Technical
July / August 2017

A Variable Sampling & Acceptance Polygon Approach for Content Uniformity

Thomas Stepinac, PhD
Mr. Muhammad Saeed
Article

We propose a method for demonstrating content uniformity in the context of variables sampling and relate this to the acceptance criteria and probability of passing the USP CU test. We demonstrate that a variable sampling plan with 99.4% coverage between 83.5% and 116.5% of label content is approximatively equivalent to (and even stricter than) the 95% probability of passing the USP test.

The views expressed in this paper are professional opinions of the authors and may not necessarily represent the position of Novartis.

To ensure the consistency of dosage units, individual batch units are controlled to achieve drug substance content within a sufficiently narrow range around the label claim. The most common test for content uniformity (CU) of dosage units is described in United States Pharmacopeia (USP) general chapter <905>.1 The USP requirements and CU acceptance values for oral solid dosage (OSD) forms (e.g., tablets and capsules) are well known and summarized in Table A, where:

AV is the acceptance value
x̄ is the mean of the samples (as percent of the label claim)
s is the standard deviation of the samples
k is the acceptability constant
If n = 10, then k = 2.4 (Stage 1)
If n = 30, then k = 2.0 (Stage 2)

The reference value M
= 98.5 if x̄ < 98.5
= 101.5 if x̄ > 101.5
= x̄ if 98.5 < x̄ < 101.5

This paper proposes a method for demonstrating CU using acceptance sampling plans and relates it to acceptance criteria and the probability of passing the USP CU test.

A variable sampling plan ensures that at least a specified proportion of a population is within a specified range with a given confidence. Variable sampling plans can be used to set acceptance criteria and support process validation or batch release. This ensures that at least a certain proportion of tablets in a batch have an assay value that falls between defined limits with a given confidence.

The shape of the acceptance region as a function of the mean and standard deviation of a sample can be approximated by a polygon, as shown in Figure 1. For more details, refer to Schilling.2

The acceptance polygon equation consists of two separate one-sided tolerance intervals with an upper limit on the maximum standard deviation

(MSD):
x̄ – ks > L
x̄ + ks < U
s < MSD

Where:
x̄ = sample mean
s = standard deviation
k = one-sided tolerance factor; the k value is a function of the confidence
level desired, proposed coverage, and number of samples
U = upper specification limit
L = lower specification limit
MSD = (U – L) * F, where F is a tabulated value

Table A: USP <905> requirements
Stage Number tested Pass if:
S1 n = 10 AV =| x̄ – M| + ks < 15
S2 n = 30 AV =| x̄ – M| + ks < 15
Figure 1: Acceptance region for a variable sampling plan
Acceptance region for a variable sampling plan
Source
Acceptance Sampling in Quality Control, Second Edition, by Schilling, Edward G.; Neubauer, Dean V. Reproduced with permission of Taylor and Francis via Copyright Clearance Center.

The lot or batch shall be considered acceptable if the applicable k and F criteria are met.

For double-sided specifications:

1. Both (U – x̄) ∕ s and (x̄ – L) ∕ s must be ≥ k to meet the k criterion
2. s ∕ (U – L) must be ≤ F to meet the F criterion
x̄ = sample mean
s = sample standard deviation,
U = upper specification limit
L = lower specification limit

Criterion 2 can also be expressed as S ≤ smax, where Smax = (U – L) * F is the MSD. The k and F values are shown in Tables H and I for 90% or 95% confidence and 99% coverage. For any confidence and coverage values, tables can be generated as follows:

k is the critical distance and can be calculated from the noncentral t-distribution:
\( k = t^{-1}(1 - ∝, n - 1, φ) / \sqrt{n} \)

Where:
1– α = confidence level
n = sample size
\( φ = \sqrt{n}*Z_p = \text{non-centrality factor} (p = \text{coverage}) \)

F can be calculated as:
1. Find the upper tail normal area p** corresponding to zp* = k
2. Find Zp** corresponding to a normal upper tail area of p**2
3. F = 1 / (2 Zp**)

Example (in R) for n = 30 with 95% confidence and 99% coverage (from Table I):
k = qt (0.95, 29, 12.74193) / sqrt (30) = 3.063901
p** = 1 – pnorm (3.063901) = 0.001092356
Zp** = qnorm (0.001092356 / 2, lower.tail = F) = 3.265592
F = 1 / (2* 3.265592) = 0.1531116

For details see Schilling and Natrella.2,3

Examples

The following section shows two examples to illustrate the method. The first represents simple sampling; the second shows stratified sampling with repeated measurements per location.

Single sample

We want to demonstrate with 95% confidence that 99% of a batch assay is between 85 and 115. (We will show later that this corresponds to > 97% probability of passing the USP test with 95% confidence.)

One sample was taken at 15 locations during the process (Tables B and C). We can therefore state with 95% confidence that 99% of the batch is between 85 and 115, and that acceptance criteria for validation have been met.

Multiple samples

We want to demonstrate with 95% confidence that 99% of a batch is between 85 and 115.

Duplicate samples (j = 2) were taken from 15 locations (i = 15) for a total of 30 samples (Table D). First the within, between, and overall standard deviations were estimated by variance component analysis. This can be done easily with statistical software such as Minitab (Table E):

The associated degrees of freedom are:

  • Between location: i – 1 = 14
  • Within location: i (j – 1) = 15

Total was calculated using Satterthwaite approximation:

$$df_{tot}=\frac{ \left(  \sigma _{T}^{2} \right) ^{2}}{\frac{ \left( \frac{ \sigma _{R}^{2}+J\ast \sigma _{L}^{2}}{J} \right) ^{2}}{ \left( I-1 \right) }+\frac{ \left(  \sigma _{R}^{2} \left( 1-\frac{1}{J} \right)  \right) ^{2}}{I \left( J-1 \right) }} $$



$$df_{tot}=\frac{ \left( 10.223 \right) ^{2}}{\frac{ \left( \frac{5.72+2\ast4.503}{2} \right) ^{2}}{ \left( 15-1 \right) }+\frac{ \left( 5.72 \left( 1-\frac{1}{2} \right)  \right) ^{2}}{15 \left( 2-1 \right) }}=23.66 \approx 24 $$

 

The calculation of the acceptance test is shown in Table F. Therefore, we can state with 95% confidence that 99% of the batch is between 85 and 115 and that the acceptance criteria for validation are met. This corresponds to > 97% probability of passing the USP <905> with 95% confidence.

Acceptance Regions And OC Curves

The purpose of validation is to provide a high degree of assurance that a process will consistently produce a product that meets its specifications and quality characteristics. For the USP <905> CU test, one approach is the “Bergum method”4 (named after its inventor), which is the basis of ASTM Standard E2810.5

Our proposed alternative to this standard, based on the concepts of acceptance sampling, is easier to use and can be applied to any number of tested samples. The concept of acceptance sampling is easy to understand and easy to justify as it is standard in quality control.

Tolerance interval methodology can be used to set acceptance criteria for validation or release2 and relates to variable acceptance sampling plans. When using acceptance sampling by variables, the acceptance region can be approximated by a polygon;8 this is the so-called “k method” for double-sided specification limits.2 In this article we will link the probability of passing the USP test to the statistical confidence level and coverage of a variable sampling plan.

We will demonstrate that a variable sampling plan with 99.4% coverage between 83.5 and 116.5 is approximatively equivalent to (and even stricter than) the 95% probability of passing the USP test.

Constructing the acceptance region

USP requirements and CU acceptance criteria for OSD forms are given in Table A. For the first Stage, 10 samples are taken and an acceptance value is calculated that must be smaller than 15. If this acceptance criterion is met, the test is passed. If this acceptance criterion is not met, another 20 samples are taken and an acceptance value on the total of 30 samples is calculated. If this acceptance criterion is met the test is passed. The probability of passing the test is the combined probability of passing Stage 1 or Stage 2.

  • 2. a. b. c.
  • 3. Natrella, M. G., “Experimental Statistics.” National Bureau of Standards Handbook 91. August 1, 1963. http://www.keysolutionsinc.com/references/NBS%20Handbook%2091.pdf
  • 4. Bergum J. S., “Constructing Acceptance Limits for Multiple Stage Tests.” Drug Development and Industrial Pharmacy 16, no. 14 (1990): 2153–2166.
  • 5. ASTM. “Standard Practice for Demonstrating Capability to Comply with the Test for Uniformity of Dosage Units.” Standard E2810-11. www.astm.org
  • 8. Wallis, W. A. “Lot Quality Measured by Proportion Defective,” Acceptance Sampling–A Symposium. Washington: American Statistical Association
Figure 2: USP Stages 1 + 2 acceptance region
USP Stages 1 + 2 acceptance region
Figure 3: Acceptance region plot and OC curves
Acceptance region plot and OC curves
Figure 4: Rescaled acceptance region
Rescaled acceptance region
Figure 5: OC curves for multiple numbers of samples
 OC curves for multiple numbers of samples

The geometric region where the USP CU test acceptance criteria are fulfilled can be represented as a function of the measured mean x̄ and standard deviations (Figure 2). Each point on the acceptance curve corresponds to an acceptance value (AV) of 15. Any point on or below the acceptance curve would pass the test (AV ≤ 15) and each point above would fail it. This acceptance region resembles the acceptance polygon of a variable sampling plan from Figure 1. Note that the standard deviation converges to zero when the mean is 83.5 or 116.5. This is why we will take 83.5 to 116.5 as our limits.

Stage 1 can be approximated with a variable sampling plan with n = 10 samples, critical distance k = 2.4, and limits between 83.5 and 116.5. Stage 2 can be approximated with a variable sampling plan with n = 30 samples, critical distance k = 2.0, and limits between 83.5 and 116.5. This is a conservative approximation, as the acceptance region is always smaller and within the USP acceptance region. (Stage 1 and Stage 2 of the USP are shown in Figure 2 as black polygons.)

All points on the polygon have an acceptance value of 15, all points inside have an acceptance value < 15, and all points above have an acceptance value > 15. Figure 2 shows an acceptance value of 15. This geometric region can be approximated by a double-variable sampling plan with Stage 1 having n = 10 samples and critical distance k = 2.4 for limits between 83.5 and 116.5, and Stage 2 having n = 30 samples and critical distance k = 2.4 for limits between 83.5 and 116.5.

Each variable sampling plan (for n = 10 and n = 30) has an operation characteristic (OC) curve. This shows how the probability of acceptance for a lot, which is based on the sampling plan utilized and changes with the percent defective of a lot (quality). The OC curves for Stage 1 (approximated), Stage 2, and combined Stages 1 + 2 are shown in Figure 3.

Figure 6: Acceptance regions for n = 30 samples
Acceptance regions for n = 30 samples
Table B: Single sample method
Location Sample Location Sample
1 98.1 9 96.9
2 98.6 10 98.2
3 101.8 11 94
4 94.2 12 96.7
5 94.5 13 96.1
6 96.5 14 99.7
7 97.4 15 102.6
8 92.4    
Table C: Single sample results
Name Variable or Formula Value
Number of samples n 15
Sample average 97.18
Sample standard deviation s 2.828
Lower specification limits L 85
Upper specification limits U 115
k value (from Table I) k 3.52
F value (from Table I) F 0.1351
Maximum standard deviation smax or MSD = (U – L) * F (115-85) * 0.1351 = 4.053
Lower quality index QL = (x̄ – L) / s (97.18-85) / 2.828 = 4.307
Upper quality index QU = (U – x̄) / s (115-97.18) / 2.828 = 6.301
Lower limit acceptance criterion
Upper limit acceptance criterion
smax acceptance criterion

QL > k Q

U > k

s < smax

4.307 > 3.52 (pass)

6.301 > 3.52 (pass)

2.828 < 4.053 (pass)

Table D: Multiple samples method
Location Sample 1 Sample 2 Location Sample 1 Sample 2
1 100.1 103.0 9 99.3 97.3
2 98.9 101.66 10 99.8 104.6
3 99.6 97.9 11 102.8 108.6
4 101.8 100.8 12 106.8 103.5
5 102.4 104.6 13 102 102.1
6 98.7 101.1 14 96.2 100.5
7 99.2 99.5 15 109.1 102.6
8 99.5 96.5      

The Stage 2 OC (red) is stricter than the combined Stages 1+2 OC (green). Furthermore, the green curve can only be calculated numerically. It is therefore an acceptable approximation to consider only the Stage 2 of the USP CU test. This corresponds to a critical k = 2 distance and 30 samples.

The probability of passing only Stage 2 of the USP CU test will always be smaller than the probability of passing the combined Stages 1 + 2 test, as seen when comparing the red to the green curve in Figure 3.

Characterizing the USP CU test based on the probability of passing Stage 2 only, therefore, is a conservative but valid approximation. Furthermore, the combined Stages 1 + 2 can only be computed numerically while the red OC has an analytical solution and can be described by the noncentral t distribution.

P (pass stage 2) = 1 – pt (2 * sqrt (30), 29, qnorm (1 – LotFractionDefective) * sqrt (30))

Example: For 0.6% tablets outside the 83.5 to 116.5 interval, the probability to pass the USP Stage 2 is:

1 – pt (2 * sqrt (30), 29, qnorm (1 – 0.006) * sqrt (30)) = 0.9493999 ≈ 0.95

This establishes a correspondence between the probability of passing the Stage 2 USP CU test (95%) and the proportion of samples inside the 83.5–116.5 interval (99.403%). The 95% probability of passing combined Stages 1 + 2, computed numerically, is 99.34. The probability of passing Stage 2 or the combined Stages 1 + 2 is fairly close in the upper domain of the OC curve. The probability of passing the USP versus the coverage between 83.5 to 116.5 is given in Table G.

Table E: Variance component analysis
Source Variance components % total SD
Location 4.503 44.05 2.122
Error 5.720 55.95 2.392
Total 10.223   3.197

Figure 4 shows rescaled acceptance regions for 95% probability passing the USP with 83.5 to 116.5 limits and 85 to 115 limits (100% confidence for infinite number of samples). The acceptance region is smaller with the narrower border, especially when the mean is close to the border.

Since the real mean and standard deviation are unknown but estimated from a finite number of samples, we need to add a confidence level (typically 90 or 95% in cases of process validation).

Figure 5 shows OC curves corresponding to 95% probability and 90% confidence to pass the USP Stage 2 for various numbers of samples. We can clearly see that this corresponds to a variable sampling plan with a rejectable quality level (β = 10%) at 0.6% lot percent defectives. In other words, a batch with 0.6% points outside the 83.5 to 116.5 interval (or 99.4% inside) has a 90% chance of being rejected (or 10% chance of being accepted).

The critical distance k and MSD for varying numbers of samples and 99.4% coverage between 83.5 and 116.5 (corresponding to > 95% of probability of passing the USP) are given in Tables J and K for 90% and 95% confidence.

These acceptance criteria provide an alternative to the ASTM approach by simply employing variables sampling plans.

Table F: Acceptance test calculation
Name Formula Value
Number of samples ≈ dftot 24.0
Sample average 101.35
Sample standard deviation s 3.197
Lower specification limits L 85.0
Upper specification limits U 115.0
k value (from Table I) k 3.181
F value (from Table I) F 0.1481
Maximum standard deviation smax or MSD = (U – L) * F (115 – 85) * 0.1481 = 4.443
Lower quality index QL = (x̄ – L) / s (101.35 – 85) / 3.197 = 5.514
Upper quality index QU = (U – x̄) / s (115 – 101.35) / 3.197 = 4.270
Lower limit acceptance criterion
Upper limit acceptance criterion
smax acceptance criterion

QL > k

QU > k

s < smax

5.514 > 3.181 (pass)

4.270 > 3.181 (pass)

3.197 < 4.443 (pass)

Matching The Acceptance Region

Some practitioners might argue that the range from 83.5 to 116.5 used by current USP is too large. In the following example, we rescaled the acceptance criteria to 85–115 by matching the acceptance region at the MSD. We previously shown that 99.403% coverage between 83.5 and 116.5 corresponds to more than 95% probability to pass the USP test when the number of samples is infinite. This corresponds to a critical distance k = 2.514 and MSD = 6.001. When we are on target with a mean at 100 and a standard deviation of 6.001, the proportion inside the 85–115 interval is 98.76%, which would be the new coverage. The corresponding k value, the left tailed quantile of normal distribution corresponding to 98.76% coverage, is 2.243. Table L gives the coverage between 85 and 115 for various probabilities of passing the USP.

Note that the rescaled acceptance region is always more conservative than the original one between 83.5 and 116.5, especially when the mean is close to the limit. Figure 4 shows the acceptance regions for 83.5–116.5 and 85–115 for an MSD of 6.001.

Table L shows a few interesting test properties:

  • 96% coverage between 85% to 115% and 50% confidence corresponds approximately to 50% probability to pass the USP test with 50% confidence. This could be a minimal requirement for releasing batches and could be used, for instance, in PAT application when more than 30 samples are taken.
  • 99% coverage between 85% to 115% corresponds approximately to 97% probability to pass the USP test. The confidence is typically chosen at 90% or 95%, since round numbers that are easy to justify and acceptance tables with 99% percent coverage are readily available.

Discussion

Variable sampling vs. ASTM E2810 Bergum4 published a method for constructing acceptance limits that relate directly to the probability of passing the USP test. These acceptance limits are defined to provide, with a confidence level (1 – α), a stated probability (P) of passing the USP test. This approach was incorporated in the ASTM Standard E2810.5

Table G: Probability of passing USP Stages 1, 2, and 1+2 vs. coverage between 83.5 to 116.5
Probability
Passing
USP Test
 
Stage 1 (n = 10) exact Stage 2 (n = 30) exact Stages 1 + 2 (n = 30) numeric
  Cov %
(83.5–116.5)
k MDS Cov %
(83.5-116.5)
k MDS Cov %
(83.5-116.5)
k MDS
99 99.995 3.898 4.061 99.69 2.741 5.571 99.67 2.716 5.616
98 99.989 3.705 4.256 99.60 2.65 5.736 99.56 2.617 5.798
97 99.983 3.583 4.388 99.52 2.592 5.846 99.48 2.559 5.911
96 99.976 3.492 4.492 99.46 2.549 5.931 99.4 2.515 6
95 99.969 3.419 4.58 99.40 2.514 6.001 99.34 2.479 6.074
90 99.923 3.168 4.904 99.17 2.394 6.254 99.07 2.355 6.341
85 99.866 3.001 5.146 98.97 2.314 6.435 98.84 2.271 6.536
80 99.795 2.87 5.353 98.78 2.25 6.585 98.62 2.204 6.698
75 99.709 2.758 5.542 98.60 2.196 6.718 98.41 2.147 6.843
70 99.607 2.658 5.721 98.41 2.147 6.842 98.19 2.095 6.98
65 99.487 2.567 5.896 98.22 2.102 6.96 97.97 2.047 7.111
60 99.344 2.48 6.07 98.03 2.06 7.075 97.73 2.001 7.241
55 99.175 2.397 6.246 97.83 2.019 7.19 97.48 1.957 7.371
50 98.973 2.316 6.429 97.61 1.979 7.306 97.21 1.913 7.504

Table H (PDF) represents 90% confidence that 99% of population is between limits.

Table I (PDF) represents 95% confidence that 99% of population is between limits.

While our variable sampling plan and the ASTM approach have similarities and fulfill the same purpose, the variable sampling plan has several advantages, in our view:

  • An acceptance polygon is easy to calculate and is defined by just two numbers: the critical distance k and the MSD. There is no need for computer-generated acceptance tables as with the ASTM method. Acceptance criteria can easily be derived for any sample size.
  • Our generalized approach relates the probability of passing the CU directly to the distribution of the batch and the USP. This is less abstract than the ASTM and allows a better understanding of the process.

Variable sampling vs. tolerance intervals

Tolerance intervals, another alternative to the ASTM E2810, have recently been discussed in the literature.7 Bergum6 proposed a double-sided tolerance interval method and a coverage of 98.58% between 85% and 115% corresponding to 95% probability of passing the USP. When rescaling our results for 95% probability of passing the USP Stage 2 or the combined Stages 1 + 2 to an interval between 85% and 115%, we found coverages of 98.76% and 98.64%, respectively—fairly close to Bergum´s value of 98.58%. The value of 98.76% was derived analytically, while the values of 98.64% or 98.58% (combined Stages 1 + 2) are based on numeric simulations.

Bergum6 showed that the shape of the OC curve is very strongly dependent on the position of the mean, while in our work it is not. This is because Bergum’s OC curve calculations are based on an 85%–115% range, while the USP test was conceived with an 83.5%–116.5% limit (Figure 2). Bergum also used the USP acceptance regions (black polygon in Figure 2), while we used the more conservative acceptance region of a variable sampling plan that best matches the USP.

In Figure 6, we compared different acceptance regions corresponding to 95% probability and 90% confidence to pass the USP for n = 30 samples. We compared the content uniformity and dissolution acceptance limit (CUDAL)/ASTM acceptance region, a variable sampling plan with critical distance k = 3.107 corresponding to 99.4 coverage between 83.5 and 116.5, and a double-sided tolerance interval corresponding to 98.58% coverage between 85 and 115. For comparison, the acceptance region corresponding to USP Stage 2 is also given.6

Differences in shape between these acceptance regions are apparent. The acceptance region calculated by CUDAL/ASTM E2810 is the narrowest, arising from the very conservative way the confidence limits are calculated using Lindgren´s simultaneous confidence region.4 This acceptance region has a triangular shape and converges to zero at 83.5 and 116.5, like the USP test. Bergum’s tolerance interval approach and our variable sampling approach nearly match at the Smax. The tolerance interval approach is more restrictive, however, with a triangular acceptance region, while the variable sampling plan acceptance region is elliptical (can be approximated by a trapezoidal polygon) and larger.

 

Table J: 90% confidence and 99.403% coverage between 83.5-116.5
(corresponding to 95% probability of passing USP with 90% confidence.)
Acceptance criteria: x̄ + ks < 116.5; x̄ – ks > 83.5; s < MSD. 
n k MSD n k MSD n k F n k MSD
10 3.8 4.158 40 3.01 5.133 70 2.871 5.351 100 2.805 5.46
11 3.705 4.255 41 3.003 5.144 71 2.868 5.356 101 2.804 5.463
12 3.627 4.339 42 2.996 5.154 72 2.865 5.361 102 2.802 5.466
13 3.562 4.412 43 2.989 5.165 73 2.862 5.365 103 2.801 5.468
14 3.506 4.476 44 2.982 5.175 74 2.86 5.37 104 2.799 5.471
15 3.457 4.533 45 2.976 5.184 75 2.857 5.374 105 2.798 5.474
16 3.415 4.584 46 2.97 5.194 76 2.854 5.378 106 2.796 5.476
17 3.377 4.631 47 2.964 5.203 77 2.852 5.383 107 2.795 5.479
18 3.343 4.673 48 2.959 5.211 78 2.849 5.387 108 2.793 5.481
19 3.313 4.711 49 2.953 5.22 79 2.847 5.391 109 2.792 5.484
20 3.286 4.746 50 2.948 5.228 80 2.845 5.395 110 2.79 5.486
21 3.261 4.779 51 2.943 5.236 81 2.842 5.398 111 2.789 5.488
22 3.238 4.809 52 2.938 5.244 82 2.84 5.402 112 2.788 5.491
23 3.217 4.837 53 2.933 5.251 83 2.838 5.406 113 2.786 5.493
24 3.198 4.863 54 2.929 5.258 84 2.836 5.41 114 2.785 5.495
25 3.18 4.888 55 2.924 5.265 85 2.833 5.413 115 2.784 5.498
26 3.163 4.911 56 2.92 5.272 86 2.831 5.417 116 2.782 5.5
27 3.148 4.933 57 2.916 5.279 87 2.829 5.42 117 2.781 5.502
28 3.133 4.953 58 2.912 5.285 88 2.827 5.424 118 2.78 5.504
29 3.12 4.972 59 2.908 5.292 89 2.825 5.427 119 2.779 5.506
30 3.107 4.991 60 2.904 5.298 90 2.823 5.43 120 2.777 5.508
31 3.095 5.008 61 2.9 5.304 91 2.821 5.433 150 2.747 5.561
32 3.083 5.025 62 2.897 5.31 92 2.819 5.437 200 2.713 5.621
33 3.072 5.04 63 2.893 5.315 93 2.818 5.44 300 2.675 5.691
34 3.062 5.055 64 2.89 5.321 94 2.816 5.443 400 2.652 5.733
35 3.052 5.07 65 2.886 5.326 95 2.814 5.446 500 2.637 5.762
36 3.043 5.083 66 2.883 5.331 96 2.812 5.449 1,000 2.599 5.832
37 3.034 5.096 67 2.88 5.337 97 2.81 5.452 2,000 2.574 5.882
38 3.026 5.109 68 2.877 5.342 98 2.809 5.455 3,000 2.563 5.904
39 3.018 5.121 69 2.874 5.347 99 2.807 5.457 Inf 2.514 6.001

Table K: 95% confidence and 99.403% coverage between 83.5-116.5

(corresponding to 95% probability of passing USP with 95% confidence.)
Acceptance criteria: x̄ + ks < 116.5; x̄ – ks > 83.5; s < MSD

n k MSD n k MSD n k F n k MSD
10 4.28 3.723 40 3.167 4.905 70 2.98 5.178 100 2.894 5.314
11 4.142 3.839 41 3.157 4.919 71 2.976 5.184 101 2.892 5.318
12 4.029 3.939 42 3.148 4.933 72 2.973 5.19 102 2.889 5.321
13 3.935 4.026 43 3.139 4.945 73 2.969 5.195 103 2.887 5.325
14 3.855 4.103 44 3.13 4.958 74 2.965 5.201 104 2.885 5.328
15 3.786 4.172 45 3.121 4.97 75 2.962 5.206 105 2.883 5.331
16 3.726 4.234 46 3.113 4.981 76 2.958 5.212 106 2.881 5.334
17 3.673 4.29 47 3.105 4.992 77 2.955 5.217 107 2.879 5.337
18 3.626 4.341 48 3.098 5.003 78 2.952 5.222 108 2.877 5.341
19 3.583 4.388 49 3.091 5.014 79 2.949 5.227 109 2.876 5.344
20 3.545 4.431 50 3.084 5.024 80 2.945 5.232 110 2.874 5.347
21 3.511 4.47 51 3.077 5.034 81 2.942 5.237 111 2.872 5.35
22 3.479 4.507 52 3.07 5.043 82 2.939 5.242 112 2.87 5.353
23 3.45 4.542 53 3.064 5.053 83 2.936 5.246 113 2.868 5.356
24 3.424 4.574 54 3.058 5.062 84 2.933 5.251 114 2.867 5.358
25 3.399 4.604 55 3.052 5.07 85 2.931 5.255 115 2.865 5.361
26 3.376 4.632 56 3.046 5.079 86 2.928 5.26 116 2.863 5.364
27 3.355 4.659 57 3.04 5.087 87 2.925 5.264 117 2.861 5.367
28 3.335 4.684 58 3.035 5.095 88 2.922 5.268 118 2.86 5.37
29 3.316 4.708 59 3.03 5.103 89 2.92 5.273 119 2.858 5.372
30 3.298 4.73 60 3.025 5.111 90 2.917 5.277 120 2.857 5.375
31 3.282 4.752 61 3.02 5.118 91 2.915 5.281 150 2.816 5.442
32 3.266 4.772 62 3.015 5.126 92 2.912 5.285 200 2.772 5.517
33 3.252 4.791 63 3.01 5.133 93 2.91 5.289 300 2.723 5.604
34 3.238 4.81 64 3.006 5.14 94 2.907 5.292 400 2.693 5.658
35 3.224 4.827 65 3.001 5.146 95 2.905 5.296 500 2.673 5.695
36 3.212 4.844 66 2.997 5.153 96 2.903 5.3 1,000 2.624 5.785
37 3.2 4.861 67 2.992 5.159 97 2.9 5.304 2,000 2.591 5.848
38 3.189 4.876 68 2.988 5.166 98 2.898 5.307 3,000 2.577 5.877
39 3.178 4.891 69 2.984 5.172 99 2.896 5.311 Inf 2.514 6.001

 

Conclusion

We derived a relationship between the probability of passing the USP and a variable sampling plan ensuring with a certain confidence that a certain proportion of values are between certain limits. The limits of the USP CU are between 83.5 and 116.5%, but narrower limits like 85% to 115% (or others) are also possible.

To assess the capability of intrabatch CU within process validation, different confidence and coverage values and interval ranges (corresponding to various probabilities of passing the USP test) are possible. We suggest taking typically 90% or 95% confidence and 99.4% coverage between 83.5% and 116.5%. This most closely matches 95% probability of passing the USP CU. A more conservative value would be 99% of tablets between 85 and 115. This corresponds to > 97% probability to pass the USP.

We recommend taking 30 samples, with 15 being a minimum. The smaller the sample size, the higher the risk to fail validation as the size of the acceptance region shrinks with a decreasing number of samples.

Table L: Matching the acceptance region at Smax
Probability of passing USP Stage 2 Smax k = 1/slope 83.5–116.5 Coverage %83.5–116.5 k = 1/slope 85–115

Coverage % 85–115

99 5.571 2.741 99.69 2.453 99.29
98 5.736 2.65 99.6 2.369 99.11
97 5.846 2.592 99.52 2.316 98.97
96 5.931 2.549 99.46 2.276 98.86
95 6.001 2.514 99.4 2.244 98.76
90 6.254 2.394 99.17 2.133 98.35
85 6.435 2.314 98.97 2.059 98.02
80 6.585 2.25 98.78 2 97.73
75 6.718 2.196 98.6 1.95 97.44
70 6.842 2.147 98.41 1.906 97.16
65 6.96 2.102 98.22 1.864 96.89
60 7.075 2.06 98.03 1.825 96.6
55 7.19 2.019 97.83 1.787 96.3
50 7.306 1.979 97.61 1.75 95.99


This paper provides a clear methodology to assess intrabatch capability based on total observed variability. Further controls on location means stratified sampling can also be pursued to protect against undesirable within batch trends that cannot be controlled by capability assessment alone.Another advantage of this method is that it is also easily applicable when there are several samples per location.


Acknowledgments

The authors thank Andrea Kramer, Head of Manufacturing Science and Technology at Novartis Wehr, Germany, for her continuous support during the preparation of this work. Many thanks also to James Crichton, Abe Germansderfer, and all nonclinical statisticians from the Novartis statistics network for their helpful discussions and comments.

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