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.
- 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.
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.
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, email@example.com
*To whom all correspondence should be addressed.
Submitted: Oct. 12, 2009. Accepted: Dec. 22, 2009.
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