Pharmaceutical Technology_ Challenges and Strategies for Imple

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November 1, 2009

Challenges and Strategies for Implementing Automated

isual Inspection for BiopharmaceuticalsBy Nitin Rathore,Cylia Chen,Oscar Gonzalez,Wenchang Ji

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

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 asepticprocessing 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 andefficacious 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 , the injection process shall bedesigned and qualified to ensure that every lot of all parenteral preparations is essentially free from visibleparticulates and every container whose contents shows eviden ce of visible particulates shall be rejected.Two methods are primarily empl oyed by the pharmaceutical ind ustry to address the need for visualinspection of filled and sealed co ntainers: manual visual inspec tion (MVI) relying on human capability andmachine based automated visua l inspection (AVI).

Benef its of automated visual inspection

As the name suggests, a manual inspection relies on the ability of human operators to de tect foreignconta minants in the filled containers. The inspection requires trained and certified inspec tors to performthe task. Use of inspection aids such as contrasting colors and magnifying glass can improve the accuracyof human inspection. In spite of this, the subjectivity involved with manual inspection impacts effectivenessand 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 theperformance is consistent and similar to or better than human inspection. Several comprehensive studiesof Knapp and coworkers [4, 5] highlight the probabilistic nature of the inspection process and provide amathematical framework for comparing the performance of an automated inspection system with human
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capability.

Automated inspection process: technologies and principles

The automated inspection machine (AIM) used in this study contains a light-transmission double-checksystem 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 thebase 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 forcedtoward the container wall. The vial is then stopped with precise timing through the application of brakes onthe machine. Because of frictional drag, the vortex collapses, thereby lifting and rotating the suspendedparticles. The image of moving particles is projected onto the SD sensor and can be sensed throughvariation in the intensity of the transmitted light which is converted to an electric signal from the affectedbits. The amount of change in the electric signal is proportional to the size of the particle and is comparedwith 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 scratchesor stains on the vial surface do not result in any movement during inspection and are not detected by theSD sensor.

Industry also uses a camera-based system to detect defects in filled drug-product containers. Unlike theSD 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, itsperformance is dependent on par ticle reflectivity and color. In addition to moving particles, a camera-basedsystem can also pick up the light reflected from surface scratches and other container defects. Dependingon the sensitivity of the system, this can result in increased false rejects. Alternatively, the system can becalibrated to detect specified cosmetic defects.

In addition to cosmetic defects, the potential benefit of a camera-based system includes the improvedperformance at lower fill levels. For very low fill volumes, the inspection window for a SD sensor-basedsystem is greatly reduced, thereby resulting in deteriorated performance. Such challenges can beaddressed by strategic placement of cameras to target a low fill-volume window. Hybrid systems that seekto combine the benefit of camera-based and SD sensor-based technology are being developed to provideimproved 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 standarddefect sets to challenge the AIM. Careful selection of experimental conditions (i.e., defect sets and machinesettings) is important to minimize the number of evaluations and still generate conclusive data for entireprocess space. This study uses a SD sensor-based AIM to evaluate the effect of key process parameterson machine performance for inspection of liquid products in vials. Tested parameters include machine

settings, formulation properties, and fill configuration.Me thods and materials

Eisai Automated Inspection Machine (Model 587-2, Eisai Machinery of USA, Allendale, NJ) was used toconduct an automated inspection. Mimic solutions were prepared by adding an estimated amount of PEG1000 into desired buffer solution to reach a target viscosity value. Upon proper mixing, a sample wastested for viscosity confirmation. The mimic solution was then filtered through a 0.22m filter andaspectically filled into vials followed by manual inspection to ensure absence of foreign particles. Theseclean vials were spiked with standard glass beads of three different sizes: 70m, 100m and 400m (DukeScientific Soda Lime Glass, Palo Alto, CA). Each vial was seeded with a single glass bead. The seededvials 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 toprepare PEG solutions of different viscosities. Formulations comprised of a commonly used stabilizingexcipient (sucrose or sorbitol) and a buffering agent (acetate). The formulations are listed as follows andare referred to accordingly in the remaining sections of this article:

Formulation A = PEG 1000 in acetate buffer + sucrose + Polysorbate 20


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Figure 3: Effect of spin speed and brakesettings on the d etection rate of themachine. A setting o f 1600 x 7 representsspin speed of 1600 rpm and brake settingof 7. Performance improves as spin speedis increased and inspection is performedquickly after stopping the vial .

Figure 4: Product properties can have asignificant impact on machineperformance. Detection rates (average of 100 m and 400 m particles) are reducedat hi gher viscosity and im prove within creased speed and brake settings.Solutions with similar viscosity can alsoexhibi t di fferences in detectio n rates baseddue to differences in density and surfacetension. A setting of 1600 x 7 representsspin speed of 1600 rpm and brake settingof 7. Yel low fil l col or representsFormul ation A whil e red representsFormulation B.

Figure 5: Contour plotsrepresenting the ma chineperformance over a wide range of spi n and brake settings for solutions of different viscosities.

Th e bl ack area represents theoperational p arameter range thatwas not studied.

fill configuration (e.g., container size and fill volume) as discussed in the following sections.

Role of product properties. In addition to the machine settings, theproduct properties can have a significant impact on the performance of the AVI system. Solution properties such as density, viscosity, and surfacetension govern the movement of the foreign particle in the flow-fieldgenerated by spinning the the vial. The speed at which meniscusrecovers, as well as the time it takes for the foreign particle to descendand stop after the application of brakes, is dependent on these solutionproperties. Figure 4 shows deterioration in performance of the AVI systemas the product viscosity is increased. As solution becomes viscous, theparticle motion relative to the solution is arrested and it becomes difficultfor the machine to detect. Detection rates can improve by increasing thespin speed and brake settings as shown in the figure by the three tracesof color (blue, red, and green) . The Figure 4 inset shows a closeup of thedata set for 2.3 cP. It was observed that the two formulations with sameviscosity but different density and surface tensions (Formulation A is

represented in yellow and has a density of 1.046 g/mL and surface tension of 48 mN/m2; Formulation B isrepresented in red and has density of 1.033 g/mL and surface tension of 61 mN/m2 at room temperature)exhibit slightly different detection rates (about 7% variation). This finding demonstrates that variation inphysical properties other than viscosity can effect the way the particles are suspended and move duringinspection, thereby affecting their detection rates. However, as the spin speed was increased, the overallperformance improved and the differences between the detections rates for the two formulations wasreduced.

In addition to the physical properties discussed above, other inherentproperties of the protein solution could affect the ability of the machine todifferentiate between and true and false rejects. If the protein has apropensity to form or trap particulates, such protein particles can beperceived by the AVI system as rejects. In such cases, kinetics of particulate formation should be characterized to assess the feasibility of using automated inspection. The liquid formulation may also have

propensity to form micro air bubbles which can be perceived by theinspection system as foreign particulates. Beccause air bubbles have atendency to rise to the meniscus, inspection view height can be carefullyselected to avoid any interference with the bubbles. Some AIMs use a pre-spin to facilitate removal of air bubbles before the inspection spin. Such

AVI process issues could be very product specific and may cause costlydelays during performance qualification. Selection of an appropriate mimicsolution for development runs is therefore critical to identify and trouble-shoot such problems early during product development.

A design of experiments can be conducted tocharacterize the performance of the AVI system

over a wide range of operational parameters. Adesign space can then be created over therange that gives acceptable performance. Such design space characterizationoffers the assurance of a consistent and robust process. Figure 5 shows resultsof a DOE study conducted over a wide range of spin speed and brake settings for a fill volume of 1.7 ml in a 3cc vial using Formulation B. The contour colorsrepresent detection rates measured by the automated inspection system for solutions of different viscosities. As discussed above the performance clearlydeteriorates with increase in solution viscosity. While higher spin speed andhigher brake settings results in improved detection rates, other operational issuescan come into play under such conditions. For example, very high spin speedmay cause the vials to shoot out of the spindles making the process operationallyunfriendly. Very high brake settings (inspecting very close to the termination of vial rotation) may not provide adequate time for the meniscus to recover. Themeniscus, in turn, puts a shadow on the sensor and can be wrongly classified as

a defect. All these operational issues should be carefully characterized to create a design space that offersacceptable performance during commercial manufacturing.


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Figure 6: Comparison of detection rates for vials of different sizes and fill volumes.Smal ler fil l vol umes are consistently mo rechallenging for automated inspection.

Role of f ill configuration. In addition to machine settings andformulation properties, fill configuration of the final drug productpresentation (i.e., size and shape of container and liquid fill volume) playsa significant role in determining the performance of an automatedinspection system. The radius of the container has a direct impact on theshape of the vortex formed when the vial is spun and the recovery of themeniscus when brakes are applied. Syringe barrels usually have smaller radii than vials and pose a bigger challenge for inspection. Higher spinspeeds are needed for syringes to obtain performance comparable withvials. The height of liquid level plays an equally important role as well. For a given vial size, as fill volume is reduced, the liquid level is lowered andthe size of the inspection window (i.e., distance between base andmeniscus level) shrinks. Figure 6 demonstrates that the automatedmachine's performance was consistently better for larger fill volumes for each of the three vial sizes.Inspecting very close to the meniscus level may result in false rejects due to the meniscus shadow beingperceived by the sensor as foreign particle. Careful selection of inspection view height is critical tominimizing such false rejects while maximizing the size of the inspection window.

Conclusions

AVI of injectable drug products offers several advantages over manual inspection, including processconsistency, 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 brakesettings 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 therebyaffect 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 falserejections. Low fill volumes are also challenging because of the smaller inspection window. It is suggestedthat any equipment qualification or process characterization work should evaluate the system performanceover a wide range of these process parameters and solution properties to arrive at a robust and consistentvisual inspection process. DOEs can be conducted to study these parameters and any potentialinteractions. Formulation properties and fill configurations can be bracketed to minimize the number of

experiments.Acknowledgments

The authors wish to thank Aarti Gidh, Deborah Shnek, Erwin Freund, and Ed Walls in processdevelopment 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 experimentalsupport toward the execution of these studies.

Nitin Rathore* , is a senior scientist, Cylia Chen is a senior associate scientist, Oscar Gonzalez is asenior engineer, and Wenchang Ji is principal scientist, all in drug product and device development at

Amgen, Thousand Oaks, CA, [email protected] 1

, tel. 805.313.6393.

*To whom all correspondence should be addressed.

References

1. N. Rathore and R. Rajan, Biotechnol. Prog., 24 (3), 504514 (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), 74107 (1990).

5. J.Z. Knapp, PDA Jrnl. of Pharma. Sci. and Technol., 75 (2), 131147 (2007).


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Table I: Lis t of machine parameters potentially impacting detection rates.Figure 1: Mechanism of vis ible particle detection through a static-divis ion s ensor-based AVI system. (ALL FIGURES ARECOURTESY OF THE AUTHORS)Figure 2: Comparis on of detection rates for particles of different sizes when us ing two different levels of sens itivity(Formulation A). The impact of sens itivity on detection rate for smaller particles is larger and higher sensi tivity may result infalse rejects (i.e., rejection of clean vials ).Figure 4: Product properties can have a s ignificant impact on machine performance. Detection rates (average of 100 mand 400 m particles ) are reduced at higher viscosity and improve with increased speed and brake settings. Solutions withsimilar viscos ity can also exhibit differences in detection rates based due to differences in dens ity and surface tension. A

setting of 1600 x 7 represents s pin speed of 1600 rpm and brake setting of 7. Yellow fill color represents Formulation Awhile red represents Formulation B.Figure 5: Contour plots representing the machine performance over a wide range of spin and brake se ttings for solutions of different viscosities . The black area represents the operational parameter range that was not studied.Figure 6: Comparison of detection rates for vials of different sizes and fill volumes . Smaller fill volumes are consis tentlymore challenging for automated inspection.


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