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Robotics and Aseptic Processing in View of Regulatory Requirements.

Andrea TanziniMarco RuggeriEleonora BianchiCaterina ValentinoBarbara ViganiFranca Ferrari BridgersSilvia RossiHermes GibertiGiuseppina Sandri
Published in: Pharmaceutics (2023)
Several nanomedicine based medicinal products recently reached the market thanks to the drive of the COVID-19 pandemic. These products are characterized by criticality in scalability and reproducibility of the batches, and the manufacturing processes are now being pushed towards continuous production to face these challenges. Although the pharmaceutical industry, because of its deep regulation, is characterized by slow adoption of new technologies, recently, the European Medicines Agency (EMA) took the lead in pushing for process improvements using technologies already established in other manufacturing sectors. Foremost among these technologies, robotics is a technological driver, and its implementation in the pharma field should cause a big change, probably within the next 5 years. This paper aims at describing the regulation changes mainly in aseptic manufacturing and the use of robotics in the pharmaceutical environment to fulfill GMP (good manufacturing practice). Special attention is therefore paid at first to the regulatory aspect, explaining the reasons behind the current changes, and then to the use of robotics that will characterize the future of manufacturing especially in aseptic environments, moving from a clear overview of robotics to the use of automated systems to design more efficient processes, with reduced risk of contamination. This review should clarify the regulation and technological scenario and provide pharmaceutical technologists with basic knowledge in robotics and automation, as well as engineers with regulatory knowledge to define a common background and language, and enable the cultural shift of the pharmaceutical industry.
Keyphrases
  • healthcare
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  • high throughput
  • machine learning
  • risk assessment
  • quality improvement
  • big data
  • deep learning
  • heavy metals
  • human health
  • current status
  • drug delivery
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