Large language models as the cognitive layer of next generation digital twins in pharmaceutical manufacturing and packaging

Digital Twins (DTs) are a virtual representation of physical processes, equipment, and products that can transform pharmaceutical manufacturing and packaging by enabling sustainable manufacturing (in-silico experimentation reducing material waste), real-time monitoring, predictive maintenance, and Quality by Design (QbD) optimization under the Pharma 4.0™ operating model. However, conventional DTs remain largely inaccessible to plant personnel, demanding specialist expertise to interpret outputs from numerical dashboards and physics based models. Large language models (LLMs), built in the transformer architecture, offer a promising cognitive layer that enables operators to interact with DTs through natural language, supporting root-cause analysis, standard operating procedures (SOP) retrieval, and automated documentation. This perspective synthesizes established DT practice with emerging LLM-DT integration concepts from Industry 4.0 literature, while explicitly distinguishing validated evidence from proposed future directions. Key challenges include hallucination risk, Good Manufacturing Practice (GMP) validation, and regulatory acceptance which are critically examined alongside a roadmap for responsible adoption.