The role of process analytical technology (pat) in green chemistry and green engineering webinar

The Role of Process Analytical Technology (PAT) in

Green Chemistry and Green Engineering – Part II

Tuesday December 1st

4am, 9am, and 2pm EST

Presenter: Dominique Hebrault, Ph.D.Senior Technology and Application

Consultant

1

The Twelve Principles of Green Chemistry

Green Chemistry and Continuous or Bio Process



5

Outline

Case Studies

- Monitoring of a Biotransformation using ReactIR™

- Development of a Continuous Process with ReactIR™

- RC1e Calorimetry: a Tool for Continuous Process Development

- Bioprocess Monitoring using RC1e Calorimetry

Conclusion

Monitoring of Baeyer-Villiger bio-

transformation kinetics and finger-

printing using ReactIR spectroscopy

Introduction

Most fermentation monitoring concerns

the determination of analyte

concentrations

ReactIR™ used for:

-Measuring progress and kinetics

-Conversion of cyclododecanone (CDD)

into lauryl lactone (LL)

-Catalyzed by a recombinant NADPH-

dependent cyclopentadecanone

monooxygenase

Source: Peter C.K. Lau et al, Biotechnology Research Institute, National Research Council, Canada; Industrial Biotechnology 2006, 138–142;

Applied and Environmental Microbiology, 2006, 2707–2720

Case Study: FTIR as PAT tool for Biotransformation


Results of CDD biotransformation as

a function of cell growth in a fed-

batch culture

Qualitative: 3-D spectral fingerprint of

CDD conversion to LL shows:

-Decrease of CDD absorbance at 1713cm-1

- Increase of LL absorbance at 1741cm-1



Quantitative: Peak profiling and

quantitative calibration model using

QuantIRTM to monitor

-Use of authentic standards of CDD and LL

-Detection sensitivity for LL: 0.2 mM


-Better understanding of reaction

kinetics

-Original utilization of ReactIR™

technology for offline qualitative and

quantitative monitoring of

cyclododecanone biotransformation



-Further development in online

monitoring and automatic controlling

-Initial expansion to a wider range of

cycloketones

9

Outline

Case Studies





Conclusion

Development and Scale-up of Three

Consecutive Continuous Reactions for

Production of 6-Hydroxybuspirone

Introduction

Control base / buspirone stoichiometry is

critical to product quality

Optimization based on offline analysis is

time consuming and wasteful

Actual feed rate adjusted based on the

feedback from inline FTIR: Flow cell and

ReactIR™ DiComp probe

Case Study: FTIR as PAT Tool for Continuous Process

Source: Thomas L. LaPorte,* Mourad Hamedi, Jeffrey S. DePue, Lifen Shen, Daniel Watson, and Daniel Hsieh, Bristol-Myers Squibb

Pharmaceutical Research Institute, NJ, USA, Organic Process Research and Development, 2008, 12, 956-966; Mettler Toledo Real Time

Analytics Users’ Forum 2005 - New York

Case Study: FTIR as PAT tool for Continuous Process

Implemented startup strategy

-Start with slight undercharge of base

(feed rate) to reduce diol 8

-Flow rate increased at 1% increments

until no decrease of Buspirone 1 signal

is observed

-Base feed rate was reduced 1-3%

-Works well because enolization fast,

equilibrium reached within minutes

KHMDS




Case Study: FTIR as PAT Tool for Continuous Process

Outcome

-Ensure product quality via proper ratio

and base feed rate

-Minimize waste of starting material

-Faster reach of steady state via real-

time detection of phase transitions

-FTIR also used for enolization

monitoring during steady state

Scale-up

-Lab reactor: Over 40 hours at steady

state

-Pilot-plant reactor: Successful

implementation (3-batch, 47kg/batch)




13

Outline

Case Studies





Conclusion

An Integrated Approach Combining

Reaction Engineering and Design of

Experiments for Optimizing Reactions

Introduction

Early phase RC1e experiments to obtain

a basic understanding of:

-Enthalpy

-Kinetics

-Mass Balance

-Type of phases

Case Study: Calo for Reaction Kinetics Screening

Source: D.M. Roberge, Department of Process Research, Lonza, Switzerland, Organic Process Research and Development, 2004, 8, 1049-1053;

Mettler Toledo 15th International Process Development Conference 2008, Annapolis, USA; Chem. Eng. Tech., 2005, 28, No. 3, 318-323

Type A: Very fast, t1/2< 1 s, controlled by

mixing

Type B: Rapid, 1 s < t1/2< 10 min, mostly

kinetically controlled

Type C: Slow, t1/2 > 10 min, safety issue

in a batch mode

50% of reactions in the

fine/pharmaceutical industry could

benefit from a continuous process

(microreactors)

RC1e allows precise measurement of

reaction enthalpy

Instantaneous reaction heat is related to

reaction rate

Results: Very fast reaction

-No heat accumulation

-Dosing controlled


Source: D.M. Roberge, Organic Process Research and Development, 2004, 8, 1049-1053; Mettler Toledo 15th International Process Development

Conference 2008, Annapolis, USA; Chem. Eng. Tech., 2005, 28, No. 3, 318-323

C=C double bond oxidized / cleaved by

aqueous NaOCl catalyzed by Ru

Type A: Very fast, t1/2< 1 s

controlled by mixing

Results: Rapid reaction

-Heat signal function of dosing rate

-Reagent accumulates and reacts

after the end of the dosage

-Lower temperatures favor high

accumulation

-Higher temperatures favor formation

of side products




Quench of ozonolysis into methanol /

dimethyl sulphide

Type B: Rapid, 1 s < t1/2< 10 min, mostly

kinetically controlled

Results: Slow reaction

-Accumulation of energy > 70%

-Most of the heat potential evolves

after the end of addition

-Typically initiated by temperature

increase or catalyst addition

-Autocatalytic reaction and / or

induction period




Knoevenagel-type reaction catalyzed by NaOH:

intramolecular aromatic ring condensation

Type C: Slow, t1/2 > 10 min, safety

issue in a batch mode

Conclusion

Real time RC1e calorimetry also for early

on kinetics and safety assessment

18

Outline

Case Studies





Conclusion

Biocalorimetry and Respirometric

Studies on Metabolic Activity of

Aerobically Grown Batch Culture of

Pseudomonas Aeruginosa

Introduction

Goal is to select an enhanced culture,

design a bioreactor, for treatment of

saline wastewater (tanning industry)

Metabolic efficiency of halobacterial

strains evaluated by RC1e calorimetry

Heat is a by-product of metabolic

processes, nonspecific, non-invasive and

insensitive to the electrochemical, and

optical properties

Source: S. Mahadevan et al, Department of Chemical Engineering, CLRI, Chennai, India; Biotechnology and Bioprocess Engineering 2007, 12, 340-

347; Biochemical Engineering Journal 2008, 39, 149-156

Case Study: RC1e Calorimetry for Biotransformation

Glucose

O2 uptake

Growth, heat

Results

Good correlation of kinetic profiles by

standard method (shaker), simulation,

and reaction heat




Biomass Concentration

Substrate ConcentrationHeat rate follows growth curve at various

glucose concentration

Shows affinity of strain to glucose

Heat yield coefficient (kJ heat evolved per

g dry cell formed) determined from total

heat versus biomass concentration




Heat yield vs biomass growth

Heat yield coefficient (kJ heat evolved per

g of glucose consumed) determined from

total heat versus substrate concentration

Substrate breakdown results in more heat

evolution than biomass growth

Heat yield vs substrate

Oxycalorific coefficient determined from

the slopes of heat generated versus

cumulative oxygen uptake

Literature reported aerobic tendency of P.

Aeruginosa confirmed here




Heat vs O2 uptake

Cell number increases until substrate(s)

depleted, then stops growing, and die

Heat flux ideal candidate to monitor

growth rate

Heat vs colony forming unit

Conclusion

-Growth and activity of P. Aeruginosa

monitored by biocalorimetry, which fits

biomass growth and oxygen uptake

rates

-Oxycalorific coefficient and heat yield

values found matches theoretical

values




Better understanding of biokinetics of

halotolerant P. Aeruginosa isolated from

tannery soak liquor

Helps efficient design of bioreactor

24

Outline

Case Studies





Conclusion

Summary

- Did the reaction work?

- Understand selectivity and reactivity

- Identify intermediates or by-products

- How long did it take?

- Endpoint, initiation-point, stall-point

- Can this process be scaled-up?

- Identify key control parameters

- Understand, measure reaction

kinetics

- Will it be safe?

- Measure reaction heat/enthalpy

- Determine heat capacity, heat

transfer coefficient

- Worst case scenario estimation

- Thermal accumulation and

conversion

Challenges of (bio)process development: ReactIR™, calorimetry, reactors

Software for Design, Data Acquisition and Analysis

Reaction Progress Kinetic Analysis: A Powerful

Methodology for Mechanistic Studies of

Complex Catalytic Reactions*

*Donna G. Blackmond, Angew. Chem. Int. Ed. 2005, 44, 4302 – 4320

Data Reaction Progress Kinetic FitSummary Simulate

Temperature Model Comment

Models

Only two data points. Rerun

DeleteNew Isothermal model

Button/menu drop down –

Options:

1) New Isothermal model

2) New temp. depend. model

3) New from selected model

Reaction Conditions

Parameter Axis Lo Hi

40.0 60.0Y axis

5.00 8.00Constant

10.0 20.0X axis

Edit Model

1.00

1.50

0.01

24.3e-4

k:

a:

b:

E act:

Apply

Time

to 9

5% co

nver

sion

of A

TA(0)

10.0

20.0 40.0

60.0

0.000

2.000

4.000

6.000

8.000

10.000

12.000

14.000

16.000

0.000 10.000 20.000 30.000 40.000

[A],[B

]

time

This point the user clicked on represents A(0)=15

and T=48 C. The entire reaction is shown at right

using these reaction conditions.

Simulation Output

Conversion of at minutes60A

Time to % conversion of 95 A

Q Peak during minute reaction60

A(0)

B(0)

T

T=48 C

Early-on kinetic evaluation

Temperature dependence model

Catalyst stability evaluation

Simulation

Internal usage only

Questions and Answers

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OR

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Don’t miss the 17th International Process Development Conference - May 16 to 19,

2010 in Baltimore, MD, USA – www.mt.com/ipdc

27

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The role of process analytical technology (pat) in green chemistry and green engineering webinar

Documents

pharmaceutical research institute

national research council

organic process research

kj heat evolved

biotechnology research institute

base feed rate

reaction kinetics screening

heat yield coefficient