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Challenges and Strategies for Implementing Automated Visual Inspection for Biopharmaceuticals
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.
Methods and materials
Eisai Automated Inspection Machine (Model 587-2, Eisai Machinery of USA, Allendale, NJ) was used to conduct an automated inspection. Mimic solutions were prepared by adding an estimated amount of PEG 1000 into desired buffer solution to reach a target viscosity value. Upon proper mixing, a sample was tested for viscosity confirmation. The mimic solution was then filtered through a 0.22μm filter and aspectically filled into vials followed by manual inspection to ensure absence of foreign particles. These clean vials were spiked with standard glass beads of three different sizes: 70μm, 100μm and 400μm (Duke Scientific Soda Lime Glass, Palo Alto, CA). Each vial was seeded with a single glass bead. The seeded vials were manually inspected a second time to ensure that each vial only had one particle and no other foreign contaminants. Two different buffer formulations (with and without polysorbate) were used to prepare PEG solutions of different viscosities. Formulations comprised of a commonly used stabilizing excipient (sucrose or sorbitol) and a buffering agent (acetate). The formulations are listed as follows and are referred to accordingly in the remaining sections of this article:
Formulation A = PEG 1000 in acetate buffer + sucrose + Polysorbate 20
Formulation B = PEG 1000 in acetate buffer + sorbitol
Experimental design. Vials seeded with particles were inspected on the Eisai machine through a double-check system comprising two inspection stations. A set of 24 vials (six clean and six each seeded with 70μm, 100μm and 400μm particles) were inspected at each inspection station. Each vial was inspected 32 times in a single run. A total of three runs were conducted to estimate statistical error in the performance. For selected experiments, a design of experiment (DOE) was performed using a space-filling design and spanning a range of spin and brake settings for a fill configuration of 1.7 mL in 3 cc. All DOE experiments were conducted for solutions with different viscosities using PEG solution in Formulation B.
The detection rate can then be calcuated for each vial type (clean, 70μm, 100μm and 400μm) by averaging the %DR values for all six vials in each group. For runs that were conducted in triplicates, the average and standard deviation were also computed for the detection rate for each particle size. For the data generated through DOEs, contour plots were generated to depict detection rates for 400 μm-particle size at various spin and brake settings.
Results and discussion
Systematic studies were conducted to evaluate the effect of key process parameters, including machine settings, product properties, and fill configuration on the performance of the AVI system.
AVI of injectable drug products offers several advantages over manual inspection, including process consistency, speed, and potential cost effectiveness. This study investigated the role of machine settings, formulation properties, and fill configuration on the performance of an AVI. Higher spin speed and brake settings were shown to improve detection rates of the AVI system. Product properties such as viscosity, density, and surface tension affect the manner and duration of particle suspension in solution and thereby affect process performance. Other inherent solution properties such propensity to form air-bubbles and/or protein particles can also cause potential interference with the inspection system resulting in false rejections. Low fill volumes are also challenging because of the smaller inspection window. It is suggested that any equipment qualification or process characterization work should evaluate the system performance over a wide range of these process parameters and solution properties to arrive at a robust and consistent visual inspection process. DOEs can be conducted to study these parameters and any potential interactions. Formulation properties and fill configurations can be bracketed to minimize the number of experiments.
The authors wish to thank Aarti Gidh, Deborah Shnek, Erwin Freund, and Ed Walls in process development at Amgen for useful discussions and suggestions for this paper. We also thank Jeff Stephens, Ari Levy, and Damien Villanueva in clinical manufacturing at Amgen for providing valuable experimental support toward the execution of these studies.
Nitin Rathore*, is a senior scientist, Cylia Chen is a senior associate scientist, Oscar Gonzalez is a senior engineer, and Wenchang Ji is principal scientist, all in drug product and device development at Amgen, Thousand Oaks, CA, email@example.com
*To whom all correspondence should be addressed.
1. N. Rathore and R. Rajan, Biotechnol. Prog., 24 (3), 504–514 (2008).
2. T.A. Barber, Control of Particulate Matter Contamination in Healthcare Manufacturing (CRC Press, 1999).
3. C. Jones, presentation before the PDA Visual Inspection Forum (Bethesda, MD, 2007).
4. J.Z. Knapp and L.R. Abramson, Jrnl. of Parenteral Sci. and Technol., 44 (2), 74–107 (1990).
5. J.Z. Knapp, PDA Jrnl. of Pharma. Sci. and Technol., 75 (2), 131–147 (2007).