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Pharmaceutical Technology Europe BIO/PHARMA OUTSOURCING INNOVATION eBOOK 2022 15 the greater the amount of allowable transmitted variation on the Y response. K sigma is used when the sample size is 30 or more, and tolerance intervals are used when the sample size is less than 30. Before setting limits, a representative data set must be available. Systems suitability and validity criteria limits are normally set post-qualification and review and finalized post-validation of the method (5). K sigma or tolerance interval approach. Tolerance intervals were developed to provide a correction for limited sample sizes and scales. The interval of risk is based on three considerations: sample size, confidence interval, and proportion of the population to be described. Tolerance intervals should be used under the following conditions: • No transfer function is available and/or possible • No adverse nor unacceptable results are associated with the parameter or response • When n => 30, use K sigma in setting limits • When n < 30 use tolerance intervals • Sample data are stable (use a control chart or regres- sion analysis to evaluate stability) • Distribution is either normal or nonnormal. The procedure for setting limits are as follows: • Generate a distribution histogram (Figure 4) • Make sure there is a representative sample of the assay validity or systems suitability measurement. • Check data for outliers. Jackknife z and three sigma cut point is typical. • Determine the distribution that best fits the data, nor- mal or nonnormal distribution, use a goodness of fit or second-order Akaike's information criterion (AICc) functions to determine the best fit. • Avoid using Johnson or Sinh-Arcsinh (SHASH) distri- butions when fitting nonnormal tolerance intervals as Uncon ECSO Standard [ H I-- • • SL 8 6 4 2 0 0.02 0.04 0.06 0.08 0.1 0.12 0.14 LogNormal-3.1625.0.35031 ) _ u Summary Statistics Mean 0.0450762 Std Dev 0.0172439 S td Err Mean Upper 95% Mean Lower 95% Mean N S um Variance N Missing 0.0030971 0.0514013 0.0387511 31 1.3973635 0.0002974 38.25488 0 Fitted LogN ormal Parameter Estimates Type Parameter Estimate Lower 95% Upper 95% Scale µ -3.16253 -3.289768 -3.035292 Shape a 0.3503129 0.2783726 0.4595399 Measure -2''LogLikelihood -173.1363 AICc -168.7077 BIC -166.2683 Diagnostic Plot . 0.95 . . . 0.9 . . • . • 0.8 . • • 0.7 ·- 0 0.6 0.5 0.4 - . . . 0 0.3 . z . . 0.2 . . . 0 . . . . 0.1 . . • . . • . 0.05 0.02 0.04 0.05 =· . . . . .· . . . -- 0.1 Uncon EC50 Standard Quantiles: Un centered and Unscaled Percentile Q uantile 0.0013499 0.0147950 0.9986501 0.1210452 Capability Analysis Specification Value Portion Lower Spec Limit 0.01 Below LSL Spec Target - Above USL Upper Spec Limit 0.13 Total Outside % Achial 0.0000 0.0000 0.0000 Quantile Sigma I Ca p abilit y Index CP 1.129 0.135 50 99.865 CPK 1.114 CPM LSL USL CPL 1.174 0 0.1 CPU 1.114 Portion Percent Below LSL 0.0019 Above USL 0.0678 Total Outside 0.0697 PPM 19.0966 678.2881 697.3848 Sigma Q ualit y 5.618 4.704 4.696 Figure 4. Distribution-based limits. The curve depth of the reference standard is another method to demonstrate a clear signal.