OR WAIT 15 SECS
Using multivariate experiments to define acceptable ranges.
A key challenge in successfully implementing Quality by Design (QbD) is achieving a thorough understanding of the product and the process. This knowledge base must include understanding the variability in raw materials, the relationship between the process and the critical quality attributes (CQAs) of the product, and finally the relationship between the CQAs and the clinical safety and efficacy of the product.
Quality by Design (QbD) is receiving significant attention in both the traditional pharmaceutical and biopharmaceutical industry subsequent to the FDA's publication of the International Conference on Harmonization (ICH) Q8 guidance, Pharmaceutical Development, in May 2006.1 Previously, in August 2002, the FDA had announced a major initiative aimed at improving the quality and management of pharmaceuticals. This "Risk Based Approach to Pharmaceutical cGMPs for the 21st Century" contained innovative concepts that FDA believed would modernize the regulation of pharmaceutical manufacturing and product quality. Several of the key concepts included in the initiative were encouraging the early adoption of new technological advances, facilitating industry application of modern quality-management techniques, and encouraging implementation of risk-based approaches. An outgrowth of using the newest technological advances was the process analytical technology (PAT) initiative. FDA outlined the expectations for PAT in its guidance PAT—A Framework for Innovative Pharmaceutical Manufacturing and Quality Assurance.2
AVECIA BIOLOGICS LIMITED
Complementing the FDA's current good manufacturing practices (cGMP) initiative, two important guidance documents were also published by the FDA that were aligned with ICH documents: ICH Q8, Pharmaceutical Development,1 mentioned above, and ICH Q9, Quality Risk Management.3 ICH Q8 described the expectations for the Drug Product Pharmaceutical Development Section of the common technical document, and ICH Q9 outlined approaches to producing quality pharmaceutical products using scientific and risk-based approaches. Much work and progress has been made in defining the application of these expectations in the biotech and traditional small-molecule pharmaceutical industry. The industry also has been working actively on applying these concepts to the development and manufacture of drug products.
Anurag S. Rathore, PhD
Nail and Searles recently reviewed various applications of QbD involving the development, scale-up, and technology transfer of freeze-dried parenteral drugs.4 Cook et al. published a case study involving design of experiments (DOE) to identify key and critical process parameters and their targets for a hydrophobic interaction chromatography step used in an antibody purification process.5 Harms et al. recently presented a case study illustrating an approach to establishing process design space for biotech products.6
This article is the fourteenth in the "Elements of Biopharmaceutical Production" series. In Part 1 of this article, we present a stepwise approach to defining a design space. Case studies from industry, including both biotech and traditional small-molecule pharmaceutical manufacturing, are used to illustrate the key aspects. Part 2, which will appear in the January issue, will present a stepwise approach to validating, filing, and monitoring the design space. It will also discuss how to implement QbD for legacy products and how to integrate QbD with process analytical technology (PAT).
According to the Q8 guidance, quality by design means:
Designing and developing a product and associated manufacturing processes that will be used during product development to ensure that the product consistently attains a predefined quality at the end of the manufacturing process.1
The concept of design space as defined in ICH Q8, is gaining popularity as a platform for communicating QbD principles for pharmaceutical products. ICH Q8 defines design space as:
The multidimensional combination and interaction of input variables (e.g. material attributes) and process parameters that have been demonstrated to provide assurance of quality.
and goes on to say:
Working within the design space is not considered as a change. Movement out of the design space is considered to be a change and would normally initiate a regulatory post approval change process. Design space is proposed by the applicant and is subject to regulatory assessment and approval.
Design space is a well-developed concept in the pharmaceutical industry.6,7 This paper focuses on presenting a stepwise approach to defining the design space for both biotech and traditional small-molecule pharmaceutical products.
Figure 1 illustrates the key phases of process development: early development, commercialization, and post-launch. The early development phase includes the first-in-human milestone and typically encompasses work up to the decision made at the end of Phase 2 about whether or not to continue developing the product. Most pharmaceutical candidates are eliminated at the end of Phase 2 because of concerns related to safety or clinical efficacy. Because of the high rate of product attrition, the objective in early development is to produce the drug substance and drug product in the amounts required to meet clinical needs. Limited process development is performed at this stage, and the use of platforms is quite prevalent for both biologics and traditional oral dosage form products. The focus is on ensuring product safety and efficacy by minimizing process variability and maximizing product quality. As a result, at this early stage, it is typical for the process to be defined by narrow operating ranges and a narrow design space.
Figure 1. An illustration of the phases of process development: early process development, commercialization, and post-launch. Process understanding continues to grow post launch through process monitoring, and information gained may be used to make the process more robust or to make process improvements.
However, once the decision is reached to further commercialize a pharmaceutical product, the purpose of process development becomes creating a robust process, identifying critical process steps, identifying critical raw materials and their attributes, and controlling the environmental factors that may affect process variability. At this later stage of development, the scope of process development work may include obtaining an empirical understanding, a mechanistic understanding, and knowledge of first principles, as appropriate. Steps in the commercialization process as illustrated in Figure 1 include: commercial process development, process characterization, process validation, and regulatory filing.
No matter how well a pharmaceutical product has been characterized, however, process understanding grows throughout the lifecycle of the product. It is common practice to continue to invest in learning efforts post-launch through a process-monitoring program. The objectives of the program may include: to confirm that the process continues to perform within the approved design space; to seek opportunities to make the process more robust and eliminate additional sources of process variability; and to make process improvements geared toward improving productivity or product quality. As illustrated in Figure 1, these opportunities may drive re-initiation of the commercial process development or process characterization step.
In the following sections, case studies from both biotech and small-molecule pharmaceutical manufacturing are presented to illustrate key steps in establishing a design space.
As shown in Figures 2a and 2b, once acceptable critical quality attributes have been established, process characterization studies can be used to define the acceptable range of process parameters. Operating within these acceptable ranges—the interaction of which will ultimately define the design space—ensures quality. Those acceptable ranges are documented in the regulatory filing.
Figure 2a. The key steps in process characterization. First, a risk analysis is performed, often using the failure modes and effects analysis (FMEA) method, to identify parameters for process characterization. Second, studies are designed using design of experiments (DOE). Third, the studies are executed and the results are analyzed to make decisions about the criticality of the parameters and to establish the design space.
The overall approach to process characterization involves three key steps.6,8 First, a risk analysis is performed to identify parameters for process characterization. Second, studies are designed using design of experiments (DOE) so that the resulting data will be amenable for use in understanding and defining the design space. Third, the studies are executed and the results analyzed for decisions about the criticality of the parameters and about establishing the design space.
Figure 2b. Illustration of the design space and its relationship to the characterized and operating ranges. The operating range denotes the range in manufacturing procedures; the characterization range is the range examined during process characterization; and the acceptable range (AR) is the output of the characterization studies. The AR defines the design space and is documented in the regulatory filing. Adapted from reference 7.
Failure mode and effects analysis (FMEA) is a commonly used tool to assess the potential degree of risk for operating parameters in a systematic manner and to prioritize activities (such as experiments) needed to understand the effect of these parameters on overall process performance.9 A team consisting of representatives from process development, manufacturing, and other relevant disciplines performs an assessment to determine severity, occurrence, and detection. The severity score measures the seriousness of a particular failure and is based on an estimate of the severity of a potential failure effect at the local (process) level or at the end product (patient impact) level. Occurrence and detection scores are based on an excursion outside the operating range that results in the identified failure. The occurrence score measures how frequently the failure might occur, and the detection score indicates the probability of timely detection and correction of the excursion before end-product use. All three scores are multiplied to obtain a risk priority number (RPN) and the RPN scores are then ranked to identify the parameters with sufficient risk to merit process characterization.
Figure 3. Pareto chart showing RPN* scores for the operating parameters of a fermentation pre-induction step in a biotech process. Adapted from reference 6.
Figure 3 illustrates the FMEA outcome for a microbial fermentation step in a biotech process. RPN scores were calculated following the procedure described above. Operating parameters that had an RPN score that exceeded a certain threshold were characterized using a qualified scaled-down model. Screening was first performed to identify the process parameters that had the greatest effect on percent solids, optical density (OD) profiles, and product titer. Twelve parameters were examined in the screening study, and based on the results, three parameters were examined further for their interactions. Those parameters were temperature, pH, and dissolved oxygen (DO). A design of experiments (DOE) study was designed to examine the main effect of these parameters on percent solids, optical density (OD) profiles, and product titer, along with their interactions.
Figure 4a. Outcome of a process characterization study of a microbial fermentation step showing parameter estimates for impact on titer. All conditions were normalized against the average of the two center point runs.
The outcome of the DOE study is illustrated in Figure 4a for the effect on product titer. It was found that none of the parameters had a significant effect on product quality (i.e., none was a critical process parameter). However, temperature, pH, and DO were found to affect cell growth and titer and thus were classified as key process parameters. According to the principles in the ICH Q8 guideline, a unit operation design space was established using the acceptable ranges for temperature, pH, and DO, as illustrated in Figure 4b. It also can be seen that the operating space, as defined by the operating ranges, is well nested inside the unit operation design space, indicating robustness of the process step per Figure 2.
Figure 4b. Illustration of design space for the fermentation process under consideration. The outer surface represents the design space and the inner one the operating space. Adapted from reference 6.
A second example involving the characterization of a crystallization step in a classical pharmaceutical process is shown in Figure 5. Initially, the active pharmaceutical ingredient (API) recovery process was stressed one variable at a time. First, the crystallization operation was tested at 35 °C and found to produce acceptable potency values at pH levels of 5.75, 6.25, and 7.0. In a subsequent experimental series, the crystallization at a pH level of 6.25 was found to be acceptable at temperatures of 33 °C, 35 °C, and 38 °C. Although the data suggested acceptable ranges for pH and temperature, little knowledge was gained about whether the two parameters interact. For example, what happens at a high pH and a high temperature (Figure 5a)? Hence, the process was further evaluated using a DOE approach that showed that potency was indeed an interactive function of temperature and pH, as well as stirring speed and time. It was determined that certain combinations of low temperature and low pH, or high temperature and high pH, affected the level of impurities. Ultimately, the unit operation design space was defined and optimized using this knowledge to produce an API of high purity.
Figure 5. Illustration of outcome of an experimental study examining performance of a crystallization step in a pharmaceutical process using A) univariate experimentation and B) design of experiments (DOE). In the univariate baseline method (A), acceptable potency levels resulted from crystallization at 33, 35, and 38 Â°C, and separately at pH values of 5.75, 6.25, and 7.0, but no interactions were studied. A DOE approach was then used to study the effect of interactions of pH and temperature on potency (shown in B).
Figure 5b illustrates the effect of temperature and pH on potency. This example highlights the need to use statistical tools in designing process characterization studies and analyzing the resulting data. Using univariate approaches can often result in wrong or misinformed conclusions.
The biotech and traditional small-molecule pharmaceutical industry has been working actively on applying the concepts of Quality by Design to the development and manufacture of drug products. Case studies such as those presented here will serve as useful tools in establishing common ground about how to develop and define a design space. They provide examples of how to carry out three key steps in process characterization: 1) performing a risk analysis to identify parameters for process characterization; 2) developing studies based on a design-of-experiments approach to study those parameters and their interactions; and 3) executing those studies and analyzing them to determine which parameters are critical and how the design space should be defined. Part 2 of this article will present a stepwise approach to validating, filing, and monitoring the design space. It will also discuss how to implement QbD for legacy products and how to integrate QbD with process analytical technology (PAT).
This article summarizes the presentations and discussions that occurred in the plenary session titled "How do you sell Quality by Design (QbD)?" at the PDA/FDA Joint Regulatory Conference held on September 24-28, 2007, in Washington, DC. The objective of the session was to discuss the challenges that are encountered when implementing the QbD paradigm.
Anurag S. Rathore, PhD, is a director of process development at Amgen, 805.447.4491, email@example.com He is also a member of BioPharm International's editorial advisory board. Stephen H. Montgomery is a law clerk at McDonnell Boehnen Hulbert and Berghoff, LLP, Azita Saleki-Gerhardt is division vice-president for quality at Abbott, North Chicago, IL, and Stephen M. Tyler is director of strategic quality and technical operations at Abbott.
1. US Food and Drug Administration (FDA). Guidance for Industry. Q8, Pharmaceutical Development. Rockville, MD; 2006 May.
2. US FDA. PAT Guidance for Industry—A Framework for Innovative Pharmaceutical Development, Manufacturing and Quality Assurance. Rockville, MD; 2004 Sept.
3. US FDA. Guidance for Industry. Q9 Quality Risk Management. Rockville, MD; 2006 June.
4. Nail SL, Searles JA. Elements of quality by design in development and scale-up of freeze-dried parenterals. BioPharm Int. 2008 Jan;21(1):44-52.
5. Cook S, Patton KA, Bazemore LR. Quality by design in the CMO environment. BioPharm Int. 2007 Dec;20(12):28-37.
6. Harms J, Wang X, Kim T, Yang J, Rathore AS. Defining design space for biotech products: case study of Pichia pastoris fermentation. Biotechnol Prog. 2008;24:655-662.
7. Rathore AS, Branning R, Cecchini D. Design space for biotech products. BioPharm Intl. 2007 April;20(5):36-40.
8. Seely J. Process Characterization. In: Rathore AS, Sofer G, editors. Process Validation in Manufacturing of Biopharmaceuticals. Boca Raton, FL: Taylor & Francis; 2005. p. 31-68.
9. Seely RJ, Haury J. Applications of Failure Modes and Effects Analysis to Biotechnology Manufacturing Processes. In Rathore AS, Sofer G, editors. Process Validation in Manufacturing of Biopharmaceuticals. Boca Raton, FL: Taylor & Francis; 2005. p. 13-30.