A Risk-Management Approach to Cleaning-Assay Validation - Pharmaceutical Technology

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A Risk-Management Approach to Cleaning-Assay Validation
The authors recommend a strategy for classifying similar nonstainless-steel surfaces into three groups based upon the analytical recovery that was observed in this study.


Pharmaceutical Technology
Volume 6, Issue 34, pp. 48-55

Results and discussion

In this study, a single analyst evaluated the analytical swab recovery from a representative set of surfaces found in the CTM manufacturing and packaging areas. The surfaces were manufactured specifically for this study to have a broad range of Ras. In addition to Ra, the effect of the material of construction, acceptance limit, compound, and method variability also were evaluated. Based upon these data sets, the authors used a strategy involving three groups of materials to represent all of the surfaces in CTM operations. Merck and Co. used a similar strategy to establish five recovery groups (9). The authors expanded on Merck's strategy by adding a detailed study supporting the groups and an approach for determining the appropriate placement of new surfaces into pre-established groups.

Roughness average (Ra). The Ra targets listed above were difficult to achieve. The intermediate Ra values were significantly lower than the target values given in the design of experiments section above. Both intermediate Ra values, initially targeted for 75 and 125 Ra, were measured to be approximately 40 μin. Although the machining process at each level yielded visually different surfaces, the measured Ra changed little from surface to surface. The authors decided to proceed with the surfaces and define smooth surfaces as Ra < 100 μin. and rough surfaces as Ra > 100 μin. This approach allowed for an assessment of the anticipated relationship between Ra and analytical recovery.


Figure 2
The Ra had little impact on the observed analytical-swab recovery, but the recovery was expected to improve with lower Ras. Figure 2 shows roughness grouped by surfaces that had a measured Ra > 100 μin. and by surfaces that had a Ra < 100 μin. Only 5- and 50-μg spikes are represented in Figure 2; the variability in the 0.5-μg spikes confused the interpretation of the data slightly, but is consistent. As Figure 2 shows, the recovery within each roughness group was approximately the same for a given analyte on a given material and did not correlate to Ra. Therefore, Ra should not be used as a predictor of analytical recovery or as a grouping criterion.


Figure 3
Material of construction. Because Ra was eliminated as a factor contributing to recovery losses, the authors performed data analysis by combining all average recovery values and assessing the effect of the material of construction. The data in Figure 3 were first separated by API, and groups were generated to represent the logical separations in recovery. Figures 3(a–c) contain the data for the 0.5-μg spikes, the 5-μg spikes, and the 50-μg spikes, respectively. The data from the 0.5- and 5-μg spikes exhibited a trend similar to that of the 50-μg spikes. The variability in the results increased as the spiked amount decreased, and the 50-μg spike results were substantially less than that of the other spike levels.

For both compounds, the Type III hard anodized aluminum exhibited the poorest recovery. The next logical break point grouped bronze and cast iron. The recovery of Compound B from bronze suggested that the material was representative of Group 1. The recovery of Compound A on bronze was lower and more variable, however, so the authors placed bronze into Group 2. For the majority of the surfaces, the recovery of Compound A was lower than that for Compound B at a given limit. In some cases, the recovery was approximately the same (i.e., of 5- and 50-μg spikes on cast iron, and of the 50-μg spike on Type III hard anodized aluminum). In addition, the predominant trend was that the average recovery of a compound increased as the spiked amount increased on a given material of construction. For example, the recovery of Compound B from stainless steel 316L was approximately 74%, 90%, and 95% at 0.5-μg, 5-μg, and 50-μg swabs, respectively.


Figure 4
Ra was originally considered a variable in the experiments previously outlined and did not affect swab recovery. To understand the surface attributes that might contribute to incomplete recovery for the different materials of construction, the authors acquired SEM images for Group 1, Group 2, and Group 3 surfaces (see Figure 4). Stainless steel is a relatively smooth surface with some striations from machining (see Figure 4a). Cast iron has a pitted surface that could provide opportunities for an API in solution to be trapped during a spiking experiment (see Figure 4b). The anodization process makes Type III hard anodized aluminum, the worst recovery surface, porous, thereby creating the greatest opportunity to lose analyte (see Figure 4c).

Note that polymers were grouped together with metals and might not be considered to be similar on first pass. The SEM image of Lexan in Figure 4d, however, illustrated that the polymer surface was smooth, albeit with some surface debris, which prevented the loss of analyte. The polymer surface was grouped with stainless steel in Group 1. The SEM images were good supporting evidence that the groupings were logical based upon surface characteristics.


Table II: Grouping of material surface of construction.
Table II is based on the data shown in Figure 3. The top surface in Table II represents the surface that was validated for recovery in each group. This recovery value represented all others within a given group. The groupings were supplied to the CTM areas, and the group number was included on the swab submission to the analytical laboratory so that the correct recovery factor was applied to each surface. In addition, the table served as a tool for engineering to determine whether newly purchased equipment contained a new product-contact surface.


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