process validation

Drug Approval Strategies in the Age of Fast Track, Breakthrough Therapy and Accelerated Approval

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Process Validation and Regulatory Review

Drug Approval Strategies in the Age of Fast Track, Breakthrough Therapy and Accelerated Approval

To meaningfully discuss the process validation and regulatory approval strategies required for drugs that have been designated Fast Track, Breakthrough Therapy or Accelerated Approval drugs, we must first clarify these designations and briefly remind ourselves what the Process Validation guidance looks like. Then we will be able to clearly identify challenges and approaches to these barriers when working to bring a Fast Track, Accelerated Approval or Breakthrough Therapy drug to market.

Fast Track designation – Fast Track drugs treat serious conditions where there is an unmet medical need. Concluding that a condition is serious and that there is an unmet medical need most definitely leaves room for judgement, but generally speaking, the conditions these drugs treat are life-threatening, and the drug in question is expected to contribute to survival, daily functioning or the likelihood that a condition will advance to a very serious state. Fast Track drugs receive the benefit of more frequent meetings and communication with the FDA, and the drug qualifies for Accelerated Approval and rolling review of the Biologic License Application (BLA) or New Drug Application (NDA).

Breakthrough Therapy – Breakthrough Therapy status can be assigned to drugs that treat a serious condition when preliminary clinical data show significantly improved outcomes compared to treatments currently on the market. Breakthrough Therapies are eligible for: Fast Track designation benefits, extensive FDA guidance on effective drug development early in the development process and organizational commitment, including access to FDA senior managers.

Accelerated Approval – The FDA established accelerated approval regulations in 1992. Accelerated Approval could be given to drugs that met a serious unmet medical need, and approval was based on a surrogate endpoint. Fast forward to 2012 when Congress passed the Food and Drug Administration Safety Innovations Act (FDASIA). This amendment to the Federal Food, Drug, and Cosmetic Act (FD&C Act) allowed approval to be based on either a surrogate endpoint per the 1992 regulations or approval based on an intermediate clinical endpoint. For example, as a result of the 2012 legislation, a cancer drug could be approved based on the surrogate endpoint of increasing the probability of cancer to going into remission or the intermediate clinical endpoint of shrinking tumor size—an outcome that is strongly correlated with the ability to much more successfully treat cancer and induce remission.

These FDA designations are clearly designed to increase the availability and speed to market of drugs treating serious conditions where unmet medical needs exist. Given that nimbleness and speed has historically not been the pharmaceutical industry’s nor FDA’s strong suit—commercialization of a drug has historically taken on average 12 years and cost up to $2.5B (including expenditure outlays and opportunity costs). The ability for these designations to save both time and money is very attractive. However, given the slow-moving nature of the industry, changes in both mindset and approaches are needed by both drug innovators and regulators to validate processes and ensure drug quality within the faster-moving constructs.

Let’s now turn to the most recent Process Validation guidance so that we may juxtapose that system with the nimble needs of Fast Track Designation, Breakthrough Therapy and Accelerated Approval drugs—ultimately, making some observations regarding needed Process Validation and overall regulatory approval approaches as the industry moves towards accelerated development processes for an increasing number of drugs.

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WHAT IS PROCESS VALIDATION?
According to the FDA’s 2011 Process Validation (PV) guidance, “For purposes of this guidance, process validation is defined as 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 product. Process validation involves a series of activities taking place over the lifecycle of the product and process.”

The Three Stages of Process Validation:
Stage 1: Process Design–manufacturing process is defined during this stage and is based on knowledge acquired through development and scale-up activities.

Stage 2: Process Qualification–process design is evaluated to determine if the process is capable of reproducible commercial manufacturing.

Stage 3: Continued Process Verification–ongoing assurance during manufacturing that the process is controlled and the outcome predictable.

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Keys for Successful Validation Include:
• Gaining knowledge from the product and process development
• Understanding sources of variation in the production process
• Determining the presence of and degree of variation
• Understanding the impact of variation on the process and end product
• Controlling variation in a manner aligned with Critical Quality Attributes (CQA) and the risk a given attribute introduces to the process

Process Qualification, a key component of Process Validation, should be based on overall level of product and process understanding, level of demonstrable control, data from lab, pilot and commercial batches, effect of scale and previous experience with similar products and processes. Process Qualification is generally recommended to be based on higher levels of sampling, additional testing and greater scrutiny of process performance than would be typical of routine commercial production.

As we will now explore, some of the demands of Process Qualification and overall Process Validation is severely challenged by the approaches required when bringing a Fast Track, Accelerated Approval or Breakthrough Therapy drug to market.

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NOVEL APPROACHES NEEDED FOR ACCELERATED APPROVALS
Historically, it has taken an average of 12 years and, according to a Tufts Center for the Study of Drug Development (CSDD) report, including expenditures and opportunity costs, an average of ~$2.6 billion to bring a prescription drug to market. This paper will refrain from making editorial comments about this pharmaceutical industry fact; however, the undeniable reality is that the speed required at every point in the industry to develop Fast Track, Accelerated Approval or Breakthrough drugs is having a profound impact.

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Approval of a Breakthrough drug, which of course is classified for Accelerated Approval, means manufacturers need to develop Chemistry, Manufacturing and Controls (CMC) data in about half the time of the traditional process. In addition, Breakthrough designation does not mean the innovator company can do less. In order to meet these accelerated timelines, they do need to start analytical methods creation and product and process characterization sooner, and handle the process differently. Validation of a process traditionally has called for sufficient data and an adequate number of runs to convince the manufacturer (and regulators) that the process works. As we will explore below, Breakthrough therapies are often in the market before the product is fully validated.

However, the guiding force behind these new approaches is that despite sharply reduced timeframes, manufacturers cannot compromise patient safety or product supply. Therefore, characterization of critical product and process attributes is typically required much earlier in the process.

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Challenges and Realities of Process Validation and Regulatory Approval within the Accelerated Drug Paradigm:
• The collaboration and communication required between the FDA and innovator companies is extensive. Given limited FDA resources and extensive resources required by the organizations of innovator companies, is the growth of the Fast Track/Breakthrough Therapy/Accelerated Approval programs sustainable?
• New Drug Applications (NDA) for Breakthrough Therapies include less manufacturing information and data requiring alternative risk-mitigation approaches and often nontraditional statistical models.
• Both patient safety and product supply is at the forefront, without the data and historical knowledge traditionally used to address these concerns.
• The primary concerns for CMC reviewers include incomplete characterization of the drug, underdeveloped analytical methods and a lack of full understanding of a product’s Critical Quality Attributes (CQA) and associated risks.
• Process Validation will, in many cases, be incomplete at product launch.

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THE CHANGED PARADIGM RESTORED TO ORDER (SORT OF)
The “restored order” for the approval of, and ultimate Process Validation for, Breakthrough/Accelerated Approval drugs will not look like anything we normally see. Again, all Breakthrough and Accelerated Approval drugs address very serious conditions and offer treatment where none currently exists, or offers benefits well above and beyond drug products currently on the market. Therefore, flexibility has been applied to segments of the traditional product review and approval process to speed the availability of treatments for these critical conditions.

Despite the flexibility in, and often changes to the product review and approval process, patient safety remains at the forefront, as well as the guarantee of consistent product supply.

Approaches for Successfully Handling the Approval and Validation of Accelerated Approval Drugs:
• Open and transparent communication with the FDA is essential throughout the entire approval and post-market process. The pharmaceutical company mindset of not wanting to learn certain information for fear of needing to revalidate based on those discoveries has no place in this new reality. New information will be learned pre- and post-launch, and plenty of amendments will need to be filed.
• Given the compressed development timeframes, less stability data will be available at submission. Additional data will be submitted via amendments during the review cycle, and in some cases, post-market.
• Launch commercial process with limited experience and optimize post-approval–the classic three runs is not the guiding force within this construct. The level of flexibility regulators will extend is determined for each specific product. Factors taken into consideration include: riskiness of product characteristics, seriousness of the condition and medical need, complexity of manufacturing processes, state of the innovator’s quality system and merits of the innovator’s risk-based quality assessment including Critical Quality Attributes (CQA).
• Novel statistical models and approaches will need to be applied in many cases. Representative samples and assays for these models will likely need to be acquired from sources, like prior knowledge and use of comparability protocols. Also, determination of the appropriate use of stability data from representative pilot scale lots will be required.
• Manufacturers should freely acknowledge where data is limited, demonstrate that the missing data pose no risk to patient safety or product supply and outline post-market strategy for acquiring the missing data. Conversations with the FDA are clearly required for successful outcomes.
• Focus on patient safety and reliable supply of quality product at launch, not process optimization. In addition, begin critical product attributes and process characterization work much earlier than a typical pharmaceutical development process. In many cases, consider broader product quality ranges for non-Critical Quality Attributes until further manufacturing experience is acquired post-approval.

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Enhance analytical methods and understanding to offset more limited process understanding and to support future comparability work. Extremely important, involve commercial Quality Control representatives in the development assay design.
• Again, CMC activities that may be incomplete at launch include: Process Validation, stability studies on commercial product, manufacturing scale/tech transfer data and complete control system data.
• A post-approval product lifecycle management plan is a must, and it needs to be included in the filing to support deferred CMC activities.

Fast Track, Breakthrough Therapy and Accelerated Approval drugs have profoundly changed the thinking and approach to Process Validation and other CMC activities.

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Sources:
Joseph A. DiMasia, Henry G. Grabowskib, Ronald W. Hansenc, “Innovation in the Pharmaceutical Industry: New Estimates of R&D costs,” Tufts Center for the Study of Drug Development, Tufts UniversityJ. Wechsler, “Breakthrough Drugs Raise Development and Production Challenges,” Pharmaceutical Technology 39 (7) 2015.Earl S. Dye, PhD, “CMC/GMP Considerations for Accelerated Development and Launch of Breakthrough Therapy Products,” Roche“Guidance for Industry Expedited Programs for Serious Conditions – Drugs and Biologics,” U.S. Department of Health and Human Services, Food and Drug Administration, Center for Drug Evaluation and Research (CDER), Center for Biologics Evaluation and Research (CBER), May 2014 ProceduralAnthony Mire-Sluis, Michelle Frazier, Kimberly May, Emanuela Lacana, Nancy Green, Earl Dye, Stephan Krause, Emily Shacter, Ilona Reischl, Rohini Deshpande and Joe Kutza, “Accelerated Product Development: Leveraging Industry and Regulator Knowledge to Bring Products to Patients Quickly,” BioProcess International, December 2014

Daniel Alsmeyer and Ajay Pazhayattil, Apotex Inc., “A Case for Stage 3 Continued Process Verification,” Pharmaceutical Manufacturing, May 2014

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Process Validation of Existing Processes and Systems Using Statistically Significant Retrospective Analysis

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Process Validation of Existing Processes and Systems Using Statistically Significant Retrospective Analysis – Part One of Three

The FDA defined Process Validation in 1987 by the following: “Process Validation is establishing documented evidence which provides a high degree of assurance that a specific process will consistently produce a product meeting its predetermined specification and quality attributes.” 1The purpose of this article is to discuss how to validate a process by introducing some basic statistical concepts to use when analyzing historical data from Batch Records and Quality Control Release documents to establish specifications and quality attributes for an existing process. In an ideal world, the qualification of processing equipment, utilities, facilities, and controls would commence at the start up of a new plant or the implementation of a new system. This would be followed by the validation of the process based on developmental data and used to establish the product ranges for in process and final release testing. However, the ideal case may not exist and thus there are incidences where commissioning of facilities or new systems occurs concurrently with the qualification and process validation; or the facility and equipment are “existing” and there is no such documentation. Some facilities, equipment, or processes pre-date the above definition by many years and therefore have never been validated on qualified equipment, utilities, facilities, and controls. Additionally, in some cases, no developmental data exists to establish the product ranges for in process and release testing.

Basics of Process Validation

Before examining the existing processes, it is important to first understand the basic concepts of Process Validation. Figure 1 is a flow chart defining Process Validation from the developmental stage to the plant floor. As a simplistic example, a process begins with the raw materials being released, then the raw materials are mixed, pH is adjusted, purification occurs by gel chromatography, excipients are added for final formulation, and the product is filled and terminally sterilized. Each of these steps has defined functions and therefore would have a designed goal. For example, purification would not begin until the desired pH is reached in the previous step. Therefore, the desired pH is an in process attribute of the pH adjustment stage and the amount of buffer used to adjust the pH is a processing parameter. Each of these steps has attributes that one would want to monitor to determine that the product is being produced acceptably at that step such that the next process step can start. The ranges for these attributes are generally determined by process development data so that if the process attributes are met, then there is a high degree of confidence that the final container is filled with a product of acceptable attributes as determined by the developmental data.

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During Process Validation, there needs to be approved Standard Operating Procedures (SOPs) in place that Plant Operators have been trained on. Analytical testing should be performed by SOPs and the Quality Control (QC) analysts should be trained in these SOPs as well. The analytical tests should have been previously validated by normal analytical methods validation and documented as such. Also, the equipment used to prepare product must be documented to be qualified for its installation, operation, and performance, commonly referred to as IQ, OQ, and PQ. There are methods for performing such qualification retrospectively, but for the purpose of this discussion, it is only important to note that the equipment must be qualified.

Relating Process Validation to Existing Processes

Existing processes may lack developmental data for in process ranges and release testing. If a retrospective analysis of existing data is used to establish process ranges, including input, output, and in process testing parameters, then the process can be treated like a new process by following the basics of Prospective Process Validation. The difference between traditional Prospective Process Validation and Prospective Process Validation based on retrospective analysis is that in place of developmental data to establish ranges, the retrospective analysis reviews data from past Batch Production Records, QC test reports, product specs, etc. Thus the items that need to be in place are: approved SOPs, and Batch Records with personnel training, equipment qualification (IQ, OQ, PQ), QC methods validated, approved SOPs and training for QC personnel. With these in place, all that is missing is a Process Validation Protocol with defined ranges. The best sources of this information are approved completed batch records, process deviation reports, QC Release data, and small-scale studies. From these, the following items must be completed:

  • Critical parameters and input and output parameters must be defined.
  • A statistically valid time frame or number of batches must be determined.
  • The data used to establish the parameters must be extracted from controlled documents.
  • The data extracted from the controlled documents will be analyzed to establish ranges.

Each one of these steps will be examined in the following sections to describe them in further detail.

Critical parameters and input and output parameters defined.

In The Guidelines on General Principles of Process Validation, 15 MAY 1987, it states that:

The validity of acceptance specifications should be verified through testing and challenge of the product on a sound scientific basis during the initial development and production phase.1

It is important to determine which parameters in your process are critical to the final product. When determining these parameters and attributes a variety of personnel with different expertise should be utilized. Assembling a team of professionals is a starting point and this committee should be a multi-disciplined team including Quality, Validation, Systems Engineering, Facility Engineering, Pharmaceutical Sciences (or R&D), and Manufacturing. When determining the parameters and attributes which are critical, it is important to consider those which if they were not controlled or achieved, then the result would have an adverse effect on the product. A risk assessment should be performed to analyze what the risk is and what the results are if a specific parameter or attribute is not controlled or achieved (e.g. the resulting product would be flawed). Risk assessment is defined by The Ontario Ministry of Agriculture, Food and Rural Affairs as:

  1. the probability of the negative event occurring because of the identified hazard,
  2. the magnitude of the impact of the negative advent, and
  3. consideration of the uncertainty of the data used to assess the probability and the impact of the components. 2

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Figure 2 is a list of general questions to consider when assessing risk while Figure 3 is an example of Fault Tree Analysis – a formal approach to evaluating risk, where a Top Level Event is observed and through questions and observations the cause of the event can be determined.

National Center for Drugs and Biologics and National Center for Devices and Radiological Health, “Guidelines on General Principles of Process Validation,” Rockville MD. 15 MAY 1987.National Center for Drugs and Biologics and National Center for Devices and Radiological Health, “Guidelines on General Principles of Process Validation,” Rockville MD. 15 MAY 1987.Ontario Ministry of Agriculture, Food and Rural Affairs (2000), Queen’s Printer for Ontario, Last Updated March 22, 2000; WEB:http://www.gov.on.ca/omafra/english/research/risk/assum1b.html.

Process Validation of Existing Processes and Systems Using Statistically Significant Retrospective Analysis – Part Two of Three

A Statistically Valid Time Frame or Number of Batches

How large of a sample set is needed of previously recorded data to determine ranges that are truly representative of the process, and will the ranges be useful in the Validation effort and not set one up for failure? This is a difficult question to answer, and it is important to note that the batches selected should have no changes between them, thus be produced with the same processing conditions. The draft FDA Guidance for Industry, Manufacturing, Processing, or Holding Active Pharmaceutical Ingredients from March of 1998 suggests that 10-30 consecutive batches be examined to assess process consistency. 1

This is a good target statistically because when selecting a sample size or population, the concept of Normality or Degree of Normality becomes important. As a general rule of thumb, the population should be large enough that the distribution of the samples within the population approaches a Normal or Gaussian distribution (one defined by a bell-shaped curve). Thus in theory if the samples are normally distributed then 99% of the samples will fall within the +/- 3 Standard Deviation (S.D.) range. 2 When considering this, one should think in the sense of Statistical Significance (the P-Value). The P-Value is the significance that a given sample will be indicative of the whole population. Thus at 99% (a P-Value of 0.01) then a given sample has 1% chance of falling outside the +/- 3 S.D. range or (assuming no relationship with other variables) the sample has a 99% statistical significance as representative of the population. Finally, when considering the central limit theorem, which has the underlying concept that as the sample size gets larger, the distribution of the population becomes more normal, then in general a sample size of 10 – 30 as the FDA suggests would have a high chance of being distributed normally.

The data used to establish the parameters must be extracted from controlled documents.
When the number of batches to review is selected, the next step is to determine from what documents the processing data will be extracted. Typically the range establishing data must be taken from approved and controlled documents (see the examples below).

Examples of Controlled Documents:

  • Batch Production Records (BPRs)
  • Quality Control (QC) Lab Reports
  • Limits establish by Licensure
  • Product License Agreement (PLA)
    – Biologic License Agreement (BLA)
    – New Drug Application (NDA) or Abbreviated (ANDA)
  • Product Release Specifications
  • Small scale bench studies simulating plant environment.

The data extracted from the controlled documents will be analyzed to establish ranges.
Having established where the data will be selected from, the data must then be analyzed for specific trends such to define ranges for the Process Validation Protocol’s acceptance criteria. This acceptance criteria will be what the “Actual” process data collected during the execution of the Protocol will be compared to, in order to verify its acceptance. This part is where much thought needs to be applied so that the acceptance criteria are not so tight that failure is eminent or so broad that the achievement of the criteria proves nothing. Listed below are general steps that can be incorporated to determine the analysis.

  1. Draw “X Charts” and analyze for outliersApply a model to determine Normality
  2. Determine the +/- 3 S.D. range and plot on Trend Chart
  3. Determine the Confidence Interval as compared to +/- 3 S.D.
  4. Process Capability Indices
  5. Recording the Maximum and Minimum
  6. Assign Acceptance Criteria Range and justify

Drawing Trend Charts

Trend Charts, also referred to as X-Charts, are a good way of plotting data points from a set of data where the target is the same metric (for example pH as measured at a specific point in the process). It is a matter of defining the X-axis by the number of samples and the Y-axis by the metric that is being used. As an example, the X-axis could be a list of the batches by batch number and the Y axis could be pH. Figure 4 is an example of a type of trend chart. This way the data is presented graphically and can be appreciated with respect to setting a range.

With the data plotted, one can quickly assess any visible trends in the data. Additionally one can no begin the task of applying statistics to the data. It is important to determine if there are outliers in the data. Outliers may exist and can usually be rationalized by adverse events in processing as long as they are reported appropriately. Outliers can also exist as samples that are “statistically insignificant.” As mentioned before, the P-Value is the significance that a given sample will be indicative of the whole population so that outliers would have a very low P-Value. One method for determining outliers is to use a box-plot where a box is drawn from a lower point (defined typically by the 25th percentile) to an upper point (typically the 75th percentile). The “H-spread” is defined as the distance between the upper and lower points. 3 Outliers are then determined to be any data that falls outside a predetermined multiplier of the H-spread. For example the lower outlier limit and upper outlier limit are defined as 4 times the H-Spread, anything above or below these limits is statistically insignificant and are outliers.

Apply a model to determine Normality

With the accumulated data plotted, the Degree of Normality should be investigated so that the data can be analyzed by the appropriate method. There are several models for determining the Degree of Normality; some common ones are the Kolmogorov-Smirnov test, Chi-Square goodness-of-fit test, and Shapiro-Wilks’ W test. 4 Once the Degree of Normality is determined a more appropriate statistical method can be applied for setting ranges. If the data is determined to be non-Normal than there are two approaches to evaluating the data. The first way is to apply a Nonparametric Statistical model (e.g. the Box-Cox Transformation 5 ), however, these tests are considered to be less powerful and less flexible in terms of the conclusions that they provide, so it is preferred to increase the sample size such that a normal distribution is approached. 5 If the data is determined to be Normal or the sample size is increased such that the data is distributed more normally, then the data can be better analyzed for it’s range characteristics.

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Determine the +/– 3 SD range and plot on Trend Chart

The data having now been displayed graphically should be analyzed mathematically. This can be done by using simple statistics where the mean is determined as well as the standard deviation. The mean refers to the average of the samples in the population. The standard deviation is the measure of the variation in the population from the mean. If the distribution proves to be normal, as from our normality tests above or by selecting a large enough population such that the central limit theorem predicts the distribution to be normal, then it stands that 99% of the data will fall within the +/–3 SD range. Using our example from Figure 4, the data is analyzed for its mean and standard deviation using the displayed formulas in Figure 5. Once this is determined, the +/– 3 SD can be applied to the trend charts by drawing them as limits at their values. This graphically displays the data as it is applied per batch and how it fits within the statistical limit of +/– 3 SD (see Figure 6.)

  1. U.S. Department of Health and Human Services, Food and Drug Administration, Center for Drug Evaluation and Research, Guidance for Industry, Manufacturing, Processing, or Holding Active Pharmaceutical Ingredients, March 1998, 36
  2. StatSoft, Inc. (1999). Electronic Statistics Textbook. Tulsa, OK: StatSoft. WEB: http://www.statsoft.com/textbook/stathome.html
  3. Rice Virtual Lab in Statistics (1993-2000), David M. Lane, HyperStat Online, Houston TX, WEB:http://www.ruf.rice.edu/~lane/rvls.html
  4. StatSoft, Inc. (1999). Electronic Statistics Textbook. Tulsa, OK: StatSoft. WEB: http://www.statsoft.com/textbook/stathome.htm
  5. Box, G.E.P., and Cox, D.R. (1964), “An Analysis of Transformations,” J. Roy. Stat. Soc., Ser. B.

Process Validation of Existing Processes and Systems Using Statistically Significant Retrospective Analysis – Parts Three of Three

Determine the Confidence Interval as compared to +/- 3S.D. With the +/- 3 S.D. ranges determined, it can be considered important to evaluate what confidence there is that the next data point will fall within this range. The rationale for determining this level is to justify that the +/- 3 S.D. range provides a confidence that 99% of the data is within that range. Similar to the +/- 3 S.D. range, the confidence interval is a range between which the next measurement would fall. This level is typically 99% or greater. Thus a 99% confidence interval means, “there is 99% insurance that the next value would be in the range.” To calculate a 99% Confidence Interval, one needs to consider the area under the standard normal curve, the mean, the standard deviation, and the population size. Determining this level can be done using the formula in Figure 7. The confidence interval can be added to our previous example and is displayed in Figure 8.

Figure 8: Trend Chart with +/- 3 S.D. and Confidence Interval.

Figure 7: Definition of Confidence Interval Formulas.

Following our example:

Confidence Interval (99.9%) = 6.23 ± snc ( 0.151202/(30) 1/2)

Confidence Interval (99.9%) = 6.95 to 6.02

The confidence interval at 99.9% is slightly more narrow than the +/- 3 S.D. range which follows the trend if the +/- 3 S.D. range provides that 99% of the data will be within the range if the data is normally distributed.

As can be seen in the trend charts, the data fits well within the +/- 3 S.D. range, and therefore the confidence level is very high that the next data point that is collected will be within this range. Therefore this range may be appropriate to use as acceptance criteria based on the statistics. If the confidence level was wider than the +/- 3 S.D., then the data would have to be analyzed such to investigate if there were errors in calculating the degree of normality, the +/- 3 S.D., the confidence level, the outliers, or errors in the sampling technique to show that it was not computational error.

Process Capability Factors

Another method to setting ranges to be used as acceptance criteria are the Process Capability Indices defined by:

CPu = (USL – µ) / s (or in our example 3 S.D.)

CPl = (µ – LSL) / s (or in our example 3 S.D.)

Where:

USL = Upper Specification Limit

LSL = Lower Specification Limit

An industry accepted standard CP would be 1.33.1 This would mean that only 0.003% of the testing results would be out of the specification or 99.997% would be within the specification. This is a similar concept to confidence level.

Recording the Maximum and Minimum

Recording the maximum and minimum values in the data is important because it is a quick way to see if the data is all within the +/- 3 S.D. range. Additionally, if the maximum and minimum are within the +/- 3 S.D. range, than there is an additional level of confidence since all of the data would be within the range. Lastly, the data may be determined to be non-normally distributed and in such case, confidence may predict to high a possibility for failure at the +/- 3 S.D. range so in the interim, the maximum, and minimum values can be selected to be the range until further data can be collected to define the range (this refers back to increasing the sample size in order to approach a more normal distribution).

Assign Acceptance Criteria Range and Justify

Using all of the above analysis techniques, knowledge of the process and agreement on by a cross-discipline committee, acceptance criteria ranges can be assigned for the critical parameters and attributes. A general course of action would be to start by recording all the data at a given point in a spreadsheet, calculating the mean, S.D., population size, +/- 3 S.D., 95% and 99% confidence intervals, plotting the trend charts with appropriate ranges, and then deciding on which range makes the best sense. When selecting the acceptance criteria, a cross-functional committee should be utilized with backgrounds of QA, Manufacturing, Validation, R&D, and Engineering present. The

ranges should be selected and justified by scientifically sound data and conclusions. The ranges should be within the PAR for the product, which means that if +/- 3 S.D. is selected, the range should be checked at the upper and lower limits to verify that acceptable product is prepared. This should be done prior to a final agreement on the range and incorporation into the validation protocol. A report should be written to document the ranges with the rationale for selecting them and the justification for determining the limits as well as any determination that the ranges are within the PAR. Additionally, those ranges which are not to be included should be discussed within the report to justify why they are not to be recorded. A process validation protocol should be prepared with theses ranges for acceptance criteria and the process should be run at a target within the acceptance criteria ranges at least three consecutive times using identical procedures to verify that the process is valid.

Summary

Since the ideal case of validating a process during its implementation does not always exist in the pharmaceutical, biopharmaceutical, biotechnology or medical device industries, it may be important to determine a way to validate these processes using historical data. The historical data can be found in a variety of places as long as it is approved (e.g. approved and completed BPRs or quality control release documents, etc). A cross-functional team should perform a risk assessment on the parameters and attributes to determine which ones would be included in the process validation. A range establishing study for the attributes and parameters should be performed to evaluate historical data and analyze the data set for the concepts of normality, variation (standard deviation), and confidence. With a high degree of confidence, acceptance criteria ranges should be set for each parameter and attribute and a process validation protocol should be written with the appropriate ranges. This protocol should be approved and executed at target settings within the acceptance criteria ranges, from the start of the manufacturing process to the finish using qualified equipment, approved SOPs, and trained operators. In a final report for the process validation, the degree to which the process is valid would be determined by the satisfaction of the approved acceptance criteria.

References

1. Box, G.E.P., and Cox, D.R. (1964), “An Analysis of Transformations,” J. Roy. Stat. Soc., Ser. B., 26, 211.

2. Kieffer, Robert and Torbeck, Lynn, (1998), Pharmaceutical Technology, (June), 66.

3. Lane, David M., Rice Virtual Lab in Statistics (1993-2000), HyperStat Online, Houston TX, WEB: http://www.ruf.rice.edu/~lane/rvls.html.

4. National Center for Drugs and Biologics and National Center for Devices and Radiological Health,(1987) “Guidelines on General Principles of Process Validation,” Rockville MD. 15 MAY.

5. Ontario Ministry of Agriculture, Food and Rural Affairs (2000), Queen’s Printer for Ontario, Last Updated March 22, 2000; Web: http://www.gov.on.ca/omafra/english/research/risk/assum1b.html.

6. StatSoft, Inc. (1999). Electronic Statistics Textbook. Tulsa, OK: StatSoft. WEB: http://www.statsoft.com/textbook/stathome.html.

7. U.S. Department of Helath and Human Service, Food and Drug Administration, Center for Drug Evaluation and Research, Center for Biologics Evaluation and Research, Center for Veterinary Medicine, “Guidance for Industry: Manufacturing, Processing, or Holding Active Pharmaceutical Ingredients,” Rickville MD. March 1998, 36.

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Statistics and Process Validation: current Findings of the FDA

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The “new” FDA’s process validation guideline has been effective since January 2011. One considerable change was made to the original validation guideline from 1987 to put a significantly greater emphasis on statistics in the context of process validation. So far, relatively few inspection deficiencies had been observed by the FDA with regard to statistics. At a conference in September 2015 co-sponsored by the FDA, Grace McNally – Senior FDA official – reported about current “findings” in the 483s deficiency reports and in Establishment Inspection Reports (EIR). Now, deficiencies regarding statistical problematics can also be found here.

For example, it has been criticised that a (statistical) sampling plan had be misinterpreted. Wrong AQL values with regard to the number of samples have been noted based on MIL-STD-105D. Moreover, it has been criticised that the company didn’t know the operation characteristics of its sampling plan.

Another criticised “finding” was that PPQ batches had been considered as “accepted” when all in-process controls and release specifications were met. It has also been criticised that no intra-batch variabilities have been examined. In addition, it has been noticed that there was no information available in the validation plan concerning the assessment of the process itself. There was also no indication about the objective of the determination of inter-batch variabilities.

Although OOS results had been found in 2 out of 4 PPQ batches, reduced IPC tests have been recommended in the PPQ report giving the justification that this was a standard procedure. Regarding this point, the FDA criticises the lack of scientific rationales for reduced sampling and monitoring. Interestingly, Grace McNally mentions possibilities for rationales of IPC sampling plans and the adaptation to a reduced size. In this context, she refers to the ANSI/ASQ Z1.4 norm and ISO 2859 whereby it is expressly pointed out that the ANSI norm recommends the production of at least 10 successful batches before reducing testing. According to the ISO norm even 15 successful batches are necessary.

The FDA notified a tablet process, criticising the fact that no rationales for warning and action limits were available. Furthermore, it has been criticised that no analyses on variabilities were available although they had been required internally and no capacity indices had been determined. There have been no analyses on the distribution of data, neither planned nor performed. The FDA also remarked that the calculation of variabilities is necessary to be able to make statements about process capacities.

Conclusion: Reinforcing the emphasis on statistics in the US FDA Process Validation Guideline from 2011 hasn’t been really often addressed in the official deficiencies reports. This seems to be changing.

see………http://www.gmp-compliance.org/enews_05077_Statistics-and-Process-Validation-current-Findings-of-the-FDA.html

 

////////////Statistics, Process Validation,  current Findings,  FDA

Determining Criticality-Process Parameters and Quality Attributes

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Determining Criticality-Process Parameters and Quality Attributes Part I: Criticality as a Continuum

A practical roadmap in three parts that applies scientific knowledge, risk analysis, experimental data, and process monitoring throughout the three phases of the process validation lifecycle.
 

As the pharmaceutical industry tries to embrace the methodologies of quality by design (QbD) provided by the FDA’s process validation (PV) guidance (1) and International Conference on Harmonization (ICH) Q8/Q9/Q10 (2-4), many companies are challenged by the evolving concept of criticality as applied to quality attributes and process parameters. Historically, in biopharmaceutical development, criticality has been a frequently arbitrary categorization between important high-risk attributes or parameters and those that carry little or no risk. This binary designation was usually determined during early development for the purposes of regulatory filings, relying heavily on scientific judgment and limited laboratory studies.

Figure 1: Process validation lifecycle.

With the most recent ICH and FDA guidances endorsing a new paradigm of process validation based more on process understanding and control of parameters and less on product testing, the means of determining criticality has come under greater scrutiny. The FDA guidance points to a lifecycle approach to process validation (see Figure 1). “With a lifecycle approach to process validation that employs risk-based decision making throughout that lifecycle, the perception of criticality as a continuum rather than a binary state is more useful.” The problem is that a practical approach of determining this criticality “continuum” using risk analysis has been left to each company to develop.

This article presents the first part of a practical roadmap that applies scientific knowledge, risk analysis, experimental data, and process monitoring throughout the three phases of the process validation lifecycle to first determine and then refine criticality. In this approach, criticality is used as a risk-based tool to drive control strategies (Stage 1), qualification protocols (Stage 2), and continued process verification (Stage 3). Overall, a clear roadmap for defining, supporting and evolving the criticality of parameters and attributes throughout the process-validation lifecycle will allow pharmaceutical companies to easily embrace the new process-validation paradigm. Furthermore, processes will be more robust and continuous improvement opportunities more easily identified.

In Part I of this series, the author used risk analysis and applied the continuum of criticality to quality attributes during the process-design stage of process validation. After using process knowledge to relate the attributes to each process unit operation, the inputs and outputs of each unit operation were defined to determine process parameters and in-process controls. An initial risk assessment was then completed to determine a preliminary continuum of criticality for process parameters.

DRUG APPROVALS BY DR ANTHONY MELVIN CRASTOhttp://newdrugapprovals.wordpress.com

In Part II, the preliminary risk levels of process parameters provided the basis of characterization studies based on design of experiments (DOE). Data from these studies were used to confirm the continuum of criticality for process parameters.

In Part III, the control strategy for the process was developed from a design space established from characterization studies. As the process-qualification stage proceeds, the continuum of criticality was used to develop equipment qualification criteria and strategies for process performance qualification. Finally, in the continued-process-verification stage of process validation, criticality was used to determine the frequency of monitoring and analysis.

From binary to continuum
On the surface, deciding whether an attribute or parameter is critical or not may seem clear and simple. After all, data are compared to acceptance criteria in countless decisions regarding clinical trials, experimental studies, qualifications, and product release. Either the acceptance criteria are met, or they are not. Companies that take this familiar path have tried to draw a definitive line between the “critical” and “not critical” sides. Once a decision has been made about criticality, there is no need to look again. It doesn’t help that the guidance documents for industry have been vague on where this criticality threshold lies. The FDA’s PV guidance avoids the issue: “attribute(s) … and parameter(s) … are not categorized with respect to criticality in this guidance” (1).

ICH Q8(R2) provides the following definitions using the term critical:

Critical process parameter (CPP). A process parameter whose variability has an impact on a critical quality attribute and, therefore, should be monitored or controlled to ensure the process produces the desired quality.
Critical quality attribute (CQA). A physical, chemical, biological, or microbiological property or characteristic that should be within an appropriate limit, range, or distribution to ensure the desired product quality.

This interpretation of CQA is most applicable to in-process and finished-product specification limits, which suggests that these limits must be critical given that they were designed to ensure product quality. During the early stages of process development and design, other quality attributes may be measured that, over the course of development, do not end up as either in-process or finished-product tests in the commercial process. These test results may show little variation and present little to no risk to product quality. In other cases, while process duration or yield is measured, they are not related to the product quality and are, therefore, not CQAs. However, even when defined as critical, not all CQAs have equal impact on safety and effectiveness (3, 5).

The definition for CPP states that a parameter is considered critical when its variability has an impact on a CQA. The amount of impact is not defined, which leads to the question, does even a small impact to a CQA mean that the parameter is critical? It is not difficult to imagine the example of an extreme shift of a process parameter having a minor impact on a CQA, whether measurable or not. Extreme temperatures can destroy many pharmaceutical products; however, if a process inherently cannot produce such temperatures, is temperature still considered to be critical and, therefore, required to be monitored and controlled?

When these definitions are strictly interpreted, some companies find themselves in one of two extremes:

• Every quality attribute is critical (they all ensure product quality); every parameter is critical (product cannot be made without controlling them)
• No parameter is critical because if they are controlled, all quality attributes will pass specifications.

Reality lies somewhere between these extremes. Logic and common sense dictate that additional criteria must be necessary to aid in determining criticality. There is great value in understanding not only if a parameter/attribute is critical (i.e., has an impact), but also how much impact the parameter/attribute has. All companies have limited time and resources; therefore, the focus must be on that which provides the greatest benefit for the effort

By using risk analysis as a means to determine criticality, an opportunity arises to help resolve these potential conflicts. CQAs should be classified based on the potential risks to the patient. CPPs should be separated into those that have substantial impact on the CQAs and those with minor or no impact. The binary yes/no decision transforms into a continuum of criticality ranging from high impact to low impact critical to not critical. As knowledge increases or as improvements are made to a process throughout the lifecycle, risks may be reduced and the level of impact for a CPP can be modified and control strategies adjusted accordingly

The number of levels in the continuum is a matter of choice and the risk analysis method used. Each company must procedurally define what risk tools and risk levels it will use and consistently apply them across similar products. In the following examples, three levels of impact are used for simplicity’s sake. The different levels drive decision-making and action plans throughout the lifecycle.

For CQAs, a continuum of criticality provides a tool to designate particular attributes as the most important to the protection of the patient. For CPPs, a continuum of criticality allows for process control and monitoring strategies to focus where the greatest impact on product quality is achieved.

A practical roadmap in three parts that applies scientific knowledge, risk analysis, experimental data, and process monitoring throughout the three phases of the process validation lifecycle.

Quality risk management
Risk is the combination of the probability of occurrence of harm and the severity of that harm (5, 6). The value of risk assessment models is the formalized evaluation criteria that comes from agreed-upon ranking tables. Even though some may argue that the assessment is not quantitative, the benefit derived from framing the evaluation to an agreed upon risk criteria dramatically improves the ability to objectively evaluate the process risk profile. Per ICH Q9, there are two primary principles of quality risk management (3):

• The evaluation of risk to quality should be based on scientific knowledge and ultimately link to the protection of the patient
• The level of effort, formality, and documentation of the quality risk management should be commensurate with the level of the risk.

Formal risk management tools such as failure mode effects analysis (FMEA) or failure mode effects and criticality analysis (FMECA) (7) can be used to provide a structured semi-quantitative summary of risk. For Stage 1, however, often a qualitative risk assessment evaluating low, medium, and high risk is sufficient to distinguish relative differences in risk.

Continuum of CQAs
Prior to the development of a new drug, companies frequently decide and document a therapeutic need in the marketplace for a new pharmaceutical. It is through this effort that the quality and regulatory aspects of the new drug are defined such as the type of dosage form, the target dose, the in-vivo drug availability, and limit of impurities. Current guidance identifies this documentation as the quality target product profile (QTPP). The QTPP provides the basis of the desired quality characteristics of the drug product, taking into account safety and efficacy (i.e., purity, identity, strength, and quality). The QTPP should not be confused with the drug product specification, which created later, is generally a list of specific test methods to perform and their acceptance criteria designed to ensure drug efficacy and safety. The QTPP is an input to these activities whereas the quality attributes and specifications are outputs.

The initial list of quality attributes from the QTPP should be created as early as possible in the development process so that data can be collected from experimental runs. To assign the continuum of criticality to that initial list of quality attributes, knowledge of the severity of the risk of harm to the patient is paramount. This comes from prior knowledge such as early safety trials and scientific principles.

Quality attributes are rated as the highest criticality level because they have a high severity of risk of harm. Severity is the primary criteria for assessing quality attribute criticality because it is unlikely to change as understanding increases over the life-cycle. For example, an impurity may be determined to severely harm the patient (high severity score) if beyond its limit. If its level does not increase in the process or on stability testing, the occurrence score is low and its overall risk to the patient may be low. However, it is still rated as high risk due to its high severity. That severity will not change and as a high-risk CQA, it has to be tested and monitored.
Examples of risk levels for CQAs:

• High: assay, immunoreactivity, sterility, impurities, closure integrity
• Medium: appearance, friability, particulates
• Low: container scratches, non-functional visual defects.

For a quality attribute to be designated as “not critical,” it has to have no risk to the patient (e.g., yield, process duration). Attributes that are not critical to quality are sometimes named process performance attributes to distinguish from quality attributes.

Not all CQAs are tested as part of finished-product testing. Some are tested in-process to define limits such as pH and conductivity. Although frequently designated as “in-process controls,” they are still quality attributes that should be assessed for their criticality. Consideration should be given to the relationship between in-process controls and finished-product CQAs when making this decision. While this is one example of how to assign a continuum of criticality to quality attributes, other examples are also available (8-10).

A practical roadmap in three parts that applies scientific knowledge, risk analysis, experimental data, and process monitoring throughout the three phases of the process validation lifecycle.
Dec 01, 2013
Volume 26, Issue 12

Cause-and-effect matrix
Once risk levels have been assigned to the CQAs, the next activity is to begin to relate which parts of the process have impact on these attributes. This cause-and-effect analysis breaks the process into its unit operations and conveys its impact on the CQAs. An example of the cause-and-effect matrix for a biologic is given in Table I.

Table I: Example of cause and effect matrix for a biologic. H = high, M = medium, L = low.

Critical quality attribute (risk level):

Unit operations

Preculture & expansion

Fermen-tation and harvest

Centri- fugation

Cation exchange chroma-tography

Anion exchange chroma-tography

Viral filtration

Concen-tration & dia-filtration

Vial filling

Appearance (M)

Low

Low

Medium

No

No

Low

Low

Low

Impurities (H)

Medium

High

Medium

High

High

Medium

Low

Low

Protein content (H)

Low

High

Medium

Medium

Medium

Low

Medium

No

Immunoreactivity (H)

Low

Medium

Low

Low

Low

Low

Low

No

Purity (H)

Low

Low

Low

Medium

Medium

Medium

Low

No

pH and ionic strength (M)

Low

Low

No

Low

Low

Low

Medium

No

Amino acid content/ratio (H)

Medium

Low

No

Low

Low

No

No

No

Bioburden (H)

Low

Low

No

High

High

Medium

Medium

Low

In-process controls
Fill weight check (M)

No

No

No

No

No

No

No

High

Visual inspection (M, L)

No

No

Medium

No

No

Low

Low

Medium

In addition to the matrix, it is important to document the justification for these decisions as part of this analysis. For example, the parameters of the cation- and anion-exchange chromatography processes are expected to have a high impact on impurities because they are designed to remove impurities of different ionic charge than the desired product. With the knowledge of which unit operations have impact on particular CQAs, it is now possible to analyze each process’ inputs and outputs to determine how process parameters affect the CQAs.

Input and output process variables
Each unit operation has both input and output process variables. Process parameters signify process inputs that are directly controllable and can theoretically impact CQAs. Process outputs that are not directly controllable are attributes. When the attribute ensures product quality, it is a CQA. The output of one unit operation can also be the input of the next unit operation. These parameters and attribute designations and their justification should be documented in either a formal process description document, or process flow diagram/drawing. This documentation should also include the scale of each unit operation, equipment and materials required, sampling/monitoring points, test methods, and relevant processing times and storage times/conditions.

The intent of assessing process parameters is to determine how they affect the process variation of CQAs along with their control strategy. Each company should clearly document their methodology for defining and assessing parameters. The following may be considered in making that assessment:

• Raw material attributes are outputs of the release of materials. Critical material attributes (CMA) should be considered along with CPPs as impacting process variability.
• Fixed parameters such as equipment scale, equipment setup, pre-programmed recipes should be documented but are assessed as either low or non-critical.
• Parameters for sterilization processes and cleaning process and the preparation of process intermediates can be included in the primary process assessment. Alternatively, they can be treated as independent processes with their own process parameters, quality attributes, criticality assessments, and process validation.
• Calibration and standardization setting for equipment and instruments are usually not included as process parameters.
• Formulation recipes can be considered fixed parameters (low or not critical); these parameters generally have relatively tight limits, which are justified during formulation development. Such a parameter, which does not vary, cannot impact process variability. An exception to this rule is the case where operators must calculate a quantity based on a variable input such as biological activity; this variable process parameter may lead to process variation.
• Holding/storage times and conditions where no processing occurs should be qualified to show little to no impact on the product. These should be documented and, if these factors are included as process parameters, they are considered low or non-critical.
• Environmental conditions during process (room temperature, humidity), such as holding times, are to have set limits so that they have little to no impact to the process. Process-specific environmental conditions such as cleanrooms, cold rooms, and dry rooms are included as process parameters because they are monitored to ensure product quality.

When a process parameter is determined to be non-critical either by process knowledge or by process study, companies may choose to further designate the parameter as a key performance parameter if that parameter impacts a process performance attribute.

From knowledge to risks
Once each unit operation is related to CQAs through a cause-and-effect matrix and the process parameters and attributes are documented, an initial risk assessment to determine the potential impact of each process parameter is performed. Prior to process characterization experiments, this risk assessment may be more high level using primarily prior knowledge and scientific principles. However, a more formal FEMCA may also be considered.

Table II is an example of an initial risk assessment for a single unit operation. Included in the justification is the expected relationship with CQAs and how the parameter may be influenced during scale-up. Fixed parameters are set to non-critical as they do not impact process variability. For the initial process characterization experiments, process parameters with medium to high impact will be included.

Table II: Example of initial risk assessment of process parameters

Process parameters Initial
risk assessment
Justification
Inoculum in-vitrocell age Low
  • Separate end of production studies have justified limit of cell age.
Osmolality Medium
  • Can affect impurities and cell viability.
  • Keep constant in scale-up.
Antifoam concentration No
  • Knowledge from previous studies have defined acceptable range to have no impact to quality.
  • Keep constant in scale-up.
Nutrient concentration Medium
  • Must be sufficient to maintain cell viability.
  • Keep constant in scale-up.
Medium storage time and temperature No
  • Knowledge of medium storage from previous studies.
  • No effect when kept within pre-established limits.
Medium expiration (age) No
  • No effect when kept within pre-established limits.
Volume of feed addition Medium
  • Related to component concentration.
  • Scale by fermentor volume.
Component concentration in feed Medium
  • Yield impact and impacts cell viability.
  • Related to volume of feed addition.
  • Keep constant in scale-up.
Amount of glucose Low
  • Glucose fed as needed to maintain cell viability. leading to different cell concentrations.
  • Scale by fermentor volume.
Dissolved oxygen High
  • Must be sufficient to maintain cell viability.
  • Impacts yield by low cell growth.
  • Controlled by rate of aeration.
  • Scale to large scale by pre-defined model.
Temperature High
  • Impacts cell growth and viability.
  • Keep constant on scale up.
pH High
  • Impacts cell growth and viability.
  • Keep constant on scale up.
Agitation rate Low
  • Speed set by previous process experience.
  • Scale to large scale by pre-defined models.
Culture duration (days) High
  • Related to nutrient concentration for cell viability.

In Part I of this series, the author looked at criticality as a continuum to apply risk analysis during process design, and to relate process unit operations to quality attributes using a cause-and-effect matrix.

In Part II, the continuum of criticality for parameter and attributes will be used to design process characterization studies using DOE. From the initial risk assessment of critical parameters, experimental data from formal studies will confirm the criticality assignment—critical or not—and help to assess the level of impact to CQAs.

References
1. FDA, Guidance for Industry, Process Validation: General Principles and Practices, Revision 1 (Rockville, MD, January 2011).
2. ICH, Q8(R2) Harmonized Tripartite Guideline, Pharmaceutical Development, Step 4 version (August 2009).
3. ICH, Q9 Harmonized Tripartite Guideline, Quality Risk Management (June 2006).
4. ICH, Q10, Harmonized Tripartite Guideline, Pharmaceutical Quality System(April 2009).
5. ICH, ICH Quality Implementation Working Group Points to Consider (R2), ICH-Endorsed Guide for ICH Q8/Q9/Q10 Implementation (6 December 2011).
6. ISO/IEC Guide 51: Safety Aspects-Guidelines for their inclusion in standards, 2nd ed. (1999).
7. IEC 60812, Analysis Techniques for System Reliability-Procedure for Failure Mode and Effects Analysis (FMEA), Edition 2.0 (January 2006).
8. ISPE, Product Quality Lifecycle Initiative (PQLI) Good Practice Guide, Overview of Product Design, Development, and Realization: A Science- and Risk-Based Approach to Implementation (October 2010).
9. ISPE, Product Quality Lifecycle Initiative (PQLI) Good Practice Guide, Part 1-Product Realization using QbD, Concepts and Principles (2011).
10. Parenteral Drug Association, Technical Report 60, Process Validation: A Lifecycle Approach (2013).

part 2

The most recent FDA (1) and International Conference on Harmonization (ICH) (2-4) guidance documents advocate a new paradigm of process validation based on process understanding and control of parameters and less on product testing. Consequently, the means of determining criticality has come under greater scrutiny. The FDA guidance points to a lifecycle approach to process validation (see Figure 1).

Figure 1: Process validation lifecycle.

In Part I of this series, the author introduced the concept of continuum of criticality and applied it to the concepts of critical quality attributes (CQAs) and critical process parameters (CPPs). In the initial phase, the CQAs had their criticality risk level assigned according to the severity of risk to the patient. Applying a cause-and-effect matrix approach, the potential impact of each unit operation on the final product CQAs was assessed and each unit operation was thoroughly analyzed for its directly controllable inputs and outputs. Finally, a qualitative risk analysis or a formal failure mode effects and criticality analysis (FMECA) was conducted for each of the identified process parameters. The purpose of this assessment is to provide a focus for the downstream process characterization work required to complete process validation Stage 1 (process design).

This initial risk assessment is performed prior to the baseline characterization work and can be used as the primary means of determining the criticality of process parameters under the following conditions:

• When a platform process that possesses similar properties and process to another commercial product (e.g., new strength or new dosage form)
• When there is a significant body of published data on the process
• When experimental studies and commercial data are available, such as when the process validation lifecycle is applied to a legacy product to substantiate the initial assessment.

In these cases, this initial assessment can be further bolstered through the addition of an uncertainty component to the traditional risk score. For example, a high-risk critical parameter with low uncertainty (due to substantial supporting data) may not require further study, but a medium-risk parameter with high uncertainty may require further experimentation to quantify the risk to product performance.

The challenge facing most organizations is how to effectively evaluate the impact of potentially hundreds of process parameters on product performance to determine what is truly critical. Few companies have the time or resources to design experimental studies around all potentially critical process parameters. The initial risk assessment provides a screening tool to sort out the parameters that have low or no risk.

Design space and design of experiments
The goal is to increase process knowledge by providing a mechanistic understanding of the relationship between process parameters, raw material attributes, and CQAs. This is defined as both the demonstration of impact and the quantification of the contribution of each parameter to the product’s performance. Through this exercise, it will be possible to identify the process design space. The ICH guidance defines three elements–knowledge space, design space, and control space–to establish a process understanding (seeFigure 2) (2).

Figure 2: Knowledge, design, and control space.

ICH Q8 defines design space as, “The multidimensional combination and interaction of input variables (e.g., material attributes) and process parameters that have been demonstrated to provide assurance of quality.”

The design space is part of an enhanced process development approach referred to as quality by design (QbD). Prior to QbD, pharmaceutical development did not require the establishment of functional relationships between CPPs and CQAs. Consequently, process characterization experiments were primarily univariate (one factor at a time [OFAT]), showing that, for a given range of a process parameter (referred to as proven acceptable range or PAR), the CQAs meet acceptance criteria. While univariate experiments can provide some limited knowledge, a compilation of OFAT studies cannot typically define a design space because it cannot substantiate the importance or contribution of the parameter to the product CQA being evaluated. To do this, multivariate studies must be performed to account for the complexities of interactions when several CPPs vary across their control ranges.

Design spaces can be developed for each unit operation or across several or all unit operations. Although it may be simpler to develop for each unit operation, downstream unit operations may need to be included to sample and test the appropriate CQAs. For example, to perform a multivariate study on a fermentation unit operation, additional processing through cell lysis and purification unit operations is needed so that CQAs may be sampled and tested. The challenge faced by most development programs is how to efficiently and cost-effectively derive maximum process understanding in the fewest number of studies. To do this, a staged approach using multiple studies is most efficient.

A staged design of experiment approach
The following is an example of a simple staged design of experiment (DOE) approach. More complex DOE designs and strategies may be required, but these designs are typical:

Screening (fractional factorial, Plackett-Burman). To identify or screen out process parameters that have no significant impact on a CQA. Screening designs can test main (individual impact and contribution) effects of each parameter being evaluated.
Refining (full factorial). Having dropped out parameters, which do not impact the product CQAs, the refining step tests both main effects and interactions between the remaining parameters and generates first-order (linear) relationships between process parameters and CQAs. The criticality level of a CPP is determined from the quantitative impact on the CQA shown in the modeled relationship.
Optimization (central composite, Box-Behnken). To generate response surfaces and illustrate second-order (quadratic) relationships between process parameters and CQA. This analysis allows optimal set points for the design space or control space to be identified to target desired CQA (or performance attributes) values.

The DOE design assists in determining which parameters are studied and what set point value is used for each experimental run. The initial risk assessment, using prior knowledge and scientific principles, provides an expected relationship as to which CQAs and their related in-process controls will be affected by the given process parameters. Although the focus is on quality impact, process performance attributes (no quality impact) should also be sampled and measured as appropriate. This step is especially important during the optimization stage because a trade-off may be required in terms of optimizing quality and performance attributes.

DRUG APPROVALS BY DR ANTHONY MELVIN CRASTOhttp://newdrugapprovals.wordpress.com


For process validation Stage 1 process characterization studies, analytical methods for measuring CQAs may not yet be fully validated, but still, must be scientifically sound.
The level of accuracy and precision of the analytical method or measurement system must be well understood because they directly impact the quantitative decision process when interpreting study results early in the process-design stage. Techniques of measurement system analysis such as Gage repeatability and reproducibility (Gage R&R) studies are recommended because they provide information on the variability of the measurement system. The Gage R&R study provides a quantitative measurement of the measurement tools contribution to variation for any measurement made. Typically, a percent contribution from R&R variability must be < 20% and demonstrate at least five distinct categories for the method result to be meaningful. The distinct categories are the number of discernable groups of measurements that can reliably span the range of the CQA.

Including replicate runs in addition to the study experimental design provides crucial data for estimating the underlying variability of the study. This is because, during each run, small unmeasured and uncontrolled variations always occur and may influence the result. Two otherwise identically configured runs may produce slightly different responses due to changes in environment, equipment, measurement, sampling, and operators, among others. Even deliberately fixed parameters (those not under study) may not be exactly identical from run to run. Together, these are called “noise” factors and are important in discerning true responses (i.e., “signal”) caused by the changing parameters from the inherent variability. Differences between sets of replicate runs allow for the quantification of this variability. Large changes in the responses between replicates may indicate either an unstable experimental platform (such as poor run-to-run control) or that a low-risk CPP or non-CPP, may have a higher impact on the CQAs than originally assessed.

Where a raw material has a critical material attribute (CMA) of medium-risk to high-risk impact to CQAs, it should be included as a parameter of the study where possible. Multiple lots or lots with extreme variation in CMAs may not always be available during early development or characterization studies. This limitation frequently is one of the primary drivers of establishing the continuous process verification (CPV) program in Stage 3 to monitor the future impact from this raw material variation. For large studies, multiple lots of raw materials may be required. Consideration should be given to either proportional mixing of the raw material lots for each run or use of a statistical technique called blocking, which incorporates change of material lots into the experimental design.

In each design, the choice must not only be made on the number of parameters to be studied, but at how many levels (i.e., set points within the range) and how many times a particular set of conditions is repeated (replication).

The number of levels is related to the mathematical relationship between the parameters and the CQA measured (e.g., two levels for linear or three for quadratic). For screening designs, it is typical to use only two levels (minimum and maximum of the range); for these designs, any known non-linear relationship may have to be mathematically transformed. For refining design, center points (midpoint of ranges for all parameters) are added to estimate variability and to detect potential curvature.

Because of the cost involved or availability of API, it is not possible to perform all experimental studies at commercial scale (such as with fermentors of 5000 to 25,000 L), hence, most biotech process development programs rely heavily on modeling the process at smaller or intermediate scale. Some process parameters may be independent of the scale or may have simple models to account for scale changes. Scale itself may be considered a parameter. Establishing similar run conditions at multiple scales is an important consideration when trying to qualify the comparability between full-scale and small-scale experiments. Substantial prior experience with scaling particular unit operations may provide key information such as dimensionless parameter groups and scaling equations.

Areas considered for experimental scale include, but are not limited to:
• Aspect ratios of bioreactors and mixing tanks
• Impeller number, size, and location
• Aeration method and effectiveness of oxygen transfer
• Location of addition ports and effect on mass transport and uniformity
• Temperature control and heat-transfer surface area
• Location of instrument sensors and control-loop tuning parameters.

Screen, refine, and optimize
The advantage of a screening design is that it can handle a fairly large number of parameters in the fewest number of runs. The disadvantage is that the interaction effect of each CPP on a CQA cannot be directly determined because the experimental parameters are confounded. Confounding refers to a scientific state where there is insufficient resolution in the experiment to resolve the interaction effects from the main contributions of each parameter studied. However, at the screening stage, the objective is to eliminate as many parameters from the potential list of CPPs as possible so that the true process design space can be determined in the refining studies.

At this stage, the criticality of parameters has not yet been verified and parameter control ranges (proven acceptable range) have not yet been determined. Although it is usually the goal to meet the CQAs’ acceptance criteria to ensure product quality, the purpose here is to show how the process responds to the parameters even if the CQAs may not meet their criteria.

Figure 3: Screening design of experiment (DOE) Pareto of parameters for critical quality attribute (CQA) (aggregates). Temp is temperature; Osm is osmolality; Med conc is medium concentration; Inoc conc is inoculum concentration; DO is dissolved oxygen.

Figure 3 is an example output chart from a screening study. This Pareto chart shows the standardized effect or relative impact, for each of eight process parameters on a CQA. A reference line at 2.45 is the threshold below which the parameter’s effect is not statistically significant for this study (p-value > alpha of 0.05). In this example, six of the parameters may be screened out of further studies, provided they do not produce significant effects for other CQAs. A similar approach can be used for process performance attributes (non-CQAs) to evaluate parameters that impact process performance, but not quality; these non-critical parameters are frequently called key parameters. If these investigations had been conducted as OFAT studies, it would be impossible to quantitatively determine which parameter had an impact on the product’s CQA and to what extent. Through the use of a DOE, it is possible to measure both and define the level of variation explained by the parameters evaluated based upon the data observed.

Once the screening DOE has been completed, parameters that have not shown strong responses to any of the CQAs are now kept constant or well controlled to reduce the number of parameters for refining studies. By employing a full factorial design, all main effects and interactions are separated with regard to the CQA responses; there is no confounding in a full-factorial design. Center-point conditions (runs at the midpoints of all parameter ranges) are recommended because they can be used to detect if significant curvature (non-linear relationship) exists in the response to the parameter and they provide replication to determine the inherent variability in the study.

Figure 4: Refining design of experiment (DOE) Pareto of parameters and interactions for critical quality attribute (CQA) (impurities).

Figure 4 is an example output chart from a four-parameter, full-factorial study. The Pareto chart shows a threshold line. Two parameters and one two-factor interaction are statistically significant for this CQA. All parameters and interactions below this threshold are not statistically significant and their effects have no more impact than the inherent run-to-run variation.

Figure 5: Refining design of experiment (DOE) Pareto of parameters, reduced model, for critical quality attribute (CQA) (impurities). DO is dissolved oxygen.

Because these parameters and interactions are not significant, they may be treated as random noise and the model for this attribute is reduced as shown in Figure 5. A mathematical model was generated using the significant factors (pH and temperature) from Figure 3 and the significant interaction (pH and dissolved oxygen [DO]):

Impurities = Constant + α(pH) + β(temperature) + γ(DO) + δ(pH) (DO)
where: Constant is the intercept generated by the DOE analysis
α, β, γ, and δ are the coefficients generated by the DOE analysis for each parameter or interaction.

Positively signed coefficients indicate the CQA increases with an increase of the parameter; negatively signed coefficients indicate the CQA decreases with an increase of the parameter. The model equation is a regression, or best fit, from the data for the experiment, and therefore, is valid for the specific scale conditions of the experiment including the ranges of the parameters tested. Models are tested for their “goodness of fit” or how well the model represents the data. The simplest of these tests is the coefficient of determination, or R-squared. Low R-squared values (such as below 50%) indicate models with low predictive capability, that is, the parameters evaluated across the defined range do not explain the variation seen in the data.

This model only represents what would be expected on average for this CQA from the unit operation(s) tested in the study. Even so, the model is a fit to the most likely mean. Recognizing that any model has uncertainty, the model can also be represented with a confidence interval (e.g., 95%) around that mean. Individual runs will also show day-to-day variation around that mean. A single-run value for the attribute cannot be predicted, but a range in which that value will likely fall can be predicted. This range for the single-run value is called the prediction interval (e.g., 95%) for the model. Empirical models such as these are only as good as the data and conditions from which they are generated and are mere approximations of the real world.

Despite the limitation, these empirical models relate not only what parameters have a statistical impact on a CQA, but also the relative amount of that impact. The range through which the parameter is tested in the study has an important relationship to the model generated. For example, perhaps the parameter temperature was initially assigned as high risk. If temperature is only tested through a tight range, the parameter may have little to no impact to CQAs in the study; its effect may be no greater than the inherent variability. If temperature is not statistically significant for the range studied (i.e., its PAR), it is designated as a non-CPP, but only for that PAR. If the temperature should ever move outside the studied PAR, there is a potential risk that it could have a quality impact become critical.

Some organization quality groups rely on the original risk assessment of the process parameter. If this parameter’s severity was initially rated high, this parameter can remain designated as critical but should be designated as a low-risk CPP as long as the parameter is in its PAR. Parameters outside the PAR would be considered outside the allowable limits for that process step because there has been no study of the parameter outside of this range.

If curvature is detected during earlier DOE stages, or if the optimization of any CQA or process performance attribute is needed, then response-surface experimental designs are used. These designs allow for more complex model equations for a CQA (or performance attributes). Two of the simpler response-surface designs are the central composite and Box-Behnken. Both designs can supplement existing full-factorial data. The central-composite design also extends the range of parameter beyond the original limits of the factorial design. The Box-Behnken design is used when extending the limits is not feasible. The empirical models are refined from these studies by adding higher-order terms (e.g., quadratic, polynomial). Even if these higher-order terms are not significant, adding more levels within the parameter ranges will improve the linear model.

Because most empirical models are developed with small-scale experiments, the models must be verified on larger scale and potentially adjusted. Applying the knowledge of scale-dependent and scale-independent parameters while developing earlier DOE designs reduces risk when scaling-up to larger pilot-scale and finally full-scale processes. The models from small-scale studies predict which parameters present the highest impact (risk) to CQAs. Priority should be given in the study design to those high-risk parameters, especially if they are scale-dependent. Since the empirical models only predict the most likely average response for a CQA, several runs at different parameter settings (e.g., minimum, maximum, center point) are required to see if the small-scale model can still apply to the large-scale process.

Significance and criticality
Statistical significance is an important designation in assessing the impact of changes in parameters on CQA. It provides a mathematical threshold of where the effects vanish into the noise of process variability. Parameters that are not significant are screened out from further study and excluded from empirical models.

A CQA may be affected by critical parameters in several different unit operations (see the Cause-and-Effect Matrix in Part 1 of this article [5]). Characterization study plans may not be able to integrate different unit operations into the same DOE study. Consequently, several model equations may exist for a single CQA; each model is composed of parameters from different unit operation. The relative effect of each parameter on the CQA can be calculated from these models using the span of the PAR for each parameter. The relative impact of each parameter on the CQA is based on the range of its acceptance criteria. Sorting each parameter from highest to lowest, the criticality of each parameter can be assigned from high to low.Table I is an example of one method for assigning the continuum of criticality.
The steps in determining the continuum of criticality for process parameters are summarized as follows:
• Show statistically significance by DOE
• Relate significant parameters to CQAs with empirical model
• Calculate impact of all parameter from model(s) for CQA
• Compare the parameter’s impact on the CQA to the CQA’s measurement capability
• Assign parameter risk level based on impact to CQA
• Update initial risk assessment for parameters.

CPP risk level % Change in CQA as CPP spans PAR
High risk > 25%
Medium risk 10% to 25%
Low Risk < 5%
Below measurement capability
Low risk in risk assessment (not in DOE)
Non-CPP Not significant in DOE
No risk in risk assessment (not in DOE)

Table I: Example of criticality risk assignment for process parameters. CPP is critical process parameter;
CQA is critical quality attribute; PAR is proven acceptable range; DOE is design of experiment.

As process validation Stage 2 (process qualification) begins, criticality is applied to develop acceptance criteria for equipment qualification and process performance qualification. Finally, in process validation Stage 3 (continued process verification), criticality determines what parameters and attributes are monitored and trended.

In the third and final part of this article, the author applies the continuum of criticality for parameters and attributes to develop the process control strategy and study its influence on the process qualification and continued process verification stages of process validation.

References
1. FDA, Guidance for Industry, Process Validation: General Principles and Practices, Revision 1 (Rockville, MD, January 2011).
2. ICH, Q8(R2) Harmonized Tripartite Guideline, Pharmaceutical Development, Step 4 version (August 2009).
3. ICH, Q9 Harmonized Tripartite Guideline, Quality Risk Management (June 2006).
4. ICH, Q10, Harmonized Tripartite Guideline, Pharmaceutical Quality System(April 2009).
5. M. Mitchell, BioPharm International 26 (12) 38-47 (2013).

Mark Mitchell is principal engineer at Pharmatech Associates.

PART 3

With the most recent FDA (1) and Inter-national Conference on Har-monization (ICH) guidances (2-4) advocating a new paradigm of process validation based on process understanding and control of parameters and less on product testing, the means of determining criticality has come under greater scrutiny. The FDA guidance points to a lifecycle approach to process validation (see Figure 1).

Figure 1: Process validation lifecycle.

In Part I, the author used risk analysis and applied the continuum of criticality to quality attributes during the process design stage of process validation. After using process knowledge to relate the attributes to each process unit operation, the inputs and outputs of each unit operation were defined to determine process parameters and in-process controls. An initial risk assessment was then completed to determine a preliminary continuum of criticality for process parameters.

In Part II, the preliminary risk levels of process parameters provided the basis of characterization studies based on design of experiments. Data from these studies were used to confirm the continuum of criticality for process parameters.

At this point in the process development stage, the design space has been determined. It may not be rectangular (if there are higher-order terms in the models) and may not include the entire proven acceptable range (PAR) for each critical process parameter (CPP). In fact, the design space is not defined by the combination of the PARs for each CPP, given that the full PAR for one CPP ensures the quality of the critical quality attribute (CQA) only when all other CPPs do not vary. The design space represents all combinations of CPP set points for which the CQA meets acceptance criteria.

Overall, the design space developed from process characterization study models represents a level of process understanding. Like all models, however, the design space is only as good as the data that drives the analysis. The CQAs, on average, may meet acceptance criteria, but individual lots–and samples within lots–are at risk of failure when operating at the limits of the design space. For this reason, the operational limits for the CPPs are frequently tighter than the design space. This tighter space is the last part of the ICH Q8 paradigm (2) (see Figure 2) and is called the control space, which equates to normal operating range (NOR) limits for each CPP.

Figure 2: Knowledge, design, and control space.

Stage 1: From models to design space to control space
At the conclusion of the process characterization studies, the design space describes each CQA as a function of process parameters of various levels of risk, or continuum of criticality. Additionally, these models have been confirmed, by experiment or prior knowledge, to adequately represent the full-scale manufacturing process. This classical multivariate approach combines impact from each CPP to predict the response of the CQA as each CPP moves through its PAR. These mathematical expressions can be represented graphically as either contour or 3-D response surface plots.

Even this view of the design space is too simplistic. To ensure a process has a statistically high probability (e.g., > 95%) that a CQA will reliably meet the acceptance criteria for a combination of CPP requires a more involved computational analysis. This analysis may lead to revising CPP set points and ranges.

Several computational statistical methods are available for analysis of process reliability. Each of these requires specialized statistical software.
These methods include:
Monte Carlo Simulation inputs CPPs as probability distributions to the design space models and iterates to produce the CQA as a probability distribution. Capability analysis can be applied to the CQA’s acceptance criteria. This method is limited by the estimations of the CPP distributions from process characterization studies, which will not necessarily represent the same level of inherent variation as the commercial process. Using sensitivity analysis on these estimated distributions may enhance this approach.
Predictive Bayesian Reliability (5) incorporates the CPPs, uncontrolled variables such as raw material and environmental conditions, inherent common cause variability, and variation due to unknown model parameters, to determine a design space with high reliability of meeting the CQAs.

Design space models often become a series of complex, multifactor equations, which are not suitable for describing the required ranges for each CPP in a production batch record. Contained within the design space, the control space consists a suitable NOR for each parameter.

Table I provides some example methods for developing the NORs and the control space together with their advantages and disadvantages. Option 1 is included only since it represents a historical approach broadly employed in NOR establishment. This approach is not consistent with the current quality-by-design (QbD) approach to process validation and will not be sufficient to defend a final NOR establishment. Issues with Option 2 have been discussed previously. Of these three options the reliability approach (Option 3) is the most robust, but requires sophisticated statistical skills. This option may be reserved for only very high risk CPPs.

Table I: Example methods for determining a normal operating range (NOR) for a critical process parameter (CPP). CQA is critical quality attribute, PAR is proven acceptable range, QbD is quality by design.

Option Advantages Disadvantages Span of range
1. NOR set equal to PAR for single CPP Simple No other CPP effects or interactions considered. Not consistent with QbD methodology (i.e., poor assurance of quality). Widest
2. NOR same as design space
(optional: rectilinear space)
Account for other CPP effects on CQA Based on process characterization models. Does not ensure lot-to-lot performance (model is “on average”). Narrower than #1
3. NOR based on reliability methods Accounts for other CPP effects on CQA. Ensures high reliability of meeting CQAs. Based on process characterization models. Requires sophisticated analysis. May be tighter than available control capability. Generally narrower than #1 and #2, possible narrower than all options
4. NOR based on design space with “safety margin” Accounts for other CPP effects. Partial allowance for lot-to lot variation. Based on process characterization models. Only partial allowance for lot-to-lot performance. Narrower than #2
5. NOR set by control capability Good for non-CPPs. Keeps CPP in tight range of control (lower risk by lowering occurrence) Range is narrow and may not allow for future unknown variability. Exceeding range does not necessarily lead to CQA failure. May be narrowest of all options depending on level of control

Option 4 is based on a “safety margin” that may be determined in a variety of ways. One choice is to measure how much an actual parameter will vary around its set point. For example, if a temperature set point is 30.0 °C, it may be observed to vary from 29.5 °C to 30.5 °C (± 0.5 °C). The safety margin of 0.5 °C is applied to narrow the CPP limit from the design space. Therefore, if the design space is 25.0 °C to 35.0 °C, the NOR becomes 25.5 °C to 34.5 °C. Additional factors, such as calibration error, can be added to provide a wider safety factor.

Option 5 is the narrowest method applied for determining the NOR. Here, the ability to control the parameter determines its range. For example, a pH set point of 7.0 may have a design space of 6.5 to 7.5. However, if the control of the pH is shown to be ± 0.2, then the NOR is 6.8 to 7.2. The primary disadvantage of such a narrow range is that even if the CPP’s NOR is exceeded, the CQA may not move outside of its acceptance range. Option 5 is suitable for setting the NOR of non-CPPs since the CQAs are not affected. For example, a mixing set point of 200 rpm is a non-CPP. If the mixer’s control is qualified for ± 20 rpm, then the NOR is 180-220 rpm.

The conclusion to process validation Stage 1 (process design) is documented by summarizing the control strategy per ICH Q10:
Control strategy: A planned set of controls, derived from current product and process understanding, that assures process performance and product quality. The controls can include parameters and attributes related to drug substance and drug product materials and components, facility and equipment operating conditions, in-process controls, finished product specifications, and the associated methods and frequency of monitoring and control (4).

The control strategy may be a single document or a package of several documents as described in the company’s process validation plan. This documentation includes or references the following:
• Continuum of criticality for process parameters
• Continuum of criticality for quality attributes
• The mechanistic or empirical relationships of CPPs to CQAs (design space)
• The set points and NOR for CPPs (control space)
• Acceptance criteria and sampling requirements for CQAs, in-process controls, and raw materials testing
• In-process hold (non-processing) time limits and storage conditions
• Key operating parameters (KOPs) and performance attributes (all non-critical), which are used to monitor process performance, set points, and ranges.

WHO publishes Draft on Process Validation

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Dated April 2014 the WHO published a proposal for the revision of appendix 7 (non-sterile process validation) as a supplementary to the Guidelines on Good Manufacturing Practices. The reason given for the revision is compliance with actual GMP requirements. The following is an in-depth analysis.

http://www.gmp-compliance.org/enews_4252_WHO%20publishes%20Draft%20on%20Process%20Validation_8377,8480,8550,8471,Z-VM_n.html