1. ChemBioPharm
Innovative combination of Quality by Design and green analytical chemistry for analytical methods in pharmaceutical sciences
Ludivine Ferey, Karen Gaudin
Laboratory of Analytical Chemistry – Faculty of Pharmaceutical Sciences – Bordeaux
ChemBioPharm – ARNA INSERM U1212 / UMR CNRS 5320
Industrial partner
Christine Boussès
Unither Développement Bordeaux

2. REGULATORY REQUIREMENTS (1/2)
ChemBioPharm
ICH Q8(R2): pharmaceutical development
Quality by Design (QbD)
« a systematic approach to development that begins with predefined objectives and emphasizes product and process understanding and process control, based on sound science and quality risk management » 
Q8(R2) Guidelines
« Quality cannot be tested into products, quality should be built in by design »

3. REGULATORY REQUIREMENTS (2/2) Use of design of experiments
ChemBioPharm
ICH Q8(R2): pharmaceutical development
Design Space (DS)
« the multidimensional combination and interaction of input variables and process parameters that have been demonstrated to provide assurance of quality » 
Q8(R2) Guidelines
Use of design of experiments

4. QbD & ANALYTICAL CHEMISTRY
ChemBioPharm
Analytical methods
used for quality control of drugs => patient safety
should provide reliable scientific data for a better knowledge of the product all along its development
need a controlled risk-based development to guarantee quality
Design Space
sub-region of the experimental domain in which the objectives of the method are reached with a defined probability
robustness domain

5. CQ QbD Global approach of QbD Analytical Target Profile
ChemBioPharm
Analytical Target Profile
(ATP)
Critical Quality Attributes
(CQA)
Critical Process Parameters
(CPP)
Design of experiments
(DOE)
Design Space
(DS)
Validation CQ
QbD

6. ANALYTICAL TARGET PROFILE (ATP)
ChemBioPharm
ATP = predefined goals according to the « intended use » of the method
intended use = stability studies of dextromethorphan (DXM) in final products
 quantitative analysis of API and degradation products
implementation in quality control laboratories
 robustness and validation ICH Q2 (R1)
Green analytical method
 respect of green chemistry principles
DXM

7. GREENING ANALYTICAL METHODS
ChemBioPharm
Reduction and proper management of waste
Reduction or replacement of toxic reagents
Reduction of the number of samples
Minimization of energy consumption
Analytical method
UHPLC fast analysis
Ethanol-based mobile phases greener solvent
Chemometrics (QbD approach) lower number of runs

8. CRITICAL QUALITY ATTRIBUTES (CQAs)
ChemBioPharm
CQAs = measurable attributes of the chromatogram that should be within an appropriate limit or range to ensure the desired quality of the method
Must have
Intend to have
Rs between DXM and impurities A, B & C  Rs >  > 2.5
Peak efficiency impurity A  N >  > 3000
Peak efficiency impurity C  N >  > 30000
Minimization of solvent consumption
EtOH volume  < 0.4mL  < 0.35mL
Mobile phase volume  < 2mL  < 1.2mL
Assassi et al, Green analytical method development for statin analysis, J. Chromatogr. A, 37 (2015)

9. CRITICAL PROCESS PARAMETERS (CPPs)
ChemBioPharm
CPPs = factors whose variability has an impact on a CQA
selected by quality risk assessment
CQAs
Injection
Column
Pump
Tubing
Mobile phase
Detection
Dwell volume
Volume
Solvent
Composition
Organic solvent %
Wavelength
Cell length
Nature
Geometry
Temperature
Length
Diameter
%/min pH
Concentration
Buffer
Flow rate
CPPs to be evaluated by screening design

10. Gradient slope (%/min)
SCREENING DESIGN
ChemBioPharm
Plackett-Burman design: 1st degree modeling
Do factors have significant effects on CQAs (responses of interest)?
Levels pH
Gradient slope (%/min)
Temperature (°C)
Flow rate (mL/min) -1 2 30
0.2 +1 6 4 50
0.4 tR
Impurity efficiency
Solvent consumption
significant factors: flow rate, gradient slope, pH, and temperature

11. Polynomial quadratic model 3-D response surface plot of Rs DXM/Imp.A
OPTIMIZATION DESIGN
ChemBioPharm
Central composite design: 2nd degree modeling
Response surface methodology
Polynomial quadratic model
Gradient slope
Flow rate
Gradient slope pH
Central composite design
High response
Medium response
Low response
3-D response surface plot of Rs DXM/Imp.A

12. OPTIMUM POINT PREDICTION
ChemBioPharm
Desirability analysis
Derringer’s desirability functions applied to CQAs
Optimum point = highest global desirability value (D)

13. OPTIMUM POINT VALIDATION
ChemBioPharm
Analysis time < 5 min
ATP 
Method validated
Impurities A & C
Green method
Ethanol consumption < 0.35 mL per analysis
Ecoscale test
Gałuszka et al, Analytical Eco-Scale for assessing the greeness of analytical procedures, TRAC-Trend. Anal. Chem, 37 (2012) 61.

14. DESIGN SPACE Contour plots
ChemBioPharm
Contour plots
Response overlay = CQA objectives displayed on the graph
Maximum desirability
Optimum point
DS:
pH:
Gradient slope: %/min
Temperature: 35-45°C
Flow rate: mL/min

15. ROBUSTNESS DS = robustness domain
ChemBioPharm
New Plackett-Burman design performed around the optimum point (24-1)
8 validation points experimentally tested by varying CPPs from extreme limits of the DS range
CQAs met the specifications for all 8 points (D = 1)
DS = robustness domain

16. RISK-BASED DESIGN SPACE
ChemBioPharm
Mean based
S > 1 min
Mean responses used for optimization
Do not provide any clue about method reliability
How well and how often the method can meet the specifications? What guarantee?
S = tR,begin – tR,end
Optimized robust assay
Take into account model uncertainty
Monte-Carlo study of the propagation of the model’s predictive errors
Quality is ensured by assessing the risk of not being within the acceptance limits
Risk based
P(S > 1 min)
E. Rozet et al, Design Spaces for Analytical Methods: what, why, how?, TrAC-Trend. Anal. Chem, 42 (2013) 157

17. CONCLUSION
ChemBioPharm
Rapid development of optimal and robust chromatographic methods
In line with ICH Q8
Better knowledge and easier introduction of new concepts (green chemistry)
Design space = robustness but method validation still necessary
C. Boussès, L. Ferey, E. Vedrines, K. Gaudin, Using an innovative of QbD and Green analytical chemistry approaches for the developpement of a stability indicating UHPLC mehod in pharmaceutical products, J. Pharm. Biomed. Anal. 115 (2015)

18. THANK YOU FOR YOUR ATTENTION

19. Penalty points (PPs) to calculate analytical Eco-Scale
ChemBioPharm
Galuszka et al. Trends in Analytical Chemistry, 37 (2012) 61-72
Chemical compound score 
Sub-total PP
Total PP
Ethanol
Amount
< 10 mL 1
Hazard
Less severe hazard
Water
None
Ammonium Formate
< 10 g
1 pictogram - Warming
Formic Acid
Σ = 3
Instrument score 
Energy
U-HPLC
≤ 0.1 kWh per sample
Occupational hazard
Analytical process hermetization
Waste
1 – 10 mL (g) 3
Recycling
Total penalty points (PP): 6
Analytical Eco-Scale total score: = 94

20. METHOD VALIDATION ChemBioPharm Protocol
Calibration set: for each impurity, the stock solutions of each impurity (0.1 mg/mL in ethanol/water mixtures, (25/75, v/v) was diluted 20 fold to obtain a solution at 0.5% of impurity. This dilution was done induplicate for each impurity which constituted the calibration set.
Validation set: dilutions of the stock solutions by 100, 20, and 8 fold were prepared in triplicate in the presence of excipients and dextromethorphan to obtain limit of quantitation (LOQ), 0.5 and 1.25% concentration levels, respectively. All these solutions were10 fold diluted while adding 6.7 mL of a DXM syrup at 3 mg mL−1.
This procedure was repeated at 3 different days and each solution was injected in the optimal chromatographic conditions.

21. METHOD VALIDATION
ChemBioPharm
Goal: to demonstrate that the method is suited for its intended use
Validation of each degradation product (ICH Q2(R1)) using accuracy profiles
specificity, accuracy, repeatability and intermediate repeatability, and linearity
range: LOQ-1.25% DXM targeted concentration
β-expectation tolerance limits within ± 20%

22. DEXTROMETHORPHAN ChemBioPharm
Cyclic tertiary amine: model compound for the study of basic substances
pKa = 8.2, a component of a cough medicine, has been analyzed by reversed-phase HPLC with some difficulties, and actually included as one of the test solutes aiming at the effect of silanols on elution of basic compounds. It has been eluted at acidic pH or in the presence of an ion-pair reagent in most reported determinations to avoid severe tailing at neutral pH in the absence of another amine.
Sensitive probe for silanol effect detection
Typical pharmaceutical case study relevant for the evaluation of robust conditions in green analytical chemistry

23. DEXTROMETHORPHAN IMPURITIES
ChemBioPharm
pKa = 9.85
0.5%

24. CHROMATOGRAPHIC CONDITIONS
ChemBioPharm
Screening of 3 columns: Acquity BEH C18 (50 x 2.1 mm, 1.7 µm) = hybrid silica, Acquity BEH Phenyl (50 x 2.1 mm, 1.7 µm) = covalently modified by phenyl grafting, Hypersil gold AQ (50 x 2.1 mm, 1.9 µm) grafted C18 silica possible to use 100% aqueuous phase
Better selectivity of Acquity BEH C18 = baseline separation of all peaks whereas on others columns coelutions of DXM and Imp. A were observed.
Mobile phase A: 10 mM ammonium formate adjusted to different pH with formic acid, and 96% ethanol (95/5, v/v)
Mobile phase B: 10 mM ammonium formate in ethanol 96% with the same proportion of formic acid as mobile phase A
Gradient: initial hold of 1 min at 21% of mobile phase B followed by a linear ramp from 21% to 36% of mobile phase B, and 6 min of equilibration
Injection volume = 2 µL and detection wavelength = 280 nm.

25. DESIGN OF EXPERIMENTS (DOEs)
ChemBioPharm
Modeling of chromatographic behavior
Screening designs: study of a large number of factors and evaluation of its main effects = 1st degree modeling
Optimization designs: determination of an optimum region (DS) = 2nd degree modeling
Full and Fractional Factorial Designs
Central Composite Designs => Response Surface Strategy

26. RISK-BASED DESIGN SPACE
ChemBioPharm
Mean based
S > 1 min
Mean responses used for optimization
Do not provide any clue about method reliability
How well and how often the method can meet the specifications? What guarantee?
S = tR,begin – tR,end
Optimized robust assay
Take into account model uncertainty
Monte-Carlo study of the propagation of the model’s predictive errors
Quality is ensured by assessing the risk of not being within the acceptance limits
Risk based
P(S > 1 min)
E. Rozet et al, Design Spaces for Analytical Methods: what, why, how?, TrAC-Trend. Anal. Chem, 42 (2013) 157