Stage 2 Process Performance Qualification (PPQ): a Scientific Approach to Determine the Number of PPQ Batches

Ajay Pazhayattil, Daniel Alsmeyer, Shu Chen, Maksuda Hye, Marzena Ingram, Pradeep Sanghvi
AAPS PharmSciTech 2016, 17 (4): 829-33
The approach documented in this article reviews data from earlier process validation lifecycle stages with a described statistical model to provide the "best estimate" on the number of process performance qualification (PPQ) batches that should generate sufficient information to make a scientific and risk-based decision on product robustness. This approach is based upon estimation of a statistical confidence from the current product knowledge (Stage 1), historical variability for similar products/processes (batch-to-batch), and label claim specifications such as strength. The analysis is to determine the confidence level with the measurements of the product quality attributes and to compare them with the specifications. The projected minimum number of PPQ batches required will vary depending on the product, process understanding, and attributes, which are critical input parameters for the current statistical model. This new approach considers the critical finished product CQAs (assay, dissolution, and content uniformity), primarily because assay/content uniformity and dissolution as well as strength are the components of the label claim. The key CQAs determine the number of PPQ batches. This approach will ensure that sufficient scientific data is generated to demonstrate process robustness as desired by the 2011 FDA guidance.

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