Interpretation and data analysis

HX-MS for proteins in lyophilized powders has developed over the past 5 years. Recent studies suggest that the method can provide detailed information on protein conformation, dynamics, and interactions with excipients in lyophilized solids and that HX with mass spectral peak width analysis can be used to screen protein formulations for the presence of nonnative subpopulations. Though the utility of the method for developing lyophilized protein formulations has not been fully tested, early results promote the wider development and application of the method. 

Over the past 25 years, there has been increasing interest in expanding the use of HX-MS. In this chapter, we have reviewed its development and application for proteins in three different environments proteins adsorbed onto solid surfaces, in frozen solutions, and in lyophilized solids. The results have demonstrated the capability of HX-MS to detect and monitor protein conformation and dynamics with high resolution in these environments that differ from bulk aqueous solution. In addition, HX-MS has provided quantitative and site-specific information, addressing many of the limitations of more established techniques such as FTIR and CD spectroscopy. 

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Prediction of the useful life, or the remaining life, of coatings from physical or analytical measurements presents many problems in data analysis and interpretation. Two important considerations are that data must be taken over a long period of time, and the scatter from typical paint tests is large. These considerations require innovative application of statistical techniques to provide adequate prediction of the response variables of interest. 

Several significant challenges exist in applying data analysis and interpretation techniques to industrial situations. These challenges include (1) the scale (amount of input data) and scope (number of interpretations) of the problem, (2) the scarcity of abnormal situation exemplars, (3) uncertainty in process measurements, (4) uncertainty in process discriminants, and (5) the dynamic nature of process conditions. 

Because the techniques for data analysis and interpretation are targeted to address different process characteristics, care must be taken in choosing the most appropriate set of techniques. For example, some techniques work best with abundant process data others, with limited process data. Some can handle highly correlated data, while others cannot. In selecting appropriate methods, two practical considerations stand out ... 

Managing Scale and Scope of Large-Scale Process Operations discusses the performance issues associated with data analysis and interpretation that occur as the scale of the problem increases (such as in complex process operations). 

With reference to Fig. 4, this section presents several examples that demonstrate how various technologies can be assembled into data analysis and interpretation systems in practical application. 

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