As the principles and technologies of Quality by Design (QbD) gradually become a second fundamental principle in the pharmaceutical industry, their application is expanding rapidly. The development of analytical methods has now become a focal point for QbD implementation. This process, similar to product development, benefits from the systematic scientific approach that QbD emphasizes. Since reliable analytical data is essential for drug development and manufacturing, there is an increasing need for more rigorous approaches in method development. A new variant of QbD, called Analytical Quality by Design (AQbD), is gaining recognition as the future direction of analytical method development.
The goal of analytical method development is to create, validate, and implement a method that provides accurate and necessary analytical information, meeting all defined objectives. The first step involves understanding the exact purpose of the measurement, akin to defining the clinical performance of a product in traditional QbD. Once this is established, it helps develop awareness and control over the key elements that influence the method's performance, reflecting the full design space of QbD.
This article explores AQbD and examines the terminology and methodologies surrounding it. Using laser diffraction particle size analysis as a model, we will walk through the practical steps of applying AQbD, analyze the characteristics of the instrument, and determine how to effectively use this innovative technique.
**Introduction to QbD Principles**
To better understand AQbD, it is important to first revisit the widely accepted definition of QbD. The term was first introduced in the International Council for Harmonisation (ICH) document Q8 (R2), which defines QbD as:
> "A systematic approach to development based on sound science and quality risk management, designed to pre-determine goals and emphasize product and process understanding and control."
Figure 1 illustrates the QbD workflow for both product and analytical method development. It outlines the key stages, such as defining the target product profile, identifying critical quality attributes (CQAs), establishing the design space, and implementing a control strategy.
**AQbD: Integrating QbD into Analytical Method Development**
The FDA’s guidance on AQbD applications highlights its potential to enhance the robustness of analytical methods across the entire product lifecycle. Like traditional QbD, compliance with AQbD offers regulatory flexibility, allowing adjustments within the method’s operational design range (MODR).
The first step in AQbD is defining the Analytical Target Profile (ATP), similar to the Quality Target Product Profile (QTPP). The ATP outlines the goals of the analytical method and ensures that the results align with the overall QTPP. Determining the purpose, use, and timing of the analytical data helps shape the ATP. Additional performance criteria, such as accuracy and repeatability, are derived from a deeper analysis of these requirements.
Next, a suitable analytical technique is selected based on the ATP requirements. Once chosen, AQbD focuses on method development, including risk assessments of factors like instrument configuration, measurement parameters, material properties, and environmental conditions. These factors are evaluated to identify those that impact the ATP, or CQAs.
Using experimental design (DoE), the MODR is defined, representing the acceptable range of parameters that ensure consistent and reliable results. After defining the MODR, method control and validation are implemented according to ICH Q2 guidelines.
Like QbD, AQbD identifies and manages variables to understand their impact. By focusing on the design space rather than fixed conditions, AQbD offers a responsive approach to everyday analytical challenges. This not only enhances daily usability but also reduces failure risks when methods transition from research labs to quality control settings.
**Practical Case: Particle Size Measurement of Active Ingredients**
To illustrate AQbD in practice, consider the determination of particle size distribution for micronized active pharmaceutical ingredients (APIs). This helps assess suitability for downstream processes and bioavailability in solid oral products.
In this case, the ATP is to determine the particle size at a specific process point, ensuring sufficient accuracy for expected material behavior. While the required precision may exceed pharmacopoeial standards, for simplicity, we assume the USP and EP acceptance criteria are adequate.
Laser diffraction is commonly used for particle size measurement due to its speed, non-destructiveness, and automation capabilities. When a laser beam interacts with particles, light scatters at various angles, allowing the calculation of particle size distribution using models like Mie theory.
Sample preparation is crucial for reliable results, especially for dry dispersion. Proper dispersion ensures representative data, and parameters like air pressure and feed rate become CQAs. Dry dispersion is preferred for its efficiency and environmental benefits, but it may damage fragile materials, making wet dispersion a safer alternative in some cases.
**Defining the MODR Scope**
Dry dispersion involves choosing between air pressure and feed rate as key parameters. Experiments, such as pressure titrations, help define the MODR. For example, using a Malvern Mastersizer 3000, different venturi systems were tested. Results showed that standard venturis provided a wider MODR, offering more stable performance across pressure ranges.
Method validation is essential to ensure the method meets ATP requirements. Repeatability and reproducibility tests, guided by USP and EP, confirm the method's reliability. Coefficients of variation (COVs) must remain within acceptable limits, even with parameter changes.
**Tools Supporting AQbD**
Software tools, such as the Malvern Mastersizer 3000 Measurement Manager, provide real-time feedback and help automate method development. Data quality assessment tools further support method validation by ensuring accurate and consistent results.
**Future Outlook**
While SOPs were once groundbreaking, analytical method development is now moving beyond rigid parameter sets. AQbD enables analysts to build a robust MODR, ensuring consistent results while remaining flexible to change. This deep understanding of MODR improves method application throughout the product lifecycle and simplifies migration.
Although QbD and AQbD require broader research, advancements in instrumentation and software significantly ease the burden. Real-time feedback and automation reduce analytical workload, making AQbD more accessible and beneficial for analysts.
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