The Critical Role of Validation Systems: Ensuring Compliance Through Meta-Qualification

In the highly regulated pharmaceutical and biotechnology industries, the qualification of equipment and processes is non-negotiable. However, a less-discussed but equally critical aspect is the need to qualify the systems and instruments used to qualify other equipment. This “meta-qualification” ensures the reliability of validation processes themselves, forming a foundational layer of compliance.

I want to explore the regulatory framework and industry guidelines using practical examples of the Kaye Validator AVS to that underscore the importance of this practice.

Regulatory Requirements: A Multi-Layered Compliance Challenge

Regulatory bodies like the FDA and EMA mandate that all equipment influencing product quality undergo rigorous qualification. This approach is also reflected in WHO, ICH and PICS requirements. Key documents, including FDA’s Process Validation: General Principles and Practices and ICH Q7, emphasize several critical aspects of validation. First, they advocate for risk-based validation, which prioritizes systems with direct impact on product quality. This approach ensures that resources are allocated efficiently, focusing on equipment such as sterilization autoclaves and bioreactors that have the most significant influence on product safety and efficacy. Secondly, these guidelines stress the importance of documented evidence. This means maintaining traceable records of verification activities for all critical equipment. Such documentation serves as proof of compliance and allows for retrospective analysis if issues arise. Lastly, data integrity is paramount, with compliance to 21 CFR Part 11 and EMA Annex 11 for electronic records and signatures being a key requirement. This ensures that all digital data associated with validation processes is trustworthy, complete, and tamper-proof.

A critical nuance arises when the tools used for validation—such as temperature mapping systems or data loggers—themselves require qualification. This meta-qualification is essential because the reliability of all subsequent validations depends on the accuracy and performance of these tools. For example, if a thermal validation system is uncalibrated or improperly qualified, its use in autoclave PQ could compromise entire batches of sterile products. The consequences of such an oversight could be severe, ranging from regulatory non-compliance to potential patient safety issues. Therefore, establishing a robust system for qualifying validation equipment is not just good practice—it’s a critical safeguard for product quality and regulatory compliance.

The Hierarchy of Qualification: Why Validation Systems Need Validation

Qualification of Primary Equipment

Primary equipment, such as autoclaves, freeze dryers, and bioreactors, forms the backbone of pharmaceutical manufacturing processes. These systems undergo a comprehensive qualification process.

IQ phase verifies that the equipment is installed correctly according to design specifications. This includes checking physical installation parameters, utility connections, and any required safety features.
OQ focuses on demonstrating functionality across operational ranges. During this phase, the equipment is tested under various conditions to ensure it can perform its intended functions consistently and accurately.
PQ assesses the equipment’s ability to perform consistently under real-world conditions. This often involves running the equipment as it would be used in actual production, sometimes with placebo or test products, to verify that it can maintain required parameters over extended periods and across multiple runs.

Qualification of Validation Systems

Instruments like the Kaye Validator AVS, which are used to validate primary equipment, must themselves undergo a rigorous qualification process. This meta-qualification is crucial to ensure the accuracy, reproducibility, and compliance of the validation data they generate. The qualification of these systems typically focuses on three key areas. First, accuracy is paramount. These systems must demonstrate traceable calibration to national standards, such as those set by NIST (National Institute of Standards and Technology). This ensures that the measurements taken during validation activities are reliably accurate and can stand up to regulatory scrutiny. Secondly, reproducibility is essential. Validation systems must show that they can produce consistent results across repeated tests, even under varying environmental conditions. This reproducibility is critical for establishing the reliability of validation data over time. Lastly, these systems must adhere to regulatory standards for electronic data. This compliance ensures that all data generated, stored, and reported by the system maintains its integrity and can be trusted for making critical quality decisions.

The Kaye Validator AVS serves as an excellent example of a validation system requiring comprehensive qualification. Its qualification process includes several key steps. Sensor calibration is automated against high- and low-temperature references, ensuring accuracy across the entire operating range. The system’s software undergoes IQ/OQ to verify the integrity of its metro-style interface and reporting tools, ensuring that data handling and reporting meet regulatory requirements. Additionally, the Kaye AVS, like all validation systems, requires periodic requalification, typically annually, to maintain its compliance status and ensure ongoing reliability. This regular requalification process helps catch any drift in performance or accuracy that could compromise validation activities.

Case Study: Kaye Validator AVS in Action

The Kaye Validator AVS exemplifies a system designed to qualify other equipment while meeting stringent regulatory demands. Its comprehensive qualification process encompasses both hardware and software components, ensuring a holistic approach to compliance and performance. The hardware qualification of the Kaye AVS follows the standard IQ/OQ/PQ model, but with specific focus areas tailored to its function as a validation tool. The Installation Qualification (IQ) verifies the correct installation of critical components such as sensor interface modules (SIMs) and docking stations. This ensures that the physical setup of the system is correct and ready for operation. The Operational Qualification (OQ) goes deeper, testing the system’s core functionalities. This includes verifying the input accuracy to within ±0.003% of reading and confirming that the system can scan 48 channels in 2 seconds as specified. These performance checks are crucial as they directly impact the system’s ability to accurately capture data during validation runs. The Performance Qualification (PQ) takes testing a step further, validating the AVS’s performance under stress conditions that mimic real-world usage. This might include operation in extreme environments like -80°C freezers or during 140°C Steam-In-Place (SIP) cycles, ensuring the system can maintain accuracy and reliability even in challenging conditions.

On the software side, the Kaye AVS is designed with compliance in mind. It comes with pre-loaded, locked-down software that minimizes the IT validation burden for end-users. This approach not only streamlines the implementation process but also reduces the risk of inadvertent non-compliance due to software modifications. The system’s software is built to align with FDA 21 CFR Part 11 requirements, incorporating features like audit trails and electronic signatures. These features ensure data integrity and traceability, critical aspects in regulatory compliance. Furthermore, the Kaye AVS employs an asset-centric data management approach. This means it stores calibration records, validation protocols, and equipment histories in a centralized database, facilitating easy access and comprehensive oversight of validation activities. The system’s ability to generate Pass/Fail reports based on established standards like EN285 and ISO17665 further streamlines the validation process, providing clear, actionable results that can be easily interpreted and used for regulatory documentation.

Regulatory Pitfalls and Best Practices

In the complex landscape of pharmaceutical validation, several common pitfalls can compromise compliance efforts. One of the most critical errors is using uncalibrated sensors for Performance Qualification (PQ). This oversight can lead to erroneous approvals of equipment or processes that may not actually meet required specifications. The consequences of such a mistake can be far-reaching, potentially affecting product quality and patient safety. Another frequent issue is the inadequate requalification of validation systems after firmware updates. As software and firmware evolve, it’s crucial to reassess and requalify these systems to ensure they continue to meet regulatory requirements and perform as expected. Failing to do so can introduce undetected errors or compliance gaps into the validation process.

Lastly, rigorous documentation remains a cornerstone of effective validation practices. Maintaining traceable records for audits, including detailed sensor calibration certificates and comprehensive software validation reports, is essential. This documentation not only demonstrates compliance to regulators but also provides a valuable resource for troubleshooting and continuous improvement efforts. By adhering to these best practices, pharmaceutical companies can build robust, efficient validation processes that stand up to regulatory scrutiny and support the production of high-quality, safe pharmaceutical products.

Conclusion: Building a Culture of Meta-Qualification

Qualifying the tools that qualify other equipment is not just a regulatory checkbox—it’s a strategic imperative in the pharmaceutical industry. This meta-qualification process forms the foundation of a robust quality assurance system, ensuring that every layer of the validation process is reliable and compliant. By adhering to good verification practices, companies can implement a risk-based approach that focuses resources on the most critical aspects of validation, improving efficiency without compromising quality. Leveraging advanced systems like the Kaye Validator AVS allows organizations to automate many aspects of the validation process, reducing human error and ensuring consistent, reproducible results. These systems, with their built-in compliance features and comprehensive data management capabilities, serve as powerful tools in maintaining regulatory adherence.

Moreover, embedding risk-based thinking into validation workflows enables pharmaceutical manufacturers to anticipate and mitigate potential issues before they become regulatory concerns. This proactive approach not only enhances compliance but also contributes to overall operational excellence. In an era of increasing regulatory scrutiny, meta-qualification emerges as the linchpin of trust in pharmaceutical quality systems. It provides assurance not just to regulators, but to all stakeholders—including patients—that every aspect of the manufacturing process, down to the tools used for validation, meets the highest standards of quality and reliability. By fostering a culture that values and prioritizes meta-qualification, pharmaceutical companies can build a robust foundation for compliance, quality, and continuous improvement, ultimately supporting their mission to deliver safe, effective medications to patients worldwide.

Navigating Metrics in Quality Management: Leading vs. Lagging Indicators, KPIs, KRIs, KBIs, and Their Role in OKRs

Understanding how to measure success and risk is critical for organizations aiming to achieve strategic objectives. As we develop Quality Plans and Metric Plans it is important to explore the nuances of leading and lagging metrics, define Key Performance Indicators (KPIs), Key Behavioral Indicators (KBIs), and Key Risk Indicators (KRIs), and explains how these concepts intersect with Objectives and Key Results (OKRs).

Leading vs. Lagging Metrics: A Foundation

Leading metrics predict future outcomes by measuring activities that drive results. They are proactive, forward-looking, and enable real-time adjustments. For example, tracking employee training completion rates (leading) can predict fewer operational errors.

Lagging metrics reflect historical performance, confirming whether quality objectives were achieved. They are reactive and often tied to outcomes like batch rejection rates or the number of product recalls. For example, in a pharmaceutical quality system, lagging metrics might include the annual number of regulatory observations, the percentage of batches released on time, or the rate of customer complaints related to product quality. These metrics provide a retrospective view of the quality system’s effectiveness, allowing organizations to assess their performance against predetermined quality goals and industry standards. They offer limited opportunities for mid-course corrections

The interplay between leading and lagging metrics ensures organizations balance anticipation of future performance with accountability for past results.

Defining KPIs, KRIs, and KBIs

Key Performance Indicators (KPIs)

KPIs measure progress toward Quality System goals. They are outcome-focused and often tied to strategic objectives.

Leading KPI Example: Process Capability Index (Cpk) – This measures how well a process can produce output within specification limits. A higher Cpk could indicate fewer products requiring disposition.
Lagging KPI Example: Cost of Poor Quality (COPQ) -The total cost associated with products that don’t meet quality standards, including testing and disposition cost.

Key Risk Indicators (KRIs)

KRIs monitor risks that could derail objectives. They act as early warning systems for potential threats. Leading KRIs should trigger risk assessments and/or pre-defined corrections when thresholds are breached.

Leading KRI Example: Unresolved CAPAs (Corrective and Preventive Actions) – Tracks open corrective actions for past deviations. A rising number signals unresolved systemic issues that could lead to recurrence
Lagging KRI Example: Repeat Deviation Frequency – Tracks recurring deviations of the same type. Highlights ineffective CAPAs or systemic weaknesses

Key Behavioral Indicators (KBIs)

KBIs track employee actions and cultural alignment. They link behaviors to Quality System outcomes.

Leading KBI Example: Frequency of safety protocol adherence (predicts fewer workplace accidents).
Lagging KBI Example: Employee turnover rate (reflects past cultural challenges).

Applying Leading and Lagging Metrics to KPIs, KRIs, and KBIs

Each metric type can be mapped to leading or lagging dimensions:

KPIs: Leading KPIs drive action while lagging KPIs validate results
KRIs: Leading KRIs identify emerging risks while lagging KRIs analyze past incidents
KBIs: Leading KBIs encourage desired behaviors while lagging KBIs assess outcomes

Oversight Framework for the Validated State

An example of applying this for the FUSE(P) program.

Category	Metric Type	FDA-Aligned Example	Purpose	Data Source
KPI	Leading	% completion of Stage 3 CPV protocols	Proactively ensures continued process verification aligns with validation master plans	Validation tracking systems
	Lagging	Annual audit findings related to validation drift	Confirms adherence to regulator’s “state of control” requirements	Internal/regulatory audit reports
KRI	Leading	Open CAPAs linked to FUSe(P) validation gaps	Identifies unresolved systemic risks affecting process robustness	Quality management systems (QMS)
	Lagging	Repeat deviations in validated batches	Reflects failure to address root causes post-validation	Deviation management systems
KBI	Leading	Cross-functional review of process monitoring trends	Encourages proactive behavior to maintain validation state	Meeting minutes, action logs
	Lagging	Reduction in human errors during requalification	Validates effectiveness of training/behavioral controls	Training records, deviation reports

This framework operationalizes a focus on data-driven, science-based programs while closing gaps cited in recent Warning Letters.

Goals vs. OKRs: Alignment with Metrics

Goals are broad, aspirational targets (e.g., “Improve product quality”). OKRs (Objectives and Key Results) break goals into actionable, measurable components:

Objective: Reduce manufacturing defects.
Key Results:
- Decrease batch rejection rate from 5% to 2% (lagging KPI).
- Train 100% of production staff on updated protocols by Q2 (leading KPI).
- Reduce repeat deviations by 30% (lagging KRI).

KPIs, KRIs, and KBIs operationalize OKRs by quantifying progress and risks. For instance, a leading KRI like “number of open CAPAs” (Corrective and Preventive Actions) informs whether the OKR to reduce defects is on track.

More Pharmaceutical Quality System Examples

Leading Metrics

KPI: Percentage of staff completing GMP training (predicts adherence to quality standards).
KRI: Number of unresolved deviations in the CAPA system (predicts compliance risks).
KBI: Daily equipment calibration checks (predicts fewer production errors).

Lagging Metrics

KPI: Batch rejection rate due to contamination (confirms quality failures).
KRI: Regulatory audit findings (reflects past non-compliance).
KBI: Employee turnover in quality assurance roles (indicates cultural or procedural issues).

Metric Type	Purpose	Leading Example	Lagging Example
KPI	Measure performance outcomes	Training completion rate	Quarterly profit margin
KRI	Monitor risks	Open CAPAs	Regulatory violations
KBI	Track employee behaviors	Safety protocol adherence frequency	Employee turnover rate

Building Effective Metrics

Align with Strategy: Ensure metrics tie to Quality System goals. For OKRs, select KPIs/KRIs that directly map to key results.
Balance Leading and Lagging: Use leading indicators to drive proactive adjustments and lagging indicators to validate outcomes.
Pharmaceutical Focus: In quality systems, prioritize metrics like right-first-time rate (leading KPI) and repeat deviation rate (lagging KRI) to balance prevention and accountability.

By integrating KPIs, KRIs, and KBIs into OKRs, organizations create a feedback loop that connects daily actions to long-term success while mitigating risks. This approach transforms abstract goals into measurable, actionable pathways—a critical advantage in regulated industries like pharmaceuticals.

Understanding these distinctions empowers teams to not only track performance but also shape it proactively, ensuring alignment with both immediate priorities and strategic vision.

Timely Equipment/Facility Upgrades

One of the many fascinating items in the recent Warning Letter to Sanofi is the FDA’s direction to provide a plan to perform “timely technological upgrades to the equipment/facility infrastructure.” This point drives home the point that staying current with technological advancements is crucial for maintaining compliance, improving efficiency, and ensuring product quality. Yet, I think it is fair to say we rarely see it this bluntly put as a requirement.

One of the many reasons this Warning Letter stands out is that this is (as far as I can tell) the same facility that won the ISPE’s Facility of the Year award in 2020. This means it is still a pretty new facility, and since it is one of the templates that many single-use biotech manufacturing facilities are based on, we had best pay attention. If a failure to maintain a state-of-the-art facility can contribute to this sort of Warning Letter, then many companies had best be paying close attention. There is a lot to unpack and learn here.

Establishing an Ongoing Technology Platform Process

To meet regulatory requirements and industry standards, facilities should implement a systematic approach to technological upgrades.

1. Conduct Regular Assessments

At least annually, perform comprehensive evaluations of your facility’s equipment, systems, and processes. This assessment should include:

Review of equipment performance and maintenance, including equipment effectiveness
Analysis of deviation reports and quality issues
Evaluation of current technologies against emerging industry standards
Assessment of facility design and layout for potential improvements

This should be captured as part of the FUSE metrics plan and appropriately evaluated as part of quality governance.

2. Stay Informed on Industry Trends

Keep abreast of technological advancements in biotech manufacturing at minimum by:

Attending industry conferences and workshops
Participating in working groups for key consensus standard writers, such as ISPE and ASTM
Subscribing to relevant publications and regulatory updates
Engaging with equipment vendors and technology providers

3. Develop a Risk-Based Approach

Prioritize upgrades based on their potential impact on product quality, patient safety, and regulatory compliance. Utilize living risk assessments to get a sense of where issues are developing. These should be the evolution of the risk management that built the facility.

4. Create a Technology Roadmap

Develop a long-term plan for implementing upgrades, considering:

Budget constraints and return on investment
Regulatory timelines for submissions and approvals
Production schedules and potential downtime
Integration with existing systems and processes

5. Implement Change Management Procedures

Ensure there is a robust change management process in place to ensure that upgrades are implemented safely and effectively. This should include:

Detailed documentation of proposed changes
Impact assessments on product quality and regulatory compliance
Appropriate verification for new and changed equipment and systems
Training programs for personnel

6. Appropriate Verification – Commissioning, Qualification and Validation

Conduct thorough verification activities to demonstrate that the upgraded equipment or systems meet predetermined specifications and regulatory requirements.

7. Monitor and Review Performance

Continuously monitor the performance of upgraded systems and equipment to ensure they meet expectations and comply with cGMP requirements. Conduct periodic reviews to identify any necessary adjustments or further improvements. This is all part of Stage 3 of the FDA’s process validation model focusing on ongoing assurance that the process remains in a state of control during routine commercial manufacture. This stage is designed to:

Anticipate and prevent issues before they occur
Detect unplanned deviations from the process
Identify and correct problems

Leveraging Advanced Technologies

To stay ahead of regulatory expectations and industry trends, consider incorporating advanced technologies into your upgrade plans:

Single-Use Systems (SUS): Implement disposable components to reduce cleaning and validation requirements while improving flexibility.
Modern Microbial Methods (MMM): Implement advanced techniques used in microbiology that offer significant advantages over traditional culture-based methods
Process Analytical Technology (PAT): Integrate real-time monitoring and control systems to enhance product quality and process understanding.
Data Analytics and Artificial Intelligence: Implement advanced data analysis tools to identify trends, predict maintenance needs, and optimize processes.

Conclusion

Maintaining a state-of-the-art biotech facility requires a proactive and systematic approach to technological upgrades. By establishing an ongoing process for identifying and implementing improvements, facilities can ensure compliance with FDA requirements, align with industry standards, and stay competitive in the rapidly evolving biotech landscape.

Remember that the goal is not just to meet current regulatory expectations but to anticipate future requirements and position your facility at the forefront of biotech manufacturing excellence. By following this comprehensive approach and staying informed on industry developments, you can create a robust, flexible, and compliant manufacturing environment that supports the production of high-quality biopharmaceutical products.

GMP Critical System

Defining a GMP critical system is an essential aspect of Good Manufacturing Practices (GMP) in the pharmaceutical and medical device industries. A critical system is one that has a direct impact on product quality, safety, and efficacy.

Key Characteristics of GMP Critical Systems

Direct Impact on Product Quality: A critical system is one that can directly affect the quality, safety, or efficacy of the final product.
Influence on Patient Safety: Systems that have a direct or indirect influence on patient safety are considered critical. This is where CPPs come in
Data Integrity: Systems that generate, store, or process data used to determine product SISPQ (e.g. batch quality or are included in batch processing records, stability, data used in a regulatory filing) are critical.
Decision-Making Role: Systems used in the decision process for product release or a regulatory filing are considered critical.
Contact with Products: Equipment or devices that may come into contact with products are often classified as critical.

Continuous Evaluation

It’s important to note that the criticality of systems should be periodically evaluated to ensure they remain in a valid state and compliant with GMP requirements. This includes reviewing the current range of functionality, deviation records, incidents, problems, upgrade history, performance, reliability, security, and validation status reports.

Building the FUSE(P) User Requirements in an ICH Q8, Q9 and Q10 World

“The specification for equipment, facilities, utilities or systems should be defined in a URS and/or a functional specification. The essential elements of quality need to be built in at this stage and any GMP risks mitigated to an acceptable level. The URS should be a point of reference throughout the validation life cycle.” – Annex 15, Section 3.2, Eudralex Volume 4

User Requirement Specifications serve as a cornerstone of quality in pharmaceutical manufacturing. They are not merely bureaucratic documents but vital tools that ensure the safety, efficacy, and quality of pharmaceutical products.

Defining the Essentials

A well-crafted URS outlines the critical requirements for facilities, equipment, utilities, systems and processes in a regulated environment. It captures the fundamental aspects and scope of users’ needs, ensuring that all stakeholders have a clear understanding of what is expected from the final product or system.

Crafting Good User Requirements

Building Quality from the Ground Up

The phrase “essential elements of quality need to be built in at this stage” emphasizes the proactive approach to quality assurance. By incorporating quality considerations from the outset, manufacturers can:

Minimize the risk of errors and defects
Reduce the need for costly corrections later in the process
Ensure compliance with Good Manufacturing Practice (GMP) standards

Mitigating GMP Risks

Risk management is a crucial aspect of pharmaceutical manufacturing. The URS plays a vital role in identifying and addressing potential GMP risks early in the development process. By doing so, manufacturers can:

Implement appropriate control measures
Design systems with built-in safeguards
Ensure that the final product meets regulatory requirements

The URS as a Living Document

One of the key points in the regulations is that the URS should be “a point of reference throughout the validation life cycle.” This underscores the dynamic nature of the URS and its ongoing importance.

Continuous Reference

Throughout the development, implementation, and operation of a system or equipment, the URS serves as:

A benchmark for assessing progress
A guide for making decisions
A tool for resolving disputes or clarifying requirements

Adapting to Change

As projects evolve, the URS may need to be updated to reflect new insights, technological advancements, or changing regulatory requirements. This flexibility ensures that the final product remains aligned with user needs and regulatory expectations.

Practical Implications

Involve multidisciplinary teams in creating the URS, including representatives from quality assurance, engineering, production, and regulatory affairs.
Conduct thorough risk assessments to identify potential GMP risks and incorporate mitigation strategies into the URS.
Ensure clear, objectively stated requirements that are verifiable during testing and commissioning.
Align the URS with company objectives and strategies to ensure long-term relevance and support.
Implement robust version control and change management processes for the URS throughout the validation lifecycle.

Executing the Control Space from the Design Space

The User Requirements Specification (URS) is a mechanism for executing the control space, from the design space as outlined in ICH Q8. To understand that, let’s discuss the path from a Quality Target Product Profile (QTPP) to Critical Quality Attributes (CQAs) to Critical Process Parameters (CPPs) with Proven Acceptable Ranges (PARs), which is a crucial journey in pharmaceutical development using Quality by Design (QbD) principles. This systematic approach ensures that the final product meets the desired quality standards and user needs.

It is important to remember that this is usually a set of user requirements specifications, respecting the system boundaries.

From QTPP to CQAs

The journey begins with defining the Quality Target Product Profile (QTPP). The QTPP is a comprehensive summary of the quality characteristics that a drug product should possess to ensure its safety, efficacy, and overall quality. It serves as the foundation for product development and includes considerations such as:

Dosage strength
Delivery system
Dosage form
Container system
Purity
Stability
Sterility

Once the QTPP is established, the next step is to identify the Critical Quality Attributes (CQAs). CQAs are physical, chemical, biological, or microbiological properties that should be within appropriate limits to ensure the desired product quality. These attributes are derived from the QTPP and are critical to the safety and efficacy of the product.

From CQAs to CPPs

With the CQAs identified, the focus shifts to determining the Critical Process Parameters (CPPs). CPPs are process variables that have a direct impact on the CQAs. These parameters must be monitored and controlled to ensure that the product consistently meets the desired quality standards. Examples of CPPs include:

Temperature
pH
Cooling rate
Rotation speed

The relationship between CQAs and CPPs is established through risk assessment, experimentation, and data analysis. This step often involves Design of Experiments (DoE) to understand how changes in CPPs affect the CQAs. This is Process Characterization.

Establishing PARs

For each CPP, a Proven Acceptable Range (PAR) is determined. The PAR represents the operating range within which the CPP can vary while still ensuring that the CQAs meet the required specifications. PARs are established through rigorous testing and validation processes, often utilizing statistical tools and models.

Build the Requirements for the CPPs

The CPPs with PARs are process parameters that can affect critical quality attributes of the product and must be controlled within predetermined ranges. These are translated into user requirements. Many will specifically label these as Product User Requirements (PUR) to denote they are linked to the overall product capability. This helps to guide risk assessments and develop an overall verification approach.

Most of Us End Up on the Less than Happy Path

This approach is the happy path that aligns nicely with the FDA’s Process Validation Model.

This can quickly break down in the real world. Most of us go into CDMOs with already qualified equipment. We have platforms on which we’ve qualified our equipment, too. We don’t know the CPPs until just before PPQ.

This makes the user requirements even more important as living documents. Yes, we’ve qualified our equipment for these large ranges. Now that we have the CPPs, we update the user requirements for the Product User Requirements, perform an overall assessment of the gaps, and, with a risk-based approach, do additional verification activations either before or as part of Process Performance Qualification (PPQ).