Process real-time analytical data

Close control of the process, with rapid response to variations in the feedstock, is impracticable. Real-time analytical data are hard to obtain, and the best process design creates dead time that makes simple feedback control difficult. 

Statistical Control. Statistical quahty control (SQC) is the apphcation of statistical techniques to analytical data. Statistical process control (SPC) is the real-time apphcation of statistics to process or equipment performance. Apphed to QC lab instmmentation or methods, SPC can demonstrate the stabihty and precision of the measurement technique. The SQC of lot data can be used to show the stabihty of the production process. Without such evidence of statistical control, the quahty of the lab data is unknown and can result in production challenging adverse test results. Also, without control, measurement bias cannot be determined and the results derived from different labs cannot be compared (27). 

The specification development process is a data-driven activity that requires a validated analytical method. The levels of data needed include assay precision, replicate process results (process precision), and real-time stability profiles. A statistical analysis of these data is critical in setting a realistic specification. Most often, aggregation and fragmentation degradation mechanisms are common to protein and peptide therapeutics. Therefore, the SE-HPLC method provides a critical quality parameter that would need to be controlled by a specification limit. 

It is therefore easy to see why this current drug safety paradigm, with its lack of standards in data collection and analysis, hinders the analysis of adverse events. Without data standards in place, it is difficult to build practical, reusable tools for systematic safety analysis. With no standard tools, truly standardized analyses cannot occur. Reviewers may forget their initial analytical processes if they are not using standardized data and tools. Comprehensive reproducibility and auditability, therefore, become nearly impossible. In practice, the same data sets and analytical processes cannot be easily reused, even by the same reviewers who produced the original data sets and analyses. Not using standardized tools slows the real-time systematic analysis... 

Sound analytical assessments require that analysts understand the manner in which the data were collected, reconfigured, migrated, and combined. These processes should be documented in a transparent way so that future investigators can readily understand the anonymization and migration in real time. 

Traditional analytical methods make extensive use of computers, but typically these methods still require constant restructuring of the data and multiple analytical tools. This endless restructuring wastes time and productivity and also makes the analytical processes difficult to document, audit, and reproduce in real time. This situation also makes it difficult to reconstruct and update analyses in real time when new adverse event data become available or when new questions need to be asked. The application of comprehensive data standards allows the use of integrated, reusable software for analyzing adverse event data. This integration facilitates the reproducibility of the results. 

Data reduction and interpretation are much aided by computer methods and the high speed of current microcomputers facilitates the real-time processing and display of data. The principle of extracting as much information as possible from analytical measurements through the application of statistical and other mathematical methods, usually with the aid of appropriate computer software, is known as chemometrics (p. 13). 

The purpose of interfacing instruments with computers is to enable raw analytical data to be collected as it is produced, then processed, stored and displayed or printed out. This may be accomplished as it is gathered, i.e. in real-time, or at some later time, i.e. post-run. Complete chromatograms or spectra can easily be stored in the main memory or RAM or transferred to disk. The immense storage capacity of mainframe computers can be used to provide large libraries of data (data banks) for future reference. 

Four serial (RS232) ports are provided for flexible communications to other instruments, i.e. autosamplers. In addition, five analogue inputs, tvsro analogue outputs and 3 TTL input/outputs are provided to ensure complete flexibility. These allow auxihary instruments under direct control and the abihty to process data generated by these in real time. These features extend the range of facihties on these expensive but worthwhile analytical techniques. There is no doubt that future developments in computing will continue to have a radical impact on these instrumental systems. 

Such real-time evaluation of process samples can be done by developing a PCA model of the calibration data, and then using this model in real time to generate prediction residuals (RESp) and leverages for each sample.3 Given a PCA model of the analytical profiles in the calibration data (conveyed by T and P), and the analytical profile of the prediction sample (xp), the scores of the prediction sample can be calculated ... 

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