Validation runs

Figure 1.8. Schematic frequency distributions for some independent (reaction input or control) resp. dependent (reaction output) variables to show how non-Gaussian distributions can obtain for a large population of reactions (i.e., all batches of one product in 5 years), while approximate normal distributions are found for repeat measurements on one single batch. For example, the gray areas correspond to the process parameters for a given run, while the histograms give the distribution of repeat determinations on one (several) sample(s) from this run. Because of the huge costs associated with individual production batches, the number of data points measured under closely controlled conditions, i.e., validation runs, is miniscule. Distributions must be estimated from historical data, which typically suffers from ever-changing parameter combinations, such as reagent batches, operators, impurity profiles, etc.

If one is less restrained in setting specification limits, a balance can be struck between customer expectations and the risk and cost of failure a review of available data from production and validation runs will allow confidence limits to be calculated for a variety of scenarios (limits, analytical procedures, associated costs see Fig. 2.15 for an example). 

Three 100-ml rinses are assumed, but the volume and number of rinses are subject to validation. Each validation run should be performed independently at least three times. 

Complete major repairs/renovations prior to validation runs if any. 

Schedule the validation runs (in conjunction with manufacturing department). 

Conduct the validation runs, including recording of all data, etc. 

Fig. 45. Steady-state NO concentrations vs. temperature in validation runs over small monolith catalyst sample with 300 cpsi. Feed 1,000 ppm NH3, 1,000 ppm NO, 1% H2O in N2 black 2% O2, red 10% Oz SV = 25,000 h symbols experimental, solid line model predictions.

Fig. 46. TPR validation run over small monolith catalyst with 200 cpsi. Feed l,020ppm NH3, 960ppmNO, 10% H2O, 10% 02 in N2, SV= 36,000h 1 symbols experimental, solid line model predictions.

Testing during these verification runs will be more frequent and cover more variables than would be typical during routine production. Typically the testing requirements at the verification stage should be the same or more than the proposed testing for process validation runs. The typical process verification analysis of tabulated product includes the following ... 

Process scale-up studies Qualification trials Process validation runs... 

After the qualification trials have been completed, the protocol for the full-scale process validation runs can be written. Current industry standard for the validation batches is to attempt to manufacture them at target values for both process... 

As a potential product moves through the various developmental stages, information is continually generated and incorporated into a master documentation file. When the validation runs are planned for the final process, they will be based on the master documentation file contents. The information generated during the validation runs is usually the last major item to go into the master documentation file. 

A written plan stating how validation will be conducted and defining acceptance criteria. For example, the protocol for a manufacturing process identifies process equipment, critical process parameters/operation ranges, product characteristics, sampling, and test data to be collected, number of validation runs, and acceptable test results [1],... 

Evaluation of cleaning procedures themselves—Many companies prefer to perform a prevalidation study (often referred to as a process capability study or engineering run ) to verify that the cleaning procedure is satisfactory prior to the actual validation runs. This is an excellent opportunity to determine if the cleaning procedures are adequately written. On some occasions, cleaning procedures are not detailed enough and may not provide enough information about, for example, the extent of disassembly of the equipment. If left to interpretation, there is the possibility that different operators may interpret the instructions differ-... 

The OQ final report is intended to summarize all relevant data that are collected during the validation run. The report gives a short description of all test functions and a discussion of the overall validation. This compilation is adequate documentation of assurance of the acceptability and validity of the packaging equipment. The basis for this assurance is the result of the data, test functions, and supporting documentation. A dossier in sections is provided in Table 5. 

After each validation run, the batch records and the freeze-drying graphic should be carefully examined. Furthermore, the validation program may include the items developed hereafter. 

The validation of lyophilization cycles is a complicated issue because the process parameters and the characteristics of the product are closely interrelated. The process affects the final product, but the characteristics of the product undergoing lyophilization impact the dependent operating parameters, the freeze-drying pattern, and dictate the basic requirements for a successful process. This interdependence limits the opportunities of using placebo formulations and most of the validation runs must be performed with active product. The high cost and limited availability of some materials, as well as the need to validate the process under conditions that are representative of routine production, justify that part of the validation is performed in a concurrent fashion. The concurrent validation should then be completed by a retrospective review of the data accumulated for commercial batches, so as to track the reproducibility, the reliability, and the trends of the process over a longer period of time. 

In the second part of the validation we used the predicted values from the model at 12 h for the acceptance criterion for three reproducibility runs. The model predictions were used since they take into account all of the data generated in the first part of the validation. The acceptance criterion for the mean level of stopper moisture was the 99% confidence interval determined from the model. The calculated 99% confidence interval for the mean value after 12 h of drying was 963 213 ig/stopper. Results from validation runs in Table 7 show that the modeling approach accurately predicted the mean moisture content of the stoppers. The moisture content of the stoppers was consistent from run to run as can be seen in the overlapping 95% confidence intervals for the mean values. 

Analysis of a videotape of repetitive prequalification should be studied for behavior and practices that may produce or harbor environmental contamination, leading to the refinement and optimization of work practices, and development of the formalized process to be instituted for the PQ validation run. The videotape may be used for identifying indicator sites, which should be incorporated into the monitoring plans, and intensively sampled during the validation run. These tapes should be retained and edited for both training and informational purposes. 

By interrupting the sterilization cycle just before the introduction of EtO gas, validation runs can be performed using currently available chamber humidity sensors, even though, care maintenance and calibration of these sensors are very important, and a real challenge is to improve their state-of-art. 

The positive control item is one that has an impact on the in vitro test system. It is often used to assess aspects such as the characteristics of the test system and if the in vitro methods gives reliable results for this positive control over time in accordance with historical data. Moreover, a positive control, that meets the predefined acceptance criteria, assures that when a test item has no effect on the in vitro method that the negative result is not caused by an error during the testing phase. Therefore, the results from the control test items are of outmost importance to show that a valid run has been performed when test data for each unknown test item are submitted to regulatory authorities. 

Guidance is provided on process validation, in-process specifications, and action limits. The concept of the use of internal action limits is described, to control the consistency of the process at less critical stages. Data obtained during development and validation runs should provide the basis for provisional action limits to be set for the manufacturing process. Approaches to the testing of raw materials, components, and excipient specifications are described. 

Validation can be a method of quantifying the performance of a process in this case measuring the performance of a quantitative method. In 1985, E. M. Fry wrote Validation has a quantitative aspect—it s not just that you demonstrate that a process does what it purports to do you actually have to measure how well its does that... then, the processes that cause variability. .. must be identified. Experiments are conducted (that is, validation runs) to ensure that factors that would cause variability, are under control (1). ... 

Depending on the situation, confirmation runs may be done prior to validation runs. Or, if you re feeling really good about the data and your understanding of the process, the validation runs (per the written protocol) may be the confirmation runs for the DOE. Remember, validation should always be just a confirmation of what you already know. Thus, you should almost never fail a validation study. Right ... 

Finally, the validation work may be performed for this process. This would most likely consist of repeating the 2 full-factorial design mentioned earlier along with three process validation runs at nominal conditions for all factors. The process validation runs would essentially be confirmation runs of the actual process to be used in future commercial manufacturing. Of course, all of these experiments would be based on an approved protocol and would have to yield acceptable data compared with the product specifications. 

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