A Review on: Optimizing Analytical Methods Through AQbD

Abstract

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• Abstract
• Introduction
• Elements of AQbD
• AQbD Strategic Principles and Implementation Steps
• Design of Experiments (DoE)
• Implementation of AQbD in the Current Practice
• Conclusion
• Source of Funding
• Conflict of Interest
• Ethical Approval
• Acknowledgment
• References

Throughout the lifecycle of a pharmaceutical product, it is crucial to establish analytical procedures, which can be costly and time-consuming if not simplified effectively using scientific knowledge and process expertise. Quality by Design (QbD) is a development concept that begins with predefined objectives and focuses on developing and controlling processes based on risk management and comprehensive scientific knowledge. When this concept is applied to the development of analytical methods, it is referred to as Analytical Quality by Design (AQbD). The main principles of AQbD involve understanding the Analytical Target Profile (ATP) and conducting a risk assessment for variables that may impact the effectiveness of the developed analytical method. AQbD enables the analytical process to operate within the Method Operable Design Region (MODR), allowing movement within this region. Compared to traditional methods, analytical methods developed using AQbD result in fewer out-of-trend (OOT) and out-of-specification (OOS) occurrences due to their robustness within the designated region (Graphical Abstract).

Figure 1: Graphical Abstract

Keywords:Quality; Quality by design; Analytical QbD; Analytical target profile; Design of experiment

At various points throughout the lifecycle of the pharmaceutical product analytical procedure must be established. The pharmaceutical industry is always looking for new guidelines or components to add or swap out with the current components of risk and quality risk management. The principle of quality by design was first introduced by renowned quality expert Joseph M. Juran. Quality by Design (QbD) ICH guidance Q8 (R2) describes QbD as, “a systematic approach to pharmaceutical development that begins with predefined objectives and emphasizes product and process understanding and process control, based on sound science and quality risk management [1-3].

Quality should be built in, it cannot be tested in the product, which is the basic rule of “Total quality management”. To achieve the goal of high-quality product innovation, the scientific background provides an overview of drug research and production. The impurities can be separated (or quantified) using chromatography techniques which can be tuned by many variables and all these variables can be optimized by QbD as shown in Table 1. The study describes how to perform quality by design-based development of UPLC, HPLC & HPTLC methods. Initially, method objectives were defined, and critical analytical attributes [4-7].

Table 1:Difference between the regulatory perspective of QbD and AQbD.

Quality by design can be extended to analytical method development called analytical Quality by Design (AQbD). It is an essential part of the recent approach toward the development of methods and helps in the systematic approach to the development of drugs. It aims to analyze the predetermined goals of the analytical technique to manage the crucial method variable that impacts the crucial analytical features, resulting in improved method performance, high robustness, flexibility, and ruggedness for ongoing development. As a component of AQbD, the design of the experiment illustrates how the input factors interact and affect the method response and outcomes at the end [9,10].

History of quality by design

A. 1990: - Dr. Joseph M. Juran developed the QbD concept of ‘quality should be designed into a product, and the majority of quality issues originate from the initial form of the product.
B. 2002: - The food and drug administration (FDA) inspires risk-based approaches and the adoption of Quality-by-design principles in drug product development, manufacturing, and regulation [11].
C. 2004 - 2012: - ICH guidelines which outline Quality by design concept [12].
D. 2004: ICH Q8 pharmaceutical development [12,13].
E. 2005: ICH Q9 quality risk management [14-16].
F. 2012: ICH Q11 development and manufacturing of drug substances
G. 2011: EMA-FDA pilot program for parallel assessment of QbD application.
H. 2017: Survey of pharmaceutical companies on the implementation of the AQbD concept.
I. 2020: Aug 2020 MHRA response and strategy for AQbD concept to pharmacopeial standards for medicines.
J. 2021: - USP & BP workshop on AQbD& analytical procedure life cycle (APLC).

Applications of AQbD

Scientific understanding of pharmaceutical processes and method: AQbD emphasizes a thorough scientific understanding of the analytical method, which helps in identifying and controlling variability. For instance, understanding the impact of pH and temperature on the separation process in High-Performance Liquid Chromatography (HPLC) can lead to better control and optimization of the method profiles shown in Figure 1 [17,18].

Figure 2:Six-phase AQbD enabled strategy for rational development of a robust analytical method.

Product design and process development: AQbD integrates product design with process development, ensuring that the analytical methods are aligned with the overall product goals. For example, designing an HPLC method that can accurately quantify both the active pharmaceutical ingredient (API) and its impurities ensures that the final product meets quality standards as shown in Table 2 [14,18-27].

Table 2:A few Examples Applying the AQbD Approach.

Science-based risk assessment: AQbD involves a systematic risk assessment approach to identify and mitigate potential risks in the analytical method. For example, using Failure Mode and Effects Analysis (FMEA) to identify potential failure points in a dissolution testing method and implementing controls to mitigate these risks.

Identification of critical quality attributes (CQAs): AQbD identifies CQAs and assesses their impact on the final product quality. For instance, identifying the CQA of particle size distribution in a dry powder inhaler and understanding its impact on the drug delivery and efficacy [23,24].

Robust method or process: AQbD develops robust methods that can withstand variations in process parameters without affecting the quality of the results. For example, an HPLC method developed using AQbD principles is less likely to be affected by minor changes in mobile phase composition or column temperature.

Provides required design space for development: AQbD defines a design space within which the method can be operated to produce results that meet predefined quality criteria. For example, determining the acceptable range of flow rates in an HPLC method still produces accurate and reliable results.

Control strategy throughout the analysis: AQbD ensures a control strategy is in place throughout the analysis to maintain method performance. For example, using real-time monitoring to guarantee consistency during gas chromatography analysis of important parameters like temperature and pressure.

Continuous improvement: AQbD allows for continuous improvement of the analytical method, even after it is validated. For example, continuously optimizing a UV-Vis spectroscopy method for better sensitivity and specificity in detecting impurities.

Flexibility in analysis: Analytical quality by design gives flexibility in the analysis of active pharmaceutical ingredients, impurities in the dosage forms, and stability in biological samples. For example, adapting an LC-MS/MS method to analyze both the Active Pharmaceutical Ingredient (API) and its metabolites in biological samples during pharmacokinetic studies [4,8].

Reduction in variability: AQbD focuses on understanding and reducing variability in analytical attributes, improving method robustness. For instance, identifying and controlling the sources of variability in a titration method to achieve more consistent results [23].

Eliminate batch failures: By ensuring robust methods and processes, AQbD helps in eliminating batch failures. For example, a robust dissolution method developed using AQbD principles reduces the likelihood of batch failure due to inconsistent dissolution profiles shown in Figure1.

Minimize deviations and costly investigation: AQbD minimizes deviations and the need for costly investigations by identifying and controlling potential issues. For instance, using a well-designed HPLC method to avoid deviations related to peak resolution and retention time variability [24-26].

Analytical target profile (ATP)

It is stated what the method has to measure like Acceptance criteria and to what level measurement is required like composition of mobile phase, PH, flow of mobile phase, column temperature, and selection of detector. Also known as Quality Target Method Profile (QTMP) [21,25].

This tool for method development has been referenced in the ICH Q8 R (2) guidelines. It specifies the method requirements that need to be evaluated to guide the method development process, containing all performance criteria necessary for the intended analytical applications. It should be established for various attributes outlined in the control strategy shown in Table 3. It outlines the measurements that must be taken to meet the acceptance criteria as well as the necessary measurement level (characteristic of performance level: precision, accuracy, range, sensitivity, and the associated performance standard) [25,27].

Table 3:AQBD lifecycle.

ATP for the analytical procedure includes a) Target analytes selection i.e., API and impurities, b) Analytical technique selection: HPTLC, GC, HPLC, ion Chromatography, etc., and c) choice of method requirements [28].

Critical analytical attributes (CAA)

It is defined as chemical, biological, physical, or microbiological properties that must be inside a proper limit or dispersal to ensure the desired product excellence.

CAAs are related to the critical method characteristics of any analytical method. Direct estimator of the performance of an analytical method Specific for a particular type of analytical method. Analytical Attributes: Assay, Peak area, Theoretical plates, Retention time, Asymmetry factor, and Capacity factor are used to determine the Signal-to-noise ratio [24,29].

Method operable design region (MODR) / design space

It is also called the analytical design space, which involves a systematic series of experiments where purposeful changes are made to input factors. This approach identifies the causes of significant changes in output responses as well as determines the correlation between factors and outputs. It evaluates all potential factors simultaneously, systematically, and efficiently [30,31].

The MODR represents the multidimensional part of the analytical knowledge space, enclosed by upper and lower levels of the coded variables, demonstrated to assure quality.

The MODR is always strictly related to a specific method. Although multiple programs might use a shared method, it is designed to meet ATP criteria. If the project criteria differ, resulting in different ATP criteria, the MODR for the shared method cannot be easily transferred [32-34].

Benefits of MODR

Robustness: Establishing a MODR ensures the method remains robust and reliable under a range of conditions, reducing the risk of method failure due to minor variations.

Flexibility and efficiency: The method can accommodate slight variations in CMPs without compromising performance, offering operational flexibility. Reduced need for frequent reoptimization and troubleshooting, saving time and resources.

Regulatory compliance: Demonstrates a thorough understanding of the method and its variability, aiding in regulatory submissions and compliance [35-37].

Control strategy: To assure method performance and product quality, a planned set of controls for Critical material attributes (CMAS) and Continuous mandatory ventilation (CMVs) is developed at the laboratory scale developmental stage, based on currently available accurate method development. It provides a comprehensive overview of how quality is ensured, based on the current understanding of the process and product. This phase also involves replicating optimized experiments, collecting data, and analyzing it to ensure that the method stays controlled [18,38].

Risk assessment: According to the ICH Q9 guideline Risk assessment strategy is defined as: “It is a systematic process to assess, monitor and evaluate quality risks throughout the product life cycle”. This step is crucial for the reliability of the method. Once a technique is identified, AQbD focuses on a detailed risk assessment of factors that can cause potential variability in the method, such as analytical methods, instrument configuration, measurement and method parameters, sample properties, preparation of sample, and environmental conditions. Traditional method development was based on method testing after a transfer, while analytical QbD requires risk assessment before method transfer and during the product life cycle [39,40]. According to ICH Q9, risk analysis can be done in three steps, viz. risk identification, risk analysis, and risk assessment [41,42]. One of the most common ways to do a risk assessment is to use a fishbone diagram, also known as an Ishikawa. Consequently, risk factors are classified into the following categories: a) High-risk factors: e.g. These should be corrected during the method development process. b) Noise Factors: These have been tested by Measurement Systems Analysis (MSA). This can be done using a cross-nested research design and variance plots, Analysis of Variables (ANOVA), etc. These factors undergo a robustness test [43]. c) Experimental factors: e.g., instrumentation and working methods. The object of the durability test and the acceptable are determined. The third step is a risk assessment, which is done using failure mode and Effect Analysis (FMEA) [44- 46].

Tools for screening parameters: Ishikawa diagrams, what-if analysis, hazard and operability analysis (HAZOP).

Tools for risk ranking: FMEA, pareto analysis, relative ranking. Experimental tool for process understanding: DOE, mechanistic models, bowtie model.

Step 1: Define the analytical target profile: by targeting analytical method validation parameters.
Step 2: Determine CAAs and CMVs or CMPs based on earlier defined ATPS Identify the potential impact of method parameters like mobile phase preparation, system suitability parameters, etc. [22,23,47,48].
Step 3: Analyse the design of experiments & perform analysis by taking the help of the Ishikawa diagram & failure mode and Effects Analysis to define our knowledge space shown in Figure 2.

Figure 3:AQbD tends to facilitate enormous savings of resources.

Step 4: Design our experiment based on the outcome of analysis by designing an Analytical Design space or MODR with a proven acceptable region.
Step 5: Verify & exhibit: the new validated method.
Step 6: Control & improvement: To achieve our intended goal, adjust the approach using control strategies for improvement over the course of the product lifecycle, taking into account modifications to the product, columns, and analytical tool [49,50].

It is a methodically structured approach to identifying the connections between the variables influencing the procedures and the outcomes. It has been suggested that DoE can provide returns in fractions of the time required to execute one-factor-at-a-time tests, four to eight times more than the cost of running the trials shown in Table 4. As per ICH Q8 standards demand Design Space in product development, MODR can also be established during the method development phase, potentially providing a reliable and economical approach [8,49,51,52] (Figure 3).

Table 4:Selection of DoE Tools.

Figure 4:Ishikawa diagram cause-and-effect fish-bone diagram for a liquid chromatographic method development.

MODR is based on the output from DoE

Similar to CQAs, MODR is the operational range for the critical method input variables that result in consistent match of the objective stated in the ATP. To give anticipated method performance criteria and method response without requiring resubmission to the FDA, MODR allows flexibility in the variety of input method parameters shown in Fig.4. Choosing the right input variable and output response requires an extensive knowledge of DoE implementation during the method development phase [53-56].

Need for design of experiment

It is a systematic, efficient method that allows scientists and engineers to study the relationship between multiple input factors and key output variables (responses) [57-59] (Figure 4).

Figure 5:Design of experiment.

In the future, along with the validation process, AQbD must be included in the method development phase and verify the performance of the method. To apply AQbD for a particular drug product, the following ideas taken into consideration. Create a QTPP which is a profile that is based on the FDA-approved product specification [60,61]. Examine the criticality of each product specification. Evaluate and support the development of the analytical approach and its applicability to choose an appropriate analytical technique such as UV, IR, and HPLC to meet ATP before moving on to QTPP [16]. Access the risk associated with the chosen approach. Determine the variable both, Qualitative and Quantitative, that has an impact on the method performance and reaction that needs to be measured. To optimize a variable and gain scientific understanding, use an appropriate DOE Evaluate the area, Robustness models, and cost-effective operations for technique variable. To demonstrate durability, validate models and MODR using experimental verification at various points. Next, verify the method’s performance in the operable region while control strategy and improvement into consideration [62-70].

Analytical Qbd implementation for method development showed many positive outcomes within the development process. The produced method showed robust, selective, and reproducible analysis. Risk assessment techniques identify and categorize the primary source of risks. The most popular advantages are techniques that are more robust and rugged, meaning they can withstand the demands of continuous use by production and quality control laboratories with a lower risk of failure. The approaches published to date emphasize the structured development of the method and provide a complete assessment of pharmaceuticals that should be included at every stage of manufacture to assure quality with QbD.

This study received no specific financing from commercial, public, or nonprofit funding bodies.

The authors declare that they have no competing financial interests.

As no animal study is involved, ethical approval is not required.

The authors are thankful to the Principal, Sinhgad Institute of Pharmacy, Narhe Pune.

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