1. GSK Approach to Enhancing Process
Understanding using Dynochem:
Reaction Kinetics Example
James Wertman
Process Engineering
GlaxoSmithKline
Overview
• Introduction
• Example for Kinetic Modeling
• Experimental Design
• Dynochem Model
– Data Reduction
– Reaction Scheme
– Preliminary Parameter Fitting
– Model Refinement and Selection
– Complete Parameter Fitting
• Optimization
• Conclusions and Future Work
Dynochem User Group Meeting
15-16 May 2007 James Wertman Page 1
1

2. Introduction
• Introduction and implementation of Quality by Design
– Strong focus on process understanding to support process
development and project decisions
• Maximize process understanding over operating ranges
from discrete experimental points
– Dynochem and other tools facilitate this implementing mechanistic
and empirical models
Dynochem User Group Meeting
15-16 May 2007 James Wertman Page 2
Reaction Example
O OH O O O
HO HO MeO MeO
O H O
O H H
Pleuromutilin Epi-Pleuromutilin "Alkene"
• Homogeneous reaction
• Considerable experimental experience
– General effects of concentration, reagents, and temperature understood
– Scaled reproducibly from < 1g to 40kg scales
– Wide range of conditions already proven acceptable
Dynochem User Group Meeting
15-16 May 2007 James Wertman Page 3
2

3. Reaction Example
O OH O O O
HO HO MeO MeO
O H O
O H H
Pleuromutilin Epi-Pleuromutilin "Alkene"
• Starting material is a fermentation product
– Highly variable impurity profile
– Large number of impurities (30+)
• Limited analytical information early in project development
– Few identified impurities
• Numerous feasible reaction pathways, mechanistically complex
Dynochem User Group Meeting
15-16 May 2007 James Wertman Page 4
Experimental Design
• Overall process focus on crystallization and isolation,
constraints imposed by the work-up procedure
– Reduce solvent/reagent amounts to safe minimum, ~3 volumes
– Maintain acid below 1.7 molar equivalents
– Maintain temperature below 40°C
• Solvent/reagent ratio fixed at 2:1
– Previous experimentation suggests negligible effect of ratio on
reaction performance
• Investigation focus on temperature and acid effect on
reaction profile
Dynochem User Group Meeting
15-16 May 2007 James Wertman Page 5
3

4. Experimental Design
• Experimental design selected as a two level factorial design
with center points to allow for statistical analysis of data if
desired
– Methanol and TMOF fixed at 2 and 1 volume respectively
– Temperature: 25 to 40°C
– Sulfuric Acid: 0.85 to 1.7equivalents
– Chromatography purified Pleuromutilin utilized to minimize impurity
complication of analysis
• Time zero is acid charge, exothermic temperature spike
minimized by equipment configuration but still present
• Sampling at 0.5, 1, 2, 4, 8...min
– Sampling extended up to 48 hours to capture “Alkene” formation
Dynochem User Group Meeting
15-16 May 2007 James Wertman Page 6
Raw Data
Pleuromutilin “Alkene”
DAD1 D, Sig=210,4 Ref=360,100 (JW092008-0801.D)
Epi-Pleuromutilin Intermediate Species?
10.487
Norm.
t = 0min
1200
1000
DAD1 D, Sig=210,4 Ref=360,100 (JW092012-1201.D)
800
10.494
Norm.
t = 4min
14.558
600
800
400
12.708
6.848
200
600
0
400
0 2.5 5 7.5 10 12.5 15 17.5 20 min
13.848
12.167
11.307
14.838
7.837
13.187
15.277
200
15.497
16.705
0
0 2.5 5 7.5 10 12.5 15 17.5 20 min
DAD1 D, Sig=210,4 Ref=360,100 (JW092021-2101.D)
14.548
Norm.
1400 t = 64min
1200
DAD1 D, Sig=210,4 Ref=360,100 (JW092035-3501.D)
1000
14.532
Norm.
800 t = 640min
1750
600
12.706
1500
10.503
400
12.901
1250
13.184
13.845
7.838
14.837
15.275
18.255
16.701
200
1000
0 750
18.076 18.241
12.705
12.900
0 2.5 5 7.5 10 12.5 15 17.5 20 min
14.837
500
10.504
12.971
13.183
13.844
7.842
14.195
15.093
15.277
18.766
15.918
16.705
17.634
17.886
17.115
250
0
0 2.5 5 7.5 10 12.5 15 17.5 20 min
Dynochem User Group Meeting
15-16 May 2007 James Wertman Page 7
4

5. Data Reduction
• HPLC areas extracted to Excel
• Area percents calculated adjusting for relative response
factors
– Pleuromutilin 5
– Epi-Pleuromutilin 1
– “Alkene” ½
– Σ(unknowns) 1 assumed
• Adjusted area percent utilized assumed to correspond to
weight percent
– Modeling performed with and without inclusion of unknown peaks in
area percent calculation
Dynochem User Group Meeting
15-16 May 2007 James Wertman Page 8
Reaction Mechanism
OH
MeOH
O O O O
CH(OCH3)3 +
HO HO MeO H MeO
O H O
+
H
O H H
Pleuromutilin Epi-Pleuromutilin "Alkene"
• Propose relevant mechanism while incorporating historical experience
– Without methanol or trimethyl orthoformate (TMOF) no product or major
byproduct is detected
– Strongest dependence to temperature and acid content
– Pleuromutilin to Epi-Pleuromutilin transformation observed to be reversible
– Multiple intermediates observed by HPLC, but no analytical information
Dynochem User Group Meeting
15-16 May 2007 James Wertman Page 9
5

6. Reaction Mechanism
OH OH O OH
O + O
HO
O H
H HO
O
H
+
HO
O
O
+ MeOH • Start with reference
H
information
O HO
Pleuromutilin A B
• Fits experimental
+
H observations except
OH
for the incorporation
O O OH
H2O +
HO
O
O
+
H
+
HO
O
HO
O
of TMOF
H H
D C
O O O
HO MeO + MeO
O + H
H H
"Alkene"
Epi-Pleuromutilin
1 H. Berner, G. Schulz, and H. Schneider,
Tetrahedron 36, 1807 (1979).
Dynochem User Group Meeting
15-16 May 2007 James Wertman Page 10
Reaction Scheme for Modeling
+ 2-
H2SO4 2 H + SO4
CH(OCH3)3 + H
+
O O
+
+ MeOH • Mechanistically relevant
reaction scheme
O OH O OH
O O
+
+
HO
O H
HO
O H
• Actual structures of
intermediates and byproducts
O O +
Pleuromutilin
O
O
selected only to fulfill mass
balances, no analytical
MeOH
information available
O OH
HO
O OH
• Concurs with experimental
HO
O
MeO O H + O
observations
+
O OH
+
H OMe
O O O
HO MeO O
MeO +
O + H
+ HO
OH
H H
Epi-Pleuromutilin "Alkene"
Dynochem User Group Meeting
15-16 May 2007 James Wertman Page 11
6

7. Dynochem Setup
+ 2-
SO4
H2SO4 2 H +
+ +
CH(OCH3)3 + H O O + MeOH
O OH O OH
+ HO HO
O O + O H O H
O O O +
Pleuromutilin O
MeOH
O OH
O OH
HO
HO
O
MeO O H + O
+
O OH
+
H OMe
O O O
HO O
MeO + MeO
O + H
+ HO
OH
H H
Epi-Pleuromutilin "Alkene"
Dynochem User Group Meeting
15-16 May 2007 James Wertman Page 12
Kinetic Parameter Fitting
• Actual temperature profiles imposed
• Initial guesses, focus on forward rate constants
– First two reactions expected to be fast
– Pleuromutilin through Epi-Pleuromutilin rates assumed faster than the
conversion to Alkene
Dynochem User Group Meeting
15-16 May 2007 James Wertman Page 13
7

8. Kinetic Parameter Fitting
• Manually adjust rate constants for rough fit of data, single scenario
Dynochem User Group Meeting
15-16 May 2007 James Wertman Page 14
Kinetic Parameter Fitting
• Manually adjust rate constants for rough fit of data for both scenarios
at 25°C
25°C, 0.85eq Acid 25°C, 1.7eq Acid
Dynochem User Group Meeting
15-16 May 2007 James Wertman Page 15
8

9. Kinetic Parameter Fitting
• Fit reaction rate constants to
both reactions at 25°C
– Acid dissociation and TMOF
‘activation’ reactions not
considered
Dynochem User Group Meeting
15-16 May 2007 James Wertman Page 16
Reaction Scheme Refinement
• Incremental reaction scheme refinement employed
• Rate constant fitting repeated for each reaction scheme for both
experiments at 25°C
• Fit statistics compared in selecting the reaction scheme for model
+ 2-
SO4
H2SO 4 2 H +
O OH O O
HO HO MeO O
CH(OCH3)3 + H
+
+ O H O + + +
H
O OH
O H
Pleuromutilin Epi-Pleuromutilin
O O O
O
HO MeO + MeO
O + H + HO
OH
H H
Epi-Pleuromutilin "Alkene"
Dynochem User Group Meeting
15-16 May 2007 James Wertman Page 17
9

10. Reaction Scheme Selection
Model Selection
Reactions SSQ F-statistic Fit Run Time
Criteria
5 0.4327 7.99e3 5.296 6.72min
4 0.4349 9.07e3 5.248 141.9sec
3 0.3991 2.05e4 5.594 101.2sec
• Best fit attained with the simplest reaction scheme
• Sufficient for characterizing acid and temperature effects
Dynochem User Group Meeting
15-16 May 2007 James Wertman Page 18
Final Parameter Fitting
• Manually adjust activation energies for a rough fit of data at 40°C
Dynochem User Group Meeting
15-16 May 2007 James Wertman Page 19
10

11. Final Parameter Fitting
• Fit activation energies with both Parameter C.I (%)
experiments at 40°C Solution.Rxn2.Ea> 8.519
• Fit reaction parameters to Solution.Rxn3.Ea> 9.949
experiments at 25°C and 40°C Solution.Rxn2.k> 8.138
• Fit reaction parameters to all Solution.Rxn3.k> 6.322
experimental data
Solution.Rxn2.Keq 42.26
• Check fit varying initial guesses
• Visually check fit to data
SSQ 1.571
F-statistic 2.64E+04
Model selection criteria 5.322
Dynochem User Group Meeting
15-16 May 2007 James Wertman Page 20
Optimization
• Defining an optimum reaction
– Starting material consumption
– Byproduct minimization
– Reaction time minimization
• ‘Optimum’ at maximum acid and temperature
– Single point during reaction, not accounting for decomposition after
optimum reaction time
• Criteria not sufficient for determining best reaction
conditions in this example
Dynochem User Group Meeting
15-16 May 2007 James Wertman Page 21
11

12. Optimization
• Defining new optimization criteria
– Reaction time = time at maximum product
– Quench window = time to consume 1% of maximum product
• Defining an objective function for optimization requires
discrete reaction time and quench window target times
• Actual goals are constraints
– Reaction time < 8 hours
– Quench window > 2 hours
Dynochem User Group Meeting
15-16 May 2007 James Wertman Page 22
Simulated Experimental Design
• Utilize statistical analysis software to model reaction time
and quench window responses
• User defined response surface design
– Simulations utilized to model responses
– Maximize resolution, 29 ‘experiments’
• Acid and temperature fit each response with polynomial
model and logarithmic transformations
– log(reaction time) = a*A + b*T + c*A2 + d*A*T...
Dynochem User Group Meeting
15-16 May 2007 James Wertman Page 23
12

13. Reaction Time Response Surface
Dynochem User Group Meeting
15-16 May 2007 James Wertman Page 24
Quench Window Response Surface
Dynochem User Group Meeting
15-16 May 2007 James Wertman Page 25
13

14. Operating Range
Dynochem User Group Meeting
15-16 May 2007 James Wertman Page 26
Conclusions & Future Work
• Simplest reaction scheme selected for model providing the best
parameter fitting
– Model sufficient to describe effects of acid and temperature only
– Additional experimentation and/or analytical information would be required
to further refine the reaction scheme and model
• Dynochem results used in combination with other tools to provide a
more complete understanding of the process
– Identified operating range based on desired reaction time and quench
window
– Developed a clear relationship of factors to process operation
– Incorporate in-situ analytical measurements with model and verification as
developed to further process understanding
• Operating conditions provided to deliver reasonable reaction time yet
allow time to perform analysis
Dynochem User Group Meeting
15-16 May 2007 James Wertman Page 27
14