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This article examines the integration of Artificial Intelligence (AI) in pharmaceutical manufacturing, particularly in regard to Good Manufacturing Practice (GMP) standards. While AI offers significant potential, challenges remain in regulation and validation. Regulatory bodies like the FDA and European authorities are working on incorporating AI into GxP processes, but specific guidelines are still in development. The report highlights recent advancements, including a proposed validation framework and examples of successful AI applications in GxP-compliant production. Overall, AI adoption in pharmaceutical manufacturing is advancing, but it should be approached cautiously to maintain product quality and patient safety.

The pharmaceutical manufacturing industry is undergoing a transformative shift towards decentralization and modularization, significantly enhanced by the implementation of digital twins, automated manufacturing, and digital reliability. These technologies improve flexibility, efficiency, and quality assurance by creating virtual replicas of devices, processes, and patients, enabling personalized medicine and optimized drug formulation. Standardization initiatives like Asset Administration Shell (AAS) and Fast Healthcare Interoperability Resources (FHIR) ensure seamless integration and interoperability across systems. Digital dependability frameworks, such as the Digital Dependability Identity (DDI), enhance safety and risk management through predictive analytics. This integration of advanced digital technologies promises to revolutionize pharmaceutical manufacturing, fostering innovation and improved patient outcomes. The aim of this article is to highlight the current advancements and significant potential of digital twins in personalized medicine, and to encourage exploration of new challenges in this field.

This article explores the evolving patent landscape at the intersection of biotechnology, pharmaceuticals, and artificial intelligence (AI), focusing on the impacts of AI on patentability and enforcement within UK and European patent law. As AI becomes increasingly integrated into biotech and pharma innovations, it introduces new complexities that can affect patent grant rates and enforceability, potentially deterring ongoing investment. The article discusses key challenges, including subject matter exclusions, the rigorous examination process, and the implications for future innovation. It also highlights the importance of adapting patent strategies to secure the necessary protection for AI-driven advancements in these critical sectors.

The article focuses on the challenges of the pharmaceutical industry that are closely linked to the production process, covering engineering, scale-up, technology transfer and operations. It covers digital, modeling- and simulation-based approaches to overcome these hurdles. The use of digital platforms allows for the collection and harmonization of vast amounts of data from various sources and consistent data integration. Model-based technologies are key for simulations and many optimization measures. Using models for optimization (e.g.: Model predictive control (MPC)) are up-and-coming methods for improving biopharmaceutical manufacturing systems. It is investigated whether the bioprocess models generated in a QbD/DoE approach can also be used online for operational support.