4.1 A significant amount of data is being generated during pharmaceutical development and manufacturing activities. The interpretation of such data is becoming increasingly difficult. Individual examination of the univariate process variables is relevant but can be significantly complemented by multivariate data analysis (MVDA). Such methodology has been shown to be particularly efficient at handling large amounts of data from multiple sources, summarizing complex information into meaningful low dimensional graphical representations, identifying intricate correlations between multivariate datasets taking into account variable interactions. The output from MVDA will generate useful information that can be used to enhance process understanding, decision making in process development, process monitoring and control (including product release), product life-cycle management and continual improvement.

4.2 MVDA is a widely used tool in various industries including the pharmaceutical industry. To generate a valid outcome, MVDA should contain the following components:

4.2.1 A predefined objective based on a risk and scientific hypothesis specific to the application,

4.2.2 Relevant data,

4.2.3 Appropriate data analysis techniques, including considerations on validation,

4.2.4 Appropriately trained staff, and

4.2.5 Life-cycle management.

4.3 This guide can be used to support data analysis activities associated with pharmaceutical development and manufacturing, process performance and product quality monitoring in manufacturing, as well as for troubleshooting and investigation events. Technical details in data analysis can be found in scientific literature and standard practices in data analysis are already available (such as Practices E1655 and E1790 for spectroscopic applications, Practice E2617 for model validation and Practice E2474 for utilizing process analytical technology).