Design space process analytical

The recent FDA initiative on Process Analytical Technology (PAT) attempts to define a design space for unit operations such as compaction... 

FIGURE 13 Interrelation between design space, PAT, and process control in a manufacturing system based on quality-by-design. (Source R. C. Lyon, Process monitoring of pilot-scale pharmaceutical blends by near-infrared chemical imaging and spectroscopy, Eastern Analytical Symposium (EAS), Somerset, NJ, 2006.)... 

Process analytical technology (PAT) can be developed early in development with a line of sight to commercial process control (ICH Qll 2011a). Easily integrated into the process equipment, as shown in Fig. 7.1, the application of in-line probes can provide unique opportunities to monitor processes in line. Spectroscopic techniques such as ultraviolet/visible (UV/VIS), near-infrared (NIR), and Raman can be utilized in line at the extmder die to ensure critical quality attributes (CQAs) such as composition and amorphous conversion of the API. Validation of these techniques at the pilot scale and transfer to the commercial scale allow for efficient manufacture of commercial material in a continuous manner and the implementation of a process control strategy that enables material outside the design space to be diverted to waste... 

Process analyzer measurements, e.g., spectra or chemical images, typically require a mathematical transformation, e.g., multivariate data analysis, to correlate the process analytical data to a more relevant critical product attribute for design space definition. For brevity, throughout this section the measurement system that yields process analytical data is noted as a PAT method and the subsequent mathematical transformation is described as a model. To forego a debate regarding what constitutes a process analyzer, i.e., temperature sensor versus a Raman fiber optic probe, herein focuses on process analyzers that yield multivariate data. 

Abstract Validation of analytical methods of well-characterised systems, such as are found in the pharmaceutical industry, is based on a series of experimental procedures to establish selectivity, sensitivity, repeatability, reproducibility, linearity of calibration, detection limit and limit of determination, and robustness. It is argued that these headings become more difficult to apply as the complexity of the analysis increases. Analysis of environmental samples is given as an example. Modern methods of analysis that use arrays of sensors challenge validation. The output may be a classification rather than a concentration of analyte, it may have been established by imprecise methods such as the responses of human taste panels, and the state space of possible responses is too large to cover in any experimental-design procedure. Moreover the process of data analysis may be done by non-linear methods such as neural networks. Validation of systems that rely on computer software is well established. 

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