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

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0 2 4 6 8Number of Factors

Nu

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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

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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

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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