GlaxoSmithKline
Jim Ward, Bob Herrmann, Teo Ching-Lay and Ann Diederich
Sequential Design – the challenge of multiphase systems
Outline
Introduction– Traditional approaches to problem solving
Our problem– Preparation of the correct crystalline form
Our approach– Mechanistic guided factor selection
Results
Proposed problem solving approach
Conventional Modeling Approaches (1)
Utility of Mathematical Model
– Prediction
– Sensitivity Analysis
Types and Issues
– StatisticalHuge number of experiments
Little mechanistic insight
Mechanistic– Requires a compete set of
constitutive equations– May not be possible for multiphase
systems (S/S/L)– May not fully understand
mechanism
Route Selection
Scoping Study
Fractional/ Screening
FoldoverRSM or Composite
Design
Robustness Study
Route Selection
Scoping Study
Fractional/ Screening
FoldoverRSM or Composite
Design
Robustness Study
A blended approach may provide benefits of both statistical and
mechanistic modeling
Motivation and the Challenge of Various Approaches
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Conventional Modeling Approaches (2)
Route Selection Scoping Study
(Scoping studies are used to narrow into the experimental
region of interest)
(4 Experiments)
Fractional/ Screening
(These designs are utilized to identify factors that affect the
process)
(16 Experiments)
Foldover
(Once the factors of interest are identified the foldover removes aliasing from the
fractional design)
(8 Experiments)
RSM or Composite Design
(utilized to determine curvature and to hone into an
optimized process)
Robustness Study
(utilized to narrow or widen process parameters)
(8 Experiments)
Fractional screening and robustness are resource consuming. May have to do at a reasonable scale if equipment sensitive. Without mechanistic knowledge, number of factors is large.
Conventional Approach: Factorial Burden
Pareto Principle2
– 80% of the effects come from 20% of the factors
– For 20 experiments, 6 factors is roughly the maximum practical limit for study
Need mechanistic data to limit factors
– DoE does not provide direct evidence of why something occurs
Even Optimized – Experimental Design can be Costly
0
20
40
60
80
100
120
140
0 2 4 6 8Number of Factors
Nu
mb
er o
f E
xper
imen
ts Full Factorial
Main Effect Screening
2 Factor InterationOptimized
2http://en.wikipedia.org/wiki/Pareto_principle
Realistically, we can only do about 20 pilot/kilo scale experiments for scale sensitive reactions, so factor selection is essential
Selected Process
Isolate Hydratevia Filtration at 25 °C
Agitate until Conversion Complete
Charge 6 volumesAcetonitrile
Heat to at least 60 C
Charge
Isolate Form AAnhydrate
Ves
sel O
ne
Fil
ter
Dri
er
Our process involves the formation of a hydrate and its subsequent desolvation to form an anhydrate (product)
Greater than 20 unit operations- which factors to study?
Our Approach: Dehydration Mechanism
Liquid Mediated (Lin and Lachman)Indomethacin
– Temperature and time control
– Mixing insensitive
– Solvent sensitive
– Effectively irreversible
– Scale insensitive
Solid State TransformationThyminde, Caffeine and Cytosine
– Very difficult to control
– Mixing sensitive
– Reversible
– Heat transfer sensitive
– Scale sensitive
Dissolutio
nCrystallization
Thermal Dehydration
Path Two
Path One
Dissolutio
nCrystallization
Thermal Dehydration
Path Two
Path One
S. R. Byrn; Solid State Chemistry of Drugs, 2nd Ed.,
Chapter 14 – Loss of Solvent of Crystallization
Know the mechanism – Narrow the factor list
Our Approach (1): Dehydration Mechanism
S. R. Byrn; Solid State Chemistry of Drugs, 2nd Ed.,
Chapter 14 – Loss of Solvent of Crystallization
Liquid Mediated (Lin and Lachman)Indomethacin
– Temperature and time control– Mixing insensitive– Solvent sensitive– Effectively irreversible– Scale insensitive
Solid State TransformationThyminde, Caffeine and Cytosine
– Very difficult to control– Mixing sensitive– Reversible– Heat transfer sensitive– Scale sensitive– PSD Sensitive / SSA
Know the mechanism – Narrow the factor list
Our Approach: Dehydration Mechanism – Experimental
ReactIR
Filtered
SaturatedSolution
Unstable Form charged
Anhydrate
Hydrate
Solvate
SeededWith Stable form
Monitor Conversion
PAT/Mechanism - ReactIR
Our Approach: Dehydration Mechanism – Results
Time
Con
cent
ratio
n
Unstable Form
Stable Form
Solution Mediated
Solid State
Time
Con
cent
ratio
n
Unstable Form
Stable Form
Solution Mediated
Solid State
Theoretical
Hydrate Charged
Co
nce
ntr
atio
nTime
Actual
PAT/Mechanism - ReactIR
The conversion is solvent mediated. Key factors are temperature and composition of the solvent affect solubility
Detailed Solubility Data
Develop detailed solubility models to enhance mechanistic understanding
– Conversion Temperature
– Conversion Composition
Presence of water increases solubility, as does increasing temperature
Driving force for crystallization can be calculated across process conditions
Establishes water composition and temperature as key factors
Our Approach: Desaturation Mechanism Determination
Dissolution Growth
Nuclea
tion
B Surface Area * ΔCb
G ΔCa
From solution to Solid
A. G. Jones; Crystallization Process Systems,
Pg 204 Eqs 7.36 & 7.38 simplified
Desaturation Mechanism - Experimental
SaturatedSolution
Unstable Form charged
PAT / Mechanism – RC1
Unstable formcharged while
Seeded with Stable form
Monitor Conversion
Monitor Conversion
Monitor Thermal Conversion by
RC1
Filtered
Our Approach: Desaturation Mechanism - Results
RC/1 can be used to estimate desaturation rates– Crystal nucleation and growth are exothermic
processes– From the heat of crystallization a rate can be
determinedThe conversion without solids present
– Autocatalytic- indicates nucleation– 5 times slower in presence of solids – indicates
affect of solids present (secondary nucleation)
2 Minutes
RC1 – Thermal Conversion
Desaturation Mechanism - Results
The conversion without solids present
– Autocatalytic
– 5 times slower
The particles with solid present
– 70% Larger- some growth
– Faster conversion rate- previous slide
UnseededX90 - 34
SeededX90 - 20
Both the conversion rate and particle size supports a nucleation dominated mechanism, with minor crystal growth also occursImportant factors: amount of supersaturation (temperature, solvent comp., agitation rate)
Factor Selection
Dissolution Nucleation
B Surface Area * ∽ ΔCb
Liquid Effects
Loss on Drying (LOD) The hydrate wet cake desolvated in a filter drier. Blowback of the hydrate wetcake prior to adding the dehydration solvent will decrease water content, lowering API solubility
Temperature As the conversion temperature is increased solubility rises perhaps effecting conversion.
Volumes of Solvent Larger solvent volumes mean higher dilution
Physical Effects
Agitation Both continuous and intermittent agitation investigated
Parameter Investigation - Results
Highly SensitiveWater content of solvent – Highly LOD of wet cake (water) results in more water being present for the conversion, which raises solubilityTemperature – Higher temperature raises solubilityInteraction of solvent composition and Temp - The highest solubility, and liquid side effects, are seen at high LOD (water content) and high temperature
Less SensitiveAgitation – Low agitation sensitivity increases confidence this product can be scaled with little physical effectVolumes – Higher throughput can be obtained because the volumes of the desolvation solvent proves to be unimportant.
Figure : D90 LOD/Temp Contour PlotFigure : D90 Agitation Contour Plot
Scale-Up of Selected Process
0
10
20
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60
70
80
90
0 5 10 15 20 25 30 35 40
Batch
X90
DoE Robustness KiloPilot PlantCampaign I/II1000x Scale
ManufacturingCampaigns I/II2000x Scale
Results: Model worked well throguh kilo lab, 1000x DOE scale.Particle size changed when going to 2000x scale. Numbers acceptable, but unexplained variance
Process surprises- a new chance to optimize
While change to x90 on scale wasn’t a large project issue, the appearance of a new solvate was
As a result, workflow repeated with previous information to guide design, and incorporating seeding
New Process Design: Using solubility data to determine solvate stability regions
Detailed Thermo
FBRM
– Indicates partial conversion to form A (2 minutes)
– Conversion to form to solvate is 180 times slower (4 hours)
Using detailed solubility models
– Conversion Temperature
– Conversion Composition
– Required Seed Load
Previous DoE
– Minimize Water
– Maximize Temperature
4 Hours
New Process Design: DOE Results
Selected Route
Isolate Hydratevia Filtration at 25 °C
Agitate until Conversion Complete
Charge 6 volumesAcetonitrile
Heat to at least 60 C
Charge
Isolate Form AAnhydrate
Ves
sel O
ne
Fil
ter
Dri
er
Selected Route
Isolate Hydratevia Filtration at 25 °C
Agitate until Conversion Complete
Charge 6 volumesAcetonitrile
Heat to at least 60 C
Charge
Isolate Form AAnhydrate
Ves
sel O
ne
Fil
ter
Dri
er
Selected Route
Isolate Hydratevia Filtration at 25 °C
Agitate until Conversion Complete
Charge 6 volumesAcetonitrile
Heat to at least 65 C
Charge
Isolate Form A
Ves
sel O
ne
Fil
ter
Dri
er
Selected Route
Isolate Hydratevia Filtration at 25 °C
Agitate until Conversion Complete
Charge 6 volumesAcetonitrile
Heat to at least 65 C
Charge
Isolate Form A
Ves
sel O
ne
Fil
ter
Dri
erCharge Water
Charge Seeds
Heat above Conversion Temp
Scale-up of modified process
0
10
20
30
40
50
60
70
80
90
0 2 4 6 8 10 12 14 16 18 20
Batch
X90
Unmodified Modified
Type11%
Batch89%
Variability Source
Both variance in particle size and form issue mitigated through guided experimental design
Alternative Workflow
Route Selection Thermodynamics
(ensure the process is on stable
thermodynamic footing)
PAT guided mechanistic studies(kinetic model not
required)
Factor selection and scoping
(using small scaleresults select factors
and design space)
4 Experiments
Factor investigation(DoE)
14 Experiments
Robustness Study
Alternative Workflow
Portions can be done on small scale– Thermodynamics– PAT Guided Mechanistic
StudiesAvoids investigating noise factors in DoE
– Fewer scale experimentsProvides a mathematical model
– Predication– Control
Provides direct scientific understandingProvides
– Confidence in robustness– Estimate of process variance– Basis of measuring scale effects
Consistent with FDA Guidance
Route Selection Thermodynamics
(ensure the process is on stable
thermodynamic footing)
PAT Guided Mechanistic Guides
(kinetic model notrequired)
Factor Selection and Scoping
(using small scaleresults select factors
and design space)
4 Experiments
Factor Investigation(DoE)
4 Experiments
Robustness Study
Route Selection Thermodynamics
(ensure the process is on stable
thermodynamic footing)
PAT Guided Mechanistic Guides
(kinetic model notrequired)
Factor Selection and Scoping
(using small scaleresults select factors
and design space)
4 Experiments
Factor Investigation(DoE)
4 Experiments
Robustness Study
FDA Guidance
A process is generally considered well understood when
(1) all critical sources of variability are identified and explained; (2) variability is managed by the process; and,
(3) product quality attributes can be accurately and reliably predicted over the design space established for materials used, process parameters, manufacturing, environmental, and other conditions.
PAT – A Framework for Innovative Pharmaceutical Development, Manufacturing, and Quality Assurance1
1www.fda.gov/cder/guidance/6419fnl.pdf
Modeling – Statistical or Mechanistic
3http://www.scale-up.com/usersarea/FDA/FDA_notes_28Feb08.pdf
Question to the FDA“the agency at the moment is much more tuned in to statistical models, in part due to the fact that drug product often requires statistical models in the absence of mechanistic detail”
FDA ResponseAgreed. Statistics and DOEs should be integrated with mechanistic modeling. We do not want to see so many experiments “in the dark” as we are seeing now. Do fewer experiments. Show us that you have identified all the really critical parameters and understand the effects of all the CPPs.
Notes of DynoChem presentation to FDA CDER, 28 February 20083
Mechanism – A word of caution
We need a word of caution at this point. Just because the mechanism and the rate-limiting step may fit the rate data does not imply that the mechanism is correct.
H. Scott FoglerElements of Chemical Reaction Engineering, 3RD Ed.Page 614
Robustness Study
Robustness StudyInvestigated factors
– Set to the widest levels the plant can provide All Adjustable Factors
– Set outside the levels future plant modifications may be wantedDesign
– Minimal 2 level DoE with no center pointsResults
– Proof of Robustness– Estimation of process variance
The Scoping Study - Experimental
For a good model the responses need to be
– Variable in region the factors are tested
– Quantifiable – Distinguishable from noise– Ideally, controlled by the factors– Contain a passable region
Scoping Study consists of– 1 Reaction at each extreme– 2 Centre Points
Scoping Study Should Result In– Confidence in factor levels– Confidence in covering controlling
factors– Estimate of pure error– Estimate of model curvature
Robustness Study
Robustness StudyInvestigated Factors
– Set to the widest levels that will allow passing of critical process parametersAll Adjustable Factors
– Set outside the levels future plant modifications may be wantedDesign
– Minimal 2 level DoE with no center pointsResults
– Proof of Robustness– Estimation of process variance