USING VALIDATION EXPERIENCE TO SET PRODUCT ALERT LIMITS

Experience gained during validation can be used to fine-tune the process for greater reliability. Several examples of changes being recommended based on study findings may be found in the section of this chapter devoted to evaluation of process data. Another application of the information gathered during valida- tion is in setting alert limits to be incorporated into the mechanism for product release. The alert limits would be the control limits (UCL and LCL) calculated as part of the review process for each analytical test; they could be made part of the written specifications for product release.

The recommendation to use control limits calculated as part of validation as alert limits is based on the expectation that test results from future production should normally fall within these limits. Indeed, this is the essence of retrospec- tive validation. Furthermore, for a stable, centered process the control limits would fall within the release specification for the test. Exceeding an alert limit therefore would not necessarily delay product release but could precipitate an investigation into the cause.

Requiring quality control to use validation experience to release product achieves two objectives: it monitors conclusions reached during validation for ongoing reliability and identifies a trend early before a rejection occurs. For quality control laboratories using a laboratory information management system (LIMS), routine performance of test result-alert limit comparisons can be auto- mated. Where such a system is not available, manually recorded test results could be transferred to a stand-alone computer for trend analysis. An x¯ plot depicting the process in relation to the alert and specification limits should be considered for monitoring trends. SeeFigure 18for an example of such a plot.

VI. RELIABILITY OF THE VALIDATED PROCESS

Once the process has been validated, controls must be put into place to make certain that operations continue to be performed as originally described. It is unrea- sonable to assume that machines, instruments, plant services, and personnel will remain static indefinitely. The FDA recognized the need for revalidation when it issued the process validation guidelines [1]. A number of resources are available to monitor for process drift. The quality assurance department can perform periodic audits of manufacturing and laboratory practices against official procedures, review equipment maintenance records including calibration history, and examine person- nel training programs. Any departures from original assumptions must be brought to the attention of the validation team for evaluation of their impact on the process. The CGMPs require the manufacturer of a product to conduct an annual review of written records to evaluate product quality [6]. A number of authors

Figure 18 Computer-generated x¯-control chart showing relationship of historical con- trol limits (UCL and LCL) and quality control release specifications.

have suggested that when done properly the review can highlight trends that might otherwise go unnoticed. Lee discusses how analytical and production data, as well as product complaint experience, can be arranged or collated for this purpose [19]. The annual review would be an expedient means of monitoring the conclusions reached during validation.

When planned changes are made to the process, equipment, or immediate operating environment, the validation team should carefully assess the nature of the change for its impact on different aspects of the process. It may not be necessary to revalidate the entire process in cases in which the change can be shown to be isolated [1]. There may be an opportunity to supplement the histori- cal experience with a prospective study specific to the planned change. To en- sure that this review occurs, a formal change control system must be in place. It would also be appropriate to have in place a written plan describing the company functions that have responsibility for monitoring the process.

VII. SELECTION AND EVALUATION OF PACKAGING DATA

To this point retrospective validation has been discussed in the context of dosage form manufacture. Some of the same concepts may be applied to validating a packaging operation. Consider the following. Packaging lines are typically con- trolled by making spot observations to confirm machinery performance and

component usage. The frequency of the inspections and the number of samples examined during each cycle are normally defined in a written procedure. Fur- thermore, the results of each monitor are generally documented in an inspection report, which becomes part of the packaging record for that lot of product. Also available from the packaging record is the number of units produced, thus the information needed to allow inferences about the reliability of a particular opera- tion is readily accessible.

If we can show that over an extended period of time an operation had a certain reliability, it is not unreasonable to expect the same level of performance for the future as long as the equipment is reasonably maintained. Conversely, any conclusion reached by such a study would be invalidated by substantial change to the equipment or its method of operation.

How many packaging runs must be examined to draw a sound conclusion about the reliability of the operation? Unfortunately, no one answer is appro- priate for every situation, but there are some rules that will aid the decision process. The sample size should be large enough to capture all variables nor- mally experienced; for instance, routine machine problems, shift and personnel changes, component vendor differences, and seasonal conditions. Furthermore, the sample must be of sufficient size to provide a high degree of confidence in the conclusion. Ten thousand observations made over 6 to 12 months of continu- ous production generally satisfy these requirements. For high-speed, multiple- shift operations the 10,000-observation figure is likely to be reached well before sufficient time has elapsed to include all avenues of variability. In these cases, time rather than units produced should be the first consideration.

To validate an aspect of the packaging operation retrospectively the fol- lowing information must be tabulated:

1. The total number of observations made for the quality attribute under review

2. The total number of nonconformances detected by the inspection process

Figure 19summarizes the retrospective validation strategy for a packaging

operation. It also takes into consideration an opportunity for process improvement. For example, we may learn from the study that a particular operation has a defect rate that in our judgment is unreasonably high. The effectiveness of remedial action could be evaluated after a suitable period of time has elapsed by repeating that phase of the validation study. In addition, the information provided by the study about machine and operator dependability permits informed replies to inquiries by customers or the FDA about alleged package defects.

A. Sources of Historical Information

A specific example can serve to illustrate how validation may be accomplished. A typical high-speed packaging line for solid dosage form products consists of

several pieces of specialized machinery, usually in series, connected by a mov- ing belt (see Fig. 20). When the line is operational, there is a roving inspection designed to evaluate the performance of each piece of equipment. For example, at the labeler the inspector would be asked to confirm that the serial number on the label matches the work order, that the correct lot number and expiration date appear on the label, and that the label is properly adhered to the bottle. The outcome of each inspection is recorded. In the event nonconformance is ob- served, packaging supervision is notified. Remedial action may take the form of a machine adjustment and/or isolation and removal of nonconforming produc- tion. These roving inspections have the effect of limiting the number of defec- tives that reach the finished goods stage.

In addition to the roving inspection, a finished piece inspection is per- formed each half hr; that is, the inspector randomly selects for examination one finished unit from the end of the line. In our example, the finished unit is a unitized bundle of 12 bottles of 100 tablets each. Each finished piece is torn down into its component parts, which are examined for specific attributes and conformance to the work order. Table 12 summarizes the tests made by the inspector, as well as the number of pieces examined at each half-hr interval. When nonconformance is detected, a notation is made in the inspection record. With 13 finished product audits performed on each shift, a considerable pool of information is readily amassed.

Table 12 Finished Product Audit: Package Attributes and Number Examined

Number examined

Attribute Each audit Each shift Each year

Intact bundle 1 13 1,300 Carton 12 156 15,600 Outsert 12 156 15,600 Bottle 12 156 15,600 Label 12 156 15,600 Lot number Expiration date Adhesion Cap 12 156 15,600 Seal Tablet counta 4 52 5,200

a_{Tablet count is performed on only four bottles. The annual figure is}

based on 100 shifts.

Because we are interested in line machinery and package attributes and not the drug product being packaged, inspection results for all 100-tablet bottle runs may be pooled. One could even argue convincingly that the type and num- ber of doses in the bottle are of no import as long as the line configuration remains constant. In any event, the pooling of production volume as well as inspectional observations substantially accelerates data accumulation. This may be an important consideration in cases in which a particular packaging line is used for multiple products and sizes.

The line to be studied runs 100 shifts per annum of a particular package size at the rate of 50,000 bottles per shift; thus, in 1 year 5 million bottles are produced. During the same period, between 1300 and 15,600 inspectional observations are made, depending on the attribute (Table 12).

B. Estimating Outgoing Product Quality

The remaining task is to count the number of defects for each attribute as re- ported by the inspector during the course of the year following the finished piece inspection. This task is more time-consuming than difficult, assuming line inspection documents are well organized. The outcome is reported inTable 13. With this information available, the maximum fraction defective at a preselected

confidence level may easily be estimated. The figures in Table 13 are derived from the Poisson approximation rather than the normal approximation to the binomial, which is adequate for this purpose [20].

According to Table 13, the cap was present for each bottle sampled; how- ever, the lip seal was not fully adhered in 16 instances. The proportion of defec- tives in the samples is 16/15,600 or 0.001 (0.1% or 1/1000). The maximum fraction defective for an incomplete lip seal in the population (production lots) is 0.0018 at the 99% confidence level. Stated another way, there is 99% assur- ance that the number of bottles with an incompletely adhered seal will not ex- ceed two units for every 1000 produced. The value has been calculated for the other quality attributes to illustrate the impact of the sample size and the differ- ent levels of machine performance on lot defectives.

Calculating the maximum fraction defective for important package attri- butes provides a clear picture of the quality of goods sent to the customer as well as machine capability. If the defect rate is uncomfortably high, an investi- gation can be made to identify the cause. Possibly the solution is to modify a practice or replace a particular item of equipment.

VIII. CONCLUSION

Under certain conditions, a firm may rely on existing production, quality con- trol, and facilities maintenance information, and consumer input to validate retrospectively the processes of marketed products. The end result of this effort

Table 13 Inspectional Results and Fraction Defective

Maximum fraction Number of Number of defective at 99% Attribute samples examined observed defects confidence limit

Intact bundle 1,300 11 16.5/1000 Carton 15,600 0 0.3/1000 Outsert 15,600 7 1.0/1000 Bottle 15,600 0 0.3/1000 Label 15,600 0 0.3/1000 Lot number 1 0.4/1000 Expiration date 2 0.511000 Adhesion 5 0.8/1000 Cap 15,600 0 0.3/1000 Seal 16 1.8/1000 Tablet count 5,200 3 1.9/1000

is the ability to predict with a degree of confidence the quality of subsequent batches. Furthermore, familiarity with the product acquired through such in- depth study can lead to process improvement, which in turn enhances overall control. The knowledge acquired and data amassed during retrospective process validation provide a performance profile against which daily release testing can be compared, to say nothing of their value as a guide when resolving production and control problems. Process validation is a CGMP requirement, and therefore an area of interest to the FDA. The program just discussed is one approach to satisfying this requirement. The chapter also extends the concept of using histor- ical data to predict future performance of packaging operations.

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