The Interactive QP Study Guide

This is an interactive (in development) copy of the 2022 QP study guide. The study guide specifies the areas of knowledge QPs are expected to be competent in. More specifically candidates are expected to “demonstrate understanding through application” in each of the areas generally described below.

An interactive guide, no matter how comprehensive it may become, will not provide an adequate substitute for first hand experience, but the content linked below is intended to give trainees a place to start and in some places a framework to orientate experience.

Additional Knowledge

Mathematics and Statistics

The practical application of basic statistical tools in pharmaceutical production and QA is essential in demonstrating the capability of processes or the acceptability of materials.

Applicants will be expected to demonstrate understanding through application of the following:

  • The underlying principles and application of Statistical Process Control to pharmaceutical manufacturing, testing, control processes and use in product quality reviews;

  • The underlying principles and application of ISO2859 “Sampling by Attributes” and ISO3951 “Sampling by Variables”, including the use of sampling plans and Acceptable Quality Levels (aqls);

  • The interpretation of stability data for modelling product performance and quality;

  • Appropriate data monitoring tools used to verify that appropriate storage and distribution conditions have been maintained;

  • Simple and statistical trending methods including but not limited to the underlying principles and application of Process Control Charts, Shewhart Charts, CUSUM Charts, Pareto Analysis, process capability (Cp/cpk) and process performance (Pp/ppk);

  • Statistics used in the planning and interpretation of process, facilities, utilities, and equipment validation;

  • Statistics applied during analytical method validation including, precision, accuracy, linearity and range, specificity / selectivity, LOD / LOQ, ruggedness, solution stability, robustness; and

  • Statistical tools for comparing data sets, e.g. Analytical test results.