Using DynoChem to Inform Experimental Design of Batch Crystallization. Rahn McKeown

Using Dynochem to Inform Experimental Design of Batch Crystallization: Case Studies in Scoping, Optimization, and Robustness

Rahn McKeown

GlaxoSmithKline RTP, NC

11-May-2011

Outline

Background

Goal

Model A – “The Nucleation Detector”

Case Studies

– Optimization study

– Robustness study

– Scoping study

Model B – “Solve the cooling curve”

Conclusions

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.

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

Model A– Nucleation detector

Modeling to predict particle size distribution is extremely difficult

– Partial differential equations

– Many assumptions

– Nucleation is unpredictable – stochastic

0

0.5

1

1.5

2

2.5

3

3.5

0 5 10 15 20 25

Su

pe

rsa

tura

tio

n

Time

With nucleation Without nucleation

Solution – cheat

– Ignore nucleation in the model

– You get a model that acts as a

“nucleation detector”

Supersaturation is the driving force to

crystallize

– If you only consider growth rate,

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

Model A- What does it look like?

Model A- How to use it…

Collect baseline data

– Solubility

– Mass transfer rate

Simulate factorial DoE with proposed ranges

Visualize

Reduce design

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)

0

20

40

60

80

100

120

140

20 25 30 35 40 45 50 55

So

lub

ilit

y (

mg

/g)

Temperature

Solubility of Compound A in MTBE/heptane @ 1.25 eq water

Baseline data

Optimization

30

32

34

36

38

40

42

44

46

40

50

60

70

80

90

100

110

0 20 40 60 80 100 120 140

Te

mp

era

ture

(d

eg

C)

Co

nc

en

tra

tio

n (

mg

/g)

Time (min)

Kinetic Data

Solution Concentration Temperature

Parameters

Optimization

Transfer in

Solubility fit

Mass transfer rate fit

Setup starting conditions

Setup DoE simulationSeeding 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%

Setting up and running in Dynochem

OptimizationSet up DoE in Dynochem

Run simulation and collate

responses

Design-Expert® SoftwareTransformed ScaleLn(Maxsuprat)

Design Points5

-0.5

X1 = E: waterX2 = D: seed

Actual FactorsA: T1 = 45.00B: rate = 0.10C: age time = 240.00

0.21 0.36 0.51 0.65 0.80

0.10

0.60

1.10

1.60

2.10

Ln(Maxsuprat)

E: water

D:

se

ed

0.5


5

-0.5



0.21 0.36 0.51 0.65 0.80

0.10

0.60

1.10

1.60

2.10

Ln(Maxsuprat)

E: water

D:

se

ed

0.5

1

Visualizing results from simulation

Optimization


Design Points5

-0.5



0.21 0.36 0.51 0.65 0.80

0.10

0.60

1.10

1.60

2.10

Ln(Maxsuprat)

E: water

D:

se

ed

Comparison to data

Optimization

Trends of “Max Supersaturation” vs. a physical property

Optimization

1

10

-1 0 1 2 3 4 5 6 7

Measu

red

SS

A (

m2/g

)

ln(max supersaturation)

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

Rationalize statistical approach with fundamentals

Robustness

A resulting full factorial design would be 128 experiments

(not including centerpoints)

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)

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]

Baseline data

Robustness

0

20

40

60

80

100

120

0 10 20 30 40 50 60

Temperature

ln(S

olu

bil

ity [

g/L

])

Experimental Fit

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

Pareto chart

Robustness

Pareto Chart

t-V

alu

e o

f |E

ffe

ct|

Rank

0.00

32.86

65.72

98.58

131.44

Bonf erroni Limit 3.64789t-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

AD

F

DFG

AGAD

DG

Factor Description

A Seeding temperature

B Aging temperature

C Isolation temperature

D Cooling rate to age temperature

E Cooling rate to isolation temperature

F Seed loading

G IMS volumes

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

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

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]

Important

interaction

MODEL B

Model B - Basics

Model B

Solve cooling or antisolvent addition curve for a given

crystallization

For a cooling crystallization:

Where dT/dC* = 1/(dC*/dT) can be derived from the

solubility curve

*

*0**

*

1

dC

dTSC

V

mCC

S

Sk

dt

dC

dC

dT

dt

dT

Liquid

seed

solid

g

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)

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

Supersaturation

Specific Surface

Area (m2/g)

Total process

time (minutes)

Processing time for linear

cooling profile (minutes)

1.25 0.9 430 900

1.5 1.3 175 300

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.

Appendix

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

-1

0

1

2

3

4

5

0.0028 0.003 0.0032 0.0034

ln (

S)

1/T (1/K)

DMSO/IPA Solubility Van't Hoff Plot

DMSO/IPA 0.25

DMSO/IPA 0.50

DMSO/IPA 1

Simulated process based on

“slow” kinetics (kg = 0.01 1/s)

and “fast” kinetics (kg = 0.2 1/s)

The results for “maximum

supersaturation” trended well

between the two result sets,

with one of the DoE edges

being the exception

Proposed 3 experiments

Most forcing

Least forcing

Discrepancy

Scoping

Particle Size Distribution

0.01 0.1 1 10 100 1000 3000

Particle Size (µm)

0

0.5

1

1.5

2

2.5

3

3.5

4

4.5

5

5.5

6

6.5

Vol

ume

(%)

GSK1265744A batch EE386725-1-2 R113237, Tuesday, October 20, 2009 9:09:38 AM GSK1265744A batch EE386725-1-2 R113237, Tuesday, October 20, 2009 9:10:00 AM



0.01 0.1 1 10 100 1000 3000

Particle Size (µm)

0

0.5

1

1.5

2

2.5

3

3.5

4

4.5

5

5.5

6

6.5

7

7.5

8

8.5

9

9.5

Vol

ume

(%)




0.01 0.1 1 10 100 1000 3000

Particle Size (µm)

0

0.5

1

1.5

2

2.5

3

3.5

4

4.5

5

5.5

6

6.5

7

Vol

ume

(%)



Most forcing: Primary size ~ 30

micron with some agglomeration

Discrepancy: Primary size ~ 45

micron with wide distribution

Least forcing: Primary size ~ 55

micron with tighter distribution

Technology

Using DynoChem to Inform Experimental Design of Batch Crystallization. Rahn McKeown