Chapter 57: Quality by Design Concepts

To understand the most current thinking behind product development and regulatory expectations, it is important to understand the principles built into the International Conference on Harmonization (ICH) Pharmaceutical Development (ICH Q8) guidance. The concepts in ICH Q8 came about as a compromise of members of the ICH, which included the United States Food and Drug Administration (FDA) and Pharmaceutical Research and Manufacturers of America (PhRMA) with their counterparts from Europe and Japan. ICH Q8 describes the suggested contents for the 3.2.P.2 Pharmaceutical Development section of a regulatory submission in the ICH M4 common technical document (CTD) format. ICH Q8 was published in the Federal Register, May 22, 2006, as a guidance for pharmaceutical product development.

Before the ICH Q8 guidance, in the United States, the design and development information submitted in an application was variable; some information was submitted in the Investigational New Drug (IND) application, some information was submitted in European Union (EU) reports, and some information was distributed in New Drug Applications (NDAs) in inconsistent format. Europe had incorporated key elements of the ICH Q8 guidance, in that submissions described formulation development, the critical product attributes, and design of the manufacturing process. Japan had limited expectations, with more information for complex dosage forms. In general, though, there was limited (regulatory) incentive to truly understand processes and products, and to optimize them.

After implementation of the ICH Q8 guidance, product quality and performance may be achieved and assured by design of effective and efficient manufacturing processes, product specifications are based on mechanistic—mathematical relationship between input and output—understanding of how formulation and process factors impact product performance, and there is the ability to affect continuous improvement and continuous real-time assurance of quality.

In the United States, the ICH guideline is discretionary and nonbinding. It represents FDA’s thinking on NDA submissions, and companies can offer alternative approaches. ICH, however, provides the preferred FDA format for submissions.

Quality by Design

Quality by design (QbD) designs quality into the product using knowledge of process. With QbD, a relationship exists between quality attributes and product efficacy and performance; with increased knowledge and control, decreased regulatory oversight is justified because all critical sources of variability are identified and explained, variability is controlled by the process, and product quality attributes can be accurately and reliably predicted over the design space established for materials used, process parameters, and environmental and other conditions.

ICH Q8 shows that pharmaceutical development is a learning process. Understanding is gained, for example, by knowledge, formal experimental designs, process analytical technology (PAT), and life cycle knowledge. Formal experimental design (design of experiments [DOE]) is a structured, organized method for determining the relationship between factors affecting a process and the output of that process. PAT is a system for designing, analyzing, and controlling manufacture through timely measurements (that is, during processing) of critical quality and performance attributes of raw and in-process materials and processes with the goal of ensuring final product quality. Both successes and failures are described as part of the life cycle story that demonstrates QbD.

Critical Process Parameter

The critical process parameters (CPPs) are the measured variables that are known to have an effect on one or more product quality attributes. All CPPs must be identified and controls established within a defined acceptable range. The ranges for these parameters may be typically obtained in later development studies (that is, full-scale, engineering studies, or technology transfer studies) aimed to optimize the process for commercialization, conducted in parallel with the human clinical trials. The process optimization efforts must ensure that the enhanced process remains equivalent to that used for the making of the clinical trials product.

Critical Quality Attributes

The critical quality attributes (CQA) are intrinsic quality characteristics that are desired or needed to ensure patient safety and benefit of the drug product. In ICH Q6A, some active pharmaceutical ingredient (API) attributes should be considered critical, regardless of the drug product end use. Identification, physicochemical properties, appearance, assay, and purity are applicable to all drug products. Particle size, microbial purity, and polymorphism depend on the drug product. The ICH Q6A decision trees can be used to determine the criticality of these quality attributes for solids, solutions, or sterile products. All CQA must be identified and have established acceptance criteria.

Design Space

Information from pharmaceutical development studies is a basis for risk management (using ICH Q9). Critical parameters carry the risks, critical formulation and process parameters generally are identified through an assessment of the extent to which their variation can have an impact on the quality of the drug product, and this assessment helps define design space. Design space is therefore defined as the multidimensional combination and interaction of input variables (for example, material attributes) and process parameters that have been demonstrated to provide assurance of quality.

Within the regulatory framework, working within the design space submitted in an application is not considered a change. Movement out of the design space is considered to be a change and would normally initiate a regulatory post-approval change process, requiring regulatory notification. Therefore, ICH Q8 Section 2.4 “Design Space” facilitates regulatory flexibility. In the traditional submission process, there is limited knowledge, and any change needs new data and new approval. With the model after Q8 implementation, an influence of factors is explored, creating knowledge, and risk analysis of impact of change is possible; there is the ability to move within a defined area post-approval that gives flexibility for continuous improvement without the need for further approval. In addition, expanded design space facilitates flexible regulatory approaches. An increased understanding of the product life cycle, including material attributes—manufacturing process and process controls—facilitates the approval and submissions process.

Validation Principles with Quality by Design

Validation is evidence of a high degree of assurance that a specific process will consistently produce a product meeting its predetermined specifications and quality attributes. In ICH Q8, continuous process verification is defined as an alternative approach to process validation in which manufacturing process performance is continuously monitored and evaluated. Product quality and performance are achieved and assured by design of effective and efficient manufacturing processes.

Traditionally, validation has focused on system operations and testing the output of the system. In the ICH Q8 validation approach, validation focuses on system design, and on testing the quality that is being built into the process. The Q8 validation approach focuses on process design space and ongoing assessments. It uses DOE to define CQA, and design space to document the process used to verify CQA. The CQA and their interrelation are documented, and the CPP for each CQA is identified. The affect that CQA variability has on the process is identified. The design space for the process is formed based on findings.

The CQA and CPP are identified through experiments, risk analysis, and analyzer assessments. Validation is based on the range of control of the process within the design space rather than map testing to the frozen specification parameters.

Process Analytical Technology Tools

FDA realized that changes were needed in its inspection approach: the perception was that the existing regulatory system was rigid and did not favor innovation. To encourage innovation, the FDA launched a new initiative in August 2002, Pharmaceutical cGMPs for the 21st Century: A Risk-Based Approach. This program included up-to-date concepts of risk management and quality systems, use of the latest scientific advances in technology, risk-based approaches that encouraged innovation, and a move to make FDA submissions and inspections more consistent. The Agency’s resources addressed the most significant health risks. The desired state of pharmaceutical process is achieved when science and engineering principles are used effectively for assessing and mitigating risks related to poor product and process quality. This process should be accomplished with design of effective and efficient manufacturing processes, specifications that are based on understanding of how formulation and process factors affect product performance, continuous real-time quality assurance, and regulatory policies and procedures that accommodate the most current level of scientific knowledge. Together with the design space concept in QbD, the FDA envisioned process analytical technology (PAT) as part of the new initiatives that would reduce the risks associated with manufacturing uncertainty; risks would be fully contained and controlled.

PAT is a system for designing, analyzing, and controlling manufacturing through timely measurements during processing of critical quality and performance attributes of raw and in-process materials and processes, to ensure final product quality. Analytical requirements of PAT include chemical, physical, microbiological, mathematical, and risk analysis. PAT is not simply online/off-line, real-time analytical measurements. PAT combines multivariate tools for design, data acquisition, and analysis, process analyzers, process control tools, and continuous improvement and knowledge management tools.

Multivariate Tools

Multivariate tools include mathematical approaches, such as statistical DOE, response surface methodologies, process simulation, and pattern recognition tools, to increase understanding of the relevant multifactorial relationships (for example, between formulation, process, and quality attributes) and applicability of this knowledge in different scenarios (that is, generalization). These tools enable the identification and evaluation of product and process variables that may be critical to product quality and performance. They may identify potential failure modes and mechanisms and quantify their effects on product quality.

Process Analytical Technology

PAT adds process analyzers for in-process multivariate (multiple variable) measurements, which are taken in one of three ways:

• At-line, where the sample is removed and analyzed close to the process stream

• Online, where the sample is diverted from the manufacturing process to an analyzer, and possibly returned to the stream

• In-line, which may be an invasive or noninvasive process that analyzes the sample while it is part of the process stream

Process Control Tools

We are most familiar with traditional in-process control tools used for monitoring such things as temperature, pH, pressure, flows, and other physical parameters (that is, univariate measurements). These measurements provide enough information to adjust equipment to more optimal settings for each variable as required, but do not typically provide enough information to learn more about the process as a whole. With PAT, rapid, dedicated in-/online testing is added to these traditional tools to monitor samples periodically and from targeted/strategic locations to actively manipulate the process to maintain a desired state. In the PAT framework, validation can be demonstrated through this continuous quality assurance, where a process is continuously monitored, evaluated, and adjusted using validated in-process measurements, tests, controls, and process end points.

Continuous Improvement and Knowledge Management Tools

Continuous learning through data collection and analysis over the life cycle of a product is important to increase the process knowledge base. A knowledge base can be of most benefit when it consists of scientific understanding of the relevant multifactorial relationships, for example, between formulation, process, and quality attributes. The desired state of pharmaceutical process gives the ability to effect continuous improvement and continuous real-time assurance of quality—continuously optimize the process within the design space—so that each batch is a new validation batch, and each batch provides the opportunity for a better process model. Continuous learning through data collection and analysis over the life cycle of a product is important to increase the process knowledge base.