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Visual inspection injectable products may be enhanced by the utilization of AI to analyze and link data to identify deviations, trends, or irregularities.
Visual inspection of pharmaceutical products is part of quality control and quality assurance. The FDA provides guidance for the development of a risk-based approach to detecting, controlling, and preventing visual particulates in drug products, including injectables.1 Injectable drug products can be the typical vial and syringe products or prefilled syringe combination products. The nature of a combination product; however, creates specific challenges for visual inspection. PharmTech spoke with Dirk Schuster, sales director Pharma at groninger & co. GmbH, to find out more about what goes into visual inspection of prefilled syringes and how artificial intelligence (AI) may be used in the process.
Schuster: The viscosity of a drug substance has a direct impact on the entire filling process. While synthetically manufactured small molecules are typically low-viscosity and relatively easy to handle, modern large-molecule drugs, such as biologics, place significantly higher demands on process stability and equipment performance. Highly viscous formulations flow more slowly and are more sensitive to external influences, such as temperature, pressure, shear forces, or pump speed. Filling therefore must be fast, highly precise, and at the same time, particularly gentle to preserve product integrity.
Compared with traditional vial filling—where more robust products and larger fill volumes allow wider process windows—prefilled syringes with high-viscosity products operate within much tighter tolerances. As a result, product requirements directly affect line performance and output, making reliable equipment operation under demanding conditions a key success factor for quality, efficiency, and patient safety.
Artificial intelligence delivers the greatest benefit where highly automated filling processes meet complex product and process requirements. This is particularly true in areas where large volumes of process, motion, and image data are generated, such as monitoring filling operations, material handling, or overall machine performance.
In our view, the value of AI lies not in replacing proven automation, but in intelligently analyzing and linking data to identify deviations, trends, or irregularities at an early stage— before they affect product quality or line efficiency. AI can also support operators by improving error detection and root-cause analysis, for example through guided, visualized, and validated format changeovers on advanced filling lines for sterile vials, prefilled syringes, or cartridges.
Supply chain disruptions affect fill/finish processes in a fundamentally changed market environment. Historically, filling lines were designed for high volumes of robust formulations, with throughput and efficiency as the primary goals. Today, targeted therapies, biologics, and peptides—often developed as integrated drug-device combinations—are more sensitive, higher in value, and increasingly processed in prefilled syringes or ready-to-use (RTU) systems.
As a result, variations in glass, stoppers, or pre-sterilized components can no longer be considered in isolation; even minor deviations may significantly impact process stability. For fill/finish equipment, this means traditional compensation mechanisms reach their limits. From a machine builder’s perspective, the focus shifts to designing robust yet flexible systems that can reliably handle material variability without compromising quality or regulatory compliance.
The number of prefilled syringes produced per aseptic manufacturing run depends largely on the characteristics of the product itself. Robust, low-viscosity drug substances allow high output rates even under sterile conditions, as they tolerate wider process windows and deviations.
By contrast, sensitive peptides or biologics require particularly stable and gentle processes due to narrow tolerances, high product value, and sensitivity to mechanical or temporal stress. In such cases, it is not sterility requirements alone but product properties that limit the economically viable batch size.
For the machine builder, this means designing filling systems consistently around the product—for example through highly automated, isolator-based RTU concepts—and defining productivity through reproducible quality, process stability, and long-term controllability rather than speed alone.
Dirk Schuster is sales director Pharma at groninger & co. GmbH as well as a member of the company’s management board. He is responsible for global sales activities in the pharmaceutical sector, with a strong focus on aseptic filling, containment solutions, and fill-finish technologies for complex drug products. With many years of experience in the pharmaceutical machinery industry, Schuster supports pharmaceutical and biotech manufacturers in developing scalable, regulatory-compliant production concepts. His work focuses on aligning customer requirements with long-term, robust filling solutions that address increasing product complexity, automation, and lifecycle considerations.