1. Dr Daniel Tray
API Chemistry, GlaxoSmithKline,
Stevenage, United Kingdom
The Role of Fractional
Factorial and D-Optimal
Designs in the Development
of QbD Pharmaceutical
Production Processes

2. Presentation Outline
Brief Overview of Research and Development at GSK
API Chemistry at GSK
 What we do
 Our approach to Quality-by-Design (QbD)
 Link between QbD and DoE
Case Study #1
 Production Process Overview
 High level control strategy intent
 DoE Investigations (Fractional factorial designs)
 Process Validation
Case Study #2
 Robustness study using D-Optimal design
 Model selection strategies
Conclusions, Learnings and Acknowledgements
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3. Research & Development at GSK
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>13,000
employees
in R&D
Dolutegravir / Tivicay

4. Research & Development for Pharmaceuticals
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 Our Pharmaceuticals business develops and makes medicines
to treat a broad range of acute and chronic diseases
 We have leading positions in respiratory disease and HIV with
a portfolio of innovative and established medicines
 Our major research centres are in the UK, USA, Europe and China
The main UK R&D Hub is based in Stevenage

5. What we do
Our approach to Quality-by-Design (QbD)
Link between QbD and DoE
API Chemistry at GSK

6. API Chemistry at GSK
Our goal in API Chemistry
Identify, develop and optimise safe, scalable and sustainable processes supporting the
manufacture of high quality medicines, allowing GSK to fulfil its mission of helping people
do more, feel better and live longer
 API Chemistry consists of more than 100 scientists based in the UK and the US
 We have expertise in the following areas:
 Synthetic Biochemistry
 Synthetic Chemistry
 Chemical Catalysis
 Isotope Chemistry
 Oligonucleotides
 Continuous Processing
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7. API Chemistry at GSK
Our goal in API Chemistry
Identify, develop and optimise safe, scalable and sustainable processes supporting the
manufacture of high quality medicines, allowing GSK to fulfil its mission of helping people
do more, feel better and live longer
 We use DoE in early phase development
 To rapidly screen reagents and solvents in a structured manner
 ... and in late phase development
 To gain process understanding: Identification of the key parameters and
interactions controlling a process, and whether we are operating in an optimum
region for quality and yield of product
 To gain process confidence: Confirmation that small deviations to the intended
parameter settings do not adversely impact quality
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8. API Chemistry and Quality-by-Design (QbD)
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• Identification of impurities that are critical to safety
and efficacy, i.e. Critical Quality Attributes (CQAs)
• Identification of parameters that influence CQAs,
i.e. Critical Process Parameters (CPPs)
Process and
Product
Understanding
• Identification and prioritisation of risk
• Mitigation of major risks through appropriate
work packages
Risk Assessment
• The overall set of controls ensuring process
performance and product quality
Control Strategy
Definition
• Flexibility to make changes in manufacturing without
compromising patient safety
Design Space
Definition
– From my frame of reference, QbD encompasses four main aspects:

9. API Chemistry and Quality-by-Design (QbD)
We use DoE as a key tool to help identify CPPs (‘factors’) and understand their effects
on CQAs (‘responses’)
 It’s not just good science
We need to be able to demonstrate and articulate this process knowledge both internally
(e.g. to colleagues in the manufacturing network) and externally (e.g. regulatory authorities
who approve our medicines)
 Note that DoE is not the only tool at our disposal
We can also demonstrate process knowledge and understanding through application of
first principle studies, e.g. kinetics
Regulatory authorities across Europe, Japan and the US have developed harmonised guidelines
(ICH guidelines) to ensure that patients receive safe, effective and high quality medicines
The successful application of DoE is key to provide process understanding, demonstrate
process robustness and to satisfy regulatory expectations
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The next few slides will present case studies illustrating how we have used
DoE in the context of late phase process development to ensure the
delivery of quality medicine for the patient

10. Production Process Overview
High level control strategy
DoE Investigations (Fractional factorial designs)
Process Validation
Case Study #1

11. Case Study #1: Production Process Overview
Background
 The synthesis of a batch of Active Pharmaceutical Ingredient (API) typically proceeds through
several discrete stages of manufacture, starting from Registered Starting Materials (RSMs)
 Each stage encompasses multiple unit operations
Process Goal
 Develop a robust final stage manufacturing process capable of delivering ca. 200 kg of API
meeting stringent quality specifications for commercial supply of a new therapeutic agent
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Final Stage Process Schematic
Input Material Reducing Agent
Non-isolated
Intermediate #1
Non-isolated
Intermediate #2
Non-isolated Intermediate #2 Aqueous reagent
Biphasic mixture containing
crude product and inorganics
Solution of crude product in
organic solvent
Part A
Part B
Salt-forming
reagent
Final API
Part C
Solution of crude product in
organic solvent

12. High Level Control Strategy Intent
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Image taken from http://www.essentialchemicalindustry.org/processes/chemical-reactors.html
12
Part A
Reaction
• Run process with parameters at appropriate settings to
maximise formation of non-isolated intermediate #2 and
minimise levels of residual non-isolated intermediate #1
(CQA ‘A’)
Part B
Reaction
• Run process with parameters at appropriate settings to
maximise formation of product and minimise levels of
residual non-isolated intermediate #2 (CQA ‘B’)
Work-up and
solvent swap
• Perform phase separations to remove inorganic
impurities followed by solvent swap to afford a solution
of crude product in organic solvent
Part C
Crystallisation
• Form stable salt and crystallise product of correct
particle size (also a CQA) under conditions which
minimise entrainment of impurities

13. High Level Control Strategy Intent
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Image taken from http://www.solidliquid-separation.com/pressurefilters/nutsche/nutsche.htm
13
Isolation
• Remove majority of liquors from the product cake
Washing
• Effective displacement washing regime to remove
remaining liquors and soluble impurities
Drying
• Deliver final API meeting stringent quality
specifications for subsequent formulation
0 5 10 15 20 25 30
Retention Time (min)
-200
0
200
400
600
800
1000
1200
Response
(mAU)
15.906
13.917
12.263
11.903
11.194
9.596
9.449
9.356
9.028
8.893
6.543
6.187
1.756

14. Develop Understanding:
Part A Reaction – Factor Screening
Factors were brainstormed using well-established techniques and investigated
using a screening DoE on automated equipment
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Image source: https://www.amigochem.com/products
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Man Machine Measurement
Materials Method Mother Nature
Response variables, including
conversion to desired non-
isolated intermediate #2 and
levels of residual non-isolated
intermediate #1 (CQA ‘A’)
Measurement considerations
for analysis (HPLC): linearity,
sensitivity, resolution etc.
Method factors: Included solvent
quantities, reaction temperature,
input reducing agent etc.

15. Develop Understanding:
Part A Reaction – Factor Screening
The Design
Resolution IV 2-level fractional factorial design, 5
factors in 20 runs, 2 blocks, 2 centre points per block
Rationale
 Main effects were aliased with 3FIs and could be
assigned with confidence
 2FIs were aliased with other 2FIs but assignment
likely based on combination of scientific intuition and
identification of main effects
 Note that 2FIs are very common in chemical
reactions
 Equipment constraints (blocks of 10)
 Block effect only aliased with 3FIs
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16. Develop Understanding:
Part A Reaction – Factor Screening
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Main Outcomes
 Four controlling factors were identified, along with several 2FIs
 Models were obtained for all important responses
 Half-normal plot and forward selection used; all practically relevant terms with
p < 0.05 were included (judgement call – avoid overfitting)
 Models reported to exhibit significant lack-of-fit – this was considered to be
‘artificial’ as the centre point reactions were highly reproducible; moreover all
diagnostic plots were acceptable
 Significant curvature detected – statistically and practically significant
 Models could not be used to predict system behaviour near the centre points,
where the predictions were at odds with experimental observations.
 Inherent weakness of fractional factorial designs which deliver linear models
 Root cause(s) of curvature unknown
 Fractional factorial designs can detect the presence of curvature but cannot
provide information on which factor(s) is / are responsible

17. Develop Understanding:
Part A Reaction – Factor Screening
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Next steps: Model the curvature in the system?
This would cost an additional 30 experiments and allow us to
model the response surface by fitting a full quadratic model
Example design: CCD built from 4 factors (3 blocks)
This study has determined the controlling factors, key
interactions and their relative importance
This information was considered to be fit-for-purpose when
combined with knowledge from other experimentation
Design-Expert® Software
Logit(CQA A)
Error estimates
Shapiro-Wilk test
W-value = 0.939
p-value = 0.604
A: Reducing agent
B: Temperature
C: Heating rate
D: Solvent composition
E: Concentration
Positive Effects
Negative Effects
0.00 0.43 0.86 1.29 1.72
0
10
20
30
50
70
80
90
95
99
Half-Normal Plot
|Standardized Effect|
Half-Normal
%
Probability
A-Reducing agent
B-Temperature
D-Solvent composition
E-Concentration
AB
AD
AE
Actual
Predicted
Predicted vs. Actual
-5
-4
-3
-2
-1
-5 -4 -3 -2 -1
A: Reducing agent (molar eq.)
B: Temperature (°C)
2 2.5 3 3.5 4
Logit(CQA
A)
-5
-4
-3
-2
-1
3
3
Interaction
CQA ‘A’

18. Develop Understanding:
Part C Crystallisation – Factor Screening
Factors were brainstormed and investigated using an equipment set-up designed to
closely mimic the characteristics of the commercial crystallisation vessel
The Design:
Resolution IV 2-level fractional factorial design, 8 factors in 20 runs, 4 centre points
Rationale:
 Main effects were aliased with 3FIs and could be assigned with confidence
 2FIs were aliased with other 2FIs but assignment likely based on combination of
scientific intuition and identification of main effects
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Image source: https://www.radleys.com/products/our-products/jacketed-lab-reactors/reactor-ready-lab-reactor
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Man Machine Measurement
Materials Method Mother Nature
Response variables,
including particle size
CQA and yield

19. Develop Understanding:
Part C Crystallisation – Factor Screening
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Main Outcomes
 Particle size CQA – Three controlling factors identified along with two 2FIs
 Yield – Two controlling factors identified
 Models were not perfect meaning predictions could only be made with caution
 Particle size model – low predicted R2 of 0.47
 Yield model – statistically and practically significant curvature
0.00 9.10 18.21 27.31 36.42 45.52 54.63
0
10
20
30
50
70
80
90
95
99
Half-Normal Plot
|Standardized Effect|
Half-Normal
%
Probability
A-Total solvent quantity
B-Temperature
E-Local energy dissipation
G-Age time
AB
BE
Particle
Size CQA
0.00 0.09 0.18 0.26 0.35
0
10
20
30
50
70
80
90
95
99
Half-Normal Plot
|Standardized Effect|
Half-Normal
%
Probability
A-Total solvent quantity
B-Temperature
Yield
This study has determined the controlling factors, key interactions and
their relative importance
This information was considered to be fit-for-purpose when combined with
knowledge from other experimentation

20. From Process Understanding to Process
Confidence: Robustness DoE and Design Space
Aims of New Study
 Demonstrate robustness and define the parametric design space for
the overall manufacturing process encompassing Parts A, B and C
 What do we mean by ‘Robustness’?
 Confirmation that our process can tolerate small realistic
deviations to the intended parameter settings without
adversely impacting API quality
 What do we mean by ‘Design Space’?
 ICH Definition: The multidimensional combination and
interaction of input variables (e.g., material attributes) and
process parameters that have been demonstrated to provide
assurance of quality
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Recap Process Goal
 Develop a robust final stage manufacturing process capable of delivering ca. 200 kg
of API meeting stringent quality specifications for commercial supply of a new
therapeutic agent

21. From Process Understanding to Process
Confidence: Robustness DoE and Design Space
Our Approach to Process Robustness
 Execution of a low-resource DoE including our critical and
important process parameters using ranges that will allow
flexibility in manufacturing;
 Demonstration that all runs from this DoE meet pre-defined quality
criteria (so in this case the API specification)
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Design Used
 Resolution III 2-level fractional factorial design, 6 factors in 12 runs, 3 centre points
 Factor generators altered from default to guarantee inclusion of ‘least forcing’
combination of parameters as part of the design
 Additional run added manually to include ‘most forcing’ combination of
parameters; row status set to “Verification” in design
 Run order manually modified such that first four runs comprised ‘riskiest’
combination of parameters and two centre points

22. From Process Understanding to Process
Confidence: Robustness DoE and Design Space
Rationale
 Main effects only to be estimated (process confidence, not process understanding)
 We wanted to minimise the number of runs due to high experimental cost – materials, time,
analytical testing requirements
 We wanted to check our proposed ranges and demonstrate reproducibility as quickly as possible
22
RHS Vessel
Used for Part A and
Part B reactions, and
phase separations
LHS Vessel
Used for solvent
swap and Part C
crystallisation
PAT probe
Used to follow
solvent swap
Dosing Pump
Used to add aqueous
reagent for Part B in a
controlled manner
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23. From Process Understanding to Process
Confidence: Robustness DoE and Design Space
Study Output
 All runs afforded excellent quality API meeting specification
 No meaningful statistical models could be obtained for the responses, with the exception of yield
 This was not unexpected and in keeping with the aims of the study
 Parametric design space was defined, i.e. the multidimensional combination of process parameters
that have been demonstrated to provide assurance of quality
23
CQA A
% w/w
CQA B
% w/w
CQA C
% w/w
CQA D
% w/w
Any
unspecified
impurity
% w/w
Total HPLC
impurities
% w/w
API
content
by HPLC
% w/w
Specification →
DoE Run # ↓
NGT
0.15
NGT
0.15
NGT
0.15
NGT 0.15 NGT 0.10 NGT 1.0
98.0 -
102.0
1 ND ND ND < 0.05 <0.05 < 0.05 99.9
2 ND ND ND < 0.05 <0.05 < 0.05 99.8
3 ND ND ND < 0.05 <0.05 < 0.05 100.1
4 ND ND ND < 0.05 <0.05 < 0.05 100.0
5 ND ND ND < 0.05 <0.05 < 0.05 100.0
6 ND ND ND < 0.05 <0.05 < 0.05 100.8
7 ND ND ND 0.08 <0.05 0.08 100.0
8 ND ND ND < 0.05 <0.05 < 0.05 100.3
9 ND ND ND < 0.05 <0.05 < 0.05 99.7
10 ND ND ND 0.07 <0.05 0.07 99.4
11 ND ND ND < 0.05 <0.05 < 0.05 99.5
12 ND ND ND < 0.05 <0.05 < 0.05 99.2
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0.000 1.000 2.000 3.000 4.000 5.000
0
10
20
30
50
70
80
90
95
Half-Normal Plot
|Standardized Effect|
Half-Normal
%
Probability
A-Input material charge
C-Solvent charge
Trend Plot of Drug Substance Particle Size by Robustness Run
Robustness Run
1 2 3 4 5 6 7 8 9 10 11 12
120
140
160
180
200
220
240
PSD
(um)
LSL
USL
Yield
Particle
Size CQA
Time to move to process validation...

24. Process Validation
Final process transferred to CRO in India
Six validation batches run which delivered > 180 kg product
24
Input
Material
Amount
kg
Output
API kg
CQA A
% w/w
CQA B
% w/w
CQA
C %
w/w
CQA
D %
w/w
Any
unspecified
impurity
% w/w
Total
HPLC
impurities
% w/w
API
content
by HPLC
% w/w
Specification
→
Validation
Batch # ↓
NGT
0.15
NGT
0.15
NGT
0.15
NGT
0.15
NGT 0.10 NGT 1.0
98.0 -
102.0
1 35.0 29.90 ND ND ND < 0.05 <0.05 < 0.05 99.8
2 35.0 30.15 ND ND ND < 0.05 <0.05 < 0.05 100.1
3 35.0 30.50 ND ND ND < 0.05 <0.05 < 0.05 100.4
4 35.0 30.14 ND ND ND < 0.05 <0.05 < 0.05 99.7
5 35.0 29.85 ND ND ND < 0.05 <0.05 < 0.05 100.3
6 35.0 30.15 ND ND ND < 0.05 <0.05 < 0.05 100.4
Trend Plot of Drug Substance Particle Size by Validation Batch
Validation Batch
1 2 3 4 5 6
120
140
160
180
200
220
240
PSD
(um)
LSL
USL
 All batches delivered high quality API meeting stringent specifications
 Levels of individual CQAs and total impurities were extremely low
 Variation in product quality was minimal
 Variation in product yield was also minimal
Particle
Size CQA
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Image source: https://support.skype.com/en/faq/FA12330/what-is-the-full-list-of-emoticons

25. Robustness study using D-Optimal design
Model selection strategies
Case Study #2

26. Case Study #2: Robustness Study
Using D-Optimal Design
Another example of a Robustness DoE – This time using a non-orthogonal design
Design Used
 D-optimal saturated design, 8 factors in 13 runs including 4 centre points
Rationale / Comments
 We are more interested in process confidence rather than process understanding –
but this design still allows estimation of main effects
 D-Optimal designs are flexible and allow the user to specify the model to be fitted,
number and allocation of runs (model points, replicates, centre points etc.)
 Fewer runs than a regular two-level fractional factorial design, minimum run resolution
IV design or Plackett-Burman design
26
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27. Case Study #2: Study Output and Model Selection
Study Output
 All runs gave product which met the pre-defined quality specification
 For some responses, meaningful statistical models could be obtained
 Small design: we have been assuming effect sparsity and the absence of interactions
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Image source: https://support.skype.com/en/faq/FA12330/what-is-the-full-list-of-emoticons
Model selection was non-trivial!
 DX10 offers several criteria to help when building and
comparing models (p-values, AICc, BIC and adjusted R2)
 We found AICc to work better where there were was no
evidence of curvature
 We found BIC to work better where there was evidence of
curvature
 Final models chosen by analysing responses as ‘factorial’
rather than ‘polynomial’
 This allows generation of half-normal plots and ‘sanity
check’ of models chosen algorithmically
 Feedback on this approach would be welcome!

28. Conclusions, Learnings and
Acknowledgements

29. Conclusions & Learnings
 The successful application of DoE is key to provide process understanding,
demonstrate process robustness and to satisfy regulatory expectations
 Studies such as those presented here are vital to ensure the delivery of quality
medicine for the patient
 The sequential approach to DoE studies (factor screening followed by optimisation and
finally robustness) allows process knowledge and confidence to be built up in stages,
along with management of resources
 Not a prescriptive workflow: Be clear on the aims of the study and what constitutes
fit-for-purpose results
 D-Optimal designs are flexible and can be resource efficient
 Often requires some trial and error to obtain the ‘best’ design
 Analysis and model selection can be non-trivial
 It is recommended to investigate multiple selection methods and criteria before
settling on a final model
29
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30. Acknowledgements
API Chemistry
 Lee Boulton
 Andrew Kennedy
 Calvin Manning
 Batool Ahmed Omer
 Rushabh Shah
 David Stevens
Analytical Sciences and Development
 Eeva-Liisa Alander
 Carl Heatherington
Thank you for your attention
30
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Process Engineering,
Particle Sciences and PAT
 Leanda Kindon
 Laura Palmer
 Sara Rossi
 Jono West
 Audrey Zilliox
Statistical Sciences
 Simon Bate
 Mohammed Yahyah