Challenges and Strategies for Implementing Automated Visual Inspection for Biopharmaceuticals

The authors used a light-transmission-based static division system to detect particles of foreign contaminants in prefilled vials.
Oct 31, 2009

This article is part of PharmTech's supplement "Injectable Drug Delivery."

Manufacture of sterile parenteral drug products involves a series of unit operations (1) and aseptic processing conducted under strict requirements with respect to product quality. The manufacturing process is designed and validated to address such requirements and to ensure supply of safe and efficacious products. Visually inspecting each filled and sealed container for foreign contaminants or particulates ensures that these high standards are met and the final drug product is safe for patient use (2).

The United States Pharmacopeia (USP) provides guidance with respect to the inspection process for injectable drug products (2, 3). According to USP General Chapter <1>, the injection process shall be designed and qualified to ensure that every lot of all parenteral preparations is essentially free from visible particulates and every container whose contents shows evidence of visible particulates shall be rejected. Two methods are primarily employed by the pharmaceutical industry to address the need for visual inspection of filled and sealed containers: manual visual inspection (MVI) relying on human capability and machine based automated visual inspection (AVI).

Benefits of automated visual inspection

As the name suggests, a manual inspection relies on the ability of human operators to detect foreign contaminants in the filled containers. The inspection requires trained and certified inspectors to perform the task. Use of inspection aids such as contrasting colors and magnifying glass can improve the accuracy of human inspection. In spite of this, the subjectivity involved with manual inspection impacts effectiveness and the speed with which the inspection can be done. In addition, the process cannot be validated. Achieving required inspection throughput for a large commercial lot would require larger number of inspectors, which can add to labor costs.

Automated inspection systems, on the other hand, rely on a machine to detect visible particulates. Compared with manual inspection, an AVI process is more consistent and can be more cost effective over a longer time period of use. The AVI system requires qualification and validation, which ensure that the performance is consistent and similar to or better than human inspection. Several comprehensive studies of Knapp and coworkers [4, 5] highlight the probabilistic nature of the inspection process and provide a mathematical framework for comparing the performance of an automated inspection system with human capability.

Automated inspection process: technologies and principles

The automated inspection machine (AIM) used in this study contains a light-transmission double-check system for detecting particles in filled and sealed drug-product containers. The AIM uses a static division (SD) system that divides the photo detector into independent bits that span a detection window from the base of the container to just below the meniscus. The first step in the inspection process is the spinning of the container at a specified speed. As the vial spins, the liquid inside the vial forms a vortex and, because of the centrifugal force, imparts momentum to insoluble particles. These suspended particles are forced toward the container wall. The vial is then stopped with precise timing through the application of brakes on the machine. Because of frictional drag, the vortex collapses, thereby lifting and rotating the suspended particles. The image of moving particles is projected onto the SD sensor and can be sensed through variation in the intensity of the transmitted light which is converted to an electric signal from the affected bits. The amount of change in the electric signal is proportional to the size of the particle and is compared with a preset sensitivity level. If the signal exceeds the threshold established by the preset sensitivity level, the vial is deemed faulty by the machine and is sent to the defect bin. Cosmetic defects such as scratches or stains on the vial surface do not result in any movement during inspection and are not detected by the SD sensor.

Industry also uses a camera-based system to detect defects in filled drug-product containers. Unlike the SD sensor, which relies on light transmittance, a camera system uses light reflection to detect particles. Because the judgment of the camera system depends on the intensity of the reflected light, its performance is dependent on particle reflectivity and color. In addition to moving particles, a camera-based system can also pick up the light reflected from surface scratches and other container defects. Depending on the sensitivity of the system, this can result in increased false rejects. Alternatively, the system can be calibrated to detect specified cosmetic defects.

In addition to cosmetic defects, the potential benefit of a camera-based system includes the improved performance at lower fill levels. For very low fill volumes, the inspection window for a SD sensor-based system is greatly reduced, thereby resulting in deteriorated performance. Such challenges can be addressed by strategic placement of cameras to target a low fill-volume window. Hybrid systems that seek to combine the benefit of camera-based and SD sensor-based technology are being developed to provide improved performance for both particle and cosmetic defects.

Irrespective of the technology selected for automated inspection, several operational parameters (e.g., machine settings) and product properties play a key role in determining the performance of the system. Detailed characterization and optimization of these parameters is critical to developing an AVI process. Each technology needs to be qualified for its ability to detect faulty containers and to ensure that non-defect containers will not be rejected. This qualification requires a series of experiments using standard defect sets to challenge the AIM. Careful selection of experimental conditions (i.e., defect sets and machine settings) is important to minimize the number of evaluations and still generate conclusive data for entire process space. This study uses a SD sensor-based AIM to evaluate the effect of key process parameters on machine performance for inspection of liquid products in vials. Tested parameters include machine settings, formulation properties, and fill configuration.