Reaction Engineeringin the Pharmaceutical Industry

David J. am EndeEngineering Technologies / Chemical R&D

Pfizer, Inc.

13th International Process Development ConferenceSeptember 17th-21st , 2006

Newport, Rhode Island

Engineering Strategy• Process Characterization

– Enable the portfolio by delivering fundamental process understanding prior to scale-up through characterization of the critical rate processes.

– Develop new process characterization tools

New Technologies for Manufacturing – Increase productivity– Reduce manufacturing costs– Process Intensify

Where do Problems Occur on Scale?Numerous physical and chemical interactions exist

Kinetics

HeatTransfer

ThermodynamicEquilibrium

Mass Transfer

Mixing

Physical PropertyChanges

Scale-up is about understanding the important rates processes as they change with scale

Engineering Focus Areas

Batch

Continuous

Process Characterization

Process Design

Process Characterization & Design

• Reaction Kinetics• Heat Transfer• Mixing• Fluid Properties• Phase Equilibria• Process Safety• Process Modeling

Avoid Surprises on Scale

Right First Time• Dynochem• Visimix• Fluent• Aspen• Cosmotherm• ReactIR/ConcIRT• Athena• Numerica• Matlab• Design Expert• FusionPro

Presentation Outline

• Continuous Flow Chemistry• Characterization of Reaction Kinetics• DOE vs Kinetic Modeling• Future Horizons

Continuous Flow

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– All of the Subtrate/Reagents experience same

reaction conditions• High Intensity Mixing typically• Efficient heat exchange• Steady state

OxidationsNitrationsHalogenationsMetallationsHigh Temp Chemistry…

Benefits in Production•Reduced solvent usage•Reduced cycle times•Reduced capital cost for new equipment / expansions•Better lot-to-lot consistency

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Reloads-Out

Reloads-IN

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

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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22

0 0.5 1 1.5 2 2.5 3 3.5 4

Tem

pera

ture

, o C

Reactor (Liquid) Volume , ml

Model Predictions

Tjacket

TreactorInitial Conditions

Actual Outlet temperatures

mCp ∆ T=UoA ∆Tlm

4.45 g/sec@ 21.5 oC

1.2 g/sec@ 9 oC

Tjacket

Treactor

U=1030 W/m2K

Modeling Temperature ProfilesHeat Exchange of Hot and Cold Water

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45

50

0 0.5 1 1.5 2 2.5 3 3.5 4

Tem

pera

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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Hea

t Flo

w, W

atts

and

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o C

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+

Oxidation with Peracetic Acid

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t Flo

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atts

and

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mass of dose

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

Pyridine Model Oxidation

• Issue of cost of substituted pyridine• Pyridine used to develop the flow platform

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Batch run of N-oxide formation with pyridine

Tem

p [C

]

Time [min]

Temperature Profile for Batch ReactorCRC-90 Experiment (all at once addition)

N NO

OH

O+

O

O O+

H2O2

2

Flow Oxidation of substituted PyridineTemperature Profiles

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Tem

p [C

]

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Internal Tr

Jacket Tj

∆ 20 oC

Results of Oxidation Flow Experiments

27%27%7.3%93%87%2 Ethyl Pyridine

97-98%82-85%Pyridine

88%64-71%16-17%92-94%91%Substituted Pyridine

ConversionIn

Receiver

ConversionAcylation

Res.time Exit

ConversionAcylationReactor 2

Exit

ConversionOxidationRes. Time

Exit

ConversionOxidationReactor 1

Exit

DOE (Pyridine Oxidation) for Extent of Conversion

• Stoichiometry Factors:– Acetic anhydride– Hydrogen Peroxide– Water

• Current conditions:– 1.5 eq hydrogen peroxide– 1.4 eq Acetic anhydride– 2.84 eq water (50% hydrogen peroxide sol)

DOE Reactor configuration

DOE Results:Steady State

Continuous Flow Conditions

Range of Robustness

High Energy Flow Chemistry: Process Safety and Extraction of Rate Parameters

Adiabatic Calorimetry• Low Phi Factor

• All at once - Batch Mode• Temperature vs time• Pressure vs time

Hettenbach-Bill

Acetic Anhydride in MeOH

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MeasuredCalculated

Tem

pera

ture

(o C)

Time (min)

O

OOO

O

OH

O

O+

Acetic Anhydride (AA) Methanol Methyl Acetate Acetic Acid

+k

Model: r =-k[AA]1[MeOH]0

= -67.6 kJ/mol (Lit.:65-70)= 1.07

Arrhenius Parameters:E = 72.6 kJ/mol (Lit.:67-75)A=5.1010/min

= 153 oC

rH∆φ

Initial Fill:MeOH = 30.18 gAA = 49.77 g

Temperature vs. Time

adT∆

H

Hettenbach-Bill

Di-t-Butyl Peroxide in Toluene

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MeasuredCalculated

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pera

ture

(C)

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O O O C2H6+2

Di-t-Butyl Peroxide Acetone Ethane(DTBP)

k

Model: r =-k[DTBP]1

= -191.3 kJ/mol (Lit:240-260)= 1.07

Arrhenius Parameters:E = 156.0 kJ/mol (Lit.:155-165)A=3.57x1017/min

= 113 oC

rH∆

Initial Fill:DTBP = 12.28 gToluene = 49.25 g

φ

Temperature vs. Time

adT∆

Hettenbach-Bill

Acetic Anhydride Hydrolysis

Model: r =-k[AA]1[H2O]0

= -54.9 kJ/mol (Lit.:58-62)= 1.06

Arrhenius Parameters:E = 65.3 kJ/mol (Lit.: 40-60)A=8.6x109/min

= 11 oC

rH∆

O

OO O

OH+ H2O 2

Acetic Anhydride (AA) Acetic Acid (P)Water

k

φ

Initial Fill:H2O = 50.0 gAA = 4.88 g

Temperature vs. Time

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Tem

pera

ture

(o C)

Time (min)

φ

φ

φ = 1.06

= 2

= 5adT∆

φ adT∆

Hettenbach-Bill

“Quality by Design”

“Designed Process Understanding (DPU)”yi=f(xi) : process mapping

• Goal of (DPU) is for improved process understanding at time of validation and will position processes for continuous improvement post launch. In addition, DPU is used for critical-process-parameter justification and to support regulatory filings.

• Quantify measured outputs as functions of input conditions…ie Process Modeling (DOE, Kinetics, etc)

ln BasesubstrateECFkdt

substrated0]][[][][ =−

Process Modeling Approaches• Traditional Approach: One Factor at a time (OFAT)

– No variable interaction– Fragmented information. No process mapping

• Process Modeling Approach :Combining a number methodologies/techniques, to maximize the information out of a limited number of experiments

• DOE/Empirical Approach (linear local approximations)– Data mining of historical information– Screening Experiments

• Efficient identification of important variables and interactions to focus experiments– Response Surface Experiments

• Quantify Interaction and Curvature• Map the design space

– No rate (impurity accumulation) information

• Physicochemical Models / Engineering Kinetic Approximations: All Batch Chemical Processes are dynamic

– Impurities are growing over time (parallel, or consecutive reactions) - rate information always important in an implicit fashion

– Time to completion affects impurity level not only throughput– Provides the starting point for PGM optimization – Fundamental knowledge with full mapping of the dynamic design space – Enhanced Process

Understanding

• Equipment Modeling / Simulation (Engineering Approximations)– Heat Transfer (exothermic reactions)– Mass Transfer (multiphase systems, heterogeneous reaction, agitation effects) Mustakis

Process Modeling and Parameter Ranges

• If nonlinearities are present, process modeling not “highly predictive”: less information

• Filing/ manufacturing: High interest to avoid amendments

• Larger number of experiments will be required

Wide ranges Narrow Ranges

• Process modeling much more predictive – Non linearities are avoided

• Filing/ manufacturing: Higher risk for amendments and restrictions on manufacturing

Design Space: Experimental design needs to balance

Watson/Mustakis

Statistical DOE• Local linear (algebraic) approximation of the experimental

space(is oblivious to chemistry or fundamental knowledge)

• Some good practices :– Include the right variables – PGM guidance is critical – “reasonable variable ranges”

• Limits no linear effects– Use center point of the design to check for curvature– Incorporate standard conditions – if possible as the center point– Replicate center point to extract experimental error– Always review the data – Be very careful in extrapolating outside experimental space– Always validate/verify

Mustakis

DOE and Hi-Lo (OFAT) approach :4 variable layoutTe

mpe

ratu

re

TFAA Mustakis

Designed Process Understanding Example(Quality Parameters)

Factors:•Ethylchloroformate (equiv)•Base (equiv)•Concentration of substrate in THF (vol)

Responses:• Impurity Homolog (wt%)• Substrate (wt%)• Product (wt%) • Total impurities

• 20 Experiments (central composite)• hplc Samples collected at 6, 12, 24, 36 hrs for each expt

• 80 chromatograms to analyze

NH

HN

O

OF3C N

HN

O

OF3C

Cl

O

O O O+

+ HCl

BaseTHF

k1

Time to Conversion(time to 0.5% starting material – interpolation)

23hr

Increasing Base

Standard Conditions

Mustakis

Base Base Base

Base

DOE provides Local Approximations“Kinetic” Information is Lost

One Model Per Sample PointModels are not connected

Mustakis

DOE Does not Utilize Rate Information

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Mustakis

Kinetic Model to Apply to all 20 DOE Experiments

Parameter Estimation R2

k1 0.259 0.242 0.276n 1.470 1.379 1.561l -0.130 -0.200 -0.061

Confidence Interval ±95%

Model1, n=1, l = 0 0.8304

Model2 0.9504

k1 0.240 0.219 0.276

ln BasesubstrateECFkdt

substrated0]][[][][ =−

Simple Kinetic SchemeStarting Point 1st order to each reactant

Mustakis

NH

HN

O

OF3C N

HN

O

OF3C

Cl

O

O O O+

+ HCl

BaseTHF

k1

Fitting all 20 Experiments at once

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Mustakis

DOE + Kinetics Model Predictions

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1010

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Eth

yl C

hlor

ofor

mat

e (e

q)

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Mustakis

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Effect of ECF Effect of Dilution

KineticModels

DOERegressionB

ase

DOE-Kinetic “Designed Process Understanding” Summary

• Kinetic modeling provides more direct and accurate description of the time dependence functions for those responses that can be accurately modeled– Ie conversion, starting material, product, major side products

etc– A single kinetic model replaced 4 separate polynomial

regression equations at 6, 12, 24, 36 hr• All 20 phosphate experiments were simultaneously fit

to second order kinetics via multivariate non-linear regression analysis MATLAB– Significantly reduced the number of parameters required to

describe the responses• DOE Response surface analysis was still required for

low level impurities (say only measurable at 36 hr or extended time points) or when the complexity is too high to model Kinetically

Kinetic ToolsCurrently Used Tools

• ALR’s , RC-1, Multimax• Calorimetry• HPLC• FTIR• UV-Vis• Raman• ConcIRt algorithms

Leveraging Multiple in-situ Analytics(The Current Challenges)

• Operational Integration (of multiple probes)

• Software Integration• Closed Software-exporting ReactIR spectra• ConcIRt beyond midIR

– Heat Flow + ConcIRt– Multiple Spectral Set e.g UV + MidIR+ Raman

• Analytical Specificity• Facilities (space, Liq N2 for IR, chiller issues)• Material Intensive

Next Generation tools need to give us more and evenbetter information per experiment….while consumingEven less material

Typical InSitu Analytics:Specificity, Sensitivity, and Cost

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100

1000

Cos

t ($K

)Technology

Spe

cific

ity

SensitivityLow Med High

Low

M

ed

Hig

h

FTIR

UV, NIRRaman NIR

UV

FTIRRaman

NMR

NMR

We need higher specificity and better structural elucidation in real timefor routine reaction characterization…NMR appears to be well suited

Functional groups

proton, carbon, etc

Integration of Chemical Reactors and Real-Time NMR

Feed

Heating/Cooling

SampleLoop

am Ende, Marquez, Mustakis

hplc pump

Numerous References :Maiwald et al, J. Mag. Resonance, 166 (2004) 135-146Hasse, Albert, et al, Chem. Eng, and Processing, 44 (2005) 653-660Horvath et al, Chem. Rev., 1991 (91) 1339-1351…

Initial NMR Kinetic ExperimentsNMR Tube Kinetics

Inject Reagent, Shake NMR Tube, & Insert in NMR

am Ende, Marquez Sept, 2005

0

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1.5

2

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Hydrolysis of Acetic Anhydride in D2O @ 25 oC

Composition vs time via NMR

Con

cent

ratio

n [M

]

Minutes

AcOD

AcOAc

1st order kinetics: Ca=Caoexp(-k*t)

measured kD=0.94 * 10-3 (sec-1)

lit. kD=0.89 * 10-3 (sec-1)

reported isotope effect =koH/koD=2.9Batts & Gold, J. Chem. Soc. A, (6), 984, 1969.

Flow NMR KineticsReactor Integrated to Flow NMR and run SemiBatch mode

am EndeMarquezMustakis

April 7, 2006

0

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1

1.5

2

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Hydrolysis of Acetic Anhydride in D2O @ 25 oC

Composition vs time via NMR flow cell

Con

cent

ratio

n [M

]

Minutes

AcOD

AcOAc

1st order kinetics: Ca=Caoexp(-k*t)

measured kD=0.84 * 10-3 (sec-1)

20 ml AcOAc dosed over 20 minutesinto 200 ml D

2O

NMR flow = 3 ml/min, Gain=40

103 * k (sec-1)• NMR tube 0.94• Lit 0.89• NMR flow 0.84

Spectra collected during the

20 min dose

NMR monitoring of a Semibatch Reaction:Alkylation of a Di-amine with Glyoxal

Feed

Heating/Cooling

ppm

Intensity

minutes

SampleLoop

NH2NH2

N

COCF3

O

O

HH

NCOCF3

NNOH

+

am Ende, Marquez, Mustakis

D2O/IPO-d8

NH2NH2

N

COCF3

O

O

HH

NCOCF3

NN

H2O

OH

+ + 2 0 20 40 600

2

4

x 104

0 20 40 600

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4000

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2

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2x 104

StartingMaterial

Product

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1

NH2NH2

N

COCF3

O

O

HH

NCOCF3

NNOH

+

H2N N

NCOCF3

HN NH

NCOCF3

HO HO

H H

4 4.2 4.4 4.6 4.8 50

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38Numerica Technology

Kinetic mechanism superstructure

The superstructure may be created by

merging several smaller mechanismsintroducing hypothesized reactions

A family of mechanism is created from the superstructure by removing various subsets of species and reactions

The figure on the right illustrates a superstructure

The boxes denote chemical species The arrows denote reactions

Automatic mechanism selection

The global dynamic optimization capabilities of JACOBIAN can be used to select simultaneously a mechanism from a superstructure while fitting the rate constants against experimental data

Merging Platform Reactor Technology with “Lab” NMR

FTIR

Reactor

Calorimetry

UV Flow Cell

Recycle Solution Phase

Powerful Tool for Elucidation:•Reaction Mechanism•Structure•Kinetics

1H, 13C, 19F, 31P

Summary• Continuous Flow

– Process intensification opportunities– Portfolio Enabling– PlugFlow reactors are fed stoichiometrically

• equivalent to all at once dosing– Highly exothermic reactions pose challenges for

acquisition of isothermal kinetics• Adiabatic calorimetry one option for extracting rate information

– Engineering Technology working to:• Developing plug flow platform for gmp installation – hot

reactions• Develop “small-scale” continuous slurry reaction,

crystallization, isolation platforms as well.• Develop holistic continuous flow processes on selected existing

processes in manufacturing – raw materials to API

Summary-Continued• Kinetic Understanding is Key for

– Scale-Up – time scale analysis for Mixing Effects• Time scale of impurity kinetics vs mixing times

– Process Understanding (Quality by Design) Mapping• Leverage Kinetic models when possible vs DOE models

– Continuous Flow Reactor Design• Rate Equations to model conversion vs length or res. time

• Next generation platform tools needed to help elucidate pathways even faster– Hardware needed to:

• Allow multiple analytic (UV, FTIR, RAMAN, etc) sensors on 10-15 ml scale

• Provide heat flow directly• Make online Quantitative NMR routine and easy

– Software needed to:• Simultaneously analyze multiple analytic data sets - FAST

– UV, FTIR, RAMAN, NMR, Tr, Qr– With constraints – Ao, Bo, dose time, Tr, etc

• Rapidly assess kinetic models• Global optimization of parameter estimation and model discrimination

for complex kinetics

Acknowledgments

• Kevin Hettenbach• Matt Jorgensen• Brian Marquez• Jason Mustakis• Geraldine Taber• Tim Watson• Eric Dias (Symyx)

Mettler-Toledo

Back-Up Lithiation Slides

Kinetic CharacterizationMetallation Example

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

Br Br n-BuLi

Br LiTHF

-78 to -40oC

Br Br

THF

Br Li

775 and 665 cm-1

Br Br

17.4 g of 2.5 M n-BuLi in Hexanes

(0.062 mols dosed in 2 minutes)

14.7 g C6H4Br2 = 0.062 molsIn 400 ml THF

-65 oC

Preparation ofLithium Bromobenzene

Under diluteconditions

-20

0

20

40

60

80

100

120

-120

-110

-100

-90

-80

-70

-60

-50

-200 -100 0 100 200 300 400

Hea

t Flo

w, W

atts

Mas

s n-

BuLi

/Hex

anes

add

ed, g R

eactor Temperature, oC

Seconds

Tr

Preparation of Lithium BromobenzeneReaction Calorimetry under Dilute Conditions

teflon tubeinserted

sub surfacenear reactor

wall

Dosing Started

Heat Flow, W = Q=Qflow + Qaccum + Qdos

∆ Hrxn = -112 kJ/mol = -26.8 kcal/mol

Br Br n-BuLi

Br LiTHF

-78 to -40oC

Qdos

0.15 M

Kinetic Pathways

Br BrLi + Fast

k0Br Li Br+

Br Br Br Li+ Br Br

Li

Br Br

Li

+ Br

+Br LiBr

k1

k2

Br Br + Br Br

Li

+Br

Li

BrBr

Br

k4

Br LiBr +fast

Br

Li

Br

k3

k5

Calorimetry Results

0

5

10

15

20

25

30

35

0 1 2 3 4 5 6

-45 C-50 C-55 C-65 C

Hea

t Flo

w (W

)

T im e (hr)

∆ Hrxn = -289 kJ/mol = -69 kcal/mol

Br Br + Br Li BrBr

+ LiBr

Br Br

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

(dosed in 5 minutes)

0.062 mols of LiBrbenzeneIn 400 ml THF

-65, -55, -50, -45 oC

Reaction of Lithium Bromobenzene

and 1,4 Dibromobenzene

ReactIR FTIR w/ Dicomp:anion solution taken as background

BrBr

Br Br

Li

Br Li

Br

740 cm-1

Br

Kinetic Modeling of the Undesired Reactions

0

2

4

6

8

10

12

0 25 50 75 100 125 150

Hea

t Flo

w (W

)

Minutes

RC-1 Data

Kinetic Model

Br Br

Br Li

Br Br

Li

BrBr

Li

0

0.01

0.02

0.03

0.04

0.05

0.06

0 25 50 75 100 125 150

Mol

s

Minutes

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

0 5 10 15 20

minutes

0.0

0.1

0.2

0.3

0.4

0.5

@ t=0 Anion =0.5 MolarDibromobenzene=0.5 Molar

-75-70

-65

-60

-55

Effect of Temperature on Rate of the Undesired Reaction

Conditions Tr (°C) Yield (%)

500 rpm -65 44

Pre-chill nBuLi -65 68

Tj = -70°C -60 72

10X dilution -65 87

Results from RC-1 Lithiation/Quench

Rate of Side Reaction isSignificant

At these Temperatures

So Long add timesResults in more side

Reactions

Ani

on (m

ols)

25 min30 min

Add time

• Hot spots and competing kinetics significantly impacted yield in RC-1 experiments

• Undesired reaction highly temperature dependent• Take Home Messages:

– Understand the kinetics of competing side reactions– Run more dilute 10X – will reduce Tad from 89 to 9 oC– Run colder –75 oC– Minimize time for Anion to “see” dibromobenzene such as in a

flow system (proposal)• Use a pre-prechilled feeds in jacketed static mixer

Conclusions of Lithiation Study

n-Butyl Lithium in Hexanes

Bromobenzene/THF

Ketone/THFcoolant

Conclusions (Cont.)• Understanding the kinetics of the Undesired Reaction

was Key to Understanding this process and how to improve it.

• Heat of reactions were measured for:– anion formation (lithium bromobenzene) = –112 kJ/mole– Biphenyl formation via exothermic benzyne route = –280

kJ/mol of anion• Activation energies were estimated = 12-13 kcal/mol

• Need to characterize undesired reaction pathways (Kinetics) to fully understand the process