The Role of Fractional Factorial and D-Optimal Designs in ... Role of Fractional... · Dr Daniel Tray. API Chemistry, GlaxoSmithKline, Stevenage, United Kingdom. The Role of Fractional

Dr Daniel TrayAPI Chemistry, GlaxoSmithKline, Stevenage, United Kingdom

The Role of Fractional Factorial and D-Optimal Designs in the Development of QbD Pharmaceutical Production Processes

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

DRT Stat Ease DoE Conference Paris Jun 18 2

Research & Development at GSK


>13,000 employees

in R&D

Dolutegravir / Tivicay

Research & Development for Pharmaceuticals


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 witha 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

What we doOur approach to Quality-by-Design (QbD)Link between QbD and DoE

API Chemistry at GSK


Our goal in API ChemistryIdentify, 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



Our goal in API ChemistryIdentify, 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


API Chemistry and Quality-by-Design (QbD)


• 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 packagesRisk 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:

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 scienceWe 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 disposalWe 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


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

Production Process OverviewHigh level control strategyDoE Investigations (Fractional factorial designs)Process Validation

Case Study #1

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


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 APIPart C Solution of crude product in

organic solvent

High Level Control Strategy Intent

DRT Stat Ease DoE Conference Paris Jun 18Image taken from http://www.essentialchemicalindustry.org/processes/chemical-reactors.html

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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

http://www.essentialchemicalindustry.org/processes/chemical-reactors.html

High Level Control Strategy Intent

DRT Stat Ease DoE Conference Paris Jun 18Image taken from http://www.solidliquid-separation.com/pressurefilters/nutsche/nutsche.htm

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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 30Retention Time (min)

-200

0

200

400

600

800

1000

1200

Res

pons

e (m

AU)

15.9

06

13.9

17

12.2

6311

.903

11.1

94

9.59

69.

449

9.35

69.

028

8.89

3

6.54

36.

187

1.75

6

http://www.solidliquid-separation.com/pressurefilters/nutsche/nutsche.htm

Develop Understanding:Part A Reaction – Factor Screening

Factors were brainstormed using well-established techniques and investigated using a screening DoE on automated equipment

DRT Stat Ease DoE Conference Paris Jun 18Image 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.


The DesignResolution 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




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



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 modelExample design: CCD built from 4 factors (3 blocks)

This study has determined the controlling factors, key interactions and their relative importanceThis information was considered to be fit-for-purpose when combined with knowledge from other experimentation

Design-Expert® SoftwareLogit(CQA A)

Error estimates

Shapiro-Wilk testW-value = 0.939p-value = 0.604A: Reducing agentB: TemperatureC: Heating rateD: Solvent compositionE: Concentration

Positive Effects Negative Effects

0.00 0.43 0.86 1.29 1.72

0102030

50

70

80

90

95

99

Half-Normal Plot

|Standardized Effect|

Hal

f-Nor

mal

% P

roba

bilit

y

A-Reducing agent

B-Temperature

D-Solvent composition

E-ConcentrationAB

AD

AE

Actual

Pre

dict

ed

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

Logi

t(CQ

A A

)

-5

-4

-3

-2

-1

33

Interaction

CQA ‘A’

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 pointsRationale: 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

DRT Stat Ease DoE Conference Paris Jun 18Image 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

Develop Understanding:Part C Crystallisation – Factor Screening


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

0102030

50

70

80

90

95

99

Half-Normal Plot


Hal

f-Nor

mal

% P

roba

bilit

y

A-Total solvent quantity

B-Temperature

E-Local energy dissipation

G-Age timeAB

BE

Particle Size CQA

0.00 0.09 0.18 0.26 0.35

0102030

50

70

80

90

95

99

Half-Normal Plot


Hal

f-Nor

mal

% P

roba

bilit

y

A-Total solvent quantity

B-Temperature

Yield

This study has determined the controlling factors, key interactions and their relative importanceThis information was considered to be fit-for-purpose when combined with knowledge from other experimentation

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


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


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)


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


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 VesselUsed for Part A and Part B reactions, and

phase separations

LHS VesselUsed for solvent swap and Part C

crystallisation

PAT probe Used to follow solvent swap

Dosing PumpUsed to add aqueous reagent for Part B in a

controlled manner

DRT Stat Ease DoE Conference Paris Jun 18

From Process Understanding to Process Confidence: Robustness DoE and Design SpaceStudy 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/wSpecification →

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 99.92 ND ND ND < 0.05 <0.05 < 0.05 99.83 ND ND ND < 0.05 <0.05 < 0.05 100.14 ND ND ND < 0.05 <0.05 < 0.05 100.05 ND ND ND < 0.05 <0.05 < 0.05 100.06 ND ND ND < 0.05 <0.05 < 0.05 100.87 ND ND ND 0.08 <0.05 0.08 100.08 ND ND ND < 0.05 <0.05 < 0.05 100.39 ND ND ND < 0.05 <0.05 < 0.05 99.7

10 ND ND ND 0.07 <0.05 0.07 99.411 ND ND ND < 0.05 <0.05 < 0.05 99.512 ND ND ND < 0.05 <0.05 < 0.05 99.2

DRT Stat Ease DoE Conference Paris Jun 18

0.000 1.000 2.000 3.000 4.000 5.000

0

10

20

30

50

70

80

90

95

Half-Normal Plot


Hal

f-Nor

mal

% P

roba

bilit

y

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 12120

140

160

180

200

220

240

PS

D (u

m)

LSL

USL

Yield

Particle Size CQA

Time to move to process validation...

Process Validation

Final process transferred to CRO in IndiaSix validation batches run which delivered > 180 kg product

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Input Material Amount

kg

OutputAPI 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 99.82 35.0 30.15 ND ND ND < 0.05 <0.05 < 0.05 100.13 35.0 30.50 ND ND ND < 0.05 <0.05 < 0.05 100.44 35.0 30.14 ND ND ND < 0.05 <0.05 < 0.05 99.75 35.0 29.85 ND ND ND < 0.05 <0.05 < 0.05 100.36 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 6120

140

160

180

200

220

240

PS

D (u

m)

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

DRT Stat Ease DoE Conference Paris Jun 18Image source: https://support.skype.com/en/faq/FA12330/what-is-the-full-list-of-emoticons

Robustness study using D-Optimal designModel selection strategies

Case Study #2

Case Study #2: Robustness StudyUsing D-Optimal DesignAnother 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

26DRT Stat Ease DoE Conference Paris Jun 18

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

27DRT Stat Ease DoE Conference Paris Jun 18Image 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!

Conclusions, Learnings and Acknowledgements

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


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


Process Engineering, Particle Sciences and PAT Leanda Kindon Laura Palmer Sara Rossi Jono West Audrey Zilliox

Statistical Sciences Simon Bate Mohammed Yahyah

Documents

The Role of Fractional Factorial and D-Optimal Designs in ... Role of Fractional... · Dr Daniel Tray. API Chemistry, GlaxoSmithKline, Stevenage, United Kingdom. The Role of Fractional