Content uniformity testing with PAT tools- how does the probability of batch acceptance change?

Process analytical technology (PAT) enables non‑destructive assessment of content uniformity (CU) and supports real‑time monitoring in pharmaceutical manufacturing processes. PAT‑based analytical methods typically rely on predictive models and therefore exhibit greater measurement uncertainty than conventional methods. Although PAT has become widely accepted for the evaluation of several critical quality attributes (CQAs), its routine application to CU testing remains limited because reliable regulatory decision strategies must be developed for each manufacturing process individually. This study provides a simulation-based evaluation of PAT-based CU assessment under the current requirements of the European Pharmacopoeia (Ph. Eur.). Monte Carlo simulations and operating characteristic (OC) curves were used to investigate the combined effects of analytical measurement uncertainty and sample size on the probability of correct batch acceptance and rejection decisions. In addition, case studies demonstrate the implementation of PAT‑based CU assessment in both real-time release testing (RTRT)- and quality‑by‑design (QbD)‑oriented environments and in conventional manufacturing. The results showed that larger sample sizes can compensate for the increasing analytical uncertainty to maintain reliable CU decisions. However, full batch (100 %) inspection was generally unnecessary, as decision reliability approached a plateau beyond a method-specific sample size. The proposed simulation method can identify these points and determine sample sizes capable of achieving predefined decision performance objectives. Case studies illustrated the application of this approach in evaluating PAT method suitability and establishing justified sampling strategies. This work, together with tables in Supplementary S5, may provide guidance for the development of regulatory-compliant PAT-based CU testing strategies in pharmaceutical manufacturing.