A Risk-Management Approach to Cleaning-Assay Validation 4

Analytical methods. The model and worst-case compound evaluation did not replace any analytical-method validation activities. Analytical recovery must be established for each compound in the portfolio, but not on all surfaces. If multiple limits are to be considered, or if a range of reporting is required, the lowest limit may be evaluated, and that recovery can be applied to all acceptance limits as a conservative estimate. In the analytical method, three recovery factors were presented: Group 1, Group 2, and Group 3. Methods could be validated for any surface within a group and could be considered representative. The authors chose stainless steel 316L because it is the most prevalent, and cast iron because it is a common material on a tablet press. Type III hard anodized aluminum is the only surface in Group 3. This strategy did not ignore any uncommon surfaces. It grouped them appropriately, swabbed them, and applied a representative recovery factor.
Variability. Method variability was evaluated by performing a control sample (Compound A, 6 replicates, 0.5-μg swab, stainless steel 316L, Ra = 3.5) each day. The mean recovery of the entire experiment was 52%. These data suggested that the swabbing ability of the analyst did not change over time. The standard deviation within a day typically was less than 6. The pooled-within-run standard deviation was 3.99 over the course of the experiments. This value was used as a criterion for grouping new surfaces. The day-to-day standard deviation was 15.34.

Figure 5
Incorporating new materials of construction into the grouping strategy. Periodically, new equipment will be introduced into the CTM area that incorporates a product-contact surface made of a material of construction that is not listed in Table II. This problem is often caused by alloys of metals that have already been evaluated and by polymers of proprietary composition. Because surface recovery must be evaluated, personnel need a way to incorporate new surfaces into the groupings outlined in Table II. When a new piece of equipment is purchased, CTM-engineering employees prepare the needed documentation to evaluate the equipment with regard to the cleaning program before use. If an identified sampling location is made of a new material of construction, the engineer asks the person responsible for the cleaning program and analytical development to perform the next steps, which are shown in Figure 5. Suppose that new equipment incorporated three new materials: a crystalline thermoplastic polyester marketed under the trade name of Ertalyte (Quadrant Engineering, Reading, PA), stainless steel 420, and stainless steel 630. Without the grouping strategy in place, method revalidation would have to occur for all compounds handled by this piece of equipment. With the strategy in place, these surfaces are placed into groups based on model-compound recovery. No method revisions are required. The analytical recovery of Compound A was evaluated for Ertalyte, stainless steel 420, and stainless steel 630 at the 5.0-μg/in.2 level. The authors used the validated method to evaluate the recovery of the three new surfaces compared with a representative surface from Group 1 (i.e., stainless steel 316L), Group 2 (i.e., cast iron), and Group 3 (Type III hard anodized aluminum). Recovery was evaluated for both the group representative and the new surfaces on the same two days with three replicates on each day. As an alternative, six replicates may be performed on the same day as the controls because the comparison of recovery is relative.

The new surfaces are placed in one of the three groups or define a new lower group, based on how close their average is to that of the group control. The authors obtained a cutoff value of 3.0% in the following manner. Based upon the data for the control, the within-day standard deviation was calculated to be 3.99 and was used in Equation 1. A series of one-sided hypothesis tests with an error rate of α = 0.10 were performed to assess whether the new surface mean was less than a specified control-surface mean. The confidence limit half-width for the difference between the means of two surfaces was computed using the within-day standard deviation because the replicates for each surface had to be run on the same two days, with each day treated as a block. For these calculations, it was assumed that this standard deviation was known. The lower one-sided confidence limit for the difference in means was derived using the following steps: This calculation did not indicate a difference between a control surface and the new surface under evaluation if the recovery differed by less than 3.0%. This approach was conservative because Eq. 1 categorized a new surface into Group 2 if it differed from stainless steel by more than 3.0%, which could be viewed as a strict criterion. Because the results were obtained on the same days and runs, the authors believed that this approach was reasonable. When evaluating the recovery of a new surface, this strategy helps personnel to place each material into the appropriate group. The grouping starts with a comparison of the new surface average to that of Group 1 (stainless steel 316L) and continues sequentially. If the new surface recovery (NSR) is more than 3.0% less than that of the group reference, it is compared with the reference surface in the next lower group until a group is found with which it does not differ by more than 3.0%. If no such group is found, then the new surface forms a new, lower group. The procedure is as follows:
  • If the NSR > mean stainless-steel recovery – 3.0%, the new surface belongs in Group 1.
  • If the NSR > mean cast-iron recovery – 3.0%, the new surface belongs in Group 2.
  • If the NSR > mean Type III anodized aluminum recovery – 3.0%, the new surface belongs in Group 3.
  • If the NSR < mean Type III anodized aluminum recovery – 3.0%, the new surface becomes a new group or becomes the worst-case surface for Group 3 and is used in all future method validations.

Table III: Swab recovery of three new materials of construction compared with controls from each representative group.
The data for the three new surfaces are outlined in Table III. Based on this approach, Ertalyte was placed into Group 1 because the difference between its recovery and that of stainless steel 316L (i.e., Group 1) was less than 3.0% (i.e., 2.48%). The recovery from stainless steel 420 and stainless steel 630 was more than 3.0% less than that from stainless steel 316L, but greater than that from cast iron. The authors placed stainless steel 420 and stainless steel 630 into Group 2. The placement of these two grades of stainless steel into Group 2 highlighted the conservative nature of this approach because their recoveries were only 4% and 8% less than that from stainless steel 316L. The recoveries were 12% and 9% greater than that from cast iron for stainless steel 630 and 420, respectively. Table II was updated to reflect this placement. Because the grouping strategy was conservative, it prevented the underestimation of recovery factors when assay values were reported and prevented the formation of additional groups for method validation unless the recovery value for a new surface is sufficiently low to warrant such an addition. 
The authors' data-driven risk-management approach to cleaning verification methods uses analytical-recovery values for a model compound to place product-contact surfaces into groupings for analytical-method validation. The data generated during the studies supported the formation of three recovery groups to validate analytical swab methods. Groups 1–3 were represented by stainless steel 316L, cast iron, and Type III hard anodized aluminum, respectively. This approach allowed all surfaces to be considered during analytical-method validation and provided an objective mechanism to incorporate new surfaces into the strategy.
The benefits of this strategy are numerous. First, only three surfaces must be validated on each compound, which drastically minimizes the number of recovery values established to support the entire portfolio. Second, the strategy includes a way to add new materials of construction to the cleaning program if new equipment is purchased. Traditionally, all swab methods must be revalidated to incorporate the new surface. With this strategy in place, a model compound is evaluated, the new surface is grouped, and no changes to existing methods are required. Third, the strategy allows for a constant state of compliance. A relative recovery value is known for any material of construction for all equipment.
Because the grouping strategy is applied to a small fraction of the total surface area, no surface material of construction is ignored, each molecule undergoes a typical method validation, and the strategy places surfaces into groups conservatively. The authors believe that the strategy controls risks appropriately and that the data set given in this study scientifically supports the strategy of grouping materials of construction to support analytical methods within the cleaning program. Acknowledgments
The authors would like to acknowledge the following colleagues at Eli Lilly: Gifford Fitzgerald, intern, for generating the swab-recovery data; Ron Iacocca, research advisor, for the SEM data; Sarah Davison, consultant chemist; Mike Ritchie, senior specialist; Mark Strege, senior research scientist; Matt Embry, associate consultant chemist; Kelly Hill, associate consultant for quality assurance; Bill Cleary, analytical chemist; and Laura Montgomery, senior technician, for their contributions and insightful suggestions throughout the project. In addition, Leo Manley, associate consultant engineer, provided the roughness measurements in support of this project.
Brian W. Pack* is a research advisor for analytical sciences research and development, and Jeffrey D. Hofer is a research advisor for statistics, discovery and development, both at Eli Lilly and Company, Indianapolis, IN, tel. 317.422.9043,

*To whom all correspondence should be addressed.
Submitted: Oct. 12, 2009. Accepted: Dec. 22, 2009.
1. ICH, Q9 Quality Risk Management, Step 5 version (2005).
2. Code of Federal Regulations, Title 21, Food and Drugs (Government Printing Office, Washington, DC), Part 211.67.
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8. ICH, Q2 Validation of Analytical Procedures: Text and Methodology, Step 5 version (1994).
9. R.J. Forsyth, J.C. O'Neill, and J.L. Hartman, Pharm. Technol. 31 (10), 102–116 (2007).

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