Materials of Construction Based on Recovery Data for Cleaning Validation - Pharmaceutical Technology

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Materials of Construction Based on Recovery Data for Cleaning Validation
The material of construction is a factor in the recovery of residue in cleaning validation. An analysis of existing recovery data showed that recovery factors for drug products on various materials of construction may be categorized into several groupings.

Pharmaceutical Technology

Table III: Second highest group mean recovery factor (RF).
Representative material from each group. This result implies that RF for any new product can be estimated from a material within this group with a standard deviation of 13 units. The mean RF for each material in the group is shown in Table III. The most conservative choice for a representative material is the lowest mean recovery, which is nylon. The mean or median recovery of the materials in the group is either brass or Nylaclast Oilon. These three materials, however, represent a very small percentage of the actual equipment. The most logical and practical representative material for recovery studies is stainless steel. Stainless steel is included in the group, is not on the extreme high end, and is the major component of manufacturing equipment. The RF from other material groups can be determined as exceptions to this group.

Table IV: Average recovery data.
Test case. As a test of the data analysis, a recent study measured the percent recovery of a development compound, MK-0524, and niacin. The study included five materials of construction from the largest group of recovery materials (see Tables II and III), including stainless steel. The study results, shown in Table IV and Figure 2, confirm the overall study conclusions. The average recoveries on the materials of construction were well within the expected Table III value 15% range.

Figure 2: Recovery variability. (FIGURE 2: MERCK & CO. INC.)
The repeatability for these results was better than typical. The standard deviation estimate of 4.3 for repeated tests across different materials compared favorably to the data in the large data set, where standard deviations were typically 13 to 14 units. The tighter data set was most likely the result of study parameters. The study included a single site and relatively large number of recoveries for each compound, conducted in the same time frame following the same procedure.

The standard deviation was tighter for this data set, so it was possible to detect statistically significant differences among some materials for MK-0524; however, the confidence intervals for recovery estimated on the five different materials were all well within the 15% expected range. The confidence intervals for niacin tested on five different materials were all overlapping.


Statistical approach for the recovery analysis.
The analysis showed that a recovery study conducted at one manufacturing site using stainless steel could serve as a representative material of construction for most materials used in drug-product manufacturing applicable across multiple sites.


The authors acknowledge the contributions of the 16 site representatives, without whom, this study would not have been possible.

Richard J. Forsyth* is an associate director of worldwide GMP quality with Merck & Co., Inc., WP53C-307, West Point, PA 19486, tel. 215.652.7462, fax 215.652.7106,
Julia C. O'Neill is a senior scientific fellow of regulatory and analytical sciences, and Jeffrey L. Hartman is a validation manager of regulatory and analytical sciences at Merck & Co., Inc.

* To whom correspondence should be addressed.

Submitted: Mar. 26, 2007. Accepted June 4, 2007.


1. FDA, Guide to Inspection of Validation of Cleaning Processes (FDA, Rockville, MD) , July 1993).

2. R.J. Forsyth and D. Haynes, "Cleaning Validation in a Pharmaceutical Research Facility," Pharm. Technol. 22 (9), 104–112 (1998).


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