Validation and Comparison of Live Microorganism Plating Analytical Procedures Following United States Pharmacopeia...

Properly validated procedures can reduce variation and lead to data that is more accurate and precise. Analytical procedure lifecycle management (APLM) is a statistical approach based on uncertainty measurements that validates procedures by demonstrating they are fit for purpose. APLM results can also be used to compare procedures. This paper builds upon previous publications that introduced, developed, and demonstrated the application of APLM as described in USP <1220> to a microbiological analytical procedure. The application of APLM is demonstrated in two MS EXCEL workbooks that are provided: Template APLM, that can be used to apply APLM to any microbiological plate count procedure and Case Study APLM detailing the APLM validation and comparison of enumeration procedures associated with a Lactobacillus acidophilus probiotic ingredient. The measurand and analytical target profile are clearly defined, a risk assessment is documented, and an analytical control strategy created. Two different procedures, ISO 20128 and USP <64>, are compared using tolerance intervals (TI) calculated from the procedures' uncertainties. Tools to ease validation and comparison and all required statistical equations are contained within the workbooks. Case study data, which is based on a Lactobacillus acidophilus, single-strain, powdered ingredient, was generated in a manufacturing facility laboratory. This example of using APLM validated ISO 20128 as fit for the purpose of enumerating L. acidophilus in a powdered probiotic ingredient by showing that the intermediate precision (0.062 log₁₀ CFU/g) was less than the target measurement uncertainty (0.097 log₁₀ CFU/g). When comparing ISO 20128 to USP <64> overlapping tolerance intervals were observed; 11.14-11.76 log₁₀ CFU/g and 11.41 to 11.62 log₁₀ CFU/g, respectively. The results indicate the procedures are similar, but not equivalent. Options for using APLM and TI information are discussed. This study shows that APLM and tolerance intervals are useful tools that improve and ease procedure selection, assist in information gathering that leads to better understanding and control of analytical procedures, and helps improve data quality.