Getting overwhelmed by the news is an everyday struggle for those wishing to stay in touch with the latest news. Our newsletter should make it easier for those interested in pharmacovigilance regulations, advancement in the relevant use of artificial intelligence, and personal growth. Approaching with care, the same as we provide PV services, we thoughtfully pick articles every month to keep you updated.
This article provides an overview of the European Medicines Agency's (EMA's) position on issues that are typically addressed in discussions or meetings with MAHs in the post-authorisation phase. Revised topics are marked 'New' or 'Rev.' upon publication.
A key focus for the MHRA as we develop our forthcoming strategy is enabling safe and timely access to innovative technologies, while maintaining public trust. This includes strengthening our approach to regulating adaptive AI, enhancing both pre-market evaluation and robust post-market surveillance, and ensuring that safety, performance and equity remain central as technologies evolve in real-world settings.
High-quality clinical documentation is essential for safe and effective care, yet its production remains time consuming and prone to error. Large language models (LLMs) have shown potential for supporting clinical note generation, but their clinical adoption depends on how the quality of generated text is assessed, and current evaluation practices vary widely.
This article provides an overview of the European Medicines Agency's (EMA's) position on issues that are typically addressed in discussions or meetings with MAHs in the post-authorisation phase. Revised topics are marked 'New' or 'Rev.' upon publication.
This article addresses the classification of changes to the RMP, submission requirements and aspects to be considered in the management of parallel procedures affecting RMP. Revised topics are marked 'New' or 'Rev.' upon publication.
EU implementation strategy of ICH E2D(R1) Guideline - Post-approval safety data: Definitions and standards for management and reporting of individual case safety reports
High-quality clinical documentation is essential for safe and effective care, yet its production remains time consuming and prone to error. Large language models (LLMs) have shown potential for supporting clinical note generation, but their clinical adoption depends on how the quality of generated text is assessed, and current evaluation practices vary widely.
Before committing to research, AI is helping pharma teams identify which concepts are most likely to resonate with their key customers
Medical text records serve as essential repositories of patient information, providing a foundation for informed clinical decision-making, accurate diagnosis, reliable prognosis, and effective treatment planning. Recent advancements in Artificial Intelligence (AI), particularly in Natural Language Processing (NLP) and Machine Learning (ML), have positioned AI-driven language models as powerful tools for analyzing, classifying, and generating medical textual data.
A key focus for the MHRA as we develop our forthcoming strategy is enabling safe and timely access to innovative technologies, while maintaining public trust. This includes strengthening our approach to regulating adaptive AI, enhancing both pre-market evaluation and robust post-market surveillance, and ensuring that safety, performance and equity remain central as technologies evolve in real-world settings.
Preventing fetal exposure to teratogenic medications is a public health priority. The Teratogenic Risk Impact Mitigation (TRIM) tool has been developed to support regulatory decisions regarding risk mitigation programs. One of the explicit TRIM criteria requires medication-specific quantification of fetal exposure risk.
In this article, we’ve compiled 10 of our favorite tips on leading organizational change, from how to avoid false starts to how to align your team when priorities change.
Pictures used in this newsletter were generated by AI.