The document outlines the implementation of Quality by Design (QbD) principles in developing High-Performance Liquid Chromatography (HPLC) analytical methods. It details the steps involved, including establishing analytical target profiles, risk assessments, modeling approaches, defining design spaces, and procedural qualifications for method validation. Emphasis is placed on ensuring robustness and accuracy in analytical procedures through statistical and design of experiments (DOE) methodologies.

1. Implementation of Quality by Design
principles to HPLC analytical method
Dimitris Papamatthaiakis
Pharma Life-cycle Consultancy

2. Step 1: Analytical Target Profile
Examples
• “The analytical procedure should be able to quantitatively determine all
known/specified related substances and degradation products of an API in the
presence of excipients and other components of a drug product from a reporting
limit to 120% of their specification limit with an accuracy within ±20.0% of the
true value”.
• The procedure must be able to quantify the analyte in presence of A, B, C
impurities at no more than 2% over a range of 80% to 120% with an accuracy and
uncertainty so that the reportable result falls within ±5% of the true value with at
least 97.5% probability.
Tome et al, Development and Optimization of Liquid Chromatography Analytical Methods by Using AQbD Principles:
Overview and Recent Advances, Org. Process Res. Dev. 2019, 23, 1784−1802

3. Step 1: Analytical Target Profile
• CMAs used:
resolutions of critical peaks
signal-to-noise ratio (S/N) of target components
peak symmetry
peak width
analysis time.
Raman et al, Analytical Quality by Design Approach to Test Method Development
and Validation in Drug Substance Manufacturing, Journal of Chemistry Volume 2015,
Article ID 435129, 8 pages

4. Step 2: Risk assessment
Karty et al, Application of QbD and QRM to Analytical Method Validation, PharmTech (2016) Volume 40 (11), 46–55

5. Step 3: Modeling
• Quantitative relationships between CMPs and CMAs are modelled either
with statistical models or with mechanism models
• Screening DOE: The screening designs most often applied are a two-level
full factorial design, a fractional factorial design, and a Plackett− Burman
design. y = b0 + b1x1 + b2x2 + residual
• Response Surface DOE: The factors are studied at more than two levels.
Examples of response-surface designs are the three-level full factorial
design, central composite design, Box−Behnken design, and Doehlert
design. y = b0 + b1x1 + b2x2 + residual + b11x12 + b22x22 + b12x1x2 + residual

6. Step 3: Modeling

7. Step 4: Design space
• As defined in the ICH guideline Q8 (R2): when analytical parameters vary within
the design space, the predetermined requirements for the method would still be
achieved.
• A combination of acceptable ranges of analytical parameters.
• Ensures robustness of the method.

8. Step 4: Design space
Tome et al, Development and Optimization of Liquid Chromatography Analytical Methods by Using AQbD Principles:
Overview and Recent Advances, Org. Process Res. Dev. 2019, 23, 1784−1802

9. Step 4: Design space
Surface plot by
Monte-Carlo Simulation

10. Step 4: Design space
Contour plot
2
2.125
2.25
2.375
2.5
2.625
2.75
2.875
3
2 2.5 3 3.5 4 4.5 5 5.5 6
pH
%ACN in MP
Contour plot %ACN in MP (B) vs. pH (D)
42.2-44
40.4-42.2
38.6-40.4
36.8-38.6
35-36.8
33.2-35
31.4-33.2
29.6-31.4
27.8-29.6
26-27.8

11. Step 5: Analytical Control Strategy
Quality By Design Approaches to Analytical Methods - FDA

12. Step 6: Procedure Qualification
• A.k.a. Method validation
Hanumant G. et al, Development and Validation of RP-HPLC Method
for Simultaneous Determination of Tramadol hydro chloride,
Paracetamol and Dicyclomine hydro chloride by using Design of
Experiment Software (DOE), Int J Pharma Sci. 2014, 4(6): 792-801

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