Review on - Analytical Quality by Design (AQbD) A Novel Approach for Method Development

Table No. 2 - Traditional approach Vs. AQbD approach

5.         Analytical Quality by Design Framework

AQbD integrates systematic planning, risk assessment, and statistical tools to ensure consistent analytical performance.

5.1       Analytical Target Profile (ATP)

The ATP defines the objective of the analytical method and specifies performance requirements such as accuracy, precision, specificity, linearity, and range. ATP acts as the foundation of AQbD-based method development. The ATP outlines the method performance requirements, while risk assessment (e.g., using Ishikawa or FMEA tools)

identifies variables that influence analytical performance. The DoE helps establish method robustness, linearity, and accuracy. [5]

5.2       Critical Quality Attributes (CQAs)

CQAs are measurable attributes that define method performance. In chromatographic methods, typical CQAs include resolution, retention time, tailing factor, theoretical plates, and signal-to-noise ratio. [6]

5.3       Risk Assessment

Risk assessment identifies method variables that may affect CQAs. Tools commonly used include Ishikawa diagrams, Failure Mode and Effects Analysis (FMEA), and risk ranking methods. [7]

5.4. Design of Experiments (DoE)

DoE is a multivariate statistical technique used to evaluate the effect of method parameters simultaneously. It enables identification of significant factors and their interactions, reducing experimental effort while enhancing method understanding. Design of experiments (DoE) is a statistics optimization tool. [8]

Full factorial design: Factorial experiment : It is an experiment whose design consist of two or more factor each with different possible values or level. FD technique introduced by “Fisher” in 1926.Factorial design applied in optimization techniques. Factor: factors can be “Quantitative” (numerical number) or they are qualitative. They may be names rather than numbers likes method 1, site B or present or absent. Factorial design depends on independent variables for development of new formulation. Factorial design also depends on levels as well as coding. [9]

Response surface methodology: It includes optimization procedure for the setting of factorial variables, such that the response reaches a desired maximum or minimum value.

[10]

Central composite design: It is an experimental design, useful in creating relatively sophisticated models, usually second order (quadratic) model for the output function without needing to use other designs. [10]

Axial or star points: These points are marked by “α”. They are located symmetrically around the design center. The value of α depends on the number of input factors involved (this value is determined a prior and could be found in statistical tables - software products)

Corner points: According to the design of the type full factorial design at two levels of variation of the factors. It is depends on preliminary experience and information. Factorsare coded as “+1” and “-1” The choice of the intervals of variation for the factors is an important task for the scientist. Central point is marked by 0. [10]

5.5       Critical Method Parameters (CMPs)

CMPs are method variables that significantly influence CQAs, such as mobile phase composition, pH, flow rate, column temperature, and detection wavelength. [11]

5.6       Method Operable Design Region (MODR)

MODR represents the multidimensional combination of CMPs that assures method performance meeting ATP requirements. Operation within MODR offers regulatory flexibility. [12]

5.7       Control Strategy and Lifecycle Management

Control strategies ensure consistent method performance through system suitability tests and continuous monitoring. AQbD supports method lifecycle management and post- approval changes. [13]

6.         Analytical Method Validation and Regulatory Perspective

Analytical method validation, as described in ICH Q2(R1), verifies that the analytical procedure is suitable for its intended purpose. Validation parameters include specificity, linearity, accuracy, precision, limit of detection (LOD), limit of quantification (LOQ), and robustness. Regulatory agencies encourage AQbD principles to ensure analytical methods are fit-for-purpose and meet compliance throughout their lifecycle. [14]

7.         Example of AQbD: Design of Experiment (DoE) [15]

Box–Behnken Design (BBD) was used to optimize the method parameters and evaluate the effect of critical method parameters on chromatographic responses.

Table No.3 Factorial Design for AQbD

Table No.4 Optimized HPLC Conditions obtained

Design Space

A design space was established using statistical analysis where acceptable resolution, retention time, and peak symmetry were obtained. Within this design space, small variations in parameters do not affect method performance.

Figure no. 1: Three dimensional response surface and two-dimensional contour plot obtained for the studied critical analytical attributes (CAAs). (a–c) retention time; (d–f) area

Figure no. 2 : Three dimensional response surface and two-dimensional contour plot obtained for the studied critical analytical attributes (CAAs).  (g–i) asymmetry; (j–l) theoretical plates.

Figure no. 3: Overlay plot showing the optimal analytical design space.