Process Validation: Using Tolerance Intervals for Setting Process Validation Acceptance Criteria - - BioPharm International


Process Validation: Using Tolerance Intervals for Setting Process Validation Acceptance Criteria

BioPharm International
Volume 20, Issue 6

Figure 6
From Figure 5, the simulated tolerance interval is from 82.7 (0.5% quantile) to 94.4 (99.5% quantile). Note that the interval is slightly wider and shifted to the left from the interval computed using equation (1). The increased width is due to the propagation of error caused by the movement in OP2 and OP3, and the shift to the left is due to OP3 being centered to the left of its setpoint (45).

Note the distribution of the PP in Figure 5 is centered at 88.6 instead of at the desired large-scale GMP mean of 89.5. Recalling that the spread of a tolerance interval is not affected by shifts in location, the interval is adjusted to the desired GMP center by taking as the lower bound 82.7 – (88.6 – 89.5) = 83.6 and as the upper bound 94.4 – (88.6 – 89.5) = 95.3.

Figure 7
Figure 7 presents actual PP values from the validation runs for which these criteria were established. The difference between the intervals for Scenarios 2 and 3 in Figure 7 is not great because there is not a particularly strong relationship between the PP and the OPs in this example. (R-square in Table 3 is only 0.241). There will be a greater disparity between these two sets of limits when the strength of the linear relationship between the PP and OPs is greater. However, note that by making use of bench data and regression analysis, intervals from scenarios 2 and 3 are much shorter and more representative of the values obtained in the validation runs than the limits computed with only the large-scale GMP data.


The procedure described in this paper is general enough to apply to more complex situations. In particular, it is often the case that random events such as differences in column feed material will increase the variability in a PP. The regression model can be modified to appropriately incorporate random effects, and the JMP simulator used to produce a tolerance interval under these conditions. Quadratic effects and interaction effects among the OPs are also easily incorporated into the regression model.

In conclusion, we have presented approaches that yield appropriate VAC. The most appropriate technique for establishing these ranges depends on the available data. For many processes, movement by an OP within the OR is expected. Combining bench-and large-scale data sets, analyzed using the simulation approach presented in this paper, results in VAC that are indicative of process control, yet are not unnecessarily restrictive.

Rick Burdick is a principal quality engineer in the Quality Engineering and Improvement department at Amgen; Tom Gleason is a senior associate scientist in the Manufacturing Science and Technology department at Amgen 303.041.1432,
Steve Rausch is a senior scientist in the Manufacturing Science and Technology department at Amgen; and Jim Seely is a director in the Manufacturing Science and Technology department at Amgen.


1. Seely JE, Seely RJ. A rational, step-wise approach to process characterization. BioPharm Int. 2003; Aug(16):24-34.

2. Kieffer R, Bureau S, Borgmann A. Applications of failure modes and effects analysis to the pharmaceutical industry. Pharm Tech Eur. 1997; Sept(9):36-49.

3. Stamatis DD. Failure modes and effects analysis; FMEA from theory to execution. 2nd ed. Milwaukee (WI):ASQ Quality Press; 2003.

4. Wald A, Wolfowitz J. Tolerance limits for a normal distribution. Ann of Math Stat 1946(17);208–15.

5. Howe WG. Two-sided tolerance limits for normal populations, some improvements. J Am Stat Assoc. 1969(64): 610–20.

6. Orchard T. Setting acceptance criteria from statistics of the data. BioPharm Int. 2006; Nov (19):22–9.

7. Hahn GJ, Meeker WQ. Statistical intervals: a guide to practitioners. New York (NY): Wiley; 1991.

8. Neter J, Kutner MH, Nachtsheim CJ, Wasserman, W. Applied linear statistical models. 4th ed. Scarborough, Ontario (Can): Irwin; 1996.

blog comments powered by Disqus



FDA and NIH Win Award for IP Licensing of Meningitis Vaccine
September 26, 2014
FDA Releases First-Ever Purple Book for Biosimilar Characterization
September 26, 2014
FDA Releases REMS Report
September 25, 2014
NIH Funds Tissue Chip for Drug Screening
September 25, 2014
UPS' Pain in the Supply Chain Results
September 23, 2014
Author Guidelines
Source: BioPharm International,
Click here