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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
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]
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.”
[ICH Q8(R2) - Guidance for Industry, Pharmaceutical Development, August, 2009]
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.”
[ICH Q8(R2) - Guidance for Industry, Pharmaceutical Development, August, 2009]
Design Space (Un-shaded Region):
• Mean Performance Only
Edge of Failure –
Mean Performance
12
FINAL DESIGN SPACE – MEAN PERFORMANCE + ROBUSTNESS
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.”
[ICH Q8(R2) - Guidance for Industry, Pharmaceutical Development, August, 2009]
Edges of Failure –Method RobustnessFinal Design Space:
• Mean Performance• Robustness
13
[ICH Q8(R2) - Guidance for Industry, Pharmaceutical Development, August, 2009]
14[ICH Q8(R2) - Page 23
QbD “DESIGN SPACE”
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.”
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
[ICH Q8(R2) - Guidance for Industry, Pharmaceutical Development, August, 2009]
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
What’s New in Fusion AE – Version 9.5.0
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
What’s New in Fusion AE – Version 9.5.0
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
What’s New in Fusion AE – Version 9.5.0
Modifications to Aqueous Linked Factor System Settings Control
• Consolidated UI for Aqueous-linked Variables
• Metadata for pH and Buffer Strength
20
What’s New in Fusion AE – Version 9.6.0
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
What’s New in Fusion AE – Version 9.6.0
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
What’s New in Fusion AE – Version 9.6.0
New Graphical Optimizer with Operating Space and Advanced Reporting
23
What’s New in Fusion AE – Version 9.6.0
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.
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]
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
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.”
[ICH Q8(R2) - Guidance for Industry, Pharmaceutical Development, August, 2009]
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
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.”
[ICH Q8(R2) - Guidance for Industry, Pharmaceutical Development, August, 2009]
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
[ICH Q8(R2) - Guidance for Industry, Pharmaceutical Development, August, 2009]
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
Experiment Setup – LC Instrument Centric Template
45
Experiment Setup – LC Instrument Centric Template
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
Inherent Data Loss – Co-elution and Peak Exchange
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 – Co-elution and Peak Exchange
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
Optimization Experiment – Design
77
Optimization Experiment – Design
78
Optimization Experiment – Design
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
Automated Numerical Search for Optimum Method
86
Automated Numerical Search for Optimum Method
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 )
Process Capability - Quantified
±3σ
LTL UTL
±4σ
33.168cp ==σσ
Rs Variation
±3σ Variation = 75% of Tolerance Limit Interval
102
Mean Value( X )
Process Capability - Quantified
±3σ
LTL UTL
±6σ
00.26
12cp ==σσ
Rs Variation
±3σ Variation = 50% of Tolerance Limit Interval
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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.
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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
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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
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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
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-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.
∆
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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
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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 …
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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
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Design and Operating Space – Mean Performance Goals Only
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Final Design and Operating Space – Mean Performance + Robustness
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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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