Process Understanding

A process is generally considered well understood when (1) all critical sources of variability are identified and explained; (2) variability is managed by the process; and, (3) product quality attributes can be accurately and reliably predicted over the ranges of acceptance criteria established for materials used, process parameters, and manufacturing environmental and other conditions. The ability to predict reflects a high degree of process understanding. Although retrospective process capability data are indicative of a state of control, these alone may be insufficient to gauge or communicate process understanding.

The emphasis on process understanding provides a range of options for qualifying and justifying new technologies such as modern on-line process analyzers intended to measure and control physical and/or chemical attributes of materials to achieve real time release. For example, if process knowledge is not shared or communicated when proposing a new process analyzer, the test-to-test comparison between an on-line process analyzer (e.g., NIR spectroscopy for content uniformity) and a conventional test method (e.g., a wet chemical test) on collected samples may be the only available option. In some cases, this approach may be too burdensome and may discourage the use of some new technologies (e.g., use of acoustic measurement patterns or signatures for process controls). An emphasis on process knowledge can provide less burdensome approaches for validating new technologies for their intended use.

Transfer of laboratory analytical methods to at-line methods using test-to-test comparisons may not necessitate a PAT approach. Existing regulatory and compendial approaches and guidances on analytical method validation should be considered.

Structured product and process development on a small scale, using experiment design and an on- or in-line process analyzer to collect data in real time for evaluation of kinetics on reactions and other processes such as crystallization and powder blending can provide valuable insight and understanding for process optimization, scale-up, and technology transfer. Process understanding then continues in the production phase when possibly other variables (e.g., environmental and supplier changes) may be encountered. Therefore, continuous learning through data collection and analysis over the life cycle of a product is important.