Implementation of Analytical Quality by Design (AQbD or QbD) in Analytical Method Development

What is AQbD?

First and foremost, it's essential to recognize that drug substances and drug products (formulated dosage forms) are indeed products. Every product is characterized by fundamental performance attributes aligned with its intended purpose. Developers or manufacturers produce these products in a manner that ensures they consistently meet their predetermined performance criteria. Quality control plays a pivotal role in this process by assessing representative samples from each production batch. Based on the results, a product is either approved for release or rejected. This traditional approach is common across various manufacturing processes.

Now, turning to analytical methods, they are also considered a form of "product" with inherent performance characteristics that correspond to their intended analytical purpose. The performance attributes of drug substances or drug products include, but are not limited to, factors like appearance, pH, solubility, water content, the percentage of active components, purity, the proportion of volatile organic impurities, and more. These attributes are used to determine if the target product can fulfill its intended objective.

Similarly, analytical methods, especially those based on High-Performance Liquid Chromatography (HPLC), have their own performance characteristics. These include the ability to resolve closely adjacent peaks, the percentage recovery of the active ingredient or impurities, linear behavior of analytes across specific concentrations and wavelengths, repeatability, sensitivity to analytes of interest, and handling potential interferences linked to diluent or excipient peaks.

Analytical methods do not undergo formal quality control testing akin to drug substances or formulated drug products, where products are either approved for release or rejected based on predefined acceptance criteria. Instead, the performance of analytical methods is assessed during the method validation process, where they must meet predefined acceptance criteria to be declared "validated."

This approach is the traditional way of dealing with both products and analytical methods. In cases of product or method failure, in most instances, the solution has been to create new batches using the same process until they align with the specified acceptance criteria.

However, Quality by Design (QbD), or more specifically, Analytical Quality by Design (AQbD), provides a different perspective. The QbD approach relegates the traditional quality control function to a less significant role when it comes to testing the final product and making release or rejection decisions. In QbD, the key idea centers on defining the characteristics of the ultimate product quality based on a design framework, rather than relying solely on data derived from the final product.

In essence, the quality of the target product is predetermined based on its intended purpose. This approach involves an exhaustive, risk-based study to optimize and establish a manufacturing or development process that assures the product's quality at each step. The concept of "passing or failing" is essentially replaced with a commitment to affirm quality at every stage. Products are manufactured in such a way that they must conform to the predetermined acceptance criteria defined from the outset based on the product's intended purpose. This is the essence of the QbD or AQbD approach.

Why is AQbD important for analytical method development?

Analytical Quality by Design (AQbD or QbD) is a crucial framework that should be incorporated into the development of any analytical method for pharmaceutical products. The primary purpose of an analytical method is to generate reliable, consistent, and reproducible analytical data, which is essential for ensuring the quality, efficacy, and safety of drug substances and drug products. Even a slight deviation in the performance of an analytical method can have serious consequences over time.

It is imperative to subject each stage of analytical method development to thorough scrutiny to understand its inherent characteristics. This scrutiny helps in determining whether the method is prone to variations at any point during its lifecycle. Variations can arise from various factors, including the expertise level of personnel involved, the use of different instruments, working in different laboratories, and variations in solvents, reagents, HPLC columns, and environmental conditions between different batches. Therefore, adopting a risk-based approach becomes essential.

AQbD focuses on the comprehensive analysis of the risks associated with each step during the development of analytical methods. This includes a deep understanding of the chemistry of analytes, their purity, sources, and how they respond to detection technologies. It considers whether detection techniques such as UV-visible, PDA, ELSD, CAD, fluorescent, or RI detectors are suitable for the task. Moreover, it considers the inherent limitations of the selected technologies and factors like the susceptibility of analytes' functional groups to hydrolysis or oxidation. The selection of appropriate solvents, mobile phases, HPLC columns, sample preparation techniques, and more is also part of this risk assessment.

This risk assessment process helps in making informed decisions about the right combination of techniques and tools during the initial stages of method development. AQbD goes beyond merely ensuring that results "pass"; it delves into understanding the potential range where results may fail. This in-depth analysis aids in selecting the most optimized method parameters and creates a comprehensive understanding of the method's performance characteristics, including potential pitfalls, based on prior knowledge.

What are the current practices in analytical method development?

Currently, many analytical methods are developed and validated using the "One Factor at a Time" (OFAT) approach. This approach is often chosen due to limitations in time and resources. In OFAT, each independent variable is examined individually. This means that one independent variable is altered at a time while keeping all other independent variables constant, and the corresponding responses are observed. The process is repeated for each independent variable, studying them in isolation from one another.

However, the OFAT approach has limitations when it comes to fully understanding the relationships between interacting variables and their combined impact on the output responses. For instance, in chemical reactions, there are typically multiple independent variables such as temperature, pressure, and the rate of reactant addition, which influence the percent yield as the output response. To achieve the maximum percent yield, it's important to optimize the values of all these variables simultaneously. Maximizing the yield by changing only one variable at a time may not reveal the true maximum yield, as it may occur at a different combination of conditions that consider the combined effects of these variables.

This same challenge is faced in analytical method development, especially in the context of Analytical Quality by Design (AQbD). When developing chromatographic methods, parameters like retention time, peak resolution, and peak shape are typically evaluated at specific buffer pH levels. The approach may involve studying the impact of various factors one by one, such as column temperature and the compositions of aqueous and organic phases. After collecting data for these different parameters, the so-called "best" set of chromatographic conditions is compiled for a particular buffer pH, column temperature, and solvent composition. However, this selected set of conditions may not truly represent the optimized parameters, and the optimal conditions might be different from what they initially appear to be.

This gap in understanding and optimization is a significant drawback of the OFAT approach in analytical method development. The AQbD framework offers a more comprehensive and systematic way to address these issues, considering the interplay of various parameters and their combined effects on the analytical method's performance.

Why do analytical methods fail and generate OOS results?

Obtaining out-of-specification (OOS) results in analytical testing is a common occurrence, despite conducting tests with precision and accuracy. These OOS results trigger investigations to identify the root causes and determine corrective and preventive actions (CAPA). Typically, OOS investigations can be attributed to various factors, which may involve the analytical method, the instrumentation used, the quality of materials, or human errors. Investigators often place strong emphasis on materials, instruments, and personnel involvement as potential sources of errors, and OOS investigations often lead to recommended corrective actions in these areas.

One notable aspect of OOS investigations is that analytical methods may be perceived as less likely to be the "culprit" if they have been validated. This validation status often directs investigators' attention towards other factors like materials, instruments, or human errors. When an analytical method has been validated, it is usually not subjected to thorough questioning during the investigation.

For OOS investigations, errors related to the materials used, such as purity, limit of detection (LOD), water content, salt forms, and expiration dates, are specific and can be relatively straightforward to address. Instrument-related problems are also typically easier to identify and may contribute to OOS incidents. Human errors, particularly those associated with material weighing, sampling, dilution, or calculation, can play a significant role and are usually resolved through simple steps during the investigation.

However, many OOS investigations tend to conclude by pointing to instrument calibration status, personnel errors, or vague explanations related to unspecified and scientifically weak factors. While these factors may indeed have some influence on OOS results, they often do not address the primary root cause.

Instrument qualification or personnel training is frequently recommended as potential corrective and preventive actions. However, it is essential to understand that well-maintained instruments may produce precise and accurate data even after their qualification period has expired, whereas poorly managed equipment may yield inaccurate results shortly after a qualification update. Therefore, instruments are not always the root cause of OOS incidents.

In some instances, the product itself may be implicated in OOS results, which can lead to extensive investigations beyond the scope of this discussion.

As mentioned earlier, analytical methods that have been validated tend to receive less scrutiny as potential sources of OOS results. This gap in OOS investigations highlights the difference between methods validated under the One Factor at a Time (OFAT) approach and those developed and validated based on the Analytical Quality by Design (AQbD) approach.

This gap provides investigators with an opportunity to question the role of validated analytical methods. There is a need to scrutinize these methods to identify any factors that might impact the analytical results or contribute to OOS results. This should be done before finalizing the investigation report and drawing conclusions based on vague or unspecified reasons. Not all OFAT-based analytical methods are problematic, but some may have inherent gaps, leading to the possibility of OOS results. In such cases, analytical methods developed and validated with inaccurately optimized parameters could indeed be a potential source of OOS results, regardless of their validation status.

Where are the loopholes in analytical methods?

Analytical methods that have been developed and validated using specific values for independent variables, such as buffer type, buffer strength, pH, particular organic solvents, their compositions, column temperature, injection volume, HPLC columns from various vendors, HPLC system manufacturers, and more, may not always be truly representative. Data that appears accurate might be "incidental," and these so-called "incidental" data can create vulnerabilities for the analytical method in the long term. Data that consistently "passes" might indicate a very limited range of study conditions, making it challenging to assess the method's consistency under varying conditions. If the study range is extremely narrow, the "incidentally passed" data may not even be reproducible with the same set of parameters, personnel, reagents, or environmental factors. The situation may become even more unpredictable when different personnel, reagents, or environmental factors are involved.

To address this issue, establishing a "failing range" for the factors listed above, in addition to the "passing range," can help clarify where the actual study ranges should be. Once acceptable ranges are determined based on an understanding of the failing range, strategies can be implemented to optimize variability.

Furthermore, analytical method development should be based on the worst-case scenario. When it comes to peak separation, it's essential to consider the most challenging sample matrix as the worst-case scenario. Simple composite mixtures of all possible analytes may not be sufficient to assess the method's capability for peak separation. Simple yet inappropriate sample preparation techniques, such as pipetting a very small volume of either a highly concentrated or highly diluted solution and then diluting it again to a very small volume, can introduce unexpected variations. A lack of proper understanding of the physical and chemical properties of the analytes can also jeopardize the entire method development process. For example, polymorphic transformations of crystalline drug substances can occur during the handling or manufacturing process of a formulated drug product, resulting in unwanted analytical results. This situation makes it challenging to determine whether the issue is related to the analytical method or the formulation process itself. Isolating the source of variation is crucial.

These are just a few examples of the challenges that need to be addressed during the initial risk assessment phase in analytical method development.

How can AQbD minimize the loopholes and fit into the current practices to fix the gaps?

AQbD conducts a comprehensive risk analysis for each parameter involved in analytical method development. This analysis considers the suitability of analytes of interest for various detection techniques based on their nature, chemistry, chromophores, binding affinity to specific types of packing materials, interactions with particular reagents or solvents used as additives in the mobile phase, and solubility characteristics in specific solvents.

AQbD conducts this extensive risk assessment to ensure the right selection of analytical technologies, detection techniques, HPLC columns, solvents, diluents, solubility, interactions with binding sites in the molecules, polar surface area, enantiomeric form, polymorphism, crystallinity, stability, and more. While some risks may be negligible and have minimal impact on the target analytical method being developed, others can significantly influence the method's performance.

This thorough risk assessment serves as the foundation for selecting the appropriate set of tools and parameters. Based on this assessment, initial method parameters are designed. While these parameters may not be perfect, they are assumed to be close to the target method. AQbD takes into account potential risks and vulnerabilities to minimize loopholes and adapt to current practices, ultimately fixing the gaps in analytical method development.

What are the core AQbD components?

In the AQbD framework, product performance characteristics are pre-defined and not solely reliant on the quality of product test data. These performance characteristics are design-based and are confirmed from the initial stages to the final steps, ensuring that the final product adheres to the predetermined acceptance criteria. These predefined performance characteristics are known as quality target product profiles (QTPP) or analytical target profiles (ATP) for analytical methods.

The quantity of QTPP or ATP is not as important as the quality of identifying these profiles. The QTPP or ATP should be selected in a comprehensive manner to encompass the genuine performance characteristics of the intended product or analytical method. It should also consider the potential impact of secondary components, which may not be part of the core QTPP but can influence it. For instance, in the case of a formulated oral aqueous suspension, the typical QTPP list might include parameters like appearance, pH, specific gravity, viscosity, assay of the active ingredient, percent release or dissolution, and degradation products.

However, some parameters that are not explicitly listed may be secondary in nature but have direct or indirect impacts on the primary QTPPs. For example, particle size (API or particle size of suspending components in the vehicle) is a crucial secondary parameter for oral aqueous suspension, influencing other QTPPs like percent release and content uniformity. While it may not be part of the core QTPP list, its impact should be assessed thoroughly.

Similarly, zeta potential, which measures the surface charges of suspending particles and affects the attractive or repulsive forces among these particles, can have a significant impact on sedimentation rates. Understanding these secondary parameters is essential for predicting the suspension's behavior.

Not all QTPPs and ATPs are equally critical. Some are straightforward and minimally influenced by process parameters, while others are critical and are impacted by other process parameters or performance characteristics. These crucial QTPPs and ATPs are identified as critical quality attributes (CQAs). In AQbD, it's essential to break down the CQAs and conduct a thorough assessment of their impacts.

How do I handle AQbD core components correctly?

The selection of critical quality attributes (CQAs) must be a meticulous and comprehensive process to ensure a thorough assessment of potential barriers to the target analytical method and its ability to meet acceptance criteria. The choice of CQAs should depend on several factors, including the type of analytical method (qualitative or quantitative), detection techniques, the reliability of the technology, the method's purpose, its scope for different phases, data utilization, and more. The primary goal is to gather all necessary information to understand the factors that may directly or indirectly influence the target responses.

In the context of an HPLC-based UV detection method, various factors can be considered as CQAs. For instance, the UV cutoff value of solvents is a CQA that may impact the method's ability to meet analytical target profiles (ATPs). Therefore, it's crucial to study the impact of different solvents and their ratios at the target detection wavelength, including other wavelengths if applicable, to select the appropriate diluent or organic solvent.

The relative strength of the mobile phase compared to the diluent can also be a CQA. Using a diluent that is stronger than the mobile phase can affect peak shape, which, in turn, influences the method's ability to meet acceptance criteria.

Analyte concentration is another CQA to consider, as overloading can lead to peak fronting and improper peak integration, impacting ATPs. Additionally, the selection of lower UV ranges should be taken into consideration, as it can result in multiple peaks in chromatograms due to the presence of ionic species in the test solution.

These are just a few examples, and the selection of CQAs should encompass various aspects, including technology, instrument setup, analyte chemistry, solvent-analyte interactions, analyte stability, sample preparation techniques, personnel errors, and variability. The specific CQAs may vary depending on the method under development.

When initiating the Design of Experiment (DOE), it's essential to select appropriate ranges of independent variables. Extreme values, such as extremely low or high buffer pH, or very narrow differences between lower and higher ranges, should be avoided. Such extremes may lead to unrealistic or uninterpretable results. Instead, selecting reasonable and practical ranges is important. For example, if the expected working pH is around 3.5, the selected pH range in the DOE might be 2.0 to 6.0 to ensure an adequate assessment of the entire range.

Similarly, this principle should be applied to other parameters like column temperature, flow rate, or solvent composition. The selected ranges should be wide enough to generate meaningful data to outline the method's realistic and workable range. At the same time, the ranges should not be so narrow that the DOE consistently produces results falling within the acceptable range, as this would defeat the purpose of AQbD.

How to Implement AQbD in Analytical Method Development

To implement Analytical Quality by Design (AQbD) in an analytical method, the initial step is to define the Analytical Target Profile (ATP), which aligns with the Quality Target Product Profile (QTPP) based on the specific objectives of the analytical method. For example, consider an HPLC-based UV detection technique designed to accurately quantify trace-level impurities, including genotoxic impurities at the ppb level, with a scientifically justified level of accuracy and precision. The target method must possess sufficient sensitivity to detect and quantify the analytes accurately. Additionally, it is crucial that the analyte of interest is well-resolved to enable correct peak integration. In this context, the Analytical Target Profile may include parameters such as the signal-to-noise ratio (S/N ratio), resolution between closely adjacent or critical peak pairs, accuracy, precision, and linearity. AQbD provides a framework to optimize method parameters so that the analytical method can consistently meet these ATPs within the Method Operatable Design Range (MODR).

Once the ATPs are established, it becomes essential to distinguish between critical and non-critical parameters, including any associated factors that might impact the ATPs. These identified critical parameters are referred to as Critical Quality Attributes (CQAs) and require thorough investigation.

In traditional method development, the 'One Factor at a Time' (OFAT) approach is employed, where the combined effects of multiple factors on specific responses cannot be assessed, and the relationships between these factors remain unexplored. This limitation of the OFAT approach underscores the importance of AQbD. Factors such as buffer pH, column temperature, flow rate, and mobile phase ratio are independent variables that can significantly affect dependent variables, such as peak separation, achieving target resolution, peak shape, and detector response. In AQbD, all independent variables are adjusted simultaneously, allowing for the evaluation of their combined impact on responses and the relationships between them. The Design of Experiment (DOE) is a valuable tool in AQbD for analyzing multivariate factors. For instance, with four independent variables, such as buffer pH, column temperature, flow rate, and mobile phase ratio, DOE enables the running of a factorial design involving 43 or a total of 64 runs, which can become quite complex as the number of variables increases. Software-controlled DOE is often used to design appropriate experiments for all relevant factors. Analyzing DOE results is critical, as it reveals the interactions between variables and their individual and combined effects on responses. Contour analysis, akin to examining the topography from the summit of a hill, is used to visualize the responses. Statistical tools like Analysis of Variance (ANOVA) are commonly employed to interpret DOE results.

It is not uncommon for a few runs out of the total of 64 DOE runs, mentioned earlier, to produce outlier results. This is a normal part of the AQbD process, as it helps provide a comprehensive understanding of how the analytical method behaves over a wider range of core parameters when changing all variables simultaneously. From this broader perspective, a workable range is extracted from the data and optimized for each parameter studied, which is known as the Method Operatable Design Range (MODR).

How AQbD Explores New Horizons

Analytical Quality by Design (AQbD) offers an innovative approach to analytical method development, expanding our understanding and capabilities. Through a scientifically justified broader range of variables assessed in Design of Experiment (DOE) data, AQbD unveils the method's true position, often referred to as the 'knowledge space.' This knowledge space provides valuable insights into the statistical position of the analytical method concerning its ability to meet the predefined Analytical Target Profiles (ATPs). It offers an in-depth understanding of the method's actual position, including the boundaries of the design space and the optimal parameter values.

As previously mentioned, the knowledge space represents the broader area within which most responses are expected to fall, signifying that the analytical method complies with the acceptance criteria for some ATPs but not for others. In contrast, the Method Operatable Design Range (MODR) is a smaller circle within the knowledge space, where all ATPs must satisfy the acceptance criteria. When validating the analytical method, the entire MODR is scrutinized for accuracy, precision, and linearity studies. Optimal method parameters are derived from the MODR.

Modifying parameters within the MODR generally does not compromise the integrity of the analytical method. Consequently, such changes do not necessitate change control or revalidation, which can be especially pertinent to regulatory considerations, including FDA requirements.

It is essential to recognize that Quality by Design (QbD) and AQbD are not distinct tools applied separately to drug substances, drug products, and analytical methods. Instead, they represent a unified approach that emphasizes the concept of risk-based design across product development, methods, processes, and other relevant domains. This approach ensures that the intended quality is built into products and processes, reducing reliance on end-product testing. Thus, QbD should be regarded as a comprehensive framework encompassing manufacturing and quality management systems.

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