European Medicines Agency Seeks Input on AI in Drug Development, Mfg

The European Medicines Agency has published a draft reflection paper and is seeking input from the bio/pharma industry and other stakeholders on the use of artificial intelligence in drug development and the product lifecycle, including manufacturing. The agency plans to hold a workshop on AI in late November and is seeking input by the end of the year.

The European Medicines Agency has published a draft reflection paper and is seeking input from the bio/pharma industry and other stakeholders on the use of artificial intelligence in drug development and the product lifecycle, including manufacturing. The agency plans to hold a workshop on AI in late November and is seeking input by the end of the year.

Seeking feedback on AI
The European Medicines Agency (EMA) has published a draft reflection paper outlining the agency’s current thinking on the use of artificial intelligence (AI) to support the development, regulation, and use of human and veterinary medicines. The draft paper, which is now open for public consultation, reflects on principles relevant to the application of AI and machine learning (ML) at any step of a medicine’s  lifecycle, from drug discovery to the post-authorization setting. Public comment is open until December 31, 2023, and EMA will hold a workshop in November 20–21, 2023, on the draft reflection paper.   

The reflection paper is part of an initiative of the Heads of Medicines Agencies(HMA)—European Medicines Agency (EMA) Big Data Steering Group (BDSG) to develop the European Medicines Regulatory Network’s capability in data-driven regulation. It has been developed in liaison between the BDSG, EMA’s Committee for Medicinal Products for Human Use and its Committee for Veterinary Medicinal Products.

“The use of artificial intelligence is rapidly developing in society and as regulators we see more and more applications in the field of medicines,” said Jesper Kjær, Director of the Data Analytics Centre at the Danish Medicines Agency and co-chair of the BDSG, in a July 19, 2023, EMA statement. “AI brings exciting opportunities to generate new insights and improve processes. To embrace them fully, we will need to be prepared for the regulatory challenges presented by this quickly evolving ecosystem.”

“With this paper, we are opening a dialogue with developers, academics, and other regulators, to discuss ways forward, ensuring that the full potential of these innovations can be realized for the benefit of patients’ and animal health” said Peter Arlett, EMA’s Head of Data Analytics and Methods and co-chair of the BDSG, in EMA’s July 19, 2023, statement.

In its draft reflection paper, EMA outlines that AI and ML tools have the potential to effectively support the acquisition, transformation, analysis, and interpretation of data across the medicinal product lifecycle. Their application can include, for example, AI/ML modelling approaches to replace, reduce, and refine the use of animal models during the preclinical development. In clinical trials, AI/ML systems may support the selection of patients based on certain disease characteristics or other clinical parameters. AI/ML tools can also support data recording and analyses, which in turn can be submitted to regulators in marketing-authorization procedures. At the marketing-authorization stage, AI applications include tools to draft, compile, translate, or review data to be included in the product information of a medicine. In the post-authorization phase, such tools can effectively support, for example, pharmacovigilance activities, including adverse event report management and signal detection. EMA points out that this range of applications brings with it challenges such as the understanding of the algorithms, notably their design and possible biases, as well as the risks of technical failures and the wider impact these would have on AI uptake in medicine development and health.

Risk-based approach to AI
The EMA’s draft reflection paper emphasizes a risk-based approach in applying AI and ML. It says that a risk-based approach for development, deployment and performance monitoring of AI and ML tools allows developers to proactively define the risks to be managed throughout the AI and ML tool lifecycle. The concept of risk includes, but is not limited to, regulatory impact. EMA says advice on risk management will be further reflected in future regulatory guidance as the impact of  system malfunction or degradation of model performance can range from minimal to critical or even life-threatening. The EMA’s draft reflection paper points out that the degree of risk may depend not only on the AI technology, but also on the context of use and the degree of influence the AI technology exerts. In addition, the degree of risk may vary throughout the lifecycle of the AI-system. For example, EMA says that marketing authorization applicants or marketing authorization holders (MAH) planning to deploy AI/ML technology are expected to consider and systematically manage relevant risks from early development to decommissioning.

EMA advises in its draft reflection paper that if an AI/ML system is used in the context of medicinal product development, evaluation, or monitoring and is expected to impact, even potentially, on the benefit–risk of a medicinal product, early regulatory nteraction is advised. This can include advice on the  qualification of innovative development methods for a specific intended use in research and development in relation or scientific advice is advised. The level of scrutiny would depend on the level of risk and regulatory impact posed by the system. EMA says a key principle is that it is the responsibility of the marketing authorization applicant or MAH to ensure that all algorithms, models, datasets, and data-processing pipelines used are fit for purpose and are in line with ethical, technical, scientific, and regulatory standards as described in GxP standards and current EMA scientific guidelines. Of note, these requirements may in some respects be stricter than what is considered standard practice in the field of data science.

AI and manufacturing
The EMA’s draft reflection paper outlines the use of AI through a medicine’s lifecycle from drug discovery  to drug development to post-authorizaton activities as well manufacturing. EMA says that the use of AI/ML in the manufacturing of medicinal products, including process design and scale-up, in process quality control, and batch release, is expected to increase in the coming years. It says that model development, performance assessment and lifecycle management should follow quality risk-management principles and take patient safety, data integrity, and product quality into account. For human medicines, guidelines under the International Council on Harmonization of Technical Requirements for Pharmaceuticals for Human Use (ICH), which brings together regulators and the bio/pharma industry to haromonize technical requirements for medicines globally, including GMP and quality standards, should be considered. Of particular note in the context of AI and manufacturing says EMA are ICH Q8 Pharmaceutical Development, ICH Q9 Quality Risk Management, and ICH Q10 Pharmaceutical Quality System. Last November (November 2022), EMA established the Quality Innovation Group to serve as a forum to support the development, implementation, assessment and inspection of novel manufacturing approaches and technologies. Part of the work plan for 2023 by EMA’s Quality Innovation Group is to address digitalization and automation in manufacturing, including AI/ML.

What’s next
EMA is seeking input from stakeholders on its draft reflection paper and on identifying opportunities and risks of AI in the field of medicines. The public consultation is open until December 31, 2023, and the topic will be further discussed during a joint HMA/EMA workshop scheduled for November 20–21 2023. EMA says it will analyze the feedback from stakeholders will be analyzed in consideration of the finalization of the reflection paper as well as future development of guidance as relevant.

AI in supply management
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