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
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.
REPLICATES AND RELEASE LIMITS
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
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, email@example.com
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,
4. USFDA. Guidance for industry: Investigating out-of-specification (OOS) test results for pharmaceutical production. Rockville,
MD; 2006 Oct.