Comparison of Different Calculation Approaches for Defining Microbiological Control Levels Based on Historical Data.

摘要

During the manufacturing of pharmaceutical drug products, the counts of microorganisms are monitored in the cleanroom environment, water, the product's raw materials, and the final product. This enables manufacturers to ensure that high numbers of microorganisms that may impair the product's microbiological quality are detected before the product is released to the patient. Microbiological control levels must be set to determine at which number a count is considered too high. Exceeding such levels may require an investigation to determine the root cause explaining why such high numbers of microorganisms occurred, and a set of actions should be performed with the aim of eliminating this root cause. In order to really differentiate higher-than-usual counts, microbiological control levels should be based on historical data. In the present work we analyzed different calculation approaches towards that purpose. We used real microbiological data and performed simulation experiments to determine which statistical method could calculate the most realistic control levels that would provide the best prediction for future routine testing. Better predictions would ensure that only significant contaminations lead to an excursion of the microbiological control level, which would avoid wasting resources by investigating non-issues or normal/controlled conditions.