Improving the Situation
It is usually more cumbersome to implement variance (precision) improvements versus matching (accuracy) improvements. Precision
improvements usually mean more samples, more testing, and more tightly controlled processes, and are therefore costly changes.
Accuracy or bias in testing is usually easier to affect by simply switching to another instrument, reference standard, etc.
However, the pushing of results into only one direction can backfire and make the situation much worse later when small changes
can add up and are not visible by themselves (because of poor precision). Decreasing the overall measurement errors or uncertainty
in test results by increasing precision has a better long-term affect because any bias in results (from production, sampling
or method changes) will be more visible and will appear sharper. Following the potency bioassay example, the sampling and
testing scheme is briefly illustrated below.
Potency In-Process Sampling and Testing Scheme
To decrease the relatively high variance in the observed potency results (SPC), a variance analysis is needed. The sampling
and assay set up for the downstream in-process potency test would then look like this (n = number of samples):
- collect n = 1 in-process sample
- split sample into two aliquots
- run two independent assay runs, each generating n = 3 results (total of n = 6 results).6
In the example, reproducibility could be significantly improved by collecting n = 3 independent samples, each run in three
independent assays with each having three replicates (total of n = 9 results). Because of the current assay set-up and sampling
process, increasing the number of samples from n = 1 to n = 3 should significantly improve precision by a factor of 1.73 (square-root
of 3, see Equation 2).6
To a lesser extent, an increase in assay runs from two to three runs should further improve the situation. Another more easily
implemented improvement would be to run the assay control at only plus or minus 2 s.d. (instead of 3 s.d.). This test system
suitability change should also improve the overall SPC variance (Figure 1). This improvement will lead to an expected 4% higher
rate for invalid assay runs. This will be a relatively small price to pay when considered the predicted return.
References
1. Krause SO. Validation of analytical methods for biopharmaceuticals—A guide to risk-based validation and implementation
strategies. PDA/DHI Publishing. Bethesda, MD; 2007 Apr.
2. US Food and Drug Administration. Guidance for Industry (CDER/CVM/ORA, US FDA). PAT—A framework for innovative pharmaceutical
development, manufacturing, and quality assurance. Rockville, MD; 2004 Sep.
3. International Conference on Harmonisation. Q8, Pharmaceutical development. Geneva,Switzerland; 2005 Nov.
4. International Conference on Harmonisation. Q9, Quality risk management. Geneva,Switzerland; 2005 Nov.
5. International Conference on Harmonisation. Q10, Pharmaceutical quality system, draft consensus guideline. Geneva,Switzerland;
May 2007.
6. Brown F, Mire-Sluis A, editors: The design and analysis of potency assays for biotechnology products. Dev Biol. Karger:
2002;107(1):117–127.
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