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…

Presentation Outline

 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

Combining Real Time Analytics & Process Control

 Characterize Particles

 Analyze Reaction Chemistry

 Expand
Productivity  Data Capture and
Understanding

Mid-IR Real-time Reaction Analysis

ReactIR

Mid-IR Real-time Reaction Analysis
In-situ reaction results

Absorbance
Time
 ConcIRT live
 Peak height profiling
 Quantitative model
Component Spectra Component Profiles

Relative concentration
Absorbance
or

Time

Case Study: FTIR, PAT tool in Pharma Development
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


 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
Known: 14.3 mg/ml
- Peak area for band at 1875 cm-1 Known: 11 mg/ml
Predicted: 10.2 mg/ml
Predicted: 14 mg/ml

- 2 models: [0-100mg/ml] and [60-350]
- Model tested in reaction mixture:
consecutive additions of TFAA

Observations

- Good prediction from calibration model



 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 Study: Dev. of Manuf. Process for LY518674

The Role of New Technologies in Defining
a Manufacturing Process for PPAR#
 Challenge
Agonist LY518674
Development of a robust impurity control

 Introduction strategy

LY518674 highly potent and selective History shows 5 impurities > 0.1%

agonist of peroxisome proliferator- despite final crystallization

activated receptor alpha (PPARR)
One single HPLC method challenging

Recently evaluated in phase II clinical because of polarity differences

studies in patients with dyslipidemia and
hypercholesterolemia

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

 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 - Shortened development cycle times
development of several challenging
crystallization processes - Process knowledge → control strategy
- Comparison of performance at
laboratory and pilot-plant scale

- Obviated the requirement of PAT for
process control at larger scale


Real Time Calorimetry: RTCal™ on RC1e
 Heat flow  Real Time Calorimetry

- Well established, accurate - Real time, no calibration, no evaluation
measurement
- Automated heat exchange area (A)
- Calibration required determination

- Allows non-isothermal calorimetry, - Insensitive to reaction mass properties
some level of expertise required (viscosity)

- Sensitive to reaction mass properties - Feedback control based on energy
output

Case Study: RTCalTM, PAT Tool for Polymerization
Effect of Monomer Grade on Inverse Initial Charge Exp. Conditions
Acryl amide Inverse emulsion
Emulsion Polymerization of Acrylamide emulsion (562 ml)
Using RTCal™ Calorimetry Technology polymerization: water in

oil, batch, shots of initiator
 Introduction Polymerization Process info
57 – 65 C, 6h Kinetics: initial rateLow
Strongly exothermic acrylamide copper > initial rateHigh copper
AIBN (5x0.1ml)
polymerization reactions Cu = monomer stabilizer

Change of acrylamide copper grade in Investigation
manufacturing: standard → low Polymerization rate determination: Comparison of
low and high copper grade based on heat flow/flux

Safety assessment/validation required Process safety evaluation: H , T Ad MTSR

(Real time) heat measurement invaluable
monitoring technology

AIBN n
n
O NH2 O NH2

acrylamide poly(acrylamide)

Source: Jeffrey H. Peltier, Kate M. Lusvardi, Michael Mitchell Ashland, Hercules Water Technologies Wilmington, DE, USA, Internal Publication,
2009

 Standard vs low Cu grades
Low copper
What makes the difference (RTCalTM)?

- Shape of heat generation curve Standard copper

- Shorter induction period and more
heat generated with low copper grade

Low copper
256kJ
Initiator

Standard copper
241kJ Low Copper grade monomer

- Higher initial heat rate using low
copper

- Higher heat removal rate needed
2009

 Process safety evaluation
- iC SafetyTM minimizes risk of error
- Maximum thermal accumulation
(danger!) at starting point (batch)

- Loss of cooling → Tcf > 200°C!

Thermal accumulation
Thermal conversion - Assessment of plant’s cooling capacity
versus change in monomer copper
Temp. cooling failure grade (low/standard): 58W versus 54W
Heat

max. heat output→ no change
required

2009


 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

AIBN n
• Easier for non expert as not
n
O NH2 O NH2
sensitive to viscosity
acrylamide poly(acrylamide)

• Faster as no calibration needed

2009

The Road to API Kingdom…

Nowhere
Reaction

Isolation

Crystallization

Case Study: Real Time Supersaturation Monitoring
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

- Poor control of supersaturation may
lead to:

- By controlling supersaturation, • long filtration time
nucleation and growth can be
controlled, allowing the crystal size to
• undesired polymorph
be controlled • low purity

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

ATR crystal Liquid-Solid Slurry
• Accurate control of temperature,
liquid addition, and mixing

to detector • Concentration feedback to control
temperature
1~2 m
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 Mid-Infrared absorbance

- Datapoints collected to build
multivariate quantitative model

Paracetamol 3.8 w/w%

water

Wavenumber (cm-1)
Publication, 2009


4
One-click
calibration
5
3 Evaluate model consistency
Visualize
1 model inputs
Select trends
(C, T)
2
Select samples

31

 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)

Publication, 2009


2
Concentration stays constant as we cool down

1
Starting point

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

 Conclusions
ATR-FTIR spectroscopy + controlled
reaction vessel facilitates crystallization
characterization:

- Nucleation and growth kinetics of
crystallization

- Identification and control of critical - Qualitative and quantitative
parameters 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
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, Nov ember 2009

Presentation Outline

 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: 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*

Summary Data Reaction Progress Kinetic Fit Simulate

Models Reaction Conditions
Edit Model Parameter Axis Lo Hi
Temperature Model Comment
k: 1.00
A(0) X axis 10.0 20.0
a: 1.50
B(0) Constant 5.00 8.00
Only two data points. Rerun
b: 0.01
T Y axis 40.0 60.0
E act: 24.3e-4

Apply
Button/menu drop down – Simulation Output
Options:
1) New Isothermal model Time to 95 % conversion of A
2) New temp. depend. model
3) New from selected model Conversion of A at 60 minutes

Q Peak during 60 minute reaction
New Isothermal model Delete

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.
• Reaction progress display
Time to 95% conversion of A

16.000 T=48 C
14.000

• Temp. dependence model
12.000
10.000
[A],[B]

8.000
6.000
4.000
60.0 2.000
10.0
0.000

• Simulation
0.000 10.000 20.000 30.000 40.000
A(0) T
40.0
time
20.0

*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/webinar


 iC SafetyTM for Evaluation of Thermal Risks of a
Chemical Reaction at Industrial Scale*

• MTSRsemi-batch trend
• Integration of DSC data
• Criticality index analysis

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


 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…

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

40

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

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