Design space is part of the US Food and Drug Administration andapos;s quality initiative for the 21st century which aims to
move toward a new paradigm for pharmaceutical assessment as outlined in the International Conference on Harmonization andapos;s
quality guidelines Q8, Q9, and Q10. The statistics required for design-space development play an important role in ensuring
the robustness of this approach.
The objective of this article is to provide concise answers to frequently asked questions (FAQs) related to the statistical
aspects of determining a design space as part of quality-by-design (QbD) initiatives. These FAQs reflect the experiences of
a diverse group of statisticians who have worked closely with process engineers and scientists in the chemical and pharmaceutical
development disciplines, grappling with issues related to the establishment of a design space from a scientific, engineering,
and risk-based perspective. The answers provided herein constitute basic information regarding statistical considerations
and concepts, and will be beneficial to a scientist working to establish a design space in collaboration with a statistician
(see Figure 1).
Figure 1: Areas of common statistical questions regarding the development of a design space. (FIGURE IS COURTESY OF THE AUTHORS)
In this series, the authors discuss the statistical tools used in the following: experimental planning and strategy, including
the use, analysis, and interpretation of historical information; the analysis of a statistically designed experiment (DoE);
defining a design space based on the results of the DoE; and evaluating the resulting design space and its graphical representation.
The concept of risk is interwoven throughout the FAQs to emphasize the notion that risk management is inherent in virtually
all development decisions (including all phases of development and clinical trials) and that the application of statistical
thinking and statistical methods is intended to investigate, mitigate, and control risk.
The role of statistics with respect to the design space is grouped into sections as follows:
- Experimental design planning (Questions 1-7, Part I of III)
- The design in statistical DoE (Questions 8-18, Part II of III)
- The analysis in statistical DoE (Questions 19-22, Part II of III)
- Presenting a design space (Questions 23-24, Part III of III)
- Evaluating a design space (Questions 25-29, Part III of III).
The set of questions and answers is not meant to be exhaustive; additional relevant questions could be posed that go into
greater detail or scope in studying a design space. Nevertheless, these questions cover a broad enough range and detail to
be useful to the practicing scientist or process engineer.
The goal of FDA andapos;s QbD initiatives and current good manufacturing practice (CGMP) for the 21st century is to more effectively
and consistently produce high quality products through better scientific understanding and risk assessment of drug-product
formulation and drug-product and drug-substance manufacture. Quality systems are modified based on designing quality into
the process and product throughout the life cycle. Early in the design of the manufacturing process, it is recommended that
a risk assessment be performed by a cross-functional team to identify the critical quality attributes (CQAs) and process parameters
believed to affect the CQAs, and the effect of the quality of the incoming materials in the manufacturing processes.
According to ICH guidelines Q8(R2), Q9, and Q10, CQA is defined as a physical, chemical, biological, or microbiological property
or characteristic that should be within an appropriate limit, range, or distribution to ensure the desired product quality.
A design space is a multidimensional combination of input variables (e.g., material attributes), their interactions, and process
parameters that have been demonstrated to provide assurance of quality. A statistical and/or mechanistic model can be used
to establish a design space. In addition to the establishment of a design space, a target operating setting within the design
space is defined to ensure a certain desired level of consistency of the product and process performance during routine operations.
As the operating range of process variables approaches the boundary defining a design space, the risk of failing a specification
may increase. Whether or not there is statistical significance to this risk (i.e., effect on quality and, thus, safety and
efficacy) depends on the proximity of the operating variables andapos; ranges to the design space boundary and the attenuation
of the control strategy to detect and mitigate the risk. The execution of an operating plan, including an appropriate control
strategy and appropriate process monitoring, is essential to the success of the overall process and product performance.
Statistics is the science of making decisions in the face of uncertainty. Statistical thinking and methods thereby bring established
tools and approaches to the determination of a design space, and help to maintain a process that is in control and capable
of producing appropriate high quality product. Statistical experimental design is one especially useful tool for establishing
a design space in conjunction with risk-based and other modeling tools. This tool provides an effective and efficient way
to simultaneously test for factor effects and interactions and to describe causative relationships between process parameters
or input materials with the quality attributes.