Validation process troubleshooting

Finally, process analytics methods can be used in commercial manufacturing, either as temporary methods for gaining process information or troubleshooting, or as permanent installations for process monitoring and control. The scope of these applications is often more narrowly defined than those in development scenarios. It will be most relevant for manufacturing operations to maintain process robustness and/or reduce variability. Whereas the scientific scope is typically much more limited in permanent installations in production, the practical implementation aspects are typically much more complex than in an R D environment. The elements of safety, convenience, reliability, validation and maintenance are of equal importance for the success of the application in a permanent installation. Some typical attributes of process analytics applications and how they are applied differently in R D and manufacturing are listed in Table 2.1. 

Validation provides confidence in the quality of products manufactured as the over quality of a particular process cannot be established due to the limited sample size. Validation leads to less troubleshooting within routine production. As a result, it reduces the number of customer complaints and drug recalls. 

It is recommended that all instalments (including microplate readers and washers) undergo validation, calibration, and routine maintenance programs established and documented to meet regulatory requirements. This eliminates instruments as a cause of poor assay performance and streamlines the assay troubleshooting process. 

Method failures can, of course, occur at any stage in the progression from transfer to validation to sample testing. Therefore, the troubleshooting process discussed in this chapter applies to any of these stages. 

Small-scale process models are an essential and valuable tool for process validation and ongoing troubleshooting [32]. Scale-down factors ranging from 10- to 10,000-fold are employed [3] with the extent of scale-down de-pending on the actual production scale and smallest scale that can reliably reproduce the process [51]. Small-scale equipment is permissible only if the scalability of the unit operation is demonstrated [12]. The same technical principles for scale-down are used as for scale-up for the step of interest [3]. 

As stated earlier, process modeling refers to the development and validation of a model for the process of interest. Successful modeling provides a process model (usually, a set of equations) that can be used in a number of applications such as what-if study, troubleshooting, optimization, control and operator training. Complexity of the developed model depends on the intended use and desired accuracy of the model as well as on available knowledge, understanding and measured data of the process. 

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