Note: The views expressed in this article are those of the authors and do not necessarily represent those of their respective employers, GxP Lifeline, its editor or MasterControl, Inc.
Process validation officially became part of the FDA's Quality Systems Regulation in 1997. Fifteen years later medical device manufacturers still struggle with determining which processes require validation. The confusion traces back to two words, "fully verified." What does "fully verified" mean? What do I do if I determine that process can't be "fully verified?" And, equally important, what are the FDA's expectations when I can't?
The answer to the "fully verify" question can have big consequences. Failure to identify a process that requires validation can cause compliance issues, including warning letters, delays in pre-market submissions and field actions. A conservative approach of validating everything can be costly. And worse yet, some people erroneously think that if they can fully verify something, the verification has to be 100%. This approach, unless automated, may be a statistically invalid approach because even manual 100% inspection is statistically not 100% effective.
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The goals of this article are:
This will be accomplished by:
The requirements for process validation established in the FDA's Quality Systems Regulation states in Part 820.75 (a)1:
"Where the results of a process cannot be fully verified by subsequent inspection and test, the process shall be validated with a high degree of assurance and approved according to established procedures."
A clear intent of the regulation is that quality cannot be inspected into a product. This has been firmly established in quality thinking. In essence, this requirement is stating that for some processes, inspection alone may not be sufficient. This is particularly true when the defect only becomes apparent or manifests after the product is in distribution or use. This could be because verification is not possible (e.g., reliability or durability) or it is not sufficient (e.g., requires destructive testing). The core of this requirement is that in order to gain confidence that the likelihood of these types of defects is small, we must use the capabilities of a process. This is clarified when we look at the definition of process validation in the regulation which states process validation is2:
"...establishing by objective evidence that a process consistently produces a result or product meeting its predetermined specifications."
Thus, we'll see that this requires understanding the sources of variation, reduction and control of variation to establish a process that is capable of consistently meeting specifications. Such specifications and their associated acceptance criteria are determined using adequate design controls.
Before we dissect the term fully verified we need to go back and see how we determine what specifications are of concern. This determination should be established using effective design controls. Most professionals typically make the connection between design transfer and process validation while they overlook essential design outputs, which are the key design outputs that we must produce. So, one of the critical connections between process validation and design controls is that design outputs define the "predetermined specifications" or the results a process needs to meet.
One of the requirements for documenting design outputs is that they contain or reference acceptance criteria. These are sometimes referred to as critical to quality (CTQ) characteristics. In this sense, quality is defined as "the totality of features and characteristics that bear on the ability of a device to satisfy fitness for-use, including safety and performance."3 These features and characteristics form the basis for acceptance criteria.
While there are many tools that can be used for developing CTQs, such as Design for Six Sigma (DFSS), Quality Function Deployment (QFD), and focus groups, from an FDA perspective a risk-based approach is imperative. Using such process tools as Fault Tree Analysis (FTA) or Design Failure Mode Effects Analysis (DFMEA) when linking product hazards to specifications is a thorough approach to effective post-control measures. So, for example, in DFMEA we ask: what are the effects of a design failing to meet its design requirements? If the failure results in a safety hazard to a patient or end user, then that specification becomes a CTQ. It is these characteristics that we'll focus on when asking if they can be fully verified.
To help understand the term "fully verified," we can go to the Global Harmonization Task Force's (GHTF) Process Validation Guidance document4. This document shows a decision tree which may be helpful in determining which process should be validated. When we look at Figure - 1, there are two fundamental questions to be answered: is the output (CTQ) verifiable and is verification sufficient and cost effective?
Most quality characteristics can be verified. Verification methods run the gamut from chemical tests such as pH or conductivity, physical tests such as tensile strength, dimension, or hardness, to electrical measurements such as voltage and impedance. In some cases, the measurements can't easily be done on routine production. Thus such dimensions are not effectively "detectable" and therefore, not capable of reliability or durability. This is the main concern for detectability as a key component of an FMEA. In other cases, the verifications are not sufficient because the method is destructive and reasonable sampling schemes may not be sufficient. One simple test to apply is asking if sampling "could be" 100% and in the case of destructive method the answer would be no. Finally, some tests (detection) lack the sensitivity to be effective as part of routine production.
The cost effectiveness of the verification can also be an important consideration. In many cases, it may be more prudent to validate the process upfront to understand and control variation, thereby improving process capabilities, increasing yields and lowering scrap. These factors generally will outweigh even reducing the cost of inspection, making a strong business case for validation.
The GHTF Guidance gives us several examples of processes that should be validated, including:
It's worth noting that the guidance says "should be." It is incumbent upon the manufacturer to understand the CTQs for its product through Quality by Design (QbD) and assess whether verification is sufficient. However, the FDA will be expecting that these processes will be validated, so if you determine that verification is sufficient, your rationale will need to provide appropriate documentation.
The best place to document your validation decisions is in the Master Validation Plan (MVP). While not required, it is best practice to document your decisions and outline the plan for processes that will need to be validated accordingly. The plan should scope out the validation effort, including the facilities, processes, and products covered by the plan. All the equipment under that scope of the plan should be identified, including any utilities such as electrical, water, and air compressors. It should also include the process equipment and any environmental controls such as clean rooms or Electro-static Discharge (ESD) controls. It is also common to include test methods and software used to automate processes . Plans will spell out the Installation, Operational and Performance Qualifications (i.e., IQ, OQ and PQ) to be done for each process. It is also common to see test methods and software used to automate processes included in the lifecycle of the product, although these may not be stated as IQ, OQ or PQ requirements.
The installation qualification shows that the equipment is installed according to its specifications. This document is typically comprised of checklists and simple verifications such as fixture inspections, gauge calibration or preventive maintenance checks. If automated software is used in the process, the IQ will check to make sure the right version is installed and validated. One can look at the requirements in Production and Process Controls Part 820.705 to use as a basis for making a good IQ checklist.
The OQ and PQ are the heart and soul of process validation. Once assured that the equipment is installed to specification, the manufacturer has greater confidence that the equipment is operating properly and can start to use the equipment to understand the sources of variation and work towards establishing a capable process. The goal of process characterization during the OQ is to understand the effects process inputs (e.g., temperature, time, and pressure) have on the outputs (e.g., burst strength for a sterile package seal).
GHTF Guidance Figures 4, 5, and 6 help illustrate the concepts. These are adopted below in Figures 2 and 3.
The goal of the OQ is to understand what causes the instability in GHTF Figure 4 and reduce and control that variation to produce the stable process on the right. We should conclude through conformation runs that the stable process has the potential to meet our capability requirements. These results are typically achieved through designed experiments which seek to explore a wide range of possible input variables through screening experiments and then refine and optimize the most significant variables to produce a stable process with acceptable process potential capabilities. While it is tempting to cut straight to the vital few variables based on knowledge and experience to eliminate some of the experimental steps, these assumptions should be carefully documented in a risk-based process model and supported by scientific and historical data that clearly shows the relationships between input and output variables. A high-risk consequence condition can trump a Pareto approach.
The goal of the PQ is to show that process is capable under conditions anticipated during manufacturing. These conditions may include multiple shifts, operators, material lots and other factors that represent potential sources of uncontrollable variation. The purpose of process validation is not so much to show you have excellent process capabilities but to demonstrate you know why you have excellent process capabilities.
Once process validation is completed, the manufacturer is required to establish monitoring and control to ensure the validated state of control is maintained (Part 820.75(b)6). The manufacturer should document that the validated process was performed by qualified operators and note the monitoring and control methods, data, date performed, the individuals performing the process, and the major equipment used.
Whether you have validated a process or determined that verification is sufficient process, monitoring and control are required. For validated processes, this is established in Part 820.75(b) and for all processes it is established in Part 820.70(a):
"Where deviations from device specifications could occur as a result of the manufacturing process, the manufacturer shall establish and maintain process control procedures that describe any process controls necessary to ensure conformance to specifications. Where process controls are needed they shall include among other things 'monitoring and control of device parameters and component and device characteristics during production.'"
Fundamental validation concepts are the same, while additional requirements may be commensurate with a validated process during monitoring and control. A risk-based approach is used for such special requirements which can then be developed as part of a control plan based on a process FMEA.
If you don't validate, do you have to do 100% inspection in order to fully verify? No - the regulation doesn't say this. It does say (820.70) "Where deviations from device specifications could occur as a result of the manufacturing process, the manufacturer shall establish and maintain process control procedures that describe any process controls necessary to ensure conformance to specifications." Where process controls are needed, they shall include, among other things, "monitoring and control of device parameters and component and device characteristics during production." It also tells us in 820.80 that we need to establish in-process and final acceptance activities. Finally in 820.250, statistical techniques are delineated - "Where appropriate, each manufacturer shall establish and maintain procedures for identifying valid statistical techniques required for establishing, controlling, and verifying the acceptability of process capability and product characteristics."
There has been at least one warning letter for not 100% testing if not validating but interpreting requirements based on one or two warning letters is difficult. In 2009, the Cincinnati District office of the FDA issued a warning letter to Hammill. The warning letter states that Hammill's response to not validating certain cleaning, passivation processes and CNC equipment because they did in-process and final inspections/test was "inadequate because you are not testing every device to assure it meets specifications."7 Of course. Reading the warning letter without the 483, let alone the Establishment Inspection Report, is difficult. We don't know about the acceptance criteria (CTQs), how they were measured, whether the testing was destructive and hence, whether subsequent inspection or tests were sufficient. So it is difficult to make the leap to a requirement that all processes that are not validated must be 100% tested. In the end the regulation clearly states that process controls should be based on appropriate statistics, which require some knowledge of risks in order to be applied properly.
An example will help illustrate how to establish controls based on risk and the significant role validation plays in establishing the basis for control. For example, the CTQ for burst strength is tied to the risk associated with non-sterile packaging due to package seal failure as shown in Figure - 4. To achieve an acceptable risk level, we had to establish better than six sigma process capabilities during process validation. The trick is to establish process monitoring that has a low risk of not detecting a shift to an unacceptable level of process capabilities of five sigma, or a greater likelihood of generating defective seals. Using statistical process control (SPC) we establish a control plan that indicates an effective control measure of burst tests will be performed on a sample of 25 units every hour of production. We are concerned with a 1.0 sigma shift in the process average burst strength as this would reduce risk to an unacceptable level. We can calculate the beta risk or risk of not detecting the shift using the beta risk formula below8
β = φ(L-kûn) - φ(-L-kûn)
β = Beta RiskL = The number of sigma in the lower and upper control limits, typically 3φ() = Cumulative Standard Normal Distributionk = Process shift in sigma unitsû= square root of
n = Sample size
For our example above, if we assume the typical binomial three sigma limits for the control limits and a desire to detect a 1.0 sigma shift, the beta risk is estimated at 0.002, giving us high probability of detection or 2 versus a 1 almost certain to detect. This, along with the occurrence ranking of 1, gives us acceptable risk. From this we can see that the combination of acceptable process capabilities and process monitoring are required to make risk acceptable.
This is just one example of how to estimate the beta risk and set SPC control measures. Average run length, or the average number of samples necessary to detect a shift can also be used, which helps bring sample frequency into the equation.
The key to understanding whether a process requires validation is to understand if it is verifiable and assessing the sufficiency of verification. It is best practice to document these decisions and plan the validation effort. Some tests cannot be done on a routine basis, are destructive or lack the sensitivity to be sufficient. The heart of validating a process is ensuring it is installed to specification, while characterized and optimized to be under control and capable of consistently meeting specifications. The risk-based approach shows that understanding how to achieve excellent process capabilities reduces the likelihood of defects in the first place. In addition, process monitoring with low beta risk assures detectability if there are problems to be addressed as necessary. Verification alone may not be sufficient to produce acceptable levels of risk.
Andrew Snow has more than 25 years' experience directing and leading operations, quality, and process design-development for medical device companies, contract manufacturers, suppliers and several successful start-up ventures. He is president of Momentum Solutions, LLC a consulting firm specializing in solutions for design control, risk management and process validation for medical device companies.
Andrew has championed several lean and Six Sigma programs reducing cycle time, improving process capability, reliability, order delivery and customer satisfaction. He is an instructor with the Association for the Advancement of Medical Instrumentation (AAMI) and was recently the subject matter expert for revisions to their process validation course. He is a graduate of Northwestern University where he obtained a master's degree from the School of Industrial Engineering and University of Arizona with a certificate in Systems Engineering.
Walt Murray is MasterControl's director of quality and compliance services. He is a specialist in the quality and regulatory professions with more than 25 years' experience to his credit, working with nationally-recognized organizations including Aventis-Pasteur, Merck, Pfizer, Stryker, USANA, Del Monte Foods and the American Red Cross National Labs. He is certified in quality systems auditing, problem solving, and process control using Six Sigma principles that support lean enterprise, including kaizen improvement and advanced planning principles. His extensive audit experience covers several industries and he's successfully brought several medical device companies to full registration under the ISO process model standard. Murray has also worked extensively in risk and supplier management.
A graduate of the University of Richmond, Murray is a member of the Society for Manufacturing Engineers (SME); Regulatory Affairs Professions (RAPS); the American Society for Quality (ASQ); and the Intermountain Biomedical Association (IBA).
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