Process Analytical Technology

Process Analytical Technology

To maximize the control over any process in pharmaceutical product development, information is required on the way in which the product is responding to changes in manufacturing variables. Historically, the information was derived from data obtained on the nature of batches produced under particular manu-facturing conditions. Knowledge of the batch properties were employed to modify the manufacturing conditions to ensure that the product was closely controlled to designated quality specifications.

In recent years, analytical methods and their application have improved to the point that real time in process measurements can be taken and fed back through control systems to the input parameters to allow for continuous mon-itoring and control of processes.

In earlier chapters, examples of the major unit operations in pharmaceu-tical manufacturing were outlined. These processes will now be considered with anecdotal evidence from the literature of methods that might lead to closer control of the product quality and thereby conform to recent regulatory direc-tives to consider such methods as part of the Quality by Design (QbD) initiative.

The Food and Drug Administration has issued a guidance document on Process Analytical Technology (PAT) (Zu et al., 2007). Processes may be divided into batch and continuous approaches. These processes can be monitored by in situ, real time, and/or feedback control analyses to assure the quality of the product (Fig. 19.1).

PAT necessarily begins with the manufacture of active pharmaceutical ingredient (API) and any additives and understanding their properties (Byrn et al., 2006; Hlinak et al., 2006). Important methods in this context address the presence of impurities (including moisture), degradation products (stability), component compatibility, and crystallinity (polymorphism). Near infrared spectroscopy has been applied in situ, real time to address the chemical com-position of API or additive during manufacture (Mendendorp et al., 2006). Near infrared laser Raman spectroscopy has been employed to monitor polymeriza-tion process (Francisco et al., 2006). A variety of particle sizing methods can be employed, but those that are in situ, real time employ laser scattering methods. Dosage form manufacturing can be optimized by direct methods of monitoring the variables involved in drying, mixing/blending (Portillo, et al., 2008), gran-ulation (Papp et al., 2008), filling, compression (Askeli and Cetinkaya, 2008; Soh et al., 2007), and coating (Bose et al., 2006; Cogdill et al., 2007). For more sophisticated dosage forms, compatibility with packaging components is also required, but this is likely to have been considered during the preliminary experimental design optimization steps.

PAT arguably is at the intersection of design space (considered in chap. 18) and control strategy, these being the major elements of QbD. These topics have been described in Product Quality Lifecycle Implementation initiative of the International Society for Pharmaceutical Engineering (Drennan, 2008). Topics of interest in this initiative have been described in the Journal of Pharmaceutical Innovation.

FIGURE 19.1 Critical steps in API manufacture. Source: Modified from Byrn et al. (2006).

FIGURE 19.2 Decision tree to define levels of criticality. Source: Modified from Garcia et al. (2008).

Figure 19.2 presents the most prominent decisions required to evaluate the criticality of variables in process development. Decisions (diamonds) are made based on the business decision in foundational classification (above dotted line), and risk assessment in developmental classification (below dotted line) that pass through a filter (rectangle) to criticality designations (rounded rectangle). With respect to criticality, those variables that are not critical have not been demonstrated to impact on safety or efficacy or factor into critical quality attributes (CQA) as defined by ICH Q(8) R and consequently do not have to be included in design space. Critical variables are those that are known to impact safety, efficacy, or other measures of biological disposition or com-pliance. Critical process parameters if varied beyond a certain range have a direct and significant influence on CQAs. These properties must be controlled within predesignated range to ensure final product quality. The empty symbol represents an alternative designation for attributes that may impact the product but represent a low risk. The designation of low risk is based on an indirect impact on safety and/or efficacy alone or in combination with other variables; mitigated risk; and knowledge transfer from noncritical variables requiring additional evaluation.

It has been suggested that criticality can be reduced to fundamental ele-ments of severity, occurrence, and detection in a compounding manner (Nosal and Schultz, 2008). These terms can be related to experimental design (frequency and variation) and analytical capability (detection). During the life cycle of the product clear differentiation of levels of criticality is required to address a control strategy based on process variables, material attributes, and their relationship to quality measures.