Process Analytical Technology Applications

LIF methods can be applied in-line, at-line and on-line for real-time monitoring as discussed throughout this chapter. In-line or in situ intrinsic LIF is by far more prevalent in real-time applications such as PAT as it is nondestructive and simple to deploy along with attractive analytical merits. In-line application can be accomplished by direct insertion in situ probes or flow cells. This type of monitoring is utilized for realtime analyte quantification monitoring and detection of process endpoints and faults. 

At-line LIF methods are either based on intrinsic detection or extrinsic approaches. The former often involves static measurements which are prone to photobleaching and thermal effects. These problems can often be addressed by optimizing the excitation source output (i.e., optical power and pulse rate) or by sample agitation. Flow injection analysis or other autonomous sample prepreparation schemes are possible to facilitate various at-line extrinsic methods such as various selective fluoroimmunoassays.  

In some manufacturing process analysis applications the analyte requires sample preparation (dilution, derivatization, etc.) to afford a suitable analytical method. Derivatization, emission enhancement, and other extrinsic fluorescent approaches described previously are examples of such methods. On-line methods, in particular those requiring chemical reaction, are often reserved for unique cases where other PAT techniques (e.g., UV-vis, NIR, etc.) are insufficient (e.g., very low concentrations) and real-time process control is imperative. That is, there are several complexities to address with these types of on-line solutions to realize a robust process analysis method such as post reaction cleanup, filtering of reaction byproducts, etc. Nevertheless, real-time sample preparation is achieved via an on-line sample conditioning system. These systems can also address harsh process stream conditions (flow, pressure, temperature, etc.) that are either not appropriate for the desired measurement accuracy or precision or the mechanical limitations of the inline insertion probe or flow cell. This section summarizes some of the common LIF monitoring applications across various sectors. 

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These four very different NIR technologies represent the mainstream analyzer types used in many process analytical technology applications. They also cover the main types of established commercially available analyzers. Given their various characteristics and the physical principles upon which they are based, it is obvious that they will have different advantages and disadvantages depending on the detailed circumstances of the application. These issues will be covered in more detail below. 

In Sections 5.3.1 to 5.3.4 we have reviewed the main NIR technologies in successful current widespread use for process analytical technology applications. NIR is an evolving technology, however, and there are a number of recent developments which may in time prove significant. This is not the place for a detailed review of such technologies, but a brief overview for orientation will be provided. 

Cogdill, R. P., Anderson, C. A., Delgado, M., Chisholm, R., Bolton, R., Herkert, T, Afnan, A. M. and Drennen III, J. K. Process analytical technology case study Part II. Development and validation of quantitative near-infrared calibrations in support of a process analytical technology application for real-time release. AAPS PharmSciTech. 6(2) E273-E283, 2005. 

Implementation of Process Analytical Technologies 21 Table 2.1 Differences in process analytics applications in R D and manufacturing... 

Raman spectroscopy was discovered over 75 years ago but has only been a viable process tool for 10-15 years. However, there has been an astounding increase in process Raman spectroscopy examples in the last five years. The United States Food and Drag Administration s (US FDA) endorsement of process analytical technology clearly set off an explosion of activity. Problems that sometimes sidelined Raman in the past, such as fluorescence or highly variable quantitative predictions from samples that were too small to be representative, are being re-examined and leading to new technology. In turn, that helps open, or perhaps reopen, new application areas. The availabihty of easy to use Raman instrumentation at many prices also helps with that. 

E. Widjaja, Y.Y. Tan and M. Garland, Application of band-target entropy minimization to on-line Raman monitoring of an organic synthesis. An example of new technology for process analytical technology, Org. Process Res. Dev., 11, 98-103 (2007). 

The first demonstration of solid state fluorescence of API dates back to 1961, while its in-line use for final drug product manufacturing was not demonstrated until recently." While in its infancy as a process analytical technology for real-time monitoring and product parametric real-time release, the applications identified and in some instances demonstrated include (i) blend endpoint API content nniformity detection " (ii) segregation monitoring or API content at various process critical control points and (iii) at-line tablet content uniformity determination. The fundamentals of solid-state luminescence spectroscopy for pharmaceutical solids has been covered by Brittain."... 

The objective of this chapter is to reduce the learning curve for the application of near-infrared (NIR) spectroscopy, or indeed any process analytical technology, to the chemical industry. It attempts to communicate realistic expectations for process analyzers in order to minimize both unrealistically positive expectations (e.g. NIR can do everything and predict the stock market ) and unrealistically negative expectations (e.g. NIR never works don t waste your money ). The themes of this chapter are value and... 

Before selecting a process analytical technology to implement, it is helpful to understand the capabilities and limitations of the technology. Good introductions to NIR spectroscopy and instrumentation can be found in Chapter 5, and to chemometric methods in Chapter 12. NIR pharmaceutical applications are covered in Chapters 13 and 14. Additional information on NIR techniques and applications can be found in Williams, ... 

C. Kaiser, T. Potozki, A. Ellert and R. Luttmann, Applications of PAT - process analytical technology in recombinant protein processes with Escherichia coli, Eng. Life Sci., 8, 132-138 (2008). 

Yu LX, Lionberger RA, Rawa AS, Wu D Costa R, Hussain AS. Applications of process analytical technology to crystallization processes. Center Drug Eval Res FDA 2003 8. 

ASTM International. Committee E55 on Pharmaceutical Application of Process Analytical Technology. http //www.astm.org/cgi-bin/SoftCart.exe/ COMMIT/COMMITTEE/E55.htm E+mystore. 

In-process controls such as stratined sampling, process analytical technology (PAT) application, and blend homogeneity. Assess modincation of dissolution through optimization of API characteristics and then perform assessment of specialized technologies [hot-melt extrusion (HME), spray-dried dispersion, solid dispersion, etc.] for long-term resolution... 

In a number of process analytical technology (PAT) applications in the pharmaceutical industry it is desirable to monitor rapidly and non-invasively the bulk content of drugs with high chemical specificity. Although NIR absorption spectroscopy has been used widely in this area its comparatively low chemical specificity places limits on its usefulness. As noted earlier, transmission Raman is particularly well suited for this application since it removes the key obstacle of conventional Raman, the subsampling problem [43]. Matousek and... 

Aside from univariate linear regression models, inverse MLR models are probably the simplest types of models to construct for a process analytical application. Simplicity is of very high value in PAC, where ease of automation and long-term reliability are critical. Another advantage of MLR models is that they are rather easy to communicate to the customers of the process analytical technology, since each individual X-variable used in the equation refers to a single wavelength (in the case of NIR) that can often be related to a specific chemical or physical property of the process sample. 

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