Process Design toward Continuous Manufacturing: a Green Perspective

David am EndeChemical Engineering

Chemical R&DPfizer, Inc., Groton, CT

13th Annual Green Chemistry & Engineering ConferenceJune 23-25, 2009College Park, MD

Current Challenges inPharmaceutical Development [1,2]

• Synthetic targets of increasing complexity• Development costs continue to increase

- Estimates of +$2Billion for NCE to NDA [3]

• High attrition rate post FIH ~90%• Regulatory demands continue to increase• External cost factors on drug pricing• Compressed development timelines• Costs of Manufacturing1Federsel, H, In Search of Sustainability: Process R&D in light of current pharmaceutical challenges, Drug Discovery Today, 11(21/22) November 2006

2 Kola & Landis, Can the pharmaceutical Industry reduce attrition rates, Nature Reviews Drug Discovery, 3, 711-715, 20043 Suresh & Basu, J. Pharm Innov (2008) 3:175-187

Complexity of API Targets

celecoxib/Celebrex varenicline/Chantix pregabalin/Lyrica

N

N

O

S

O

N O

O

NN

N

sildenafil/Viagra atorvastatin/Lipitor

NN

CF3

H3C

SO

O

H2N

NN

CF3

H3C

SO

O

H2N

Manufacturing Costs: A Significant Driver of the Financial Returns of Pharma

COG's26%

R&D15%Admin

32%

Margin27%

COG'sR&DAdminMargin

[1] The Gold Sheet, January 2009, page 3, Attention Turns to the Business case for Quality by Design.[2] Adapted from Codgill, Knight, Anderson, Drennen, The Financial Returns On Investments in Process Analytical Technology andLean Manufacturing: Benchmarks and Case Study, J. Pharm Innov (2007) 2:38-50.

Major Branded Pharma, Median%, (n=10), 2006 Survey of Annual Reports[2]

COG’s average about 25% of Sales in an Industry with $600 Billion of Annual sales; or COG’s of $150 Billion [1]

Lean Manufacturing: Improve Profitability by Eliminating “Waste”

• Overproduction• Waiting• Transportation• Unnecessary processing• Uneccessary inventory• Uneccessary motion• Production of defects

#1 greatest waste is excess Inventory

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6000

1 2 3 4 5

RawsWIPFG

$Millions Big Inventories of Big Pharma

The accepted standard carrying costof Inventory is 20% [1]

[1] Codgill et al, J. Pharm Innov. (2007) 2:38-50

(cost-of-capital =12% overhead =8%)

$4 Billion Inventory = $800 million Carrying Cost

“Typical” API Cost Reductions Post Launch under Different Development Paradigms

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Future state effectively creates revenue:Challenges R&D to launch with low cost processes

Savings

$/kg

“Typical” Annual API Production Costs Post-Launch under Different Development Paradigms

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FutureState

Example: $2000/kg with peak of 50 MT/yr

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∫ ⎟⎟⎠

⎞⎜⎜⎝

⎛∗= dt

yeartkgtCOGCost )()(

Single API : Potential Cumulative Savings of $400 MM vs Traditional

Cumulative Cost

R&D: Greater Focus on COG’s

• Emphasis on having the best chemistry, at lowest cost, at the time of launch

• Emphasis on Process Design; Integrating best chemistry with the best engineering design – to achieve safe, green, robust, & well understood processes, while meeting or exceeding quality specifications.

Innovation is Key to Driving Down costs

Opportunities: Areas of Emphasis

• Predictive/Process Modeling• Green Chemistry• Biocatalysis• Chemistry “Lab of the Future”• Process Design• New Manufacturing Technologies• Continuous Processing

Quality by Design

Value Proposition for Process Modeling [1]

Increase Results

• Solubility models for improved purifications

• Distillation models to improve quality and performance

• Extraction modeling models • Kinetic modeling to improve

yield and selectivity and reactor design

• Process models that improve control

Reduce Resources

• reduced experimentation• opportunities for energy and

waste minimization• Capacity models identify

minimal resources to produce at given capacity

• Production allocation models to minimize total operating costs

• Supply chain models minimize inventory while maintaining adequate supplies

Productivity = Results obtained / Resources required

Dramatic increases in productivity via predictive screening!…frees resource to drive further optimization and cost reductions

1 McGarvey, Bernard, Chemical Engineering Progress, September 2007, 41-47

Process Modeling Areas in Chemical Engineering (API)• Phase Equilbrium Models

– Solubility prediction– Distillation prediction– Extraction Selectivity

• Reaction Kinetics and Reaction Equlibria– Model Identification and Discrimination

• Unit Operation Models– Filtration/centrifugation performance– Crystallization (growth rates)

• Equipment Models– Mixing times (micro, meso, macro) vs Reaction Kinetics– Solids Suspension in Tanks– Shear Rates (crystal or agglomeration breakage)– Batch and Continuous Flow configurations

• Quality by Design Process Mapping• Retrosynthesis and Costing Models

• Aspen • Athena• Cosmotherm• Design Expert• Dynochem• Fluent• Mettler iControl• Numerica/Jacobian• Visimix

Software Tools*

*Pfizer avails of a wide selection of software tools to aid in process mapping, and are not explicitly endorsing the software tools listed here

Predictive/Process Modeling

Increase Productivity by Increasing Number of Users

Expert Users(Engineering &

Technology Groups)

Occasional Users(available to

all R&D)

BuildUser

FriendlyWeb

Interfaces(for TypicalScreeningProblems)

…further leveraged by making accessible to the masses

e.g. Solubility Screening, Distillation Modeling, Extraction Modeling

…is one of the keys to attacking COG’s

s-surface

s-profile

Relative Solubility

chloroform 9.187238Ethoxy ethanol 5.388971methanol 2.751922n-butanol 2.240603IPA 1.790016t butanol 1.386694DCM 1.112964Water 11,2 dicholoethane 0.650617dimethylformamide 0.54352Ethyl Acetate 0.493754Me-THF 0.458001t-butyl acetate 0.452312MTBE 0.434394PhCF3 0.426536Toluene 0.412605carbon tetrachoride 0.330425iso octane 0.315709hexane 0.314053heptane 0.312642cyclohexane 0.309965

First Principle Models Solubility Predictions (CosmoTherm)CoConductorlike nductorlike SScreening creening MoModeldel

Solvent database s-profile

HB-Acceptor

Weak HB-Donornonpolar

CosmoBaseCOSMO-charge surface are qualitatively describing

molecules :• Reactive Sites• Polarity• Hydrogen Bonding• Lipophilicity / Hydrophilicity

(no experimental data required)

Experiments

Solvent Screening in minutes instead of days

Web Interface Developed by R&D, to enable Solubility Modeling to wide-range of users

Crystallization Design Applications

CP-548495

CP-549864 “meta-impurity”

Screening Prediction :DCM very high solubility – reaction solvent

MeOHCP-548495 / CP-549864 = 0.73

Methanol good solvent to selectively isolate CP-548495

Agrees with experimental Data

Varenicline Recrystalization

Torcetrapib API Solubility Relative Solubility CosmoThem

Experimental Solubility (mg/ml)

Experimental Relative Solubility

H2O 5.9E-09 0.1 0.0002heptane 0.0140 20 0.0333hexane 0.0172 25 0.0417EtOH 0.0728 225 0.3750MeCN 0.2011 600 1.0000EtOAc 1.0000 600 1.0000

CosmoTherm predicts the ranking(high solubility is more difficult to calculate since the solute can act as its own solvent)

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% wt of Ethanol

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ubili

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Prediction of anti-solvent effect (water-ethanol system)

Jason Mustakis

Distillation Screening & Modeling

B1

• Development of eLN-Ready Tools• Promotes Green Chemistry by Ranking• Enables Quick Screening of Solvents• Entry into Aspen BatchSep Models

Eric Cordi

Impacts of Poorly Understood Distillation

Wasted cycle time: heat, cool, sample . . . heat, cool, sample . . .Degradation of compounds in solutionFoaming through the ventUnexpected/uncontrolled formation of solids or oilExcessive solvent use

Eric Cordi

Benefits of Good Distillation Design

Efficient exchange of solvents – green chemistryEffective solvent drying before water-sensitive stepsChoice of distillation pressure for beneficial azeotropeChoice of strip & replace or constant-level distillationTracking distillation temperature to preserve chemical stabilityFollow solvent composition vs. volume for solubilityFit to available vessel minimum stir volume

Eric Cordi

Solvent Screening Tool

T-xy Phase Diagrams

Eric Cordi

Liquid Extraction Screening & Modeling

• Property predictions• Distribution coefficients• Complex mixtures available• Temperature sensitive• Portable to Excel interface• Screening for lab efficiency

• COSMOtherm• Aspen Properties• Excel with VBA• Portability for eLN

Log P and Activity Coefficient Modeling

in COSMOtherm

Innovation is Key to Driving Down Costs

• Predictive/Process Modeling• Green Chemistry• Biocatalysis• “Lab of the Future”• Process Design• New Manufacturing Technologies• Continuous Processing• WorkFlows

Areas of opportunity and current focus in Chemical R&D/PGRD

Process Design: An Expanding Tool Box

• Wiped Film Evaporation• Centrifugal Extraction• CCCE – continuous counter-current extraction• Membrane separations• High Shear Mixing• Rapid mixing technology• Continuous processing

– Enabling selected steps– Holistic: reaction through isolated API– Vision: Holistic API through DP

“Evolution to Continuous ProcessingSeen as the Next Step after Quality by Design” [1]

• Novartis-MIT Center for Continuous processing– Blue-Sky Vision …a major transformation in manufacturing– API-DP manufacture fully integrated in one facility

• Different organizational structures• “capital cost for batch processing is about $24.6 MM

while under continuous manufacturing is estimated to be $7MM”

• Operating Costs $8.6 vs $6.4MM (Batch vs Cont.)• Reduction in Footprint - 10% of current

[1] “The Gold Sheet” Vol. 43 No 2. February 2009.

Scope of Continuous Processing

• Single Step Reaction or Unit operation–“continuous” Batch

• Multistep Reaction/Unit Operation interfaced to Batch Processes

• Continuous DP batches (ie continuous tableting)

• Holistic API processes operate 24/7– Significant equipment/facility footprint

• Holistic API + DP operate 24/7

Potential Advantages of Continuous FlowBenefits in R&D

– Enable High Energy Chemistry– Reduced Inventories of hazardous intermediates– Scale-Up of 2X to 4X (on pipe diameter) vs 50 to

1000X– Reactants experience steady state conditions

• High Intensity Mixing typically• Efficient heat exchange• Robustness Enabling:

OxidationsNitrationsHalogenationsMetallationsHigh Temp Chemistrydiazotizations

Benefits in Production•Reduced solvent usage•Reduced cycle times•Reduced capital costs for new capacity•Better lot-to-lot consistency•Less Inventories – (elimination of WIP’s)•Less labor/overhead/smaller plants/lower costs

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Reloads-Out

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Unique-Out

Unique-IN

Enabling Hot Chemistry via Flow

Enable Potentially Hazardous Chemistry via Flow

• Oxidations

• Nitrations

• Diazotizations

• Halogenations

• Reactions Requiring High Containment due to toxicity concerns

Estimated 10% Current Portfolio

Often Chemistry is Re-Routed to avoid a hazardous steps

T

T

T

T

T

Thermocouple

Solution ofProduct

to Quench/Work-up

PAT

• Plug and Play Reactor Components• Heat Transfer is 3X higher than open Tube• Pulseless syringe pumps for lab and Kilo-lab

Real-Time Data Acquisition• Flow Rates • FTIR• pH• Temperatures• FB-control capable

Equipment Configuration

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50

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Tem

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ture

s, o C

Reactor Volume, ml

PhosphoricFeed

4.45 g/sec@ 16.6 oC

1.15 g/sec@ 24.3 oC

Tj18% NaOH

1.11 g/sec@ 20 oC

15% H3PO4

Calculated Temperature Profiles

within ±1 oC

Reactor Temp

Jacket Temp

ActualTemperatures

∆H=42.7 kJ/mol NaOH

Modeling Temperature ProfilesExothermic Fast Neutralization Reaction

Oxidation with Peracetic Acid

108 g AcOAc 128 g H2O2

substituted pyridine/ 2 vols EtOAC

30 min add40 min add

Dual Addition

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atts

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mass of dose

minutes

H2O2

acetic anhydride

Heat Flow

Tr

aq 30-50% H2O2 NO

N

substituted pyridine

AcOAc

substituted N-oxide

R2

R3

R1 R1 R2

R3O

O O+

OH

O+

EtOAc

Oxidation with Peracetic Acid

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minutes

H2O2

acetic anhydride

Heat Flow

Tr

Gut – Bill – Jorgensen – VanAlsten

O

O OH2O

OH

O2k1

OH

Ok2H2O2+

+

O

O

OHH2O+

N

k3

O

O

OH NO

OH

O+ +

R1 R2

R3

R1 R2

R3

Continuous Flow Oxidation Reactor Set-up

DOE Results:Steady State

Continuous Flow Conditions

Range of Robustness

A Simple Reaction

• Lithiation Reaction performed in RC-1 calorimeter at -50ºC.– During nBuLi addition, reaction went from colorless-orange-green-

black.– Large volume of solids produced, forming a “cap” at top of solution.– Subsequent runs at -65ºC produced similar results, although color

change was slower.

• Comparison with bench-top experiments– Reaction always proceeded as expected in RBF experiments from

0.1 – 22L scale.

Br Br n-BuLi

Br LiTHF

-78 to -40oC

BrR1

R2

OHR1 R2

O

Problem….why are we having problems running in the RC-1

From the Literature

• Reaction turns black upon warming to room temperature

• Both para- and meta- linkages suggest bromobenzyne intermediate

Br

Br

t-BuLi (1.5 eq)THF, -78oC

Li

Br

Warm to r.t.

Br

m n

Tour, J. M.; Stephens, E. B.; Davis, J. F. Macromolecules 1992, 25, 499-500.Stephens, E. B.; Kinsey, K. E.; Davis, J. F.; Tour, J. M. Macromolecules 1993, 26, 3519-3532.

Leroux, F.; Schlosser, M. Angew. Chem. Int. Ed. 2002, 41, 4272-4274.Gilman, H.; Gaj, B. J. J. Org. Chem. 1957, 22, 447-449.

Br

R

Br BrRR-Li

Li

Li

R = Br

H+

R

Br BrR

Kinetic Modeling of the Undesired Reactions

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RC-1 Data

Kinetic Model

Br Br

Br Li

Br Br

Li

BrBr

Li

0

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s

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Br Br Br Li+ Br Br

Li

Br Br

Li

+ Br

+ LiBrBr Li+ Br

Li

Br

slow

slow

k1

k2

-55 oC

14.8 g (0.062 mols) of dibromobenzene + 25 ml THF

0.062 mols of LiBrbenzeneIn 400 ml THF

Experimental MeODQuenched samples

• Run more Dilute 10X – will reduce Tad from 89 to 9 oC• Run colder –75 oC• Minimize time for Anion to “see” dibromobenzene

– Use a pre-prechilled feeds in jacketed static mixer

– dual or co-axial flow of both feeds near impeller

Improved Mode of Operation

n-Butyl Lithium in Hexanes

Bromobenzene/THF

Ketone/THFcoolant

Internal Use

Batch Processing…not just chemical reactionsComplex flow of material and informationTombstone inventory (material sits)Multiple schedules with frequent changesLarge inventories of materials or backlogs

Work methods adapted to changing circumstances (compress cycle times, change sequence of work, …)Customers are distant, needs unclearNot certain when to raise a flag CONFUSION

“Leveling is the smoothing of production. It is the most important condition by Kanban and for minimizing idle time in regard to manpower, equipment, and work in process. It is the cornerstone of Toyota Production System.”

“Toyota Production System” Monden

Raw Materials Reaction Separations Crystallization

Internal Use

Batch Processing

Can Batch Processing Achieve…?Highest qualityLowest costShortest time

Material Costs

Reagent AReagent BBaseSolvent

t1

t4

t3

t2

t5

t1=t2=t4=t5= waste of time

t1t4

t3t2

t5 t1t4

t3t2

t5 t1t4

t3t2

t5Unit Op #1 Unit Op #2 Unit Op #3

Produciton Costs

Step AStep BQuenchCrystallization

time

Internal Use

Continuous Process

Run in parallelImmediate feedbackStability

t1t4

t3t2

t5

t1t4

t3t2

t5

Unit Op #1

t1t4

t3t2

t5Unit Op #2

Unit Op #3

Internal Use

Autonomation

Build in quality at the source-inspect and fixDefects are not passed onMachines adjust/stop for abnormalitiesCauses of defect easier to traceAvoid reliance on final inspection and extra rework process.

Internal Use

Life Cycle Production Strategy

Development Growth Stability Decrease

Launch 2 4 6 8 10Life Cycle in Years

MT API

Ci,Pi,Ti Vi

Fao

Internal Use

Turndown-Option

CSTR 1Step 1

Vi

Internal Use

Ci,Pi,Ti

Turndown-Option 2c-kineticsStep A DynoChem

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Internal Use

Bigger Picture

How to integrate API and DP continuous flowWhat will facilities of the future look like?

Smaller, dedicated facilitiesHow will flow be integrated across the company?

Careful of implementing pieces…leads to confusion of the actual goalCareful of trying to do too much—if training is not in place a continuous process will not succeed but again cause confusionSmall team dedicated to a common goal for a flow process will work

Filing Strategies?Batch with quick continuous follow-on?Continuous with batch as back-up?Continuous and batch?

Pla

nt C

osts

Cost savings – reduce footprintReduce overhead, depreciation, laborTo maintain excess capacity

Continuous Processing:Strategic Vision and Future State

= HTF Su pply to m odule = Delta V co nnection

= HTF Retur n fro m mo dule = Power connect io n

pH adjustdissolutionspeck fr ee filtra tion

Crystalliza tion

Pr ocess Area Pro cess Ar ea Process Are a Pr ocess Area Pro cess Ar ea Process Are a Pr ocess Area Proce ss Ar ea Process Are a Pr ocess Area Proce ss Ar ea

Isolation/Dr yin g Area Iso la tion/Drying Are a Isolation /Dr ying Area I solation/Drying Ar ea Isolation /Drying Area Isolation/Drying Ar ea Isolat io n/Drying Area Isolation/Dr yin g Ar ea Iso la tion/Drying Are a

4SAPH/IPA slur ry Isolation M odule, a ssume a ctual location Drying M odule, a ssume in room suitable Isolat io n Mod ule, assum e actu al location Dr yin g Mod ule, assum e in r oom Isolat io n Mod ule, assum e actu al location m ake-u p tank is o n 2nd floor/m ezzanine for packout of API is on 2nd floo r/me zza nine suitable fo r packo ut of API is on 2 nd floor /mezzan ine 2nd Floor a bove th e dryer abo ve the d ryer abo ve the d ryer

Process 1 = Celecox ib Pr ocess 2 - Med ium Volume Pr ocess 3 - Sm all Vo lu me

Diket one Rxn: PF R

Celecoxib Rxn: CST Rs

Continu ous Centr ifuge

Solidaire Pa ddle Dryer

4M AP

ET FA

TF A

Na OMe

Continuou s Centrifu ge

Continuo us Pla te Dryer

Heinkel HF- 300

To rusdisc Dryer

WFEReaction Modu le

Hydr ogena tion Mo dule

Reaction Mo dule

WF E

Reaction Modu le Centrifu gal Separ ation

Mod ule

Speck Fre e F iltration

Crystallize r

Tr ica nter Modu le

Reaction Mod ule Rea ction

M odule

Reaction Mod ule

WF E

Crystallizer

Sp eck Fr ee Filtra tion

rpjack:Large Reactor M odule

r pjack:M edium Reactor Module

rpj ack:Small Work -up Module

rpj ack:Small Reactor M odule

4SAPH - IPA Slur ry

= HTF Supp ly to m odule = De l taV connection

= HTF Re turn from modu le = Power c onnec tion

pH adjustdiss olu tionspec k free fil tra tion

Crysta l li za tion

Process Area Proc es s Area Proc ess Area Process Area Process Area Process Area Proc es s Area Proc ess Area Process Area Proc es s Area Proc ess Area

Is olation /Drying Area Is ola tion/Dry ing Area Is ola tion/Dry ing Area Is o la tion/Dry ing Area Iso la tion /D rying Area Iso la tion /Drying Area Iso lation /Drying Area Iso lation /Drying Area Is olation/Drying Area

4SAPH/IPA s lu rry Is olation Module , ass um e actual loc ation D rying Modu le, as sum e in room su itable Iso lation Modu le, ass um e actual loca tion Drying Module , ass um e in room Is ola tion M odule , ass um e ac tua l loc ation make-up tank i s on 2nd floor/m ez zan ine fo r pack ou t of API i s on 2nd floor/m ez zanine s ui tab le fo r pack ou t of API i s on 2nd floo r/m ezzan ine 2nd Floo r abov e the d rye r abov e the d rye r above the d rye r

Process 1 = Ce lec ox ib Proces s 2 - M ed ium Vo lum e Process 3 - Sma l l Vo lume

Dike tone Rx n: PFR

Ce lec ox ib Rxn : CSTRs

Continuous Centri fuge

So lidai re Paddle Dry er

4M AP

ETFA

TFA

N aO Me

Continuous Cen tri fuge

Con tinuous Pla te Dry er

He ink el HF-300

Torus disc Drye r

WFEReaction M odu le

Hy drogenation Module

Reaction Modu le

WFE

Reac tion Modu le Centri fugal Separation

Module

Speck Free Fi ltra tion

Crys ta l li ze r

Tricante r M odu le

Reaction M odu le Reac tion

Module

Reac tion Modu le

WFE

C rysta ll i ze r

Spec k Free Fi ltra tion

rp jack :L arge Re actor Modul e

rpjac k:Me di um Reac to r Mod ule

rp jack :

S mall Work-up Mod ul e

rpja ck:Smal l R eactor Modu le

4SAPH - IPA Slu rry

vs vs

Flexible Batch facility(e.g Tank Utilization is < 50%)

Flexible Batch facility with Investment in Continuous Flow Trains

(e.g Batch Tank Utilization 30%)Flow Trains are approx 100% Capacity

Dedicated Flow Facility Flow Trains are

approx 100% Capacity

Cost savings inReduction of footprint,

overhead andLabor to maintain excess,

Flexible, capacity

API POF strategic vision analysis

Cost savings – withContinuous flow processingIn existing batch facilities

10%

20%-40%

A Lean Case Study: Convert Batch to a holistic continuous API flow process [1]

• > 60% reduction in capital cost• 40-fold reduction in utility requirements• > 80% reduction in plant footprint• > 7 fold reduction in total effluent• > 100 fold solvent inventory reduction• Elimination of multiple high-containment

operator interfaces

[1] Huw Thomas, Foster-Wheeler, Transforming the Pharma Industry: Lean Thinking AppliedTo pharmaceutical manufacturing , 7th World Congress of ChE, Glasgow 2005

Comment: significant reduction of fugitive emmissions

Internal Use

CSTR Systems

Internal Use

Plug Flow Reactor

E-1

E-2

P-2

P-3

E-6

E-7

E-8

E-9

E-10

P-5

P-7

E-12

E-13

P-8

orKenics

Sulzerstatic mixer

elements

Feed Tank A

Feed Tank B

Reactor and Residence Time

Chamber

Receiver or QuenchVessel

Pressure Drop, ∆P considerations with lengthAnd number of static elements

Continuous Flow vs Batch Process Designs for Manufacturing of API’s and DP’s

(currently have various installations and investments across the company)

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%

Years Post LaunchLaunch

ContinuousBatch

Summary• Process (predictive) modeling is one of the

ways COG’s are being lowered and greener processes designed

• Developing/rollout models for the masses• Screening models – narrows the experimental space

• Process Design• Integrating chemistry and engineering to drive down

cycle times and COG’s• Expanded optimal selection of unit operation

technologies for manufacturing/unit operations• Continuous Processing

• Long term substantial costs savings envisaged• Need to figure out the transition• API and DP – Facilities of the future• Need thorough Life-Cycle and cost analysis…• Sustainability favorably impacts the bottom line

Green Engineering

Acknowledgements

• Steve Guinness• Michele Drexler• Jerry Salan• John Van Alsten• Jason Mustakis• Eric Cordi• Tom Ljubicic• Yuriy Abramov

• Matt Jorgensen• Andy Palm• Kevin Hettenbach• Tim Watson• Gerry Taber• Buzz Cue

Back-Up Slides

Perceptions: Design-Related Challenges in the Industry [1]

• “Products are not optimized for production when launched”

• “There is a lack of product and process understanding…we have empirical understanding…we don’t understand the complex physicochemical relationships…”

• “There is a high failure rate in both launch and manufacturing

• “There are long development timelines”• “Resources not always deployed on the highest

net present value topics”

1Ted Fuhr, McKinsey &Co., Attention turns to the business case for Quality by Design,The Gold Sheet January 2009.

“Typical” API Cost Reductions Post Launch under Different Development Paradigms

0

20

40

60

80

100

120

0

20

40

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120

0 1 2 3 4 5 6 7 8 9 10 11 12

CO

G's

Norm

alized Volum

e (% of M

ax)

Year After Launch

Volume

Traditional

Future

Potential Savings

Thermodynamic Relationship for Solubility

• Activity coefficients are strong, nonlinear function of solvent composition, weak functions of temperature

⎟⎟⎠

⎞⎜⎜⎝

⎛−

∆−≅

∆−=

m

fusfusSATi

SATi T

TRTH

RTG

x 1lnγ

Solid in equilibriumSingle solid phase, pure solidActivity coefficient: Solvent Effect in

Solid solubility

COSMO-RS : Thermodynamic model that allow the calculation of thermodynamic properties in solution (like activity coefficient)

In order to solve the equation we also need ∆Gf•Estimation by a QSPR method (default) •Approximation with ∆Hf and melting point (DSC data) •This term contains most of the information for the polymorph