21st International Conference Organic Process Research & Development 2010 San Diego

Value of Process Automation, Real-Time

Measurements to Improve Operational Efficiency

from Laboratory to Production

Dominique Hebrault

Sr. Technology & Application

Consultant

San Diego, January 21, 2010

The Paradigm of Faster and Better…

Case Studies

- Process Research using ATR-FTIR Spectroscopy with ReactIRTM

- ReactIRTM, FBRM®, and PVM® for Process Development

- RTCalTM Calorimetry : Enabling Real Time Process Characterization

- Understanding Crystallization with ReactIRTM and EasyMaxTM

Conclusions

Presentation Outline

Analyze Reaction Chemistry

Expand

Productivity

Characterize Particles

Data Capture and

Understanding

Combining Real Time Analytics & Process Control

Mid-IR Real-time Reaction Analysis

ReactIR

Component Spectra Component Profiles

In-situ reaction results

ConcIRT live

Peak height profiling

Quantitative model

Mid-IR Real-time Reaction Analysis

Time

Absorb

ance

or

Rela

tive c

oncentr

ation

Time

Ab

so

rba

nce

Study of lactol activation by trifluoroacetic

anhydride via in situ Fourier transform

infrared spectroscopy

Introduction

Accurate charge of TFAA critical to

minimize by-product and reagent use

Chromatography not appropriate: TFAA

reactivity, activated lactol unstable

Rapid, reliable, quantitative method

needed to determine activation endpoint

Source: Yadan Chen, George X. Zhou∗, Nicole Brown, Tao Wang, Zhihong Ge, Merck Research Laboratories, Rahway, NJ, USA, Analytica Chimica

Acta 497, 2003,155–164; Other examples: Mettler Toledo 15th International Process Development Conference 2008, Annapolis, USA

Case Study: FTIR, PAT tool in Pharma Development

Project Challenges

TFAA amount is key to reaction control:

-TFAA hydrolysis with moisture

-Unstable activated lactol → lactol

-Excess TFAA reacts with chiral alcohol

-Undercharge of TFAA → dimer


HPLC Prep



Reference spectra


Initial/qualitative investigation

Stepwise changes

-1- Acetonitrile (solvent) at -5°C, 2-

lactol in solvent, 3- intentional TFAA

overcharge (1.04 eq), 4- more lactol

added


Observations

-Lactol poorly soluble in solvent

-Rapid reaction upon TFAA (5’) addition

-Activated lactol profile qualitative only

-10°C rise: safety/quality issue



Quantitative Experiments

TFAA model in reaction mixture

-TFAA spiked into solvent

-Peak area for band at 1875 cm-1

-2 models: [0-100mg/ml] and [60-350]

-Model tested in reaction mixture:

consecutive additions of TFAA

Observations

-Good prediction from calibration model


Known: 14.3 mg/ml

Predicted: 14 mg/mlKnown: 11 mg/ml

Predicted: 10.2 mg/ml



Order of reactant addition

Slow addition of TFAA to lactol

-Exotherm is feed-controlled → safer,

better quality

-0.2-0.5mol% dimer still present




Reverse addition: Lactol to TFAA

-No free lactol in the reaction mixture

-Less dimer (<0.15mol% HPLC)

Conclusions

Key parameters to prevent dimer

-Temp.: -5 → 0⁰C; dimer < 0.3mol%

-Undercharge TFAA (bp 39°C) favors

dimer > 0⁰C

-Overcharge TFAA suppresses dimer

even above 30⁰C


Benefits of ReactIR™ for this project

-Used to determine conditions leading

to high level of dimer impurity

-Amount of dimer determined by HPLC

-Helped identify critical process para-

meters, and obtain kinetic information

in real time



Case Studies





Conclusions


The Role of New Technologies in Defining

a Manufacturing Process for PPAR#

Agonist LY518674

Source: Mark D. Argentine, Timothy M. Braden, Jeffrey Czarnik, Edward W. Conder, Steven E. Dunlap, Jared W. Fennell, Mark A. LaPack, Roger R.

Rothhaar, R. Brian Scherer, Christopher R. Schmid, Jeffrey T. Vicenzi, Jeffrey G. Wei, John A. Werner, and Robert T. Roginski, Lilly Research

Laboratories, IN, USA; Org. Process Res. Dev., 2009, 13 (2), 131-143

Case Study: Dev. of Manuf. Process for LY518674

Introduction

LY518674 highly potent and selective

agonist of peroxisome proliferator-

activated receptor alpha (PPARR)

Recently evaluated in phase II clinical

studies in patients with dyslipidemia and

hypercholesterolemia

Challenge

Development of a robust impurity control

strategy

History shows 5 impurities > 0.1%

despite final crystallization

One single HPLC method challenging

because of polarity differences





Towards a “One-Pot Process”

using innovative PAT approach

-ReactIRTM to develop kinetic model for

KOCN concentration → control KOCN

and minimize 20

-FBRM® and PVM®: Design

crystallization to reach 17<0.5%





ATR-FTIR spectroscopy to

minimize by-product 20

-Develop kinetic model

-Calibration model developed for [OCN-]

-Integration over the 2088-2254cm-1

-1st order in 15 and KOCN

-Rate constant determined





Results from the model

-Model time for cyanate conversion to

reach completion

-For three different cyanate addition

times

-99.9% cyanate consumed within 5-6 h

-Little impact from addition time (0.25h

versus 1h)

-20 minimized





FBRM® and PVM® to improve

purification of 16:

-History: impurity 17 ≈ 0.1-1.2%

depending upon washing protocol

(goal<0.5%)

-17 more soluble in 5N aq. HCl-FBRM ®: 5N aq. HCl → Count # large

particles drops, fine particles count

increases

-PVM®: Needle shaped small particles

not visible to the eye identified as 22

-Crystallization of 22 prevented by

decreasing concentration: From 11mL/g

to 16mL/g 15





Conclusions

Extensive use of various Process

Analytical Technologies at lab and pilot

plant scale

-ReactIRTM used to develop a kinetic

model for a one-pot preparation of a

semicarbazide intermediate

-FBRM® and PVM® to help in the

development of several challenging

crystallization processes

-Shortened development cycle times

-Process knowledge → control strategy

-Comparison of performance at

laboratory and pilot-plant scale

-Obviated the requirement of PAT for

process control at larger scale

Case Studies





Conclusions


Real Time Calorimetry

-Real time, no calibration, no evaluation

-Automated heat exchange area (A)

determination

-Insensitive to reaction mass properties

(viscosity)

-Feedback control based on energy

output

Heat flow

-Well established, accurate

measurement

-Calibration required

-Allows non-isothermal calorimetry,

some level of expertise required

-Sensitive to reaction mass properties

Real Time Calorimetry: RTCal™ on RC1e

Effect of Monomer Grade on Inverse

Emulsion Polymerization of Acrylamide

Using RTCal™ Calorimetry Technology

Introduction

Strongly exothermic acrylamide

polymerization reactions

Change of acrylamide copper grade in

manufacturing: standard → low

Safety assessment/validation required

(Real time) heat measurement invaluable

monitoring technology

Source: Jeffrey H. Peltier, Kate M. Lusvardi, Michael Mitchell Ashland, Hercules Water Technologies Wilmington, DE, USA, Internal Publication,

2009

Case Study: RTCalTM, PAT Tool for Polymerization

NH2O

n

O NH2

nAIBN

acrylamide poly(acrylamide)

Initial Charge

Acryl amide

emulsion (562 ml)

Polymerization

Exp. Conditions

Inverse emulsion

polymerization: water in

oil, batch, shots of initiator

Process info

57 – 65 C, 6h Kinetics: initial rateLow

copper > initial rateHigh copper

Cu = monomer stabilizer

Polymerization rate determination: Comparison of

low and high copper grade based on heat flow/flux

Process safety evaluation: H , T Ad MTSR

Investigation

AIBN (5x0.1ml)

Standard vs low Cu grades

What makes the difference (RTCalTM)?

-Shape of heat generation curve

-Shorter induction period and more

heat generated with low copper grade



2009

Low Copper grade monomer

-Higher initial heat rate using low

copper

-Higher heat removal rate needed

Low copper

Standard copper

Low copper

Standard copper241kJ

256kJInitiator

Process safety evaluation

- iC SafetyTM minimizes risk of error

-Maximum thermal accumulation

(danger!) at starting point (batch)

-Loss of cooling → Tcf > 200°C!



2009

-Assessment of plant’s cooling capacity

versus change in monomer copper

grade (low/standard): 58W versus 54W

max. heat output→ no change

required

Thermal accumulation

Temp. cooling failure

Thermal conversion

Heat


2009


NH2O

n

O NH2

nAIBN

acrylamide poly(acrylamide)

Conclusions

-Validation of low copper grade acrylic

acid for manufacturing scale

polymerization → no major change

-Validation of RTCalTM as an alternative

to heat flow calorimetry

•Easier for non expert as not

sensitive to viscosity

•Faster as no calibration needed

The Road to API Kingdom…

Crystallization

Reaction

Isolation

Nowhere

Case Studies





Conclusions


Paracetamol/water Supersaturation

Monitoring Using In Situ Mid Infrared

Spectroscopy

Why is supersaturation important?

-Supersaturation is the driving force for

crystal nucleation and crystal growth

-By controlling supersaturation,

nucleation and growth can be

controlled, allowing the crystal size to

be controlled

Source: Anthony DiJulio, Novartis Pharmaceuticals Co., NJ, USA; A. Burke, D. O’Grady, D. Hebrault, METTLER TOLEDO, MD, USA, Internal

Publication, 2009

Case Study: Real Time Supersaturation Monitoring

-Poor control of supersaturation may

lead to:

•long filtration time

•undesired polymorph

•low purity


Publication, 2009


Equipment used

-Hardware and software combination

•ReactIR 45m probe based in situ

real time mid-IR spectroscopy

•EasyMaxTM automated reactor

system-Benefits

•Real time overlay of temperature,

dosing, concentration data, and

heat flow

•Accurate control of temperature,

liquid addition, and mixing

•Concentration feedback to control

temperature

from source

to detector

ATR crystal

1~2 m

Liquid-Solid Slurry


Publication, 2009


Experimental procedure: Model

-Charge 100ml water

-Add incremental amount of

paracetamol (1.3, 0.5, 0.8, 1.2g)

-For each concentration, collect

spectra at 2 temperatures, 10⁰C apart,

from 25 ⁰C to 60 ⁰C

-Datapoints collected to build

multivariate quantitative model

Paracetamol 3.8 w/w%

water

Mid-Infrared absorbance

Wavenumber (cm-1)

31

1

Select trends

(C, T)

2

Select samples

3

Visualize

model inputs

One-click

calibration

4

5

Evaluate model consistency



Publication, 2009


Experimental: Crystallization

-Cool down 3.8 w/w% paracetamol

solution: 1⁰C/min, 55 → 20⁰C

-Load multivariate calibration model,

visualize concentration evolution in

real time

-Crystallization onset:

•Concentration drop ≤ 38°C

•Exotherm detected (heat flow)

1

Starting point

2

Concentration stays constant as we cool down

3

Nucleation

4

De-supersaturation

5

Final drop to solubility curve


Solubility curve: Mitsuko Fujiwara, Pui Shan Chow, David L. Ma, and Richard D. Braatz, Crystal Growth & Design, 2002, 2 (5), 363-370

On-Demand Webinar : “Calibration Free Supersaturation Assessment and Control for the Development and Optimization of Crystallization

Processes”, Mark Barrett*, Mairtin McNamara and Brian Glennon, Crystallization Research Group, University College Dublin, November 2009


Conclusions

ATR-FTIR spectroscopy + controlled

reaction vessel facilitates crystallization

characterization:

-Nucleation and growth kinetics of

crystallization

-Identification and control of critical

parameters

-Qualitative and quantitative

supersaturation method facilitates

development of process map

-Combination of supersaturation

assessment with FBRM® and PVM®

for quantitative understanding of tech

transfers and scale-ups

-Constant supersaturation control

possible

Case Studies





Conclusions: Software for Design, Data Acquisition and Analysis


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; Live webinar from Donna G. Blackmond on April 28, 2010 “Reaction

Progress Kinetic Analysis: A Powerful Methodology for Streamlining the Study of Complex Organic Reactions” see www.mt.com/web inar

• Reaction progress display

• Temp. dependence model

• Simulation

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


iC SafetyTM for Evaluation of Thermal Risks of a

Chemical Reaction at Industrial Scale*

Source: “Thermal Safety of Chemical Processes: Risk Assessment and Process Design”, Francis Stoessel, 2008, ISBN 978-3527317127,

on-demand webinar from Francis Stoessel available at www.mt.com/webinar

• MTSRsemi-batch trend

• Integration of DSC data

• Criticality index analysis


ConcIRT Pro: Advanced post-process analysis of

single or multiple experiments from the same

spectroscopy technique or from two different

spectroscopy techniques (e.g., Raman and FTIR,

UV/Vis and FTIR, UV/Vis and Raman)

On Adopting New Technologies…

4040

Acknowledgements

Lilly Research Laboratories, IN, USA

- Mark A. LaPack

Merck Research Laboratories, NJ, USA

- George X. Zhou

Ashland-Hercules Water Technologies Wilmington, DE, USA

- Michael Mitchell

Novartis Pharmaceuticals Co., NJ, USA

- Anthony DiJulio

21st International Conference Organic Process Research & Development 2010 San Diego

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