Quality by Design (QbD) is a decade-long approach that was first introduced by quality expert Joseph M. Juran. Juran proposed a "Juran Trilogy", of which quality and innovation were two of the pillars necessary to ensure breakthroughs in developing new products, services and processes. Quite simply, Juran believed and evidence has shown that companies could plan for quality and innovation. These principles have been embraced in the automotive industry and even by the U.S. Food and Drug Administration (FDA). While the pharmaceutical industry has somewhat implemented QbD, a combination of industry conservatism and institutional economics means that it has not really taken hold.
The Theory Behind QbD
In the Juran Trilogy, "quality" refers to features that lead to customer satisfaction, and to their reliability. As failure leads to customer dis-satisfaction, it is incumbent on a firm to eliminate failure to enhance quality. This process of developing features and reducing failures is at the heart of the Juran process for developing new products, services and processes. The design model proceeds in six steps:
Set design targets and goals.
Define the target market.
Understand the target market's needs.
Develop the necessary features to meet those demands.
Iterate over the design process to improve those features.
Create process controls to ensure that the design can be produced in operations.
In the paper, "Analytical QBD Approach to Redefine the Quality of Pharmaceuticals: A Review", researchers Shaik Ayesha Ameen and Nagaraju Pappula, explained that QbD could be beneficial within the pharmaceutical industry. Already, it has been used in the development of chromatographic methods (e.g., HPLC, LC-MS); bioanalytical techniques for biological fluids; impurity profiling and stability studies; and in the use of non-destructive analysis tools like Raman spectroscopy.
As a systematic, science-based approach, Ameen and Pappula find that QbD's benefit lies in its emphasis on designing quality into products rather than relying on end-product testing to achieve quality, making it a superior framework for quality product development. They found in their review of the evidence that it achieved consistent product quality, and was in alignment with the International Conference on Harmonization (ICH) Guidelines, Q8, Q9, and Q10, and addresses variability in manufacturing and analytical processes. In harmony with Juran's initial thesis, they found that the core concepts of analytical QbD (AQbD) are finding the analytical target profile (ATP), which defines the purpose and performance criteria for analytical methods, guiding development and validation; defining the critical quality attributes (CQA) that the product must meet in order to realise product quality, such as purity, stability, and assay accuracy; and having critical method parameters (CMPs), which are the factors affecting CQAs, such as mobile phase ratio and instrument settings, which require control for robustness. Five steps were found from reviewing the literature:
Implementation Steps
Define ATP to establish method objectives.
Identify CQAs and CMPs using risk assessment tools like Ishikawa diagrams.
Use Design of Experiments (DoE) for robust method optimization.
Establish the Method Operable Design Region (MODR) for validated operational flexibility.
Implement a control strategy for continuous monitoring and improvement.
Their review of the evidence found that AQdP led to robust methods, flexibility, efficiency, and regulatory alignment. Indeed, it is clear from the evidence that AQbD offers potential for greater innovation, risk reduction, and cost savings in analytical method development. However, it remains under-used because it is seen as being rather complex and because there is a lack of mandatory regulatory requirements. The proliferation of different terminologies such as ATP and Method Operable Design Region (MODR) has led to confusion and a failure to cross-pollinate ideas and techniques, and so, there is a need for more training to facilitate adoption.
QbD in Practice
A real-world example of how QbD is used can be found in the paper, "Quality by design-based approach for the development of an analytical method for quantifying ponatinib in rat plasma" by Nahyun Koo and other researchers, QbD was applied specifically in developing an analytical method to quantify ponatinib, a tyrosine kinase inhibitor, in rat plasma. This case illustrates QbD's value in pharmaceutical analysis. They had a number of goals:
Accurate Quantification: They needed to develop a highly sensitive method to measure ponatinib concentrations in plasma with a lower limit of quantification (LLOQ) of 1 ng/mL.
Efficiency: They wanted to create a method with a shorter analysis time compared to existing techniques to facilitate high-throughput sample analysis.
Robustness: They wanted to ensure that the method performs consistently across various experimental conditions with minimal variability.
Regulatory Compliance: Finally, they wanted to align the method with FDA and European Medicines Agency (EMA) guidelines for analytical method validation to support preclinical and clinical studies.
The researchers identified several critical method parameters (CMPs) through a systematic design approach, including:
Organic Solvent Ratio in the Mobile Phase, which is a key factor influencing peak resolution and retention time.
Flow Rate, which affects sensitivity and separation efficiency.
Buffer Strength, which impacts resolution and overall chromatographic performance.
Buffer pH and Column Temperature, which were included in the initial screening as factors that could influence method performance.
These factors were optimized using the Taguchi screening method and Box-Behnken Design (BBD). The Taguchi method identified the most influential factors, while BBD refined these factors to optimize the chromatographic conditions for peak area, resolution, and retention time. This ensured robustness and minimized variability in outcomes.
Source: Science Direct
The researchers used QbD because they believed in its ability to produce a systematic and data-driven development process. Their process can be simplified into the following four considerations:
Systematic Optimization: they used statistical tools such as Design of Experiments (DoE) so that they could explore and optimize multiple variables in the most efficient way possible. The result of this is that they did not have to do as many experimental runs as they ordinarily would have, while, at the same time, they enriched their understanding of the interactions among CMPs.
Robustness: Because they defined their analytical design space, they made sure that their findings could be translated into an operational setting, allowing for reproducibility.
Efficiency and Sensitivity: Unlike traditional methods such as HPLC-UV and LC-MS/MS, QbD empowered the researchers to enhance sensitivity (1 ng/mL LLOQ) and efficiency (measured as shorter analysis time, and simpler preparation) while complying with FDA and EMA regulations.
Mitigation of Variability: The emphasis on identifying and controlling a design's sources of variability proved beneficial for quantifying a biological matrix as complex as plasma.
The result of this process is that they were able to find that a 60% organic solvent ratio, 0.8 mL/min flow rate, and 15 mM buffer strength, provide the best balance between sensitivity, efficiency, and robustness. In addition, they found that the method met FDA and EMA standards for validation, showing accuracy, precision, and linearity across the desired range of 1 to 1000 ng/mL. Finally, they validated the method by applying it to a pharmacokinetic study in rats, confirming its utility in preclinical research. This is a brilliant example of how applying QbD principles can drive breakthroughs and support future pharmacokinetic investigations with impressive reliability and efficiency. It has lessons for the wider pharmaceutical industry. Six simple principles, and impressive outcomes.