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 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.
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.
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