Statistical Analyses of Product Acceptance Criteria
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Contributing OfficeCenter for Drug Evaluation and Research
Abstract
Background
It is critical that drug units have a drug substance content near the label claim. To ensure dosage homogeneity, the U.S. Food and Drug Administration (FDA) requires products to pass USP GC <905> Uniformity of Dosage Units (UDU) acceptance testing. However, the UDU test is not a statistical test and thus is not recommended for batch release testing. Limitations of the UDU test have become apparent with novel manufacturing methods. Continuous manufacturing uses process analytical technology (PAT) for non-destructive, real-time, online analysis of drug substance content. Continuous manufacturing is desirable for several reasons; notably, it allows for a large number of samples to be analyzed throughout the manufacturing process. The UDU test has overly strict acceptance criteria for large sample sizes; this creates a regulatory barrier for manufacturing process development. To overcome these limitations, several statistical tests, with updated sampling methods and acceptance criteria, have been proposed, such as one- and two-sided parametric tolerance interval tests, parametric two one-sided tolerance interval test, ASTM E2709 and E2810, and Bayesian approaches.
Objectives
The project aims to analyze established and proposed sampling methods and acceptance criteria in order to determine new acceptance standards, with an ultimate goal to develop software program(s) and an R Shiny application to facilitate statistical analysis.
Methods
A literature review was conducted to determine relevant information for the standard and proposed sampling and testing plans. Analysis of operating characteristic curves and other empirical methods were completed for each plan. Standardized tables, graphs, and other tools were developed for implementation of statistical techniques. The standards will then be used to construct an R Shiny application.
Results
The project is currently on-going, but development of standardized statistical analyses and acceptance criteria evaluation for large sample sizes will improve efficiency and reproducibility of results. This will benefit multiple offices within CDER, including the Office of Pharmaceutical Manufacturing Assessment, the Office of Quality Surveillance, and the Office of Biostatistics. Standardized programs and tools may assist the implementation of KASA and other standardized approaches for drug quality assessment.