Speaker at seminar "The Pharmaceutical quality system: ICH Q8/ICH Q9" - University of Parma, 18 May 2012. Describing steps, tools, and approaches developed for application of QbD to manufacturing processes that have analogous application to the development and use of analytical methods.

1. UNIVERSITA’ DEGLI STUDI DI PARMA
Master di II livello in Tecnologie Farmaceutiche e Attività Regolatorie
Applicazione dei principi di QbD
allo sviluppo di un metodo
analitico
SEMINARIO
The Pharmaceutical Quality System:
ICH Q8/ICH Q9
Parma, 18-19 Maggio 2012
Dott.ssa Annalisa Forlenza
Analytical Scientist, Alfa Wassermann

2. Outline
Why QbD for analytical methods?
Comparison of traditional and QbD approches
Framework for applying QbD to analytical methods
Study

3. QbD for Analytical methods
QbD, as defined in ICH Q8(R1), is a systematic approach
to pharmaceutical development beginning with
predefined objectives that emphasize product and
process understanding as well as product and process
control
QbD
Process analytical methods
Product quality data
If the analytical method is considered a process whose
output is quality data , the concepts of QbD can be
applied to develop analytical methods

4. QbD for Analytical methods
ICH Q8(R2) doesn’t explicitly discuss analytical method
development.
However, concepts apply:
−Application of Science and Risk based methodology
−Systematic approach that includes: risk assessment, defining
a design space, control strategy and continual improvement to
increases method robustness and understanding
Steps, tools, and approaches developed for application
of QbD to manufacturing processes have analogous
application to the development and use of analytical
methods.

5. Why QbD for Analytical methods?
To enhance method understanding and robustness
– beyond ICH validation “check box” exercise
– understand, reduce and control sources of variability
To aligne method with processes
– better understanding and control of method variability
– increase the understanding and control of the
process variability
To facilitate continuous improvement
– advanced regulatory approaches

6. Why QbD for Analytical methods?
Traditional approch of method validation does not take
account of modern Six Sigma concepts and of statistical
approaches, to understanding/controlling variability
Nevalainen et al., have estimated analytical performance
at the 3.85-sigma level
All analytical methods used to monitor and control our
manufacturing processes are directly linked to the risk
assessment performed during the process control
definition

7. Comparison of traditional and QbD
approaches to analytical methods
one-factor-at-a-time"
(OFAT) approach, is
carried out by selecting
one instrument parameter
to study while holding all
other parameters fixed.
Pharmaceutical technology, 34 (2) 2010

8. Comparison of traditional and QbD
approaches to analytical methods
Pharmaceutical technology, 34 (2) 2010

9. How QbD for analytical methods is driven by
the overall process-control strategy
Phil Borman et al.
PHARMACEUTICAL TECHNOLOGY ( 2007)

10. Method performance requirements
the criteria that must be met
1.a :method performance criteria
These criteria are driven by an understanding of the process
monitoring and control requirements; that is, the process
critical quality attributes (CQAs) and specification limits.
For methods measuring these CQAs, criteria, such as the
following, would need to be met:
Precision—the need for method variability to be a small proportion of the specification
Selectivity—being clear on which impurities actually need to be monitored at each step in
a process and ensuring adequate discrimination between them
Sensitivity—ensuring the method is sufficiently sensitive relative to the specification limit.

11. Analytical Target Profile
The ATP can be
used to describe
method
requirements
necessery to
adequately
measure the
defined CQAs of
the drug product
Quality by Design
V McCurdy - Process Understanding, 2011
- Wiley Online Library

12. Analytical Target Profile
The ATP attempts to address such questions as:
What is the method’s purpose (to quantitate the major
component, or stability indicating)?
What are the specificity requirements?
What are the accuracy and precision requirements?
What are the resource constraints (instrumentation, run
time, reagents)?
Are there other constraints such as solution stability?

13. Method performance requirements
1.b: Method operational intent
These criteria address the aspects of the method that
are required to facilitate ease of use in routine operation
(e.g., analysis time, acceptable solvents, available
equipment).

14. 2: Method development
The design and selection of the method takes place 
“method scouting”

15. 3: Risk assessment and analytical
design-space definition
It is imperative to reach a high degree of confidence.
It means that the analytical method will meet all method performance
criteria under all conditions of use as it proceeds through the
lifecycle.
This confidence level can be achieved:
– using a rigorous approach for identifying all the potential method
factors that may need to be controlled to ensure method
performance
– through the use of risk assessment tools and prioritized
experimentation that eliminate areas of risk.

16. 3: Risk assessment and analytical
design-space definition
Typically DoE (Design of Experiment) is used to find
ranges for instrument operating parameters, to
understand sample preparation variations and variations
of method precision.
– Example terminology for design space: MODR (method operable
design range)

17. 4: Analytical method control strategy
Using the appropriate risk-assessment tools, the critical
factors and their acceptable ranges (from the risk
assessment or experimental work) are explicitly defined
in the method.

18. Robustness: a fundamental criteria
of quality in an HPLC separation
Ensure that methods would be robust and rugged throughout
their lifecycle of use - the same goal as QbD for
manufacturing process
Rugged, as defined by the United States Pharmacopeia
(USP)
The degree of reproducibility of test results obtained by the
analysis of the same samples under a variety of normal test
conditions:EXTERNAL FACTORS
Robust, as defined by ICH Q2(R1)
A measure of its capacity to remain unaffected by small but
deliberate variations in the method parameters and provides
an indication of its reliability during normal use: INTERNAL
FACTORS

19. Robustness: a fundamental criteria
of quality in an HPLC separation
RUGGEDNESS: ROBUSTNESS:
– Different laboratories – Change in flow rate
– Analyst – Concentration of organic acid
– Instruments in mobile fase
– Reagent batches
– Analysis days
– Assay temperature

20. Robustness: a fundamental criteria
of quality in an HPLC separation
In the past, robustness testing was tipically carried out
during the final stage of a method development process
during the validation stage
This led to undesired surprises being found late on
The method had to be redeveloped and reoptimized

21. Robustness: a fundamental criteria
of quality in an HPLC separation
To avoid these costly repetitions, there is an increasing
tendency of including multifactorial robustness
evaluation at the early stage of development, to built in
quality from the outset
By defining method operating conditions not as discrete
points but as a working spaces with known tolerances,
we obtain:
– the flexibility of a method is increased
– The likelihood of method failure is reduced
– The method can withstand small changed
By design.

22. Multifactorial robustness evaluation
A modern QbD based treatment of the robustness of an
HPLC method requires the assessment of all parameters
which most strongly influence selectivity alone and in
combination
The critical parameters are:
– Gradient time (tG)
– Temperature (T)
– pH eluent A (pH)
– Ternary eluent composition (tC)
– Stationary fase
Other parameters such as flow rate, start B%, end B%, dwell
volume, may also be important.

23. Multifactorial robustness evaluation
In HPLC analysis, for a method to be accurate, precise
and robust the sample must first be well separated
Prerequisite for robustness is critical resolution:
resolution between the least well separated peak pair
For baseline separation
Rs≥ 1,5
All condition for which
the Rs remains above
a given value (
1.5 - 2.0) are robust

24. Study
ELUENT AND REAGENT:
ACN – MEOH – WATER PURIFIED
ELUENT A: 50mM KH2PO4 IN H20/ACN 95/5 v/v
pH adjusted to 2.1, 2.7, 3.3, 6.8, 7.4, 8.0 prior to adding ACN
ELUENT B: 50 Mm KH2PO4 IN H20/ACN 20/80 v/v
50 Mm KH2PO4 IN H20/ACN/MEOH 20/40/40 v/v/v
50 Mm KH2PO4 IN H20/MEOH 20/80 v/v
EQUIPMENT:
UPLC (binary solvent pump, PDA detector, cooled autosampler,
temperature controlled column compartment)
Waters HSS t3 C18 column

25. Study
SAMPLE:
Eye drop solution containing 2 APIs (A-B) and 9 known impurities
( 4 API-A imp.+ 5 API-B imp.) each spiked at 0.1 % level of their
respective API
Journal of Chromatography A,
1232 (2012) 218– 230

26. Workflow
Method intent
1. Design of experiments
2. Design space generation
Method design &
3. Visualize robusteness
selection
4. Select working point
Method evaluation 1. Robusteness evaluation
2. Formal validation
Method Control

27. Method intent
The aim of this study was to develop a fully validated
UHPLC method in accordance with QbD principles for
the assay of two APIs and impurities for an eye drop
sample, providing a fast and robust stability indicating
analysis
All impurities must be separeted from each other and
from the main peaks
Method target performance criteria was baseline
separation for all peaks within a robust working region
All acceptance criteria pertinent to a formal validation
should be met

28. Method design:DOE
Journal of Chromatography A,
1232 (2012) 218– 230
3 different 3D resolution models were constructed:
- tG-T-pH (acidic pH, eluent B:ACN)
- tG-T-pH (neutral pH, eluent B:ACN)
- tG-T-tC ( eluent A: pH 2.7)
Flow rate 0.3 ml/min; gradient range 0 -> 100 %B remain constant

29. Method design:DOE
TG T°C TC pH
TG1 TG2 T1 T2 TC1 TC2 TC3 pH1 pH2 pH3
tG-T-Ph 15 30 25 50 ACN 2.1 2.7 3.3
(acidic)
tG-T-pH 15 30 25 50 ACN 6.8 7.4 8.0
(neutral)
tG-T-TC 15 30 25 50 ACN ACN/ MeOH 2.7
MeOH

30. Method design: DOE
Primary systematic and scientific evaluation of the
critically influential separation parameters:
Log D diagrams for column selection and best pH working
range:
pH 2-4
pH 11-13
Journal of Chromatography A,
1232 (2012) 218– 230

31. Method design:DOE
The acidic pH region was chosen for this study: in the
acidic pH range the logD values for most components is
below 0 indicating that compounds contained in the eye
drop sample are very polar
Waters HSS T3 column is suitable for the separation of
polar compounds.

32. Design space generation
First, the influence on relative retention of gradient
time, temperature and pH of eluent A – in the
acidic pH range – were investigated in a
simultaneous fashion by means of 3D resolution
cubes (Cube A).
To verify the decision to work at acidic pH, another
tG–T–pH cube was generated in the neutral pH
range (Cube B)
Once an optimal acidic pH was determined, a further 3D
resolution cube modeling gradient time, temperature and
ternary eluent composition was constructed (Cube C)
at optimal pH.

33. Design space generation
Cube A
Journal of Chromatography A,
1232 (2012) 218– 230

34. Design space generation
Resolution models map the
critical resolution for each value
of the critical resolution (Rs,crit )
is represented in color so that:
– warm colors show large Rs,crit values
– cold colors show low values
corresponding to inefficient
separations.
– Specifically, in red regions the
resolution is baseline or above (Rs,crit
= 1.5) and dark blue lines signalize
peak overlaps (Rs,crit = 0).

35. Design space: robustness
Journal of Chromatography A, 1232 (2012) 218– 230
All combination of measured parameters with Rs ≤ 1,5 are removed
from the resolution spaces
Red regions represent robust above baseline separations

36. Method selection
Two criteria for selection of working point:
1. It should be contained within the largest robustness
space
2. It should have the shortes run time
Working point:Cube A
TG: 7 min
T: 25°C
pH 2.7

37. Method evaluation:tolerances
Evaluation of robustness and ruggedness of selected
working point
Determination of parameters’ tolerances
TG: 7±1 min T: 25±2 °C pH: 2.7± 0.1
12 new experiments were carried out: they were used to
construct a new smaller cube which proved to yield
critical resolution above 1.5 in the whole range
In combination with the previously ± value, a number of
different tolerances were evaluated:
Flow rate start B% end B%

38. Method evaluation:tolerances
Parameters ± tolerance
TG T °C pH Flow rate start B% End B%
A 7 25 2.7 0,3 0 100
B 7±1 25±2 2.7±0.1 0.3±0.01 0±0.1 100-0.1
C 7±1 25±2 2.7±0.1 0.3±0.05 0±0.5 100-0.5
D 7±1 25±2 2.7±0.1 0.3±0.1 0±1 100-1
Results
(Rs crit) avg (R s crit )max (R s crit )min
A 2.14 2.14 2.14
B 2.14 2.35 1.93
C 2.12 2.60 1.67
D 2.10 2.88 1.28

39. Method evaluation:tolerances
The final tolerances, selected for this study, were those
indicated for test C as they are larger than the instrument
precision
They allow the largest experimental tolerance without
compromising the separation

40. Method evaluation:
column & ternary eluent
composition
Models were constructed with a theorical lower plate
number than the experimental observed
3 columns were predicted to give equivalent selectivity
(Fs< 3) to the one used in this study
Cube C was also investigated: Acn yielded a larger
robust region at lower analysis times than MeOH or
mixtures of both

41. Method control
The final method has been designed with sufficient
robusteness.
So in a GLP environment no critical factors need to be
tightly controlled in order to meet method performance
criteria

42. Benefits of application of QbD
approach to analytical methods
Methods will be more robust and rugged, resulting in fewer
resources spent investigating out-of-specification results and
greater confidence in analysis testing cycle times
The introduction of new analytical methods using a QbD
approach will lead to a higher transfer success rate than with
traditional technology-transfer approaches
Regulatory flexibility
– Movements within “Design Space” are not considered a
change in method

43. The QbD approach to analytical
methods faces several barriers
Current validation guidance does not lead to methods that can
always be reliably operated
External guidance must be developed in this area; ICH
guideline Q2(R1) requires revision (or removal)
A common language for some of the new terms is required
(analytical method design space, analytical method control
strategy, and method performance criteria)
Analysts must learn new tools and skills
A consistent worldwide approach is required for this initiative
to be effective.

44. aforlenza@alfawassermann.it