with an Example of for an HPLC Procedure

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1 Peer Reviewed: Analytical Procedure Journal of Validation Technology The Use of the Analytical Target Profile in the Lifecycle of an Analytical Procedure with an Example of for an HPLC Procedure Jane Weitzel, Robert A. Forbes, and Ronald D. Snee Introduction The lifecycle management of an analytical procedure applying Quality by Design (QbD) concepts and using the analytical target profile (ATP) has been introduced by the USP stimuli article Lifecycle Management of Analytical Procedures: Method Development, Procedure Performance Qualification, and Procedure Performance Verification [1]. Current regulatory guidance around process validation is moving away from a simple once-and-done validation, and toward a continuous process verification approach, also called a lifecycle approach [2]. The recent FDA method validation draft guidance also includes some discussion of a lifecycle approach for analytical methods [3]. A number of articles have been written introducing the lifecycle management of analytical procedures [4 7]. These articles introduced the concept that analytical procedures are processes and, as such, the tools, approaches and statistical analyses for manufacturing processes can be applied to analytical procedures. The benefit of recognizing and acknowledging the similarity between quality by design for manufacturing processes and analytical quality by design (aqbd) is that it will allow the application of the experience, knowledge, tools, and statistics from QbD to be applied to aqbd. Thus, all parties, laboratories, companies, Quality Assurance, production, as well as regulatory bodies can leverage their experience with QbD in developing an approach to aqbd. A key component of the aqbd paradigm is the definition of the Analytical Target Profile (ATP) [1], analogous to the Quality Target Product Profile (QTPP) in the ICH [8]. The ATP defines the requirements for the result of a test method and is based on the suitability for use of that result. This paper illustrates how the ATP is developed and applied to assess the performance of the method at various stages of its lifecycle. The use of traditional Gage R&R statistical analysis and control sample charting, which are commonly used in manufacturing process validation and improvement, will also be illustrated in the development of an analytical procedure. The similarities, contrasts, and differences between the lifecycle approach, and the traditional approach to validation of an analytical procedure as described in the USP [9 11] and the ICH [12], will be discussed and illustrated in the example. The terminology used with both approaches will be compared and explained. The aqbd approach is illustrated using an example of the potency test for a drug substance (DS) with specification limits of 98.0% to 102.0%. The potency test is one component in the DS control strategy utilized to ensure the safety and efficacy of a drug product (DP) manufactured from the DS. The test ensures that the DS is of adequate potency, so that when used in the DP, the patient will receive the appropriate dose of the active ingredient. In this way, potency is a critical quality attribute (CQA) of the DS that is linked to a CQA for the DP [13]. For simplicity, we will assume the limits are on the as-is basis, and no correction for water or solvents is necessary. Obviously, there are other specification tests (e.g. impurities, residual solvents, etc.) that are required to ensure the DS meets its CQAs, but these will not be considered in this illustration. It should also be noted that the DS potency results used for this illustration, while actual results, are provided mainly for illustrative purposes. In this example, we will focus on the precision and uncertainty performance characteristics of the potency test method to demonstrate how the gage R&R tool and experimentally designed intermediate precision studies are used for Procedure Performance Qualification and Verification. The acceptance criterion for the precision of the potency method will be set using the Lifecycle approach where the intended use of the method is defined using a decision rule. The decision rule defines the acceptable level of probability of making an incorrect decision. For this discussion, the null hypothesis is The potencyof the sample is within specification, so there are two different types of incorrect decisions. First, a batch that has acceptable potency may test outside the specification limits. Second, a batch with a potency truly outside of the specifications limits, could test within limits and be accepted. The first error can be considered a false positive (positive test for unacceptable potency), or a type I error. The second errror would be a false negative (negative test for

2 Jane Weitzel, Robert A. Forbes, Ronald D. Snee unacceptable potency) or a type II error. The consequences of each of these types of errors are taken into consideration when developing the decision rule. The ATP uses the probability of each of these errors to define the maximum allowed measurement variation (uncertainty) for the reported result, which translates into an the laboratory sample, continues with the treatment of that sample which may involve taking a test-portion, comminution, extraction, dissolution, etc. and concludes with the output, the reportable result as shown in Figure 2. The FDA definition for process validation is Figure 1 shows that the intended use of the reportable result defines the decision rule, which includes the acceptable probability of making a wrong decision. The Analytical Target Profile uses the probability to determine the maximum allowed measurement variation for the reported result or target measurement uncertainty (TMU). The TMU is an acceptance criterion for the performance characteristics. The process is continuous because it can be repeated if the requirements of the ATP cannot be met. acceptance criterion for the qualification of the analytical procedure as shown in Figure 1. The Analytical Procedure as a Process A process is a series of actions or steps taken in order to achieve a particular end or output. The output of an analytical procedure is a reportable result. The series of steps begins with the collection and evaluation of data, from the process design stage through commercial production, which establishes scientific evidence that a process is capable of consistently delivering quality products [2]. Treating an analytical procedure as a process, the analogous definition is the collection and evaluation of data and knowledge from the method design stage Figure 2 shows the analytical procedure as a process that starts with the taking of the laboratory sample and concludes with the output of a reportable result. Figure 3 QbD for analytical procedures is analogous to that for production processes. This figure shows the analogous throughout its lifecycle of use which establishes scientific evidence that a method is capable of consistently delivering quality data [4]. For this article, the method is the same as the analytical procedure and includes one full execution of the method, starting from the original sample, to produce a reportable result as defined in the US Food and Drug Administration out-of-specification (OOS) guidance [14]. The reportable result is compared to the specification to determine compliance. The analogy between QbD for the production process and for an analytical procedure is illustrated in Figure 3. QbD in both areas starts with a clear definition of the intended use of the output, or product, of the process. For production of a drug product, this considers items such as the route of administration, dosage form, bioavailability, strength, and stability. For analytical procedures, the output/product is the reportable result, and a decision rule can be used to clearly and quantifiably state the intended use of the result [15-16]. For an analytical procedure, the predefined objective is the analytical target profile (ATP). Criteria defined in the ATP refer to the quality data attributes of the reportable result (i.e., bias [accuracy] and target measurement uncertainty (TMU) [precision]), which includes all sources terms for the major steps in the QbD approach. of variability. For the production process, the critical quality attributes are determined. The analogy for the analytical procedure is the performance characteristics required of the method. These include the accuracy/bias, linearity, limit of detection, repeatability, intermediate precision, etc. The critical variables are identified and characterized and a suitable control strategy is designed. For production the production design space is defined, while for an analytical procedure, the performance characteristics that make up its design space are defined. Decision Rules Construction of a Decision Rule Decision rule construction requires four components: the measurement result, its measurement uncertainty, the specification limit or limits, and the acceptable level of the probability of making each type of wrong decision [15-16]. The analytical quality by design approach uses risk analysis, including assessment of probabilities, to determine those four components of a decision rule. The relationship between the four components allows one to determine if an analytical procedure is fit for use and to set acceptance criteria that the analytical procedure must meet. Journal of Validation Technology Volume XX Issue 4

3 Peer Reviewed: Analytical Procedure With this decision rule, the probability for either type of error is a function of both the process variability and the measurement uncertainty (i.e., measurement variation). A type II error occurs when the potency test result is within specifications, when the true batch potency is outside of specifications. In this example, the type II error rate will be assumed to be negligible, which means that the process delivers consistent, high quality DS (process variation is negligible). The DS is also assumed to be of high purity, containing negligible water (non-hygroscopic), solvents, and inorganic content, which means the true DS potency is approximately 100%. The probability of a type I error (rejection of a batch when the true batch potency is within specifications) is determined by the measurement variation in combination with the width of the acceptance zone. This probability is also called the type I error rate. Figure 4 The figure illustrates a simple decision rule for a specification with both upper and lower limits. If the measurement result lies in the acceptance or specification zone, product is accepted, otherwise it is rejected. Journal of Validation Technology to a measurand (the quantity being measured), based on the information used [17]. For an analytical procedure, the uncertainty includes all the random sources of variability, such as repeatability, intermediate precision and the uncertainty in the estimates of the accuracy or bias. As such, it can be viewed as a high level, overall requirement for the total precision. It is important to note that there are many definitions of different types of precision based on the sources of variability they include. Additionally, different industries, regulatory bodies, and references define the types of precision differently. As a result, it is best to clearly define the precision terms being used by identifying the sources of variability that are included. The VIM takes this approach by including the conditions of measurement in its definition of precision: 2.15 Measurement Precision closeness of agreement between indications or measured quantity values obtained by replicate measurements on the same or similar objects under specified conditions Note 2 The specified conditions can be, for example, repeatability conditions of measurement, intermediate precision conditions of measurement or reproducibility conditions of measurement The VIM definition of precision is very similar to the ICH definition in the analytical procedure validation guideline [12]. Reproducibility is the broadest measure of method precision as it includes all sources of variability in the estimate, including multiple testing laboratories. The ICH uses the term intermediate precision to distinguish the precision of the method within a single lab from precision over multiple labs (reproducibility). Normally the test for compliance of a given batch with specification limits is carried out in a single laboratory. Therefore, the intermediate precision estimate is generally the most appropriate precision estimate for the assessment of method capability. In this work, the term repeatability will be used to describe short-term variability (i.e., repeat preparations within a single run) and intermediate precision to describe long-term variability within a single lab (i.e., preparations analyzed over different runs/setups). Note that the terminology associated with the use of the Gage R&R tool does not make the distinction between within-lab and inter-lab precision estimates, so here the term reproducibility means the same thing as intermediate precision. The measurement uncertainty characterizes the dispersion of the values being attributed to a measurand. This dispersion is assumed to have a normal distribution. The basis for this assumption is discussed in Annex G of the GUM [18]. Even if the distribution is not normal, the same approach will be used. Uncertainty can be illustrated by imposing Simple Decision Rule For the drug substance potency example, both the decision rule and the acceptance limits are generally defined by the expectations of regulatory authorities, so the manufacturer may not have much flexibility to use alternative approaches. A range of 98.0 to 102.0% is generally expected for a commercial drug substance. This range ensures that the drug product manufactured from the DS will meet its potency specification limits, which are generally 90 to 110% in the US and % in the EU. This approach is described by a simple decision rule in which the specification zone, the values between and including the specification limits, is the same as the acceptance zone and defines the values for which the product is accepted. The values outside the acceptance zone comprise the rejection zone. This simple decision rule is illustrated in Figure 4. This simple decision rule can be stated as: The product will be considered compliant if the measurement result is within the acceptance zone. The consequences for a type I error will determine what probability is acceptable. Since there is little flexibility to increase the acceptance zone, the measurement uncertainty is the only parameter that can be adjusted in order to meet the acceptable error rate. Measurement Uncertainty Measurement uncertainty is defined in the international vocabulary of metrology (VIM) as a non-negative parameter characterizing the dispersion of the quantity values being attributed Figure 5 shows the normal distribution in relation to the specifications of 98.0% to 102.0%. The width of the normal distribution is chosen so that the probability the true result is below98.0% is 2.5% and above 102.0% is 2.5%.

4 Jane Weitzel, Robert A. Forbes, Ronald D. Snee a normal distribution over the decision rule as shown in Figure 5. In this example, the true drug substance potency is assumed to be 100.0%, which establishes the center of the normal distribution. The width of the normal distribution is determined by the standard deviation which was set to 1% to coincide with a 2.5% probability of obtaining values each above and below the limits. As shown in Figure 5, 5% of the potency results for the sample will fall outside the limits due to measurement variation (type I error rate) for the assumed drug substance potency and precision described above. This distribution only captures the measurement uncertainty and does not include process variation (variation in the true batch to batch potency), which was assumed to be negligible. If a 5% type I error rate is acceptable, the standard deviation of 1.0% becomes the target measurement uncertainty for the Analytical Target Profile. Target measurement uncertainty (TMU) is defined in the VIM as the measurement uncertainty specified as an upper limit and decided on the basis of the intended use of the measurement results [19]. Any analytical procedure that yields a reportable result with a standard uncertainty of 1.0% or less, given there is no bias, will be fit for its intended use. The probability associated with the decision rule is based on the theoretical normal distribution and the specified standard deviation. It does not take into account the variability in the estimates of the uncertainty that will be obtained in the qualification and verification experiments. That is dealt with in the experimental designs of the development and qualification experiments, and the desired confidence in their resulting precision estimates. Generally, higher confidence limits will require more experimental runs to provide better estimates of Figure 6 (a) shows that if the normal distribution is centered over 99.0% the probability of making a wrong decision is increased to 16.3%. (b) shows the target measurement uncertainty must be reduced to 0.61% in order that the probability of making a wrong decision remains at <5%. method precision to avoid failing to meet the TMU. Larger studies with more runs are more costly in terms of time and resources, so one must weigh the benefit of greater confidence in the precision estimate, versus the additional cost of larger studies. Relationship Between Accuracy and Target Measurement Uncertainty The probability of not meeting the specification depends on where the true distribution is centered. If the true distribution is not centered at 100.0%, as in the above example, then a different proportion of the measured results are expected to be outside the specifications. When this occurs, the target measurement uncertainty must be improved in order to maintain the same type I error rate. However, the analytical procedure could have a bias that cannot be removed in method development. If it is not corrected for, then the TMU will have to be adjusted. Or if the drug substance does not have a potency of 100.0%, this will impact the degree of precision that the method will need to attain. For example, if the DS has an actual potency value of 99.0%, then the TMU must be reduced to 0.6% (Figure 6). Analytical Target Profile Based on a risk analysis that includes considerations such as the cost of OOS investigations, the known characteristics of the product (the drug substance is known to have few impurities and the potency is close to 100%), and the capability of the analytical technique (HPLC) a company can decide the probability it is willing to accept associated with making an incorrect decision (type I error) concerning a product specification. In the below example, the probability selected is 5%. The decision rule can be more detailed now: The batch of drug substance will be considered compliant if the test result is within the range of 98.0 to 102.0%. The probability that an acceptable batch will fall outside this range must be NMT 5%. The analytical target profile can now be determined. The laboratory qualifies the analytical procedure for a concentration range wider than the specification to allow for variability in sample concentration. The matrix and measurand are defined clearly. There is only one known impurity, Impurity A, and it occurs below 0.1%. The ATP can now be written: The analytical procedure must be able to quantify the drug substance in the presence of impurity A, and potential degradation products, over a range of 80% to 120% of the nominal concentration with an accuracy and uncertainty so that the reportable result falls within ±2% of the true value with at least a 95% probability. Performance Characteristics for the Analytical Procedure The ATP can now be used to determine the performance characteristics for the analytical procedure. The following performance characteristics need to be included in the method qualification: accuracy, precision, specificity, linearity, and range. The acceptance criteria for specificity (i.e., resolution of the main peak from impurity A and potential degradation products) and linearity are determined based on their impact on the accuracy and precision. As long as their impact on the accuracy and precision is such that the target measurement uncertainty can be met, they are acceptable. This is a key advantage of this approach; it defines the performance that is good enough. One can confidently accept the performance because it is directly linked to the intended use. The performance characteristics and the acceptance criteria are summarized as follows: The target uncertainty is NMT 1.0%. The combination of bias and uncertainty need to be such Journal of Validation Technology Volume XX Issue 4

5 Peer Reviewed: Analytical Procedure that the probability of not meeting the specification is NMT 5%. To assess the accuracy of the method when performed in a new laboratory, a reference standard, or a previously tested sample with a defined potency could be used. Results obtained in the new laboratory would be compared to the defined result to test for a bias. If a bias is detected, the combination of the uncertainty of the bias with the intermediate precision estimate would be compared to the allowable measurement uncertainty defined by the ATP. For this example, the bias (accuracy) is assumed to be negligible so that the precision component can be focused on. If the bias were significant, then the analytical procedure should be improved to remove that bias, or the bias should be corrected for and the uncertainty of that correction included in the uncertainty estimate. Now that the ATP has been established, its use in lifecycle management of the analytical method will be demonstrated. Procedure Performance Qualification During the validation of the analytical method, the precision of the method is evaluated by studies to determine its repeatability, and its intermediate precision. Because it includes both long-term and short-term sources of variation, the intermediate precision estimate is considered a representative measure of the capability of the method for ongoing batch release testing. Generally performed as one component of a method validation protocol, a statistically designed intermediate precision study is carried out using multiple setups, incorporating multiple analysts, multiple HPLC instruments, and HPLC columns with different packing batches. In order to meet the ATP, the acceptance criterion for the study is that the overall variability, the combined uncertainty, must be NMT 1.0% as a standard uncertainty or standard deviation. Traditionally, the qualification protocol includes a criterion only for the SD estimate obtained. However, the uncertainty in the precision estimate obtained in the qualification study should also be taken into account. Generally a confidence interval (CI), or upper confidence limit (e.g., 95% UCL), is calculated for the standard deviation estimate to provide an upper limit below which the actual standard deviation is likely to fall. The width of this interval is impacted by both the desired confidence for the estimate and the number of setups in the study. For more detail on the calculation of the confidence interval, see the stimuli article in the USP Pharmocopoial Forum [20]. The design and results of one such study are shown in Table 1, where four analysts produced duplicate potency results for one batch on each of 8 setups using a different HPLC instrument and column on each setup. For this study we will choose 80% as the desired confidence for the precision estimate. This means that if we obtain a standard deviation of 1.0%, we will accept a 20% chance that the actual standard deviation may be greater. This also means that the acceptance limit for the standard deviation is effectively NMT 0.74%, which corresponds to an 80% UCL of 1.0% for an 8-run study. Since the method prescribes averaging the results for two sample preparations on each setup, the reportable result for a batch is the average (AVG). The overall standard deviation (SD) obtained Table 1, Results of Intermediate Precision Study for DS Potency Method. Journal of Validation Technology in this study was 0.15%, with an 80% UCL of 0.20%, which met the acceptance criteria of NMT 1.0% SD. A limitation of this study was the fact that only a single DS batch was tested, so potential variability caused by differences in behavior of different batches of the DS was not included. In the case of a high-purity DS, with very consistent properties from batch to batch, this source of variation is generally small but this is not always the case. Procedure Performance Verification To further illustrate the utility of the ATP, its use in the next stage of the lifecycle of the potency method will be considered. This is the stage in which the method is installed in the release testing quality control laboratory (QCL). At this stage, the method has been fully developed and validated to demonstrate its appropriateness for its intended use, and some history of its performance to test manufactured DS batches at a development scale exists. The DS is a new chemical entity that has been submitted for regulatory approval and the manufacturing process is being installed and validated in the long-term manufacturing facility. The analytical method is being transferred to the long-term release lab, and is undergoing performance qualification at the new site. Some data to characterize both the capability of the method and the capability of the manufacturing process are available. Installation of the DS potency method in the QCL is described as a Type 3 change in the USP stimuli article, involving the need to operate the method in a different environment [1]. To support this activity, a procedure performance qualification (PPQ) protocol would be written to describe the analytical method validation or comparison activities needed to demonstrate that the method meets its performance requirements as required by the ATP in the new environment. These activities are governed by current guidance around method transfers [2, 9]. From the perspective of a lifecycle approach, a key element of the transfer activity is to show that the precision of the method as performed in the QCL is sufficient to meet the required uncertainty as described in the ATP. The required precision would be included in the PPQ protocol as an acceptance criterion for the intermediate precision study; in our example the acceptance criteria would be NMT 1.0%RSD. One approach to evaluate the method precision after transfer to the QCL would be to perform an intermediate precision study similar to the study performed for method qualification. An alternative approach is to use the Gage R&R tool which measures the repeatability and reproducibility of the method [21]. While the Gage R&R study may be new to many in the pharmaceutical and biotech worlds, the method has been used widely in the process industries including auto, chemical and electronics. Gage R&R studies are also widely used in Six Sigma process improvement studies [22]. It is not uncommon to find process problems are due to poor measurement systems with the problem significantly reduced, if not eliminated, by improving the measurement system. Note that for the Gage R&R study (as described under Measurement Uncertainty above) we use the term reproducibility to mean the same thing as the intermediate precision, and not inter-laboratory variation as defined by the ICH. Repeatability is the within setup precision, reproducibility comprises precision across multiple setups. In a Gage R&R Study 5-10 samples are evaluated by 2-4 analysts (reproducibility) using 2-4 repeat tests (repeatability) sometimes involving 2-4 test instruments (reproducibility). For example if 3 analysts measure each of 10 samples of a product in duplicate the study will produce 3x10x2=60 test results. A variance components analysis is then performed on the measurements to obtain estimates of the repeatability (within-setup variability) and reproducibility of the method. These statistics are then used to evaluate the fitness of the method for use for product release

6 Jane Weitzel, Robert A. Forbes, Ronald D. Snee and to support process improvement efforts. The variance component estimates are often used to determine how many within-run replicates and/or setups are required to give the desired method intermediate precision. If the within-run variability (repeatability) is high, then more within-run replicates maybe required to generate a reportable result with the desired precision level. If setup-to-setup variability (reproducibility) is high, then multiple setups including one or more analysts, instruments, and columns maybe required to generate a reportable result with the desired precision level. The reportable result consists of the average of multiple within run and/or setups as prescribed in the method and is the value that is compared to the specification and applied to the decision rule. This kind of a replication strategy can be employed to reduce the variability test method that currently has a higher than desired level of variability to continue to release product until a better test method can be developed. In this way production doesn t have to be stopped (or not initiated) because of a highly variable test method. To illustrate the utility of this approach for verification of the method transfer to the new laboratory, a Gage R&R study was performed. Table 2. Gage R&R Study Results for DS Potency Method Transfer. Journal of Validation Technology Volume XX Issue 4 Samples from five API batches were tested on four setups by two analysts using each of two instruments (each instrument used a unique column for total to two columns). New mobile phase was prepared for each setup and each analyst prepared a fresh set of standards for each run. Samples were prepared and analyzed in duplicate on each run, producing a total of 5x2x2x2 = 40 Potency(%) test results. The data are summarized in Table 2. The results of the variance components analysis for the potency measurements are summarized in Table 3 and shown in Figure 7. In this study, multiple analysts, instruments and columns were used to obtain more realistic variability estimates, and could not be separately evaluated for statistically significant effects. The variance components show that 84% of variability is due to the setup and the remaining 16% due to repeatability (within-setup). The standard deviation of the test method (intermediate precision for two preparations on a single setup) is 0.71% with an upper 80% confidence limit of1.16%. The intermediate precision estimate of 0.71% meets the acceptance criterion of NMT 1.0% that was defined based upon the ATP. However, the upper confidence limit exceeds 1.0%, indicating that we cannot say with 80% confidence that the standard deviation is not NMT 1.0%. If the verification protocol utilized an acceptance criterion of NMT 1.0% for the 80% UCL of the SD, these results would not pass the criterion. This study provides an interesting example where the method would pass a traditional assessment, but does not pass when a more rigorous ap- Figure 7. Variability Chart showing the results of the Gage R&R study to verify performance of the method in a new laboratory. Table 3. Gage R&R Study Repeatability and Reproducibility Variance Components proach incorporating a confidence limit is used. ber of setups and within setup replicates. The method may not be good enough for its Table 4 demonstrates that increasing the intended use. To address this, additional setups could be performed to obtain a better SD peatability) from one to two has little effect on number of within setup measurements (re- estimate with a tighter CI. For example, if the the method intermediate precision. This is because the within setup (repeatability) variance results for all the batches are pooled, with the assumption that there are no significant differences between them, a standard deviation of ance (reproducibility). Generally, the standard is much smaller than the setup-to-setup vari- 0.63% with an 80% UCL of 1.03% (rounds to practice for a DS potency method is to prepare 1.0%) is obtained, which meets the acceptance samples in duplicate. While the decrease in criterion with an 80% confidence level. variation for this method is marginal, the major If the method still did not meet the acceptance criterion with additional runs, the test preparation errors by setting an acceptance cri- benefit of the two preparations is to control for method variation can be reduced by employing terion on their difference. Since reproducibility a replication strategy according to Equation 1. was the larger component of variance, analyzing Table 4 shows the effect of increasing the num- the samples on multiple setups would be more effective at reducing the method standard deviation, than preparing more samples for the same setup. By including multiple setups (n=2) the method intermediate precision could be reduced to approximately

7 Peer Reviewed: Analytical Procedure Table 4. Method Standard Deviation versus Replication Strategy. 0.5%, showing that this would be an effective way to improve the intermediate precision if necessary to meet the ATP requirements. These results illustrate a key benefit of the R&R study to understand the variance components. Because it is easier, some laboratories may be preparing replicates at the repeatability level thus wasting resources and giving a false source of assurance that precision has been improved. The estimate of the standard deviation obtained in the QCL (0.63%, 80% UCL = 1.0%, pooling the batches) was larger than the standard deviation estimate obtained during method validation (0.15%, 80% UCL = 0.20%). However, the 80% UCL for both estimates met the acceptance criterion of NMT 1.0% from the ATP, demonstrating the suitability of the method for its intended use. There may be many reasons for the apparent difference in method performance between the two studies. For example, the analysts performing the first validation study may have been more experienced in performing the method. Continued Procedure Performance Verification Following a successful installation of the method in the QCL, the method moves into stage 3 of its lifecycle where ongoing procedure performance verification (PPV) is required [1]. This is analogous to the requirement for Continued Process Verification called out in the FDA Process Validation Guidance, and is appropriate since we have argued that the execution of a test method is a process [2]. The lifecycle approach requires one to assess the repeatability and intermediate precision of the test method as it is used throughout the life of the method, not simply as a once-and-done validation, or site certification, exercise. Continued performance verification is accomplished in part by monitoring the results of the internal method system suitability requirements. System suitability requirements ensure that the method has been set up appropriately and that the equipment is functioning correctly. Peak shape (tailing or asymmetry) and resolution requirements ensure the peak can be precisely integrated, appropriate selectivity is provided by the HPLC column, and that the mobile phase has been prepared properly. Injection precision ensures that the sample injection system is functioning properly, and surfaces of internal parts have not become scratched and worn. Precision requirements for duplicate standard and sample preparations ensure that the analyst has made the preparations with adequate care. All these internal method requirements work together to ensure the measuring system continues to be capable to meet the ATP uncertainty requirements. System suitability results should be monitored and control charted to maintain a state of PPV, and to provide an early warning of drift or changes in the method capability. An effective way to assess the long-term stability of a test method is to periodically submit blind control samples (also referred to as reference samples, or method performance samples), from a common source, for analysis along with routine production samples in a way that the analyst cannot determine the difference between the production samples and the control samples. Nunnally and McConnell conclude Journal of Validation Technology there is no better way to understand the true variability of the analytical method [23]. The control sample must be homogeneous and stable in order to avoid out-of-control signals unrelated to the measurement system. The control samples are typically tested 2-3 times (depending on the test method) at a given point in time. The sample averages are plotted on a control chart to evaluate the stability (intermediate precision) of the method. The standard deviations of the sample replicate preparations are plotted on a control chart to assess the stability of the repeatability of the test method [21]. When using control charts typically two types of non-random patterns are observed: 1. Test measurements outside of the control limits (typically set at the process average ±3 standard deviations). Such events are referred to as out-of-control (OOC) signals. 2. Non-random patterns inside the control limits; such as trends, drifts, and shifts. These events are referred to as out-of-trend (OOT) signals and are detected using the Western Electric rules [21]. Both OOC and OOT signals are based on statistical tests of significance and are included in many statistical software packages [24]. Continued Procedure Performance Verification by Control Charting Example The data shown in Figures 8-10 are from an evaluation of a DS potency test method over a six-year period. Periodically blind control samples were submitted to the lab for analysis. Figure 8 is an I-MR (Individual Moving Range) control chart. The top control chart is a plot of the averages of the duplicate analyses of each sample, and the lower control chart shows the moving range of the averages. Control limits were calculated from the MR chart and thereby include both the setup-to-setup variation as well as the within setup (repeatability) variation. The standard deviation estimate calculated from the MR chart was 0.48, and is an estimate for the intermediate precision of the method for this six-year period. The MR chart in Figure 8 (bottom) shows only one point above the control limit. The individuals chart in Figure 8 (top) shows two points outside the control limits. No trends or shifts were observed over the 48 samples. However, after sample 23 the method intermediate precision was observed to improve. After further investigation, samples 1-23 were found to have all been analyzed by analyst A, and the majority of the samples afterward were tested by other analysts. Analyst technique appears to be the cause for the observed decrease in method variability following sample 23. Potential instrument and column impacts on the results were also investigated but determined to have little to no effect on the observed decrease in the method precision. When the data are split into two sets: Stage 1 (Samples Figure 9) and Stage 2 (Samples Figure 10) no practical differences between the average values of the two stages were noted. But as mentioned above, the method intermediate precision is greater in the first stage than in the second stage likely due to the practices of analyst A. The stage 2 data (Figure 10) showed one sample average and one MR outside of control limits. Another important use of the control sample results is to verify that the method meets the requirements of the ATP during this period of time. The standard deviation of all the reportable results for the control sample (sample averages) generated during the six-year period was found to be 0.62% (80% UCL = 0.83%), which meets the ATP criterion of NMT 1.0%. Furthermore, this estimate of the method intermediate precision is based on a large number of independent method setups consisting of multiple analysts, instruments, and columns. This estimate of the intermediate precision is also consistent with the estimate from the Gage R&R study (0.71%).

8 Jane Weitzel, Robert A. Forbes, Ronald D. Snee Figure 8. I-MR Control Charts for Sample Averages (n=2): All Samples Figure 9. I-MR Control Charts for Sample Averages (n=2): Samples 1-23 Figure 10. I-MR Control Charts for Sample Averages (n=2): Samples Recommendations for Interpreting the Control Charts When all the results are within the control limits and no OOT patterns are observed the method is performing as expected. However, over the course of time OOC and/or OOT may be observed. The course of action will depend on the specific situation but some general guidance can be given. The following recommendations are similar to those regarding process monitoring when using control charts. First when out-of-control (OOC) or out-oftrend (OOT) signals are obtained the data collection, analysis, and control charting procedures are examined to ensure that no mistakes have occurred. If mistakes are found the data should be corrected and the analysis repeated. When mistakes are ruled out then an investigation should be considered to find the cause of the OOC or OOT signals. The investigation will likely examine if that signal can be traced to a specific change in materials, reagents, instruments, columns, reference standards, or analysts. The action taken will then depend on the cause and the specific situation. However, sometimes the cause of the OOC or OOT cannot be determined. If this is the case, the practical significance of the signal should be considered. For example, the associated results may be close to the control chart limits and isolated with no OOC or OOT points before or after those results. The process may be performing at a very high level relative to specifications, suggesting that measurement variation may not be a problem. In the case when the variation has changed increased or decreased revised limits should be considered along with the appropriate approvals and change control actions taken. Such changes should be accompanied by appropriate training of the analyst and supervision involved. A control plan should also be created and put in place to ensure that the new procedures are followed. If unacceptable variation continues to be observed, it may be appropriate to consider performing a robustness evaluation of the test method as discussed above. A test method that is not robust can be expected to exhibit increased repeatability, intermediate precision, or both over time. Product Quality Review Summarize Test Method Performance The performance of the test method should be reviewed periodically in accordance with the reviews for the process and product. As part of that review, the results of the test method continued process verification system would be provided. The control charts of the test results for the control samples and summary statistics which document the test method performance including the repeatability and intermediate precision standard deviations and relative standard deviations, results of recent calibration studies and other information depending on the test method and situation should be included in the summary. The performance summary documentation should be stored and disseminated in accordance with the knowledge management expectations of the ICH [25]. Conclusion In this paper, we have illustrated the application of the lifecycle management approach for an analytical procedure (a drug substance potency test) and showed how a decision rule was used to develop the analytical target profile (ATP). The ATP was then used to define the fitness of the method for its intended use, which in turn enables an objective assessment of the method performance during initial validation, transfer to the quality control laboratory (QCL), and its ongoing performance in the QCL. The lifecycle approach is essentially applying Quality by Design (QbD) principles to the execution of the method to produce a result, in an analogous way that QbD is applied to a manufacturing process producing a product. We have also demonstrated the use of the Gage R&R Journal of Validation Technology Volume XX Issue 4

9 Peer Reviewed: Analytical Procedure study approach to determine the repeatability and reproducibility (intermediate precision) of the test method within the QCL after transfer, as a means to verify its performance, versus the ATP criteria has also been demonstrated. The estimate of the method precision from the QCL results will often be based on a larger sample size and encompass more variability than the development studies did, leading to a more robust estimate of the long-term measurement precision. The Gage R&R approach utilizes a greater number of test batches than a typical intermediate precision study, which provides a higher degree of replication for assessment of the measurement uncertainty. The use of a control sample for continued procedure performance verification has also been explained, which is an important stage in the lifecycle of a method that is often overlooked. The use of an analytical method control sample to monitor performance of the method versus the ATP requirements during its longterm use in the QCL has also been demonstrated. Not only does the control sample provide assurance that the ATP is being met, but it also detects special cause variation and/or shifts in common cause variation. This enables continual improvement of the method performance by identifying opportunities for analyst training, the need for equipment repairs, or detrimental changes in consumables such as the HPLC columns or reagents. References 1. USP Stimuli Article, Lifecycle Management of Analytical Procedures: Method Development, Procedure Performance Qualification, and Procedure Performance Verification, Pharmacopeial Forum 39 (6), online, stimuli-article-lifecycle-management-analytical-procedures-posted-comment, accessed December 3, FDA, Guidance for Industry Process Validation: General Principles and Practices (Rockville, MD, January 2011). 3. FDA, Guidance for Industry Analytical Procedures and Methods Validation for Drugs and Biologics (Rockville, MD, Draft--February 2014), online, gov/downloads/drugs/guidancecomplianceregulatoryinformation/guidances/ ucm pdf, accessed December 3, P. Nethercote, et al., QbD for Better Method Validation and Transfer, PharmaManufacturing.com, online, html, April 13, 2010, accessed December 3, M. Schweitzer, et al., Implications and Opportunities of Applying QbD Principles to Analytical Measurements, Pharm. Tech. 34 (2), 52 59, Phil Nethercote, Joachim Ermer, Quality by Design for Analytical Methods, Implications for Method Validation and Transfer, PharmTech.com, online, www. pharmtech.com/pharmtech/feature+articles/quality-by-design-for-analytical-methods-implicati/articlestandard/ Article/detail/791903, October 2012, accessed December 3, Joachim Ermer, A Lifecycle Concept for Pharmaceutical Analysis, European Pharmaceutical Review, 16 (3), 16-24, 2011, online, content.yudu.com/library/a1sipk/ PATampQbDSupplement/resources/index.htm?referrerUrl=http%3A%2F%2Fwww.yudu.com%2Fitem%2Fdetails%2F345094%2FPAT---QbD-Supplement accessed December 3, Journal of Validation Technology 8. ICH, Q8(R2), Pharmaceutical Development Part II: Pharmaceutical Development - Annex, Step 4 version (August 2009). 9. USP 37-NF 32 General Chapter <1224>, Transfer of Analytical Procedures, USP 37-NF 32 General Chapter <1225>, Validation of Compendial Procedures, USP 37-NF 32 General Chapter <1226>, Verification of Compendial Procedures, ICH, Q2(R1), Validation of Analytical Procedures: Text and Methodology, Step 4 version (November 2005). 13. ICH, Q11, Development and Manufacture of Drug Substances (Chemical Entities and Biotechnological/Biological Entities), Step 4 version (May 1, 2012). 14. FDA, Guidance for Industry Investigating Out-of-Specification (OOS) Test Results for Pharmaceutical Production (Rockville, MD, October 2006). 15. ASME, Guidelines for decision rules: considering measurement uncertainty in determining conformance to specifications, B Eurachem/CITAC Guide, Use of uncertainty information in compliance assessment, First edition (2007), online, eurachem.org/images/stories/guides/pdf/ Interpretation_with_expanded_uncertainty_2007_v1w.pdf, accessed December 3, JCGM, International vocabulary of metrology Basic and general concepts and associated terms (VIM), 3rd ed., JCGM 200:2012(E/F), online, org/utils/common/documents/jcgm/ JCGM_200_2012.pdf, accessed December 3, ISO/IEC, Uncertainty of measurement Part 3: Guide to the expression of uncertainty in measurement (GUM:1995), 98-3: ICH, Q2(R1), Validation of Analytical Procedures: Text and Methodology, Step 4 version (November 2005). 20. USP Proposed General Chapter <1210>, Statistical Tools for Procedure Validation, Pharmacopeial Forum 40 (5), D. C. 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