1. Using Dynochem to Inform Experimental
Design of Batch Crystallization: Case
Studies in Scoping, Optimization, and
Robustness
Rahn McKeown
GlaxoSmithKline RTP, NC
11-May-2011

2. Outline
Background
Goal
Model A – “The Nucleation Detector”
Case Studies
– Optimization study
– Robustness study
– Scoping study
Model B – “Solve the cooling curve”
Conclusions

3. Background
What is crystallization?
– Formation of a solid phase of a chemical compound
from a solution in which that compound is dissolved
– “If you’re not part of the solution, you’re part of the
precipitate”
Why crystallization?
– Separation and Purification
– Product Performance
How to crystallize?
– Stable solution with compound dissolved is
destabilized
– Physics: Supersaturation, solubility, kinetics, etc.

4. Goal
Useful generalizations
– Modeling crystallization accurately is difficult
– To enhance separation, purification, and product performance in
standard unit operations…
Bigger particles pretty much always win
Big particles generally result from keeping supersaturation low
– We also need to balance the reality of a commercial process
“Slow down” enough to grow large particles
Maintain a realistic manufacturing time
Goal
– Create a simple tool for scientists unfamiliar with crystallization
kinetics to aid in experimental design
– Demonstrate usefulness for several different types of experimental
design

5. Model A– Nucleation detector
Modeling to predict particle size distribution is extremely difficult
– Partial differential equations
– Many assumptions
– Nucleation is unpredictable – stochastic
Solution – cheat
3.5
– Ignore nucleation in the model 3
Supersaturation
– You get a model that acts as a 2.5
“nucleation detector” 2
1.5
1
Supersaturation is the driving force to 0.5
0
crystallize 0 5 10 15 20 25
– If you only consider growth rate, Time
With nucleation Without nucleation
overall crystallization rate will be
underestimated in cases where
nucleation rate is significant
– Because of this, the peak
supersaturation during the process
will be overestimated

6. Model A- What does it look like?

7. Model A- How to use it…
Collect baseline data
– Solubility
– Mass transfer rate
Simulate factorial DoE with proposed ranges
Visualize
Reduce design

8. Case Study: Optimization
Optimization
Compound A-hemihydrate is produced via
recrystallization of intermediate grade Compound
A-hemihydrate from MTBE/n-heptane/water
Water content (0.0 to 3.0 equivalents) has a
significant impact on solublity
Process operating range needs to be understood
and optimized conditions identified
Target physical property: Specific Surface Area
(SSA)

9. Baseline data
Optimization
Solubility of Compound A in
MTBE/heptane @ 1.25 eq water
140
120
Solubility (mg/g)
100
80
60
40
20
0 Kinetic Data
20 25 30 35 40 45 50
110 55 46
Temperature 44
Concentration (mg/g) 100
Temperature (deg C)
42
90
40
80
38
70
36
60
34
50 32
40 30
0 20 40 60 80 100 120 140
Time (min)
Solution Concentration Temperature

10. Parameters
Optimization
Transfer in
Solubility fit
Mass transfer rate fit
Setup starting conditions
Setup DoE simulation
Seeding temperature – 40 to 45C
Age time – 0 to 4 hours
Cooling rate – 0.1 to 0.25 C/min
Water content – 0.5 to 1.0 eq.
Seed loading – 0.1 to 2.1%

11. Setting up and running in Dynochem
Optimization
Set up DoE in Dynochem
Run simulation and collate
responses

12. Visualizing results from simulation
Optimization
Ln(Maxsuprat)
2.10
0.5
1.60
D : seed
1.10
Design-Expert® Software Ln(Maxsuprat)
Transformed Scale 2.10
Ln(Maxsuprat)
5 0.5
0.60
-0.5
1.60
X1 = E: water
X2 = D: seed
D : seed
0.10 Actual Factors Design-Expert® Software Ln(Maxsuprat)
0.21 A: T1 = 42.50
0.36 0.51 0.65
1.10 Transformed
0.80 Scale 2.10
B: rate = 0.18 Ln(Maxsuprat) 1
C: age time = 120.00 Design Points
E: water 5
-0.5 1.60
0.60
X1 = E: water
D : seed
X2 = D: seed
1.10
0.10 Actual Factors
0.21 A: 0.36 = 40.00
T1 0.51 0.65 0.80
B: rate = 0.25
C: age time = 0.00
E: water 0.60
0.10
0.21 0.36 0.51 0.65 0.80
E: water

13. Comparison to data
Optimization

14. Trends of “Max Supersaturation” vs. a physical
property
Optimization
10
Measured SSA (m2/g)
1
-1 0 1 2 3 4 5 6 7
ln(max supersaturation)

15. Case Study: Robustness
Robustness
Compound B process was reviewed during a QbD
exercise
Process: Seeded, cooling crystallization with 2 linear
cooling steps after seeding
Total of 7 factors identified for study
Some data existed on primary effects
Important to understand interactions
Ranges selected via known variability in commercial scale
equipment or based on previous work

16. Rationalize statistical approach with fundamentals
Robustness
A resulting full factorial design would be 128 experiments
(not including centerpoints)
Factor ID Factor Units Low Mid High Dynochem Variable
A Seeding temperature °C 49 52 54 T1 [°C]
B Aging temperature °C 30 35 40 T2 [°C]
C Final temperature °C -5 0 5 T3 [°C]
D Cooling rate to the aging temperature °C/minute 0.1 0.3 0.5 rate1 [°C/minute]
E Cooling rate to the final temperature °C/minute 0.1 0.3 0.5 rate2 [°C/minute]
F Seed amount wt% 0.1% 1% 1% Crystals.CompoundB [wt/wt]
G Solvent amount L/kg 7 8 9 Solution.Solvent [kg]
Teams initial thoughts were to run an 27-3 (16 experiments)
design, but this will only tease out main effects. The
minimum design to get 2-factor interactions is 27-1 (64
experiments)

17. Baseline data
Robustness
120
100
ln(Solubility [g/L])
80
60
40
20
0
0 10 20 30 40 50 60
Temperature
Experimental Fit

18. Poor design highlighted with zero wasted
experiments
Robustness
First proposed design (upon simulation) was shown to be
very poor – based purely on solubility curve and MSZW

19. Pareto chart
Robustness
Pareto Chart
A D Factor Description
131.44
A Seeding temperature
B Aging temperature
C Isolation temperature
D Cooling rate to age temperature
E Cooling rate to isolation temperature
t- V a lu e o f | E ffe c t|
98.58
F Seed loading
G IMS volumes
F
65.72
DF
G
32.86
AG
AD
DG
Bonf erroni Limit 3.64789
0.00 t-Value Limit 1.9801
1 2 3 4 5 6 7 8 9 10 11 12 13 14 15 16 17 18 19 20 21 22 23 24 25 26 27 28 29 30 31
Rank

20. Cut up that design!
Robustness
Based on Pareto chart, 3 factors can be removed as having little to no
impact on the process with respect to particle size
Factor ID Factor Units Low Mid High Dynochem Variable
A Seeding temperature °C 49 52 54 T1 [°C]
B Aging temperature °C 30 35 40 T2 [°C]
C Final temperature °C -5 0 5 T3 [°C]
D Cooling rate to the aging temperature °C/minute 0.1 0.3 0.5 rate1 [°C/minute]
Important
E Cooling rate to the final temperature °C/minute 0.1 0.3 0.5 rate2 [°C/minute]
interaction
F Seed amount wt% 0.1% 1% 1% Crystals.CompoundB [wt/wt]
G Solvent amount L/kg 7 8 9 Solution.Solvent [kg]
Discussion with the team brought on an additional variable that was
not simulated: Agitation rate
The team then elected to perform a 25-2 design (8 experiments)
eliminating 4 of the 8 possible designs based on the DF aliasing and 2
of the remaining 4 designs based on the model predictions for “span”
of supersaturation.
Model reduced experimental burden from 64 to 8 experiments and
allowed for non-random selection of an information rich quadrant of
the possible 25-2 designs

21. MODEL B

22. Model B - Basics
Model B
Solve cooling or antisolvent addition curve for a given
crystallization
For a cooling crystallization:
dT dT dC* kg S 1 * 0 mseed dT
*
C C SC
dt dC* dt solid S VLiquid dC*
Where dT/dC* = 1/(dC*/dT) can be derived from the
solubility curve
Common expression Derivative
C*=exp(A + BT) dC*/dT = B exp(A + BT)
C*=exp(A+BT+CT2) dC*/dT = (2C T+B)*exp(A+BT+CT2)
C*=exp(A + B/T) dC*/dT = - B/T2 * exp(A+B/T)
C*=exp(A+B/T+C/T2) dC*/dT = -(2C + BT)/T3 * exp(A+B/T+C/T2)
C*=exp(A + B/T+C lnT) dC*/dT = (C T(C+1) - B TC )/T2 * exp(A+B/T)
C*= ai Ti dC*/dT = i * ai*T(i-1)
C*= ai/Ti dC*/dT = - ai * i * T(-i-1)

23. Model B – Example
Model B
Run the cooling curve at
several S values
Program the fit
Approximate as multiple
linear or exponential
decay
Analyze results
Specific Surface Total process Processing time for linear
Supersaturation Area (m2/g) time (minutes) cooling profile (minutes)
1.25 0.9 430 900
1.5 1.3 175 300

24. Conclusions
The models presented have physical relevance and it has
been demonstrated that the model output correlates well
to physical properties
Simple models for crystallization, such as these, can still
inform and improve experimental design and are very
useful for data poor systems
The methods presented can be made into easy to use,
macro-driven excel/Dynochem templates for use by
scientists who do not have a background in crystallization
or engineering
Cautionary note: these models can only inform design
where the target output is related to supersaturation; this is
not always the case.

26. Appendix

27. Case Study: Scoping
Scoping
Compound C is a early phase. It is crystallized as a seeded
antisolvent, cooling crystallization from DMSO/IPA.
No data on kinetics; very little for solubility
Simulated process based on
“slow” kinetics (kg = 0.01 1/s) DMSO/IPA Solubility Van't Hoff Plot
and “fast” kinetics (kg = 0.2 1/s) 5
The results for “maximum 4
supersaturation” trended well 3
ln (S)
between the two result sets, 2 DMSO/IPA 0.25
with one of the DoE edges 1
DMSO/IPA 0.50
being the exception 0
DMSO/IPA 1
Proposed 3 experiments -1
Most forcing 0.0028 0.003 0.0032
1/T (1/K)
0.0034
Least forcing
Discrepancy

28. Scoping
Most forcing: Primary size ~ 30
micron with some agglomeration
Particle Size Distribution
Particle Size Distribution
7
6.5
6
5.5
5
4.5
4
Volume (%)
3.5
3
0.1 1 10 100 1000 3000
Particle Size Distribution
Particle Size (µm) 2.5
725-1-2 R113237, Tuesday, October 20, 2009 9:09:38 AM GSK1265744A batch EE386725-1-2 R113237, Tuesday, October 20, 2009 9:10:00 AM
725-1-2 R113237, Tuesday, October 20, 2009 9:15:08 AM GSK1265744A batch EE386725-1-2 R113237, Tuesday, 2
October 20, 2009 9:15:26 AM
1.5
1
0.5
0
0.01 0.1 1 10 100 1000 3000
Particle Size (µm)
GSK1265744A batch EE386725-3-2 R113237, Tuesday, October 20, 2009 9:32:27 AM GSK1265744A batch EE386725-3-2 R113237, Tuesday, October 20, 2009 9:32:46 AM
Discrepancy: Primary size ~ 45
GSK1265744A batch EE386725-3-2 R113237, Tuesday, October 20, 2009 9:38:03 AM GSK1265744A batch EE386725-3-2 R113237, Tuesday, October 20, 2009 9:38:21 AM
micron with wide distribution
0.1 1 10 100 1000 3000
Particle Size (µm)
5-2-2 R113237, Tuesday, October 20, 2009 9:21:39 AM GSK1265744A batch EE386725-2-2 R113237, Tuesday, October 20, 2009 9:21:58 AM
5-2-2 R113237, Tuesday, October 20, 2009 9:26:06 AM GSK1265744A batch EE386725-2-2 R113237, Tuesday, October 20, 2009 9:26:24 AM
Least forcing: Primary size ~ 55
micron with tighter distribution