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


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

Replicate #1 (HPLC #1) → 24.806

Replicate #2 (HPLC #1) → 24.964

Replicate #3 (HPLC #1) → 25.032

Replicate #4 (HPLC #2) → 24.757

Replicate #5 (HPLC #2) → 24.702

Replicate #6 (HPLC #2) → 25.151

Final value (rounded to the spec) → 24.9

%RSD → 0.70%

In this case, the "true" value is OOS. However, only three of the replicates (Replicate #1 on HPLC #1 and Replicates #4 and #5, which were run on HPLC #2) are OOS despite the "true" value and the average being OOS. Once the result is rounded appropriately, the result meets the specification.

Case #3. An outlier has been substituted into the data set. The following six replicates were generated (except for the first data point):

Replicate #1 (HPLC #1) → 24.800

Replicate #2 (HPLC #1) → 25.286

Replicate #3 (HPLC #1) → 25.377

Replicate #4 (HPLC #2) → 25.218

Replicate #5 (HPLC #2) → 25.477

Replicate #6 (HPLC #2) → 25.306

Final value (rounded to the spec) → 25.2

% RSD → 0.91%

Although not obvious, Replicate #1 on HPLC #1 is an outlier as shown by the Grubbs' test (p < 0.05). An investigation into this data point may be warranted in this instance. The statistical evidence suggests that something is different about this data point. However, the investigation is only initiated if there is a %RSD criterion (or another criterion such as a confidence or tolerance interval) in the system suitability to address these occurrences. If no %RSD criterion existed, the justification to investigate should be based on the fact that the data point was different from the others in the population, not that the data point was OOS. The root cause investigation would need to determine and address the source of the aberrant data and probably implement a %RSD criterion.


In cases where the "true" result approaches the specification limit, the possibility of generating a replicate result that is OOS increases significantly. Even with precision measurement, individual replicates can be OOS. Conversely, for a "true" OOS result, individual replicates can be within specification. Replication enables the analyst to get closer to the estimation of truth. Therefore, it makes sense to apply the specifications once all of the replicate results have been averaged for a final result.

Release limits can be used to deter these instances. Release limits are internal limits designed to ensure a lot will meet specifications through expiry by taking into account the analytical variability (and its associated uncertainty) and the stability of the molecule (and its associated uncertainty) by calculating acceptable criteria.

By focusing investigations in the right places—process shifts and other statistically valid signals versus single point OOS results—better corrective and preventative actions can be implemented to improve quality and allow for better use of resources.

Brian K. Nunnally, PhD, is an associate director and Deedra F. Nunnally is a laboratory manager, both at Wyeth, Sanford, NC,
919.566.4772. John S. McConnell is a consultant at Wysowl Pty Ltd, Warner, Australia.


1. Kuwahara SS. A history of the OOS problem. Biopharm Int. 2007; 20(11):42–52.

2. Nunnally BK, McConnell, JS. Six Sigma in the pharmaceutical industry. Boca Raton: CRC Press; 2007.

3. Shewhart WA; Statistical method from the viewpoint of quality control. The Graduate School of Agriculture: Washington, DC; 1939.

4. USFDA. Guidance for industry: Investigating out-of-specification (OOS) test results for pharmaceutical production. Rockville, MD; 2006 Oct.

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