A paradigm shift is underway in process validation. The FDA revised the guidance to industry for process validation in January 2011.  This guidance defines process validation 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.” The new emphasis is on “scientific” rather than “documented” evidence. ^[1]

This new approach is aimed at increasing process understanding and control. The consequences of ineffective process validation are significant as evidenced by increased cost of compliance: Adverse events, warning letters, delays in product approval and launch, plant remediation, field alerts, recalls and product shortages.

Implications For Industry

Pharmaceutical manufacturers must now make deliberate decisions about which statistical tools and analyses are appropriate for their products and processes.  For example, the traditional “rule of three” for the number of product batches required to validate a process has been challenged. The use of statistical tools are now encouraged not only to determine the number of batches required to validate a process but throughout the process life cycle to demonstrate product integrity, safety, and efficacy.

The larger paradigm shift is that the former approach to validation focused only on what is now called Stage 2: Process Qualification. The new guidance looks at the life cycle beginning with Stage 1: Process Design and ending with Stage 3: Continued Process Verification, in addition to the existing Stage 2.

This has produced a vexing problem for many pharmaceutical executives, especially problematic for midsize to small firms. The standards and expectations are being raised at the same time that drug products are becoming more sophisticated. Historical formulations were generally simpler; now products have complex release mechanisms making it more challenging to demonstrate equivalent bioavailability. Risk assessment is now integral to the validation process and must consider supply chain not under direct control, such as global raw material sources. There is also pressure to drive costs down and reduce production cycles.

New questions are raised: How is an understanding of process design demonstrated? What variables must be considered? What are the inputs and outputs? How are the new risk assessments accomplished? How are statistically valid results demonstrated? Is the internal statistical staff current with FDA guidance?

Some of the specific technical statistical skills that would be required to fully cover the new FDA Guidance include Design of Experiments (DOE), sample size determination, linear regression and correlation, nonlinear regression, Analysis of Variance (ANOVA), and measurement system analysis, to name a few. In addition to competency with the statistical tools, the ability to interpret data and integrate the science of the process with the specific statistical tools is critical. The higher the level of physical-chemical knowledge and pharmaceutical science knowledge coupled with a broad base of statistical skills, the better the outcome.

The benefits of statistical analysis are numerous (think “Cheaper/Better/Faster”): accelerated innovation cycle time, reduced total cost of process and product, improved compliance levels, enhanced quality and safety, reduced rework and recalls and improved patient safety and efficacy.

Practical Statistical Applications

The 2011 Guidance promotes a life-cycle approach to process validation that includes scientifically sound design practices, robust qualification, and process verification. The lifecycle concept links product and process development, qualification of the commercial manufacturing process, and maintenance of the process in a state of control during commercial production and throughout the ownership of the product by the manufacturer.

Stage 1- Process Design: The commercial manufacturing process is defined during this stage based on knowledge gained through development and scale-up activities. The objective of this stage is to demonstrate understanding and robustness of the design space. Statistical applications include:

• Risk assessment / cause and effect matrix to identify potential variables that impact the process and any risks in the system
• DOE to explore process variables
• Sample size determination to specify the number of samples needed to detect significant differences in the variables
• Multiple regression analysis to determine the significant variables and define the operating regions of the variables
• Development of the control strategy to document the operating ranges for the variables to be controlled
• Analytical method validation to obtain an adequate mechanism for measuring the in-process and release parameters
• Analysis to ensure the measurement system is repeatable and reproducible

Stage 2- Process Qualification: During this stage, the process design is evaluated to determine if the process is capable of reproducible commercial manufacturing. The objective of this stage is to demonstrate process capability and repeatability. Statistical applications include:

• Sample size determination
• ANOVA to detect inter/intra batch variability
• Development of specifications
• Process capability demonstration
• Refinement of the control strategy to the operator level
• Stability analysis for determination of shelf-life
• Statistical documentation to support to the CMC filing

Stage 3 – Continued Process Verification (CPV): Ongoing assurance is gained during routine production that the process remains in a state of control. The objective of this stage is to demonstrate a stable and effective process. Statistical applications include:

• Customer complaint analysis for ongoing monitoring
• Setting up and ongoing use of control charts
• Ongoing monitoring of process capability
• Implementation of measurements and controls
• Integration of Annual Product Review (APR) statistics and CPV

Legacy Products

The three stages of the pharmaceutical life cycle as described above are applied to new products as they are developed and introduced. However, most products currently in operation are legacy products and may not have had the benefit of the new guidance.

Legacy products and processes are vulnerable because knowledge can be fragmented, often caused by: step-wise development cycles, multiple tech transfers, compartmentalized data, incompatible information systems, ineffective supplier relations and aggressive cost reduction. Statistical tools can be used to characterize legacy products and processes in the existing facilities and prior to product transfer to other manufacturing operations.

The solution is within the organization. Objectives include: defining a continuum of drivers and variables, embedding process understanding, building an early warning and signaling process, and developing and adjusting process know-how. Statistical tools must be integrated into the fabric of the manufacturing operation for addressing deviations and managing change control.

Problematic for many pharmaceutical operations is a lack of fundamental understanding of their processes and products. When a deviation occurs, this lack of understanding makes it difficult to determine the root cause of the deviation. The investigator has no baseline to examine for sources of variation or causal events. Lack of root cause determination for deviations is a common FDA citation.

Organizing For Success

An increase in the use of statistics is inevitable. There is no single statistician that can possibly maintain all of the required skills at a high enough level to meet the overwhelming challenges of the new process validation guidance. There are alternative ways to address this challenge:

• Insource statistical skills
• Outsource statistical skills
• Hybrid approach

Insourcing completely requires a large staff, however complete coverage of statistical needs can be achieved. This is appropriate for large firms that can leverage these skills. However, the disadvantage is in the cost of maintaining this staff. Even large firms may face pressure to reduce the cost of analytics and may seek some level of outsourcing. In addition, a “next generation” gap could be forming as statisticians face retirement and replacements are strictly from academia without industry knowledge.

Outsourcing completely is appropriate for very small firms at the startup stage. However, this approach creates a dependency that may put the firm at continuity risk.

A Hybrid approach leverages the statistical competence of the existing internal staff while reaching out to fill the remaining skill gaps. This begins with the coupling of basic statistical training for the internal staff with a “back-room” network of external subject matter experts who supplement on project work as needed.

Conclusion

The use of statistical tools has the potential to greatly improve the process of demonstrating that scientific evidence exists to show that a process is capable of consistently delivering quality product, as required by the FDA Guidance. This more scientific approach also accelerates product development cycle-time and reduces the cost of regulatory compliance.

References

1. “Process Validation: General Principals and Practices” FDA Guidance

Dr. Janet Woodcock of the Federal Drug Administration (FDA) once said that a mutual goal of the pharmaceutical industry and regulatory agencies would be to have a “desired state” which is an efficient, agile flexible pharmaceutical manufacturing sector. This sector would be one that would reliably produce high quality drug products, without extensive regulatory oversight.[1]  The desired state would be achieved based upon the risk based implementation of Quality by Design (QbD) outlined in the International Conference on Harmonization of Technical Requirements for Registration of Pharmaceuticals for Human Use (ICH) guidance’s: Q8 (Pharmaceutical Development), Q9 (Quality Risk Management) and Q10 (Pharmaceutical Quality System)[2].

Any pharmaceutical company that initiates and implements QbD (ICH Q8, Q9, and Q10)  will effectively and efficiently fully leverage a competitive advantage in 21st century. The prudent implementation of this strategy based on good science and an accurate grasp of the available regulatory knowledge will create transformational value by providing high product quality assurance for patients, more efficient regulatory oversight and cost savings for the company. In this article, I will give an overview on how QbD relates to process validation, as well as outline the three stages to the lifecycle approach to process validation.[3]

QbD Helps Maintain The Validated State Of Your Process

Process validation of pharmaceutical dosage forms is the process of establishing documented evidence of a drug product’s quality.  This evidence provides a high degree of assurance that a specific manufacturing process will consistently produce a drug product meeting pre-determined quality attributes when operated within its critical process parameters ranges (CPPs). Once a drug product manufacturing process has been validated, the validated state must be maintained through implementation of operating procedures, preventive maintenance, calibration programs, atypical investigation procedures, annual product review programs, and change control procedures. Any changes to the process must be evaluated through the process change request (PCR) system as outlined by each corporation’s quality policy, procedure, guidelines, and procedures for change control to determine if the validated state will be compromised.

Designing a process validation requires the demonstration of process controls. For example, a company must define upfront the necessary critical process parameters (CPPs) as well as the in-process and finished product critical quality attributes (CQAs) that it intends to achieve with each run of the manufacturing process.  A CPP is an input process variable that is maintained within a specified range to ensure that an intermediate or final product will be within the CQA limit. A CQA is a measured property of the intermediate or final product that is considered critical for establishing the intended purity, efficacy, safety, and bioavailability of the drug product. The demonstration of consistency of the CPP and CQA is ultimately consolidated to represent a validated process for manufacture of a pharmaceutical dosage form.  

A Quality Management System Is Crucial For Process Validation Stages

All systems have to be in compliance with the current regulatory guidelines prior to conducting any validation studies. A big part of this is establishing a quality management system (QMS). QMS is defined by the American Society for Quality (ASQ) as a formalized system that documents the structure, responsibilities and procedures required to achieve effective quality.[4]  These systems also represent a set of interrelated processes that, working together, assure product safety, strength, identity, purity, and quality.  In developing quality management systems, FDA recommends six core quality systems on which companies should base their QMS: 1) quality, 2) facilities and equipment, 3) materials, 4) production, 5) packaging and labeling and 6) laboratory control. These core systems are vital to assuring that a system is well developed and worthy of being validated. These FDA recommendations are broad enough to encompass the main aspects of a system that must be considered in creating a QMS. Performing validation studies on a poorly designed system is a waste of a company’s resources and time. Therefore, companies must be in compliance with these six core systems prior to conducting any process validation studies.

All pharmaceutical products manufactured worldwide are to be prospectively validated. A life cycle approach to process validation should be comprised of three stages over the life of the product and process.

Stage 1 – Process Design

The process design stage is the activity of defining the commercial manufacturing process that can consistently deliver a product that meets its CQAs. Process design activities should include the following:

• Identify potential CQAs of the drug product, based on target product profile (TPP);
• Determine relevant CQAs of the drug substance, excipients, and packaging in order to deliver drug product of the desired quality; and
• Define the manufacturing process to be qualified and the appropriate control strategy.

Stage 2 – Process Qualification

During this stage, the process design is evaluated to determine if the process is capable of reproducible commercial manufacturing.  Process Qualification is proof of process knowledge and control. This stage has two elements. The first is the design of the facility and qualification of the equipment and utilities.  The second is the process performance qualification (PPQ).

Process re-qualification is proof of knowledge and control after a planned (e.g. significant process change, API source change, etc) or unplanned (e.g. process drift, atypical event) change in the manufacturing process. The proof is demonstrated via technical justification & supporting documents and Qualification study defined by a process performance qualification protocol.

Stage 3 – Continued Process Verification   

Ongoing assurance is gained during routine production that the process remains in a state of control. Once stage two process qualifications are completed, processes are monitored via the continued process verification and annual review process. Significant deviation from desired process performance uncovered through continued process verification, annual review or via a typical event, warrants consideration for process requalification. It is required that the data generated is continually documented into the process design knowledge of the product (i.e. controlled strategy, design space, and risk assessment documents).

With the FDA’s guidance document providing these general stages, each pharmaceutical company should implement a QbD approach specific for each manufacturing process of a drug product. Each process should be tailored on a case-by-case basis to the specific drug product. These validations strategies could facilitate continual improvement and innovation throughout the product life cycle. Additional product and process understanding can lead to enhanced manufacturing efficiency and regulatory flexibility, thereby providing the availability of safe, effective and high-quality drug products.

In my subsequent columns, I will address validation strategies with respect to solids and controlled release dosage forms; liquids, suspension and semisolid dosage forms; and sterile dosage forms (small and large molecules).