models rely on AI algorithms and their unique capacity of self-tuning themselves by learning from data; different data mean different tuning needs. Therefore, qualification of the AI algorithm is a prerequisite before a machine learning model can even be validated. Only explain- Qualifying AI Algorithms for Manufacturing Toni Manzano Qualified algorithms enable validation of machine learning models that can be used for process optimization. Toni Manzano, toni.manzano@ aizon.ai, is chief science officer and co-founder at Aizon. YINGYAIPUMI - STOCK.ADOBE.COM