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Nordic Users Training – Helsinki – Sept. 8, 2011

Richard VerseputPresidentS-Matrix CorporationEureka, CA USA

Fusion AE – Quality by Design Software for LC Method Development

Nordic Users Training – Helsinki – Sept. 2011

Thursday, September 8, 2011

15:00 – 15:30 Why QbD for LC Method Development

15:30 – 16:00 S-Matrix Quality by Design software – the complete solution for your LC Method Development workflow

16:00 – 16:30 What's new with Fusion AE Version 9.6

16:30 – 17:00 What does the future hold? (Interactive)

LC Process Flow

Separation(Column)

AcceptableVariation

Raw MaterialComposition

(Mobile Phase)

HeatingChamber

(Column Oven)

Measurement(Detector)

X─API Resolution

= variation around setpoint

QbD for Method Development ? LC – a “Process in a Box

Method A - Small VariationMethod B - Large Variation 3

Why QbD for Method Development ? ICH Q8 (R2)

Objective of the Guideline:

… The guideline also indicates areas where the demonstration of greaterunderstanding of pharmaceutical and manufacturing sciences can create a basis forflexible regulatory approaches. The degree of regulatory flexibility is predicated onthe level of relevant scientific knowledge provided.

Pharmaceutical Development:

… The information and knowledge gained from pharmaceutical developmentstudies and manufacturing experience provide scientific understanding to support theestablishment of the design space*, specifications, and manufacturing controls.

Design Space:

The multidimensional combination and interaction of input variables (e.g., materialattributes) and process parameters that have been demonstrated to provide assuranceof quality. Working within the design space is not considered as a change. Movementout of the design space is considered to be a change and would normally initiate aregulatory post approval change process. Design space is proposed by the applicantand is subject to regulatory assessment and approval.

Formal Experimental Design:

A structured, organized method for determining the relationship between factorsaffecting a process and the output of that process. Also known as “Design ofExperiments”.

Fusion AE – Formal Experimental Design engines driving automated LC method development experimentation

Why Fusion AE for Method Development ? ICH Q8 (R2)

Experiment run on HPLC in walk-away mode.

CDS generates chromatogram results.

6

Automated analysis, graphing, and reporting.

Report output formats: RTF, DOC, HTML, PDF.

Experiment Design

Ready-to-runmethods & sequences

File-less Data Exchanges

Steps 1 and 2 Step 3

Step 4

Step 5

Fusion AE – Automation Supported QbD Workflow

• Select study variables

• Define study variable ranges

Define

Experimental

Region

• Build experimental design

• Run & Analyze results build equations

Develop

Knowledge

Space

• Define best method conditions

• Establish & verify robust design spaceEstablish

Design Space

A Quality-by-Design experimental approach consists of four distinct steps

1

2

3

• Define & Document Operating Space

• Establish Process SOP

Establish Operating

Space4

7

FORMAL EXPERIMENTAL DESIGN

“A structured, organized method for determining the relationship between factors affecting a process and the output of that process. Also known as “Design of Experiments.”

[ICH Q8(R2) - Guidance for Industry, Pharmaceutical Development, August, 2009]

Simple, Template-driven Experiment Setup

8




Instant One-clickExperiment DesignGeneration

9

Automation Reduces Risk

“Eliminate manual data transcription and checking via fully-automated, e-signature controlled, and fully automated data exchanges between regulatory compliant applications.”

Automated, Regulatory Compliant Data Exchanges

ChromatographyData Systems

Experiment Designs

ChromatogramResults

10

Developing the Knowledge Space“The information and knowledge gained from pharmaceutical development studies and manufacturingexperience provide scientific understanding to support the establishment of the design space,specifications, and manufacturing controls.”


pH

Oven Temp

Initial%

5.0

15.0

30.0 40.0

Rs = 9.3 + 4.2(X1) -5.4(X2)2 + 12.7(X4)2 + 1.3(X1*X3) + 1.6(X1)2X 2+ …

Linear Additive Effects Curvature Effects Higher-order Effects

11

INITIAL DESIGN SPACE – MEAN PERFORMANCE

QbD Design Space - “The multidimensional combination and interaction of input variables (e.g., material attributes) and process parameters that have been demonstrated to provide assurance of quality.”


Design Space (Un-shaded Region):

• Mean Performance Only

Edge of Failure –

Mean Performance

12

FINAL DESIGN SPACE – MEAN PERFORMANCE + ROBUSTNESS



Edges of Failure –Method RobustnessFinal Design Space:

• Mean Performance• Robustness

13


14[ICH Q8(R2) - Page 23

QbD “DESIGN SPACE”


Curves defining the acceptable performing regions, and the design space (region of overlap), are generated by equations (models)

OPERATING SPACE – Design Space + Control Strategy

“CONTROL STRATEGY – the planned set of controls, derived from current product and process understanding thatensures process performance and product quality.”

VerificationRuns


Operating Space

• Proven AcceptableRanges

15

16

What’s New in Fusion AE – Version 9.5.0

Support for the Waters Acquity H-Class UPLC System and additional Waters Detectors

• Waters Acquity H-Class Column Manager (all stack configurations)

• Waters Acquity H-Class Direct Inject Sample Manager

• Waters Acquity H-Class Quaternary Solvent Manager

17


Implement new User Interface for the Pump Program

• Support for 2 – Step Gradient Studies

• Optional Step Inclusion/Exclusion Toggles

• Equilibration Time Integration

• Isocratic Pump Program Integration

• Gradient Slope / Gradient Curve Controls

• Pump Program Chart

• Coordination of Pump and Solvent Settings

18


Modifications to Solvents Settings Controls and Logic

• Strong Solvent Settings Dominance

• Support for More Than 1 Weak Solvent

• Solvent Type Labels based on the selected Chromatography type.

• Integration of the ‘Mobile Phase Blend Study’ factor

• Available Reservoirs Selection

19


Modifications to Aqueous Linked Factor System Settings Control

• Consolidated UI for Aqueous-linked Variables

• Metadata for pH and Buffer Strength

20


Advancements in Connectivity and Modeling

• Windows 7 and Windows Server 2008 Compatibility

• Empower 3 Compatibility

• Citrix ZenApp 5 and 6 Compatibility – Certified Citrix Partner

21


Advanced pH effects Modeling

Fairly Stable Region

1.00

2.00

9.0

pH

Rs

6.53.0

Desired Level of Characterization – the workable region for an optimization study.

Most Stable Region Least Stable Region

22


New Graphical Optimizer with Operating Space and Advanced Reporting

23


New Fusion Product Development Module

Tablet Coater Dissolution(Time Release)

Processing(Immediate Release)

Nordic Users Training – Helsinki – Sept. 2011

Friday, September 9, 2011

07:00– 08:00 Breakfast08:00 – 09:30 Orientation to Fusion LC Method Development (FMD)

Design a column/solvent/pH screening experimentOrientation to the Screening Experiment chromatogram processing

protocol within Empower09:30 – 10:00 Analyze the Screening Experiment results – identify best pH range, column,

mobile phase, and initial gradient conditions to promote to optimization10:00 – 10:30 Coffee Break

10:30 – 12:00 Design a method optimization experimentOrientation to the Optimization Experiment chromatogram processing

protocol within EmpowerAnalyze the Optimization Experiment results – identify best pH, temperature, and

final gradient conditions in terms of mean method performance12:00 – 12:45 Use Robustness Simulator to enter (1) expected variation of the study parameters,

and (2) method robustness performance requirementsConnect mean performance and robustness requirements to identify FDA Design

Space and Safe Operating Ranges for method transfer12:45 – 13:00 Summary and end of training

25

Design of Experiments – an equation building methodology.

Equations – are derived from real experimental data.

Knowledge – quantitative characterization of the study parameter effects on the critical performance characteristics.




26

Linear Equation (Model): Y = m(x) +b

1.00

2.00

40.0Oven Temp (°C)

Rs

y intercept = b0

0.0

∆y

30.0∆x

Rs = b0 + b1(x1)

1.80

1.30

∆y∆xSlope (b1) =

0.5010.00=

= 1.15 + 0.05(°C)

)x(bbR 110s +=

Example Equation

27

● Linear Effect – One Factor

)x(b)x(bbR 22110s ++=

Example Equation

28

● Linear Additive Effects – 2 Factors

(X1)

(X2)

Example Equation

29

● Pairwise Interaction Effect )x(bbR 110s +=

)x(bbR 110s +=

pH = 4.0

pH = 6.0

Example Equations

30

● Pairwise Interaction Effect

)xx(b)x(b)x(bbR 2*11222110s +++=

31

2111110s )x(b)x(bbR ++=

● Curvature

Example Equation

32

)x()x(b)x(b)x(b)x(b)x(bbR 22

11122

222222

111110s +++++=

● Curvature + Interaction = Complex Combined Effects

Example Equation

33

● Equation Expressed Across the Combined Study Ranges

Response Surface Graph

INITIAL DESIGN SPACE – MEAN PERFORMANCE



Design Space (Un-shaded Region):

• Mean Performance Only

Edge of Failure –

Mean Performance

34

35

Mean Performance Versus Robustness

Condition A – Good RobustnessCondition B – Poor Robustness

Conditions A and B – Identical Mean Performance ≠ identical robustness

FINAL DESIGN SPACE – MEAN PERFORMANCE + ROBUSTNESS



Edges of Failure –Method Robustness

Final Design Space:

• Mean Performance• Robustness

36

OPERATING SPACE – Design Space + Control Strategy

“CONTROL STRATEGY – the planned set of controls, derived from current product and process understanding thatensures process performance and product quality.”

VerificationRuns


Operating Space

• Proven AcceptableRanges

37

38

“Difficult to Control” Parameters Next = LC Configuration change

• pH (manually switching reservoirs usually required)• Organic Solvent Type• Ion Pairing Agents (manually switching reservoirs usually required)• Column Type (manually switching columns required)

Traditional Approach to Method Development

Usually Done by Sequential Studies

“Easy to Control” Parameters 1st = Changeable in the method

• Gradient Time• Temperature

Even when studied together – traditional approachesdo NOT characterize interactions!}

39

Phase 1 ─ Column/Solvent Screening = Major Effectors

• Gradient Time (constant slope - 5%-95%)• pH (wide range – automated solvent switching)• Column Type (multiple columns – automated column switching)• Solvent Type (w/wo blending)

Phase 2 ─ Method Optimization = Additional Effectors

• Pump Flow Rate• Gradient Slope (vary starting or end point % Organic) • Ion Pairing Agents (can be included)• pH (narrow range - robustness optimization)• Temperature (can be included in Phase 1)

QbD – a Risk-based Approach to Method Development

Capitalizes on automation!

40

Column/Solvent/pH Screening

Experimental Design

Screening Experiment – Design

41

Experiment Variable Range or Level SettingsPump Flow Rate .5 mL/min

Injection Volume 1.0 uL

Gradient Time (min) 3.0 — 9.0 min Reasonable range – from Sample Workup

pH 2.0, 3.0, 4.0, 5.0, 6.0, 7.0 Wide range is screened – recommend 5-6 levelspKa of Primary Compound – 4.8

Column Temperature 30 C

Column Type Four Columns: Column screening – recommend wide selectivity rangeBEH C18 – all columns support pH rangeBEH Shield RP 18BEH PhenylBEH C8

Organic Solvents Mobile Phase A1-1: Aqueous Buffer, pH 2.0Mobile Phase A1-2: Aqueous Buffer, pH 3.0Mobile Phase A1-3: Aqueous Buffer, pH 4.0Mobile Phase A1-4: Aqueous Buffer, pH 5.0Mobile Phase A1-5: Aqueous Buffer, pH 6.0Mobile Phase A1-6: Aqueous Buffer, pH 7.0

Mobile Phase B1: AcetonitrileMobile Phase B2: Methanol

Experiment Setup

42

Experiment Setup – LC Instrument Centric Template

43

44


45


Enter a pKa value only when the pKa is within the experimental range of pH.

Generate Design

One Click:

Software maps the experimental design to the study factors.

46

C4

C3

C2

Gradient Time

pH

2.0

7.0

3.0 9.0

4.5

C1

Generate Design – Statistical Efficiency

47

5 levels of Gradient Time

6 levels of pH

4 levels of Column Type

2 levels of Strong Solvent Type

5x6x4x2 = 240 possible combinations

Fusion Screening design = 46 runs

~ 5x efficiency.

Export Wizard:

Software automatically reconstructs experimental design within the chromatography data software (CDS) as instrument methods and sample sets.

Export Design to the CDS

48

49

Overnight Execution in Walk-away Mode

Solvent Selection Valve4-RelayPanel

LANCard

CustomerNetwork

Empower™

• Column screening experiments, even those done by DOE, often have significant inherent data loss in critical results such as resolution.

• The data loss is due to both compound co-elution and also changes in compound elution order (peak exchange) between experiment trials.

These changes are often due to the major effects that pH and organic solvent type can have on column selectivity.

Complexity of Traditional Results Data

50

Inherent Data Loss – Co-elution and Peak Exchange

LC Method Development Traditionally Involves Peak Tracking

But how do you track moving or disappearing peaks? 51

2.27

5

2.86

2

3.22

13.

302

3.52

9

4.15

1

5.52

0 6.76

2

AU

0.00

0.50

1.00

1.50

2.00

Minutes0.00 1.00 2.00 3.00 4.00 5.00 6.00 7.00 8.00 9.00 10.00 11.00 12.00

1.81

0

2.01

5

2.50

42.

646

3.40

7

4.30

1

5.10

7

5.78

0

AU

0.00

0.50

1.00

1.50

2.00

2.50

Minutes0.00 0.50 1.00 1.50 2.00 2.50 3.00 3.50 4.00 4.50 5.00 5.50 6.00 6.50 7.00 7.50 8.00 8.50 9.00


Trial 37

52

Trial 41

Co-elution / Peak Exchange

Run No. Gradient Time pH Column Type Imp E - USPResolution Imp F - USPResolution Imp G - USPResolution1.a.1.a 8.8 2 C18 1.4 2.32.a.1.a 6.3 2 Phenyl 1.783.a.1.a 10 2 C18 1.45 2.364.a.1.a 5 2 C18 1.155.a.1.a 10 2 Phenyl 1.06 2.256.a.1.a 5 2 Phenyl 1.667.a.1.a 10 2 RP8.a.1.a 5 2 RP9.a.1.a 7.5 2 RP 2.9510.a.1.a 7.5 4.5 C18 0.97 1.18 3.811.a.1.a 7.5 4.5 Phenyl 1.24 2.2712.a.1.a 7.5 4.5 RP 2.08 2.1513.a.1.a 5 4.5 RP 0.9814.a.1.a 7.5 4.5 C18 1 2.63 3.8515.a.1.a 7.5 4.5 Phenyl 1.29 2.2616.a.1.a 7.5 4.5 RP 2.0817.a.1.a 5 7 C18 2.3518.a.1.a 10 7 Phenyl 1.45 1.08 2.4519.a.1.a 5 7 Phenyl 2.0320.a.1.a 10 7 RP 3.0521.a.1.a 5 7 RP 1.0222.a.1.a 8.8 7 Phenyl 1.42 2.3423.a.1.a 6.3 7 RP 1.5424.a.1.a 10 7 C18 1.89 2.99 2.8225.a.1.a 10 7 C18 1.87 2.95 2.8126.a.1.a 5 7 C18

53

Peak exchange = inconsistent resolution data.

Co-elution = missing peaks = missing results.


Inherent Data Loss Can Not Model Chromatography

Compound Name R2-Adj. Value

Impurity F 0.1785

Impurity H 0.9125

54

Inaccurate Predictions

Accurate Predictions

Problem – data often not accurately modeled.

Result – Phase 1 is reduced to a “Pick the Winner” Strategy.

Regression Model Statistics

On a scale of Zero to One (0 – 1), with 1.0000 = the perfect model:

55

Trend Responses™

56

QbD Requires Good Data.

Chromatogram Processing Protocol – Trend Responses

57

Chromatogram Processing Protocol – Trend Responses

Import Results from CDS

58

One Click:

Software automatically imports results data from the CDS.

Import Wizard. Multiple Results Sets™

59

When the experiment is constructed as two or more sample sets due to instrument automation limitations, a single Import Results operation can bring in the results from all results sets linked to the experiment.

Import Wizard. Auto-computed Trend Responses™

60

All CDS Responses can be utilized throughflexible Trend Response operators.

For example:

Max Peak # – can track API peak(s).

Tailing and/or Area can become additionalmetrics of method performance.

Analysis Wizard. Automated Mode

61

pH Level (X1)

Model Results

One Click: Software automatically builds an equation (model) from the CDS resultsfor each critical method performance characteristic (referred to in ICH Q8 as a Critical Quality Attribute, or CQA).

62

222222112

2111110s )x()x()x*x()x()x(R ββββββ +++++=

Gradient Time Level (X2)

Visualize Results

63

One Click:

Software wizard generates graphical representations of each model – in this case the model of study factor effects on the resolution of a critical peak pair.

Note – effects are not always independent (linearly additive)

Note: different Critical Method Attributes have different regions of good performance.

64

Trellis Visualization of Effects

Automated Numerical Search for Optimum Method

65

Numerical Solution Search – Best Conditions

66

For this design the software conducted 46 separate solution searches.

Answers are ordered from closest to furthest away in terms of simultaneously achieving all defined goals.

An Overlay graph of all responses for which method performance goals are defined is automatically generated.

For this presentation we will “build” the overlay graph one response at a time.

Graphical Solution Search – Best Region Overall

67

Fusion AE Overlay Graph.

Each color on the graph corresponds to a response for which goals have been defined.

A region shaded with a given color shows the study variable level setting combinations that will NOT meet the goals for the corresponding response.

Note: the un-shaded region corresponds to level setting combinations that meet all response goals.

Note: Shaded region indicates pHand Gradient Time combinations that do NOT meet performance requirements.

68

Graphical Optimizer

Graphical Optimizer

Unshaded Region WithPredicted Best Settings:

~Gradient Time = 6.0 minpH = 3.0Mobile Phase = MeOHColumn = BEH Shield RP18

69

Graphical Optimizer

Unshaded Region WithPredicted Best Settings:

~Gradient Time = 6.0 minpH = 3.0Mobile Phase = ACNColumn = BEH Shield RP18

70

Point Predictions

71

1.47

5

1.85

0

1.99

92.

105

2.21

2

2.50

5

3.16

5

3.80

4

4.37

0

AU

0.00

0.50

1.00

1.50

2.00

2.50

3.00

Minutes0.00 0.50 1.00 1.50 2.00 2.50 3.00 3.50 4.00 4.50 5.00 5.50 6.00 6.50 7.00 7.50 8.00 8.50 9.00

Analyze Results to Define Variable Levels for Optimization

72

1.72

1

1.91

4

2.03

0

2.16

3

2.26

6

2.43

7

2.85

6

3.23

5

3.53

4

AU

0.00

0.50

1.00

1.50

2.00

2.50

3.00

Minutes0.00 0.50 1.00 1.50 2.00 2.50 3.00 3.50 4.00 4.50 5.00 5.50 6.00

Methanol

Acetonitrile

There is still optimization work to do for 2 critical peak pairs.

73

Method Optimization

Experimental Design

74

Results: Optimize method for mean performance AND robustness.

DOE Optimization Design: Rapid Method Development Template 2Use column, pH, and solvent type defined in Phase 1.

Chromatographic Performance: Step 1. Model all factor effects on chromatographic characteristics.Step 2. Automatically compute robustness of each experiment method.Step 3. Model all factor effects on method robustness.

Note: no additional experimentsneeded to address robustness

Robustness Demonstration Experiment

Method Validation

MeetsALL PerformanceRequirements?

Yes

No

QbD Strategy

Phase 1 – Column/Solvent Screening

Phase 2 – Method Optimization

75

Optimization Experiment – Planning

Optimization Experiment – Design

76

Experiment Variable Range or Level SettingsPump Flow Rate (mL/min) 0.5 mL/min

Injection Volume 1.0 uL

Gradient Slope(End Point % Organic)

Vary Starting point = 5.0 — 15.0 Vary Starting Point – Separation issues in1st half of chromatogram

End Point = 70.0%

Gradient Time (min) 6.0 minutes (Constant) Screening study result

pH 2.5, 3.0, 3.5 Recommend at least 3 levels

Oven Temperature 30.0 — 40.0 C Recommend at least 5.0 C

Column Type One ColumnBEH Shield RP 18 Screening study result

Organic Solvents Mobile Phase A1-1: Aqueous Buffer, pH 2.5Mobile Phase A1-2: Aqueous Buffer, pH 3.5Mobile Phase A1-3: Aqueous Buffer, pH 4.5Mobile Phase B1: Acetonitrile Screening study result


77


78


79

80

Optimization Approach to pH

1.00

3.00

3.5

pH

Rs

3.02.5

2.00

Desired Level of Characterization – Optimum pH for Mean Performance and Robustness.

Data Analysis

Method Optimization

81

Import Results Wizard. Single Results Set.

82

Import Wizard. Tracked-peak Responses

83

Optimization studies normally use identified (tracked) peak results data sets. All results computed from the CDS are available.

Format Data for Analysis

84

Analysis Wizard. Automated Mode

85


86


87

Analyze Results to Define “Optimum” Method

Chromatogram = Predicted Best Conditions.

88All critical Peaks Well Resolved. Excellent Mean Performance.

Graphical Optimizer – Mean Performance

89

Edges of Failure

Graphical Optimizer – Mean Performance

90

Integrating Robustness into Method Development

ICH Q2A – Robustness

FDA – Reviewer Guidance: uses ICH Q2A

ICH Q2A:The robustness of an analytical procedure is a measure of its capacity to remain unaffected by small, but deliberate variations in method parameters and provides an indication of its reliability during normal usage.

In the case of liquid chromatography, examples of typical variations are:

• Influence of variations of pH in a mobile phase• Influence of variations in mobile phase composition• Different columns (different lots and/or suppliers)• Temperature• Flow rate

Note – the text “but deliberate” refer to the deliberate perturbation of critical instrument parameters about their method setpointsdone as part of a Validation-Robustness experiment.

92

Method ScreeningSelect column & solventby visual inspection of

chromatograms

Method Development• Only evaluate mean

method performance

• Do NOT study parameterinteraction effects

Method ValidationFormal experiment to demonstratemethod robustness

Re-development• Method fails robustness

on validation testing

• Method fails robustness in the field

Traditional Method Development Approach to Robustness

93

A Revealing Comment

Quality Implementation Working Group on Q8, Q9 and Q10 –Questions & Answers

Q: Does a set of proven acceptable ranges alone constitute a design space?

A: No, a combination of proven acceptable ranges (PARs) developed from univariate experimentation does not constitute a design space [see Q8(R1), Section 2.4.5.].

Proven acceptable ranges from only univariate experimentation may lack an understanding of interactions between the process parameters and/or material attributes. However, proven acceptable ranges continue to be acceptable from the regulatory perspective but are not considered a design space [see ICH Q8(R1) Section 2.4.5].

Quality Implementation Working Group on Q8, Q9 and Q10 -Questions & Answers, Current version dated June 10, 2009

94

95

Setpoint Variation Envelope Around Target

Actual Nature of Setpoint Variation in Many LC Parameters

5 10 15 20

Method Run Time

Gra

dien

t % O

rgan

ic

Magnitude of shift from target varies from peak to peak. It is not a uniform bias of the chromatogram – puts OFAT approach at risk.

FDA Reviewer Guidance –Validation of Chromatographic Methods

Methods validation should not be a one-time situation to fulfill Agency filingrequirements, but the methods should be validated and also designed by the developer or user to ensure ruggedness or robustness.

Regulatory Statements and Expectations

ICH Q2A – Text on Validation of Analytical ProceduresIX. ROBUSTNESS (8)The evaluation of robustness should be considered during the development phase and depends on the type of procedure under study. It should show the reliability of an analysis with respect to deliberate variations in method parameters.

96

Method ScreeningSelect column & solvent

using quantitativeTrend Responses

Formal Method Development & Optimization

Method ValidationFormal experiment to demonstratemethod robustness

Characterize and model ALL study parameter effects on ALL critical method performance attributes

Method MeanPerformance Models

Method RobustnessModels

• Establish ICH Design Space

• Identify Optimal Method

• Establish Operating Space

Fusion QbD Approach to Robustness

97

The quantitative metric of performance robustness that we will use is derived from traditional Process Capability studies.

The Robustness Metric

Considering the LC Instrument as aProcess-in-a-Box

98

Process Flow

Separation(Column)

AcceptableVariation

Raw MaterialComposition

(Mobile Phase)

HeatingChamber

(Column Oven)

Measurement(Detector)

X─API Resolution

= variation around setpoint

Method Performance Variation – 100 Injections

Method A - Small VariationMethod B - Large Variation 99

iationvar6LTLUTLCp σ

−=

Process Capability (Cp) – a direct, quantitative measure of process robustness used routinely in Statistical Process Control (SPC) applications. The classical SPC definition of “Inherent Process Capability” (Cp) is

UTL and LTL = Tolerance Limits (tolerance width).

6σ Variation = ±3σ process output variation.

Traditional Goal ≥ 1.33

- based on setting the UTL and LTL at ±4σ ofmethod performance variation.

- Note: a 6-sigma method would have a Cp = 2.00

Process (LC Method) Capability - Quantified

LTL UTL

API Resolution

6σVariation

X─

100

Mean Value( X )

Process Capability - Quantified

±3σ

Rs Variation

LTL UTL

±3σ Variation = Tolerance Limit Interval00.166cp ==σσ

101

Mean Value( X )


±3σ

LTL UTL

±4σ

33.168cp ==σσ

Rs Variation

±3σ Variation = 75% of Tolerance Limit Interval

102

Mean Value( X )


±3σ

LTL UTL

±6σ

00.26

12cp ==σσ

Rs Variation

±3σ Variation = 50% of Tolerance Limit Interval

103

Method Robustness

Class Exercise 8 – Method Robustness Optimization

Previous Optimization – Mean Performance Only

Design and Operating Spaces – Mean Performance Goals Only

Not knowing the Edges of Failure for Robustness means the Operating Limits must be set “well within” the region of acceptable mean performance. 10

5

Automated Stepwise Procedure for Generating Cp

S-Matrix Robustness Simulator™

Monte Carlo simulation using DOE-derived Models.

No additional experiment runs required.

We will demonstrate this 4-step process in the following slides.

106

LCL UCL

Step 1 – Define Confidence Interval Around Setpoint for Each Variable

-0.1 +0.1

Maximum Expected Variation( 3σ Value) = ± 0.01

6σ ≈ 99.7%

Setpoint

E.g. – pH

NOTE - Use SOP for Buffer Preparation.

Expected 3σ Variation Limits

107

LCL UCL

Maximum Expected Variation( 3σ Value) = ± 2.0

6σ ≈ 99.7%

Expected 3σ Variation Limits

Setpoint

Confidence Interval Around Setpoint

-2.0 +2.0

E.g. – Initial % Organic

NOTE - Use manufacturer’s specs for the ±3σ value or extend it based on the least-capable system which will be used on transfer. 10

8

NOTE - Use manufacturer’s specs for the ±3σ value or extend it based on the least-capable system which will be used on transfer.

Confidence Intervals Around Setpoints

109

-0.50 +0.50

USP Resolution

Step 2 – Define Maximum Tolerance Limits for each Response (CQA)

Mean Result Tolerance Limit Delta( distance) = ± 0.50X

IMPORTANT: the Tolerance Limit Delta values define the maximumacceptable limits on method performance variation.

This is normally your System Suitability Specification.

∆

110

Robustness is calculated for each key performance metric.

NOTE - the Tolerance Limit Delta values define the maximum acceptable limits on method performance variation.

This can be your System Suitability Specification.

Maximum Tolerance Limits Around CQAs

111

Robustness Simulator* = Predicted Variation

* - U.S. Patent No. 7,606,685 B2

pH (X1)2.5

Initial %(X2)15

Step 3 – Predict Response Variation of each experiment run

E.g. Experiment Run 1 Method

RS

X —

RS = 9.3 + 4.2(X1) + 1.3(X1*X3) + 1.6(X1)2X2 - 5.4(X2)2 + 12.7(X4)2 …

112

Variation AroundSetpoint

Variation AroundSetpoint

Tolerance Width Delta( distance) = ± 0.50

.I.C6LTLUTLCp σ

−=

0.70 1.20 1.70

Peak 4 – USPResolution

∆

6σ C.I.

0.92 1.4957.000.1

92.049.170.070.1cp =

−−

=

Step 4 – Compute Robustness Cp for each experiment run

Cp = 1.75

E.g. Experiment Run 1 Method

LTL UTL

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Run No. Initial % Organic Oven Temperature pH Peak 4 - USPResolution Peak 4 - USPResolution - CpCondition Column - 1 5 30 2.501.a.1.a 5 30 2.50 1.20 1.752.a.1.a 15 30 2.50 2.43 2.963.a.1.a 5 30 2.50 1.20 1.754.a.1.a 15 30 2.50 2.42 2.96Condition Column - 2 10 30 3.005.a.1.a 10 30 3.00 2.77 1.39Condition Column - 3 15 30 3.506.a.1.a 15 30 3.50 5.12 0.627.a.1.a 5 30 3.50 5.54 0.41Condition Column - 4 10 35 2.508.a.1.a 10 35 2.50 2.03 1.91Condition Column - 5 10 35 3.009.a.1.a 10 35 3.00 2.85 1.5910.a.1.a 10 35 3.00 2.85 1.5911.a.1.a 10 35 3.00 2.85 1.5912.a.1.a 15 35 3.00 3.17 2.4813.a.1.a 5 35 3.00 2.36 1.18Condition Column - 6 10 35 3.5014.a.1.a 10 35 3.50 5.50 0.54Condition Column - 7 15 40 2.5015.a.1.a 15 40 2.50 2.56 6.3316.a.1.a 5 40 2.50 1.55 1.6817.a.1.a 5 40 2.50 1.57 1.68Condition Column - 8 10 40 3.0018.a.1.a 10 40 3.00 2.92 1.85Condition Column - 9 5 40 3.5019.a.1.a 5 40 3.50 5.52 0.4720.a.1.a 15 40 3.50 4.39 0.72

Computed ResolutionRobustness

ResolutionResults from CDS

Good mean performance

Poor Robustness

Both sets of results are modeled to identifythe best performing, most robust method.

Step 4 – Robustness Cp for All Experiment Runs

Good mean performance

Good Robustness

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Robustness prediction models are automatically derived.

Link the Mean Performance and Robustness equations via the

numerical and graphical optimizers to identify one or more

combinations of variable level settings that meet or exceed mean

performance and performance robustness goals for each response.

Step 5 – Generate a Robustness Cp Prediction Equation for Each CQA

115

Design and Operating Space – Mean Performance Goals Only

116

Final Design and Operating Space – Mean Performance + Robustness

117

Step 3 – Generate the Point Predictions

3. Navigate to the Point Predictions activity, and enter the points into the prediction dialog.

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Step 4 – Export the Point Predictions to Empower and Run the Sample Set

4. The Point Predictions wizard will generate the predicted results for each method performance metric, including all robustness metrics. You then export these points to the CDS as a ready-to-run sample set and methods.

Documentation Notes

1. Maintain the verification run chromatograms in the Empower project.

2. Maintain the Fusion Method Development files in an archival system (e.g. NuGenesis).

Verification chromatogram –Operating Space Center Point

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