Avoiding the Pain of Out-of-Specification Results - Failing to apply important statistical principles leads to unnecessary investigations and wastes time and resources. Control charts can help. - Bio

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Avoiding the Pain of Out-of-Specification Results
Failing to apply important statistical principles leads to unnecessary investigations and wastes time and resources. Control charts can help.


BioPharm International
Volume 21, Issue 6

OOS RESULTS IN THE ANALYTICAL LABORATORY

One common analytical issue concerns effective comparison of the data against specifications. This becomes tricky when replication is used to combat analytical variability. According to FDA's guidance on Investigating Out-of-Specification (OOS) Test Results for Pharmaceutical Production, all OOS results must be investigated.4 FDA defines OOS results as, "all test results that fall outside the specifications or acceptance criteria established in drug applications, drug master files, official compendia, or by the manufacturer."4 It is acceptable to average multiple results when the sample is homogeneous or when the assay has a large amount of variability. It is not acceptable to average multiple results when testing for content uniformity or when the averaging will hide the variability between individual results in an investigation. Replicates must not be compared to the specification; only the final reportable result, generated by averaging all replicates that meet the acceptable limit for variability, should be compared to the specification. Consider the following set of analytical data for a representative assay method. This is a high-performance liquid chromatography (HPLC)-based method that determines the concentration of a therapeutic protein. As part of a variability reduction effort, it was determined that weighing was a significant source of variability (because of material hygroscopicity) along with the HPLC columns. Because the resultant concentration was used as part of the formulation process, it was important to reduce the variability to ensure a robust formulation process. Therefore, the analytical control strategy was:

  • Three replicate weighings; each was solubilized individually to make a set of three solutions.
  • Each solution was injected once on two separate HPLC units.
  • All six replicates (three from each HPLC unit) were averaged together to get a single result.

The specification was applied to the final reportable result, but not to the individual replicates. (Note: the assay was not used for content uniformity.) This was the correct application of statistical principles.

APPLICATION OF SPECIFICATIONS

Analytical variability is a fact. It is impossible for two things to be identical. Ultimately, they may be similar enough so that the differences between them are rendered meaningless, but they are still different. No two HPLCs ever generate the same results; no two analysts ever prepare a sample the same way. In this world of variation, replication is used to reduce the analytical variability in the data generated to assist the production unit with generating quality product.

For the case studies listed below, let us apply a specification of not less than (NLT) 25.0; OOS results will be indicated in bold. In each case study, a set of replicates for the assay will be shown. The more replicates generated, the closer the approximation should be to the true value.

Case #1. "True" average concentration is 25.1. Using the random number generator in Microsoft Excel, the following six replicates were generated:

Replicate #1 (HPLC #1) → 25.040

Replicate #2 (HPLC #1) → 25.188

Replicate #3 (HPLC #1) → 24.937

Replicate #4 (HPLC #2) → 25.129

Replicate #5 (HPLC #2) → 25.086

Replicate #6 (HPLC #2) → 25.056

Final value (rounded to the spec) → 25.1

%RSD → 0.34%

As seen in the data, Replicate #3 on HPLC #1 was OOS, despite a low relative standard deviation (%RSD) of 0.34%. (The actual %RSD of the assay was closer to 0.68% as determined by re-evaluation data. An OOS investigation into this result would be a waste of time because the OOS data were actually just a random event for a lot with a potency value close to the specification. Based on the analytical variability, it was likely that one of the replicates would be outside of the specification. Further, the point of the assay control strategy was to reduce the analytical variability by averaging across the critical sources of variability.

Case #2. "True" average concentration is 24.9. Using the random number generator in Microsoft Excel, the following six replicates were generated:


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