Pharmaceutical Process Robustness -Manufacturing Durability

“The process is the foundation of success. Trust it, embrace it, and let it guide you towards greatness.” — John Addison

Preamble-

The pharmaceutical industry increasingly emphasizes deepening process understanding, driven by manufacturers’ strong motivation to cultivate resilient processes. Well-understood and robust processes offer a heightened sense of predictability regarding yields, cycle times, and waste levels. Moreover, they enable manufacturers to maintain lower inventories of final products, assuming the manufacturing process’s reliability.

ICH Q8 emphasizes the utility of assessing process robustness in risk assessment and reduction. Establishing robust processes benefits patients, regulatory agencies, and firms by consistently delivering safe, effective, and cost-efficient products.

However, achieving robustness goes beyond simply meeting final specifications; it necessitates integrating it into the product’s design and development stages. Continuous monitoring of product and process performance throughout scale-up, introduction, and routine manufacturing is crucial to maintain robustness and make necessary adjustments.

Process robustness refers to a process’s ability to maintain acceptable quality and performance despite input variations. It’s influenced by both formulation and process design and encompasses factors such as raw material composition and manufacturing parameters.

Before understanding Process Robustness, we must first understand the definitions of a few common words in this topic. Glossary definitions clarify key terms, aiding in effectively implementing robustness strategies.

Glossary

Robustness: The ability of a system, process, or product to maintain stable and consistent performance despite variations or uncertainties in external conditions, input parameters, or operating environments.

Design Space: The design space is the established range of process parameters that have been demonstrated to assure quality. Design space refers to the multidimensional combination and interaction of input variables (e.g., material attributes and process parameters) that have been demonstrated to assure quality. Process parameters and product attributes can be varied within specified ranges within the design space without compromising product quality. The design space is established through systematic studies during product development to demonstrate the relationship between input variables and product attributes and to identify the operating ranges within which the desired product quality can be consistently achieved. It provides process optimization and scale-up flexibility while ensuring product quality and regulatory compliance.

Manufacturing Science: Manufacturing science is a multidisciplinary field encompassing the study, analysis, and application of principles and practices related to the design, development, optimization, and control of manufacturing processes and systems. It involves the integration of various scientific and engineering disciplines, such as materials science, mechanical engineering, electrical engineering, chemical engineering, and computer science, to understand, improve, and innovate manufacturing processes and technologies. Normal Operating Range The Normal Operating Range (NOR)- refers to the range of values within which a process or system operates under typical or expected conditions while maintaining desired performance, quality, and safety standards. It encompasses the acceptable variation of parameters or attributes that are considered normal and acceptable for the functioning of the process or system. Deviations from the normal operating range may indicate potential issues or anomalies that require attention or intervention to prevent adverse effects on the process or product. Establishing and monitoring the normal operating range ensures stable and reliable manufacturing processes and systems operation.

Process Analytical Technologies (PAT)—Process Analytical Technologies (PAT) refers to a system of tools, strategies, and methodologies used in the manufacturing industry to monitor and control manufacturing processes in real-time. PAT uses analytical techniques, sensors, and data analysis tools to understand and optimize a production process’s critical parameters and attributes.

Proven Acceptable Range (PAR): A characterized range at which a process parameter may be operated. The PAR represents the boundaries within which parameter variations or attributes are considered acceptable without compromising product quality or safety.

Critical Process Parameter (CPP) — A Critical Process Parameter is a process input that directly and significantly influences a Critical Quality Attribute when varied beyond a limited range.

Critical Quality Attribute (CQA) — A Critical Quality Attribute (CQA) is a measurable physical, chemical, biological, or microbiological property or characteristic of a product that ensures its safety, efficacy, and quality. These attributes are critical because they directly affect the product’s performance, efficacy, or safety and must be controlled within predefined limits to ensure the product meets its intended quality standards. CQAs are identified and defined during the development and manufacturing process of pharmaceuticals, biologics, medical devices, and other regulated products to ensure consistency and compliance with regulatory requirements.

Quality: Quality can be defined as the degree to which a product or service meets or exceeds customer expectations and requirements or the degree to which a set of inherent properties of a product, system, or process fulfills requirements. It encompasses various attributes such as reliability, durability, performance, safety, and consistency. Quality is not just about the absence of defects but also about meeting customer needs and delivering value. It involves continuous improvement efforts to enhance processes and outcomes to achieve higher levels of satisfaction and excellence.

Quality System: A formalized system that documents the structure, responsibilities, and procedures required for effective quality management.

Requirements: Needs or expectations that are stated, generally implied, or obligatory by the patients or their surrogates (e.g., health care professionals, regulators, and legislators).

Repeatability: Repeatability refers to obtaining consistent and similar results when the same experiment or process is repeated multiple times by the same operator, using the same equipment and procedures, under the same conditions. It measures the precision and consistency of measurements or outcomes within a single set of conditions or parameters.

Reproducibility: Reproducibility refers to the ability to achieve consistent and similar results when an experiment or process is repeated under similar conditions by different operators or in different settings. It indicates the reliability and consistency of experimental or observational findings.

What is process Robustness- The term “robustness” denotes the capacity of a manufacturing process to withstand the inherent variability in raw materials, operational conditions, equipment, environmental factors, and human involvement.

In other words, the ability of a manufacturing process to tolerate the expected variability from sources like raw materials, operating conditions, process, equipment, environmental conditions, sampling variability, and human factors for non-automated processes is referred to as robustness.

Developing a Robust Process- 8 steps process

Adopting a systematic, team-based approach to development enhances process comprehension and ensures the creation of a robust process. Despite the absence of explicit guidance on robust process development, this section aims to outline a systematic method and identify which parameters qualify as CPPs.

Steps for Developing a Robust Process:

Step 1. Team Formation: To develop a robust process, assemble a team comprising technical experts from R&D, technology transfer, manufacturing, statistical sciences, and relevant disciplines. This team, led by individuals with deep knowledge of the product, production process, analytical methods, and statistical tools, fosters collaboration and ensures early alignment on technical decisions. Forming this team early in the process, ideally before optimization and scale-up, is crucial.

Step 2. Process Definition: A typical process comprises several unit operations. Before proceeding with developing a robust process, the team must agree on the unit operations under scrutiny and define the process parameters and attributes. Process flow diagrams or flowcharts are commonly used for this purpose, providing sufficient detail to understand each step’s primary function. Additionally, the team should identify all possible product attributes and agree on potential Critical Quality Attributes (CQAs), including assay, dissolution, degradants, uniformity, absence of microbial growth, and appearance. Determining process parameters involves considering categories such as materials, methods, machinery, personnel, measurement, and environment. Tools like Fishbone or Ishikawa diagrams can help in capturing these parameters.

It’s essential to emphasize that documenting results is a critical aspect of this process, and comprehensive records should document all development findings.

Step 3: Prioritizing Experiments

Developing a robust process requires a comprehensive understanding of the process and its parameters. However, studying every potential relationship between process parameters and attributes isn’t practical or essential. Instead, the team should employ a structured analysis method like a prioritization matrix to identify and rank both process parameters and attributes for further investigation. Unlike more statistically-focused approaches, a prioritization matrix primarily draws on the process knowledge and technical acumen of the team members involved, though data from designed experiments may also be incorporated.

Step 4: Analyze Measurement Capability: All measurements are subject to variability. Therefore, the process analysis cannot be meaningful unless the measuring instrument used to collect data is both repeatable and reproducible. A gauge repeatability and Reproducibility study (R&R) or similar analysis should be performed to assess the measurement system’s capability for both parameters and attributes. Measurement tools and techniques should be of the appropriate precision over the range of interest for each parameter and attribute.

Step 5: Identify Functional Relationship Between Parameters and Attributes: The next step is to identify the functional relationships between parameters and attributes and to gather information on potential sources of variability. The functional relationships can be identified in many different ways, including computational approaches, simulations (small-scale unit ops), or experimental approaches. Where experimental approaches are needed, one-factor-at-a-time experiments can be used but are least preferred. Design of Experiments (DOE) is the recommended approach because of its ability to find and quantify the interaction effects of different parameters.

Step 6: Assessing Measurement Capability:

Understanding the variability inherent in measurements is crucial. Hence, analyzing a process requires reliable data collection instruments that are both repeatable and reproducible. Conducting a Gage Repeatability and Reproducibility (R&R) study or similar analysis is essential to evaluate the measurement system’s capability for parameters and attributes. Measurement tools and techniques must exhibit suitable precision across the range of interest for each parameter and attribute.

Step 7: Establishing Functional Relationships Between Parameters and Attributes:

The subsequent phase involves identifying the functional connections between parameters and attributes while pinpointing potential variability sources. These relationships can be discerned through various methods, such as computational approaches, simulations (like small-scale unit operations), or experimental techniques. Although one-factor-at-a-time experiments can be utilized when necessary, Design of Experiments (DOE) is preferred due to its ability to uncover and quantify interaction effects among different parameters.

Well-designed experiments can optimize scientific insights while minimizing resource expenditure because:

· Pre-planning experiments reduce the need for additional trials.

· Fewer studies are necessary.

· Each study encompasses a broader scope.

· Multiple factors can be manipulated simultaneously.

The design of experiments often involves a two-stage process: screening experiments to identify primary factors and response surface methodologies to refine the understanding of functional relationships between key parameters and attributes. Table C provides an example of statistical DOE for studying a direct compression tablet.

Step 8: Validating Critical Quality Attributes (CQAs) and Critical Process Parameters (CPPs):

Once sufficient process understanding is attained, it becomes feasible to validate previously identified CQAs (from step 2). In the direct compression tablet case study, critical quality attributes included dissolution, assay, tablet uniformity, and stability. CPPs, defined as process inputs directly influencing CQAs, are typically identified using the functional relationships established in Step 5. For instance, in the direct compression tablet case study, tablet press speed and compression pressure were identified as CPPs affecting dissolution. Figures 5 and 6 illustrate the impact of these parameters on dissolution. These functional relationships aid in employing optimization strategies to identify optimal process set points or operating regions for press speed and compaction pressure.

Enhancing Manufacturing Process Robustness-

During the Research and Development (R&D) phase, a structured development plan is executed, comprising discrete experiments to formulate the product, define the manufacturing process, and gain insight into the key relationships between parameters and attributes. However, when the product transitions to Manufacturing, it often encounters a broader range of parameter variations than observed in R&D. This could result from increased variability in raw material parameters. Hence, the assessment of true process capability and robustness, along with any necessary process improvements or corrections, begins during this phase.

Manufacturing generates a wealth of empirical process performance data, invaluable for various purposes. Periodic analysis of this data is crucial to assess process capability and robustness, prioritize improvement initiatives, and identify correlative relationships. Feedback loops to R&D may also enhance product quality during this phase.

Monitoring Robustness State-

Manufacturing should monitor both parameters and attributes over time, emphasizing critical ones, based on the operating range established by R&D. Statistical Process Control (SPC) charts, coupled with capability index calculations, offer effective means to monitor process stability, detect potential issues, and evaluate control status. Run charts and control charts visualize process trends and variability, while capability indices gauge the process’s ability to meet specifications.

Process Specific Improvement or Remediation-

Manufacturing must adhere to development-defined bounds to achieve and sustain an ideal process state. Any deviations detected, whether through trend analysis or single-point anomalies, prompt investigation. Various tools aid in this, such as flowcharts for process visualization, Ishikawa diagrams to map cause-and-effect relationships, and Quality Function Deployment (QFD) to translate customer requirements into technical specifications. Failure Modes and Effects Analysis (FMEA) identifies, prioritizes, and mitigates risks. Kepner-Tregoe (KT) techniques provide systematic procedures for critical thinking, facilitating understanding and decision-making. Pareto charts help prioritize improvement efforts by identifying significant causes.

In the pharmaceutical industry, ensuring process robustness is critical due to the stringent regulatory requirements and the need to produce high-quality products consistently. Here are some key indicators and methodologies to assess pharmaceutical process robustness:

Key Indicators of Pharmaceutical Process Robustness

1. Process Capability Indices (Cpk, Cp):

• Measure the process's ability to produce output within specification limits. Higher values indicate a more capable and robust process.

In general, the higher the CPK, the better. A Cpk value less than 1.0 is considered poor, and the process is incapable. A value between 1.0 and 1.33 is considered barely capable, and a value greater than 1.33 is considered capable.

2. Control Chart Analysis:

• Monitor critical quality attributes (CQAs) and critical process parameters (CPPs) over time to detect trends, shifts, or any unusual variations that might indicate a lack of robustness.

3. Consistency of Critical Quality Attributes (CQAs):

• Ensure that CQAs such as potency, purity, dissolution rate, and stability remain consistent across different batches and production runs.

4. Reproducibility and Repeatability:

• Assess the ability to reproduce the same results under consistent conditions and repeat the process reliably over multiple cycles or batches.

5. Process Yield and Efficiency:

• Evaluate the consistency of yield and overall efficiency. Significant variations may indicate robustness issues.

6. Deviation and Non-Conformance Rate:

• Track the frequency and severity of deviations and non-conformances. A lower rate generally indicates a more robust process.

7. Change Control Impact:

• Analyze how process changes (e.g., raw material changes and equipment updates) affect product quality and performance.

Methodologies to Assess Process Robustness

1. Design of Experiments (DOE):

• Use DOE to systematically investigate the effects of multiple factors on process performance. This helps identify critical factors and their interactions, leading to a more robust design.

2. Quality by Design (QbD):

• Implement QbD principles to design and develop processes with a thorough understanding of the variability sources and controls. QbD emphasizes building quality into the process from the beginning.

3. Risk Assessment and Management:

• Conduct thorough risk assessments (e.g., FMEA) to identify and mitigate potential risks affecting process robustness. Continuous risk management ensures ongoing control.

4. Statistical Process Control (SPC):

• Employ SPC tools such as control charts and process capability analysis to monitor and maintain process stability.

5. Multivariate Data Analysis (MVDA):

• Use MVDA techniques to analyze complex data sets, uncover patterns, and understand relationships between multiple process parameters and quality attributes.

6. Stress Testing and Edge-of-Failure Studies:

• Conduct stress tests to push the process to its limits and identify failure points. This helps in understanding process robustness under extreme conditions.

7. Continual Improvement Programs:

• Implement programs like Six Sigma or Lean methodologies to continually analyze and improve the process. This involves regular review and refinement to enhance robustness.

Example Indicators and Tools:

• Process Capability Analysis (Cpk, Cp): Software tools like Minitab or JMP.
• Control Charts: SPC software such as Minitab, JMP, or in-house developed systems.
• DOE: Tools like JMP, Design-Expert, or Minitab.
• MVDA: Software such as SIMCA or MATLAB.
• Risk Management: FMEA templates, risk matrices, and tools like RiskWatch.

Summary

In the pharmaceutical industry, process robustness is assessed through a combination of statistical analysis, risk management, design experiments, and continuous improvement methodologies. By closely monitoring key performance indicators, conducting thorough risk assessments, and applying robust design principles, pharmaceutical companies can ensure their processes consistently produce high-quality products, meet regulatory requirements, and withstand variability and unexpected conditions.

Conclusion-

Establishing a robust process enhances quality and reduces costs. Initiating R&D, emphasizing building quality into the product, and continuing through Manufacturing, with vigilant monitoring and improvement activities, ensures sustained process robustness.

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