The Use of Simulation Tools for Crystallisation Process ... Barce… · Regressed UNIFAC Solubility...

© ESTEVE QUÍMICA (2019)

The Use of Simulation Tools

for Crystallisation Process

Development.”Steve Winter

Crystallisation Workshop

Barcelona, April 11, 2019


ESTEVE GROUP

ESTEVE PHARMACEUTICALS, S. A. (SPAIN)/ ESTEVE FARMA

(SPAIN), LDA (PORTUGAL) / PENSA PHARMA, S. A. (SPAIN)/

PENSA PHARMA, sPa (ITALY) / toLife (PORTUGAL) / PENSA

PHARMA ILAÇ (TURKEY) / PENSA PHARMA AB (SPAIN)/ ESTEVE

QUÍMICA, S. A. (SPAIN) / SINTENOVO, S.A. (MEXICO) /

ZHEJIANG HUAYI PHARMACEUTICAL, CO. LTD. (CHINA) /

ESTEVE HUAYI PHARMACEUTICAL CO., LTD. (CHINA) / ISDIN,

S.A. (SPAIN)/ ESTEVE TEIJIN HEALTHCARE (ETH) (CHINA)

2

Family owned business with international presence

Esteve QuímicaCorporate HQ and R&D - Barcelona

Manufacturing – Celrà (Girona), Banyeres de Penedes (Tarragona), Mexico, China

Process Development and Manufacture of APIs


Lab optimization

PAR studies Scale-up Manufacturing

Process Development: old approach

Sequential activities with the objective to demonstrate that the

process works.

3

Why use simulation?


Process Development: QbD approach

Lab optimization

PAR studies Scale-up Manufacturing

4

Build the quality by understanding the process and be able to predict

the possible impact of the changes

How to integrate the different phases of a

development into a solid knowledge platform

Using simulation tools

QbD


Process Development: QbD approach

Models enable systematic analysis of experimental data and

quantification of effects on process change

Less experimental trials

Less resources

Deeper knowledge

Global knowledge platform

Good customer perception

Future trend for regulatory submission

5


Simulation Tools

Several different commercially available

packages

Crystallization

•Solvent selection

•Optimization

Reaction kinetics

•Process optimization

•PAR studies

Unit operations

•Distillation

•Solvent swap

•Extractions

Scale up attributes

•PSD control

•Filterability

•Drying

•Reactor design

Safety studies

•Safety tests

•Safe scale up

6


Solid Form Development Workflow7

API

Solid form

screening

Solvent

screening

Normally some limited solubility data generated

Basic solubility data for a range of solvents

Crystallisation

process

development

Thermodynamic modelling (accurate solubility data)

Kinetic modelling


Solvent Selection Workflow8

Solid form

knowledge

Solubility

modelSolubility

experiments

Solubility

predictions

Thermal data (m.p. DH0f)

solubility info. fromsolid form screen

XRPD recovered solid

Solvent

systems for

development

Solubility model (e.g. UNIFAC) allows for predictions in solvent systems

outside of the experimental range (e.g. temp., mixtures, different

solvent with similar functional groups)


Regressed UNIFAC Solubility Model9

UNIFAC model Main groups = 51

The UNIFAC method (UNIQUAC Functional-group Activity Coefficients) is

a semi-empirical system for the prediction of non-electrolyte activity in

non-ideal mixtures.

UNIFAC uses the functional groups present on the molecules that make

up the liquid mixture to calculate activity coefficients.

Each group is characterized by a Van der Waals surface area (Q) and

volume (R) and binary interaction parameter (ij).

Regressed UNIFACUNIFAC Main groups + solute, the API

51 + 1= 52 groups


UNIFAC – Experimental screen10

Define solvents and enter measured data Vol frac 1 Vol frac 2 Temp Measured

Solvent1 Solvent2 - C wt%

Ethanol <select solvent> 1 0 25 0,13

acetonitrile <select solvent> 1 0 25 0,06

acetone <select solvent> 1 0 25 0,01

THF <select solvent> 1 0 25 0,06

1_4_Dioxane <select solvent> 1 0 25 0,01

MEK <select solvent> 1 0 25 0,00

Acetic_acid <select solvent> 1 0 25 14,42

water <select solvent> 1 0 25 0,27

Ethanol water 0,5 0,5 25 2,39

acetonitrile water 0,5 0,5 25 10,02

acetone water 0,5 0,5 25 3,32

THF water 0,5 0,5 25 6,70

1_4_Dioxane water 0,5 0,5 25 3,10

MEK water 0,5 0,5 25 5,16

Acetic_acid water 0,5 0,5 25 13,21

Include relevant functional groups to allow predictions outside the

experimental range


Screen solvent mixtures Select your 10 solvents using row 3, then click 'Screen' button Temperature 20 C

Solubility (g/L) vs comp (vfrac) Acetonitrile MTBE Acetone 2_MeTHF i_propyl_acetate Isopropanol MIBK n_heptane n_pentane

Acetonitrile

MTBE VERDADERO

Acetone VERDADERO VERDADERO

2_MeTHF VERDADERO VERDADERO VERDADERO

i_propyl_acetate FALSO FALSO FALSO FALSO

Isopropanol VERDADERO VERDADERO VERDADERO VERDADERO FALSO

MIBK VERDADERO VERDADERO VERDADERO VERDADERO FALSO VERDADERO

n_heptane VERDADERO VERDADERO VERDADERO VERDADERO FALSO VERDADERO VERDADERO

n_pentane VERDADERO VERDADERO VERDADERO VERDADERO FALSO VERDADERO VERDADERO VERDADERO

Water VERDADERO VERDADERO VERDADERO VERDADERO FALSO VERDADERO VERDADERO VERDADERO VERDADERO

Max solubility (g/L) Acetonitrile MTBE Acetone 2_MeTHF i_propyl_acetate Isopropanol MIBK n_heptane n_pentane

Acetonitrile

MTBE 0,31 Maximum solubilities over the composition range

Acetone 0,43 0,05

2_MeTHF 0,33 0,01 0,06

i_propyl_acetate 6,23 1,01 1,50 1,01

Isopropanol 1,37 0,48 0,89 0,76 11,80

MIBK 0,31 0,00 0,05 0,01 1,01 0,31

n_heptane Immiscible 0,00 0,05 0,01 1,01 0,24 0,00

n_pentane Immiscible 0,00 0,05 0,01 1,01 0,24 0,00 0,00

Water 684,52 Immiscible 363,35 Immiscible Immiscible 93,76 Immiscible Immiscible Immiscible

Solvent Selection Model –Regressed UNIFAC

11

https://dcresources.scale-up.com/#q=SOLUBILITY___173

https://dcresources.scale-up.com/#q=SOLUBILITY___284

Predicted solubilities used

for guideline purposes

Not 100% accurate!


Examples Solvent Selection Model12

0

100

200

300

400

500

600

700

800

900

10 20 30 40 50 60

So

lub

ility

(g

/L D

is)

Temperature (ºC)

Solubility Vs Temperature

Ethanol

Methanol

Single solvent

S Methanol > S Ethanol

Binary mixtures

Solvent-water

S Methanol-water < S Ethanol-water

0

10

20

30

40

50

60

70

80

0 0.2 0.4 0.6 0.8 1

So

lub

ility

(g

/L)

Volume fraction

Solubility Vs Volume fraction

Ethanol

Methanol

Process efficiency improved from 100 to 30 volumes


Solvent Selection Workflow13

Solid form

knowledge

Solubility

modelSolubility

experiments

Solubility

predictions

Thermal data (m.p. DH0f)

solubility info. from

solid form screen

XRPD recovered solid

Solvent

systems for

development

What are the objectives?• Yield

• Purity

• Polymorph

• Particle Size Distribution


Particle Size Considerations14

As long as it filters OK

and dries OK, we don’t

mind.

I’ve got a crystallization process. The

yield is 90%, the purity is 99.5%, but….

What PSD do we need?

We don’t know yet. But it

will be VERY important!

We need all plant batches to have

the same PSD as that first lab batch

you gave us!

Some months later….

Solid form

scientist

Plant

scientist

Formulation

scientist


Scale-up Challenges15

Objectives

1. Correct polymorphic form

2. Optimise yield, purity

3. Correct particle size distribution

mg g kg

Requirements

1. Thermodynamics (solubility)

2. Kinetics of crystallisation


Some Words of Wisdom16

“Trying to develop or troubleshoot a solution

crystallisation process without knowledge of the solubility

curve and metastable zone width is akin to hiking though

the wilderness without a map or compass.”

Chris Price

ChemEng Prog 34-43 (1997)


Solubility Curves or Models17

Co

nce

ntr

atio

nUnstable Zone

Stable Zone

Meta-stable Zone

spontaneousnucleation

Temperature

What if we have a solvent mixture? – Better to have a solubility model.


Solubility Models for Binary Mixtures18

Van’t Hoff Equation:

Solubility models:

where x1 is solvent fraction

R

ΔS

RT

ΔHnKl

Model Equation

0

1

2

3

4

RT

xDxC

RT

BAC 10

100

0lnln

RT

xDxC

RT

BAC 1

1111

1 explnln

)exp(ln 122122

12 xDRT

C

RT

xBxAC

RT

xDxC

RT

BAC 13

133

3 )exp(lnln

44142

14ln DRT

C

RT

xBxAC

1. Experimental data

2. Data regression

3. Choose best fitting model

2/3 temperatures × 4/5 compositions = 8 – 15 experiments


Solubility Model

Binary Mixture Example19 Dynochem Simulation of Solubility Curves

Variation of Crude 4093 Solubility with Water Vol. Fraction

0

50

100

150

200

250

300

350

400

10 20 30 40 50 60 70 80 90

Temperature (ºC)

Co

ncen

trati

on

(g

4093 /

L o

f so

lven

t)

0.21 vol fraction

0.25 vol fraction

271 g/L concentration

308 g/L concentration

bp 655mmHg 75ºC

bp 760mmHg 80ºC

65-70ºC seeding range

So

lub

ility

(g

/Kg

)

Temperature (ºC)Solubility model aids basic process design

But how can it help us control PSD?


Crystallisation Process Development

First step – solubility curve / model20

Co

nce

ntr

atio

n

Unstable Zone

Stable Zone

Meta-stable Zone

spontaneousnucleation

Temperature

Conventional wisdom: for large PSD seed close to solubility curve and cool slowly.

Is this always true? And what if we don’t want large particles?

We need to understand crystal growth and nucleation.



“Remember that all models are wrong; the

practical question is how wrong do they

have to be to not be useful.”

George E. P. Box (1919 – 2013)

“Trying to develop or troubleshoot a solution crystallisation

process without knowledge of the solubility curve and

metastable zone width is akin to hiking though the wilderness

without a map or compass.”

Chris Price, ChemEng Prog 34-43 (1997)


Crystal Growth Modelling22

Growth rate

Δc = (CL-C*)

B = KB · Δcb

G = KG · Δcg

Nucleation rate

Supersaturation

To model crystallization kinetics:

Need to know KG, g, KB, b and the solubility (C*)

Simplified approach: Turn off nucleation!

Peter Clark, DynoChem ACT, 16th Larson Workshop October 5, 2009


Crystallisation kinetics23

Supersaturation

Growth rate

Nucleation rate

Growth rate

Δc = (CL-C*)

B = K · Δcb

G = K · Δcg

Nucleation rate

Supersaturation

Use growth only model, vary process conditions

and track supersaturation

If the model shows high supersaturation

small particles due to nucleation

Rahn McKeown

https://dcresources.scale-

up.com/#q=Crystallization___2982

Typically g << b

PSD is determined by the relative rates of

growth, nucleation, attrition,

agglomeration


Simplified Supersaturation Model24 G = K·Δcg

*)CC(AKdt

dMLSL

t

SS V·aA

*)CC(VKdt

dMLSG

t

dt*)CC(VKM L

t

0t

SGt

G growth rate

Δc supersaturation

K constants

Mt mass of formed crystals

AS area of crystals

VS volume of crystals

CL concentration

C* solubility

g ~ 1

This model is wrong – but is it useful?

Determine KG and solubility model (C*) experimentally

Follow crystal mass over time, but more importantly we can track DC


Example: Crystallisation Development

Compound A25

Tem

pera

ture

Charge base, IPA

and water

Addition of acidSeeding

Addition IPA Filtration

Polish filtration

Parameters:

Seeding T, charge, PSD

Maturation time

Cooling ramp

Final T

Antisolvent addition rate

Agitation

Time


Experimentation vs Simulation26

Many Parameters:Seeding T, charge, PSD

Solvent volumes

Maturation time

Cooling ramp

Final TAntisolvent addition rate

Agitation

Computer simulationMany variables, many levels

Virtual DoE

Hundreds of simulations in a few minutes

Semi-empirical modelPredictions not limited by design space

Experimental DoEMany experiments

Only practical to study a few levels

Lots of analytical time

Statistical model based on exp. dataOnly valid within design space

Complementary approaches


Supersaturation model27

dt*)CC(VKM L

t

0t

SGt

Solubility model (C*)

Crystal growth equation

RT

xDxC

RT

BAlnCln 10

100

0

So far the only experimentation has been determination of the solubility model

Calculate the maximum supersaturation (CL-C*)

experienced during the crystallisation process

Virtual DoE Full factorial

three levels

Factors Seeding temperature

Cooling ramp

Volumes water

Volumes IPA

-0,4

-0,3

-0,2

-0,1

-0,0

0,1

0,2

0,3

0,4

0,5

0,6

0,7

0,8

0,9

1,0

1,1

1,2

maxsupratio maxsuper

Investigation: sita_kg0.01_3 (MLR)

Normalized Coefficients

N=81 Cond. no.=6,203 DF=66

Temp

ram

H2O

IPA

Temp*Temp

ram*ram

H2O*H2O

IPA*IPA

Temp*ram

Temp*H2O

Temp*IPA

ram*H2O

ram*IPA

H2O*IPA

MODDE 9.1 - 2013-03-13 16:31:59 (UTC+1)

-0,4

-0,3

-0,2

-0,1

-0,0

0,1

0,2

0,3

0,4

0,5

0,6

0,7

0,8

0,9

1,0

1,1

1,2




N=81 Cond. no.=6,203 DF=66

Temp

ram

H2O

IPA

Temp*Temp

ram*ram

H2O*H2O

IPA*IPA

Temp*ram

Temp*H2O

Temp*IPA

ram*H2O

ram*IPA

H2O*IPA

MODDE 9.1 - 2013-03-13 16:31:59 (UTC+1)

KG = 0.01 s-1

slow growth kinetics

Factors:

ramp,

ramp*ramp

-1,2

-1,0

-0,8

-0,6

-0,4

-0,2

0,0

0,2

0,4

0,6

0,8

1,0




N=81 Cond. no.=6,203 DF=66

Temp

ram

H2O

IPA

Temp*Temp

ram*ram

H2O*H2O

IPA*IPA

Temp*ram

Temp*H2O

Temp*IPA

ram*H2O

ram*IPA

H2O*IPA

MODDE 9.1 - 2013-03-13 16:36:32 (UTC+1)

KG = 0.2 s-1

fast growth kinetics

Factors:

water,

seeding temp


Effect of Seeding T and Ageing Time

KG = 0.01s-128Virtual DoE Factors

Seeding temperature (4 levels)

Ageing time (2 levels)

Lowest max.

in supersat

Highest max.

in supersat

How does this compare with experimentation?


Experimental DoE29

IPA H2OTemp. seed

Cooling Ramp

- - - -

+ + - -

- + + -

+ - + -

- + - +

+ - - +

- - + +

+ + + +

0 0 0 0

0 0 0 0

0 0 0 0

0 0 0 0

FactorCentre Point

Lower Value

Upper Value

Volumes IPA (ml/g) 3.43 3.31 3.81

Volumes water (ml/g) 0.89 1.00 1.06

Seeding Temperature (ºC) 73 65 70

Cooling Ramp (ºC/min) 0.3 0.1 0.5

Resolution IV fractional factorial design

24-1, 8 experiments + 4 centre point

Analysis by laser diffraction

(Malvern Mastersizer)


Comparison of Models:

Simulation vs DoE30

Two main effects are statistically significant:

Seed temperature(marginal) and cooling ramp.

The effect of cooling ramp is non-linear.

-0,30

-0,20

-0,10

-0,00

0,10

0,20

0,30

ram

ram

*ram

Tem

p

Effects

Effects for D01~

N=12 R2=0,963 RSD=0,02695

DF=8 Q2=0,917 Conf. lev.=0,95

Investigation: sita (MLR)

MODDE 9.1 - 2013-03-13 16:46:56 (UTC+1)

ramp

ramp * ramp

temp

-1,2

-1,0

-0,8

-0,6

-0,4

-0,2

0,0

0,2

0,4

0,6

0,8

1,0




N=81 Cond. no.=6,203 DF=66

Temp

ram

H2O

IPA

Temp*Temp

ram*ram

H2O*H2O

IPA*IPA

Temp*ram

Temp*H2O

Temp*IPA

ram*H2O

ram*IPA

H2O*IPA

MODDE 9.1 - 2013-03-13 16:36:32 (UTC+1)

-0,4

-0,3

-0,2

-0,1

-0,0

0,1

0,2

0,3

0,4

0,5

0,6

0,7

0,8

0,9

1,0

1,1

1,2




N=81 Cond. no.=6,203 DF=66

Temp

ram

H2O

IPA

Temp*Temp

ram*ram

H2O*H2O

IPA*IPA

Temp*ram

Temp*H2O

Temp*IPA

ram*H2O

ram*IPA

H2O*IPA

MODDE 9.1 - 2013-03-13 16:31:59 (UTC+1)

KG = 0.01 s-1

Factors:

ramp,

ramp*ramp

KG = 0.2 s-1

Factors:

water

seeding temp,

The experimental PSD results correlate inversely with

the supersaturation simulation for slow growth kinetics

(lower KG).

D[0.5]


Plant Batches31

Batch A particle size distribution was

too fine.

Batch B was better. Please provide

future batches like this.

Formulation

scientist

Batch ScaleSeed Temp

Seed load

Time after seeding

Cooling time

Agitation D[0.1] D[0.5] D[0.9]

Kg ºC wt% min hours rpm µm µm µm

A 50 69 0.5 30 5 75 4.7 20.6 89.7

B 50 69 0.5 15 5.1 70 8.5 49.4 162.3

Your process isn’t robust

enough!

Plant

scientist

DoE suggested cooling rate is main factor

Can our crystal growth / supersaturation model be improved?


Determination of KG32

Cooled crystallization

Samples taken, filtered and analysed (HPLC or gravimetrically)



RT

xDxC

RT

BAlnCln 10

100

0



dt*)CC(VKM L

t

0t

SGt


35

Determination of KG

dt*)CC(VKM L

t

0t

SGt


36

dt*)CC(VKM L

t

0t

SGt

Determination of KG

KG = 0.08 s-1


Virtual DoE with KG = 0.08 s-1

37

65 66 67 ºC6463

0.1

0

.3

0.5

ºC

/min

Co

olin

g r

ate

Seeding Temperature

Maximum supersaturation levelVariables:

Seed temperature (9 levels)

Cooling rate (3 levels)


Particle Size Considerations38

You must be joking! We need a

seeding temperature range of

at least 5ºC, preferably 10ºC

Well… we can try.

OK, the formulation scientists want

larger particles, so we should seed

at 64-66ºC and cool at 0.1ºC/min

Solid form

scientist

Plant

scientist


Seed load

Time after seeding

Cooling time



A 50 69 0.5 30 5 75 4.7 20.6 89.7

B 50 69 0.5 15 5.1 70 8.5 49.4 162.3


Plant Batches39

Erm, sorry…. But the PSD of

batches C and D are actually a

little too large. Please can we

have all batches like batch B?

Formulation

scientist


Seed load

Time after seeding

Cooling time



A 50 69 0.5 30 5 75 4.7 20.6 89.7

B 50 69 0.5 15 5.1 70 8.5 49.4 162.3

C 42.2 66 0.6 20 10 70 10.1 68.6 211.1

D 60 66 0.5 15 10 75 10.3 70.1 236.3

###*****!

Solid form

scientist

Seeding at lower temperature, slower cooling

larger particles

Still room for improvement!



“Remember that all models are wrong; the

practical question is how wrong do they

have to be to not be useful.”

George E. P. Box (1919 – 2013)

“Trying to develop or troubleshoot a solution crystallisation

process without knowledge of the solubility curve and

metastable zone width is akin to hiking though the wilderness

without a map or compass.”

Chris Price, ChemEng Prog 34-43 (1997)

“Everything should be made as simple

as possible, but not simpler.”

Albert Einstein (1879-1955)


Particle Size Distribution Simulations41

Requirements:

1. Improved crystal growth modelling

2. Add equations for nucleation

(and attrition and agglomeration)

3. Particle distribution tracking


More Sophisticated Growth Equation42

Temperature dependent growth,

non-linear with respect to supersaturation

Previous approximation with g = 1

gCRT

EakG D

exp

g

ref

ref CTTR

EakG D

)

11exp( ln_

gCKG D

g ≠ 1


More Sophisticated Growth Equation43

Experiments performed at low supersaturation to avoid nucleation

Large amount of seed to increase area for reasonable growth

Data fitted to determine Ea, Kref_ln and growth_order

Takes into account temperature dependence of growth constant

g

ref

ref CTTR

EakG D

)

11exp( ln_








Secondary Nucleation Equation45

DC = supersaturation (calculated)

MT = slurry density (calculated)

Power per unit mass (calculated)

Secondary nucleation rate constant (fitted experimentally)

Secondary nucleation order (fitted experimentally)

N_exp = exponent on agitation (fitted experimentally)

Power per unit mass is our equipment dependent variable

that effects nucleation

J. Mullin, Crystallization, 4th edition, Butterworth-Heinemann, 2001, p.249H-H. Tung et al, Crystallization of Organic Compounds: An Industrial Perspective, John Wiley & Sons, 2009. p.86


Calculation of Power per Unit Mass46

https://dcresources.scale-up.com/#q=solid-

liquid+Liquid+Mixing___5578








Population Balance Modelling

Method of Moments48

Approximation assumes a lognormal

distribution


Determination of nucleation parameters

Cooled crystallization with FBRM data49

Fit data (concentration

and moments)

to determine

sec_nuc_rate_ln,

N_exp and

sec_nuc_order


Crystallisation model with growth,

nucleation and population modelling50

Problem: only one lab experiment with FBRM data

no variation in power per unit mass over data set

N_exp and sec_nuc_rate_ln not properly resolved

Solution: second lab experiment changing scale and agitation

Or alternatively…

use data from one of the plant batches (Malvern PSD only)


Summary of Plant Batches51

BatchScale Seed temp Seed load

Time at T after seeding

Cooling time AgitationPower per unit

mass

Kg ºC wt% min hours rpm W/Kg

A 50 69 0.5 30 5 75 0.35

B 50 69 0.5 15 5.10 70 0.31

C 42.2 66 0.6 20 10 70 0.45

D 60 66 0.5 15 10 75 0.32

E 90 68 0.5 15 10 75 0.21

Experimental PSD Dynochem Models

Batch D[0.1] D[0.5] D[0.9]max

supersatwith nucleation and population modelling

µm µm µm g/KgD[0.1]

µmD[0.5]

µmD[0.9]

µm

A 4.7 20.6 89.7 39 12.0 44.9 166.9

B 8.5 49.4 162.3 39 12.9 47.2 172.4

C 10.1 68.6 211.1 28 21.0 67.1 214.2

D 10.3 70.1 236.3 30 21.9 70.0 223.3

E 10.6 55.4 168.0 30 21.9 69.9 223.1

Still room for improvement, but getting close!


Crystallisation Development52

Solubility

Model

Crystal growth /

supersaturation

model

PSD model with

nucleation

One or two experiments allows for model that can

guide effects of process parameters on

supersaturation (and therefore PSD trends!)

Experiments with good PSD and concentration

data and varying power / unit mass (agitation,

scale).

Scale up

predictions

Essential first step for any crystallization model!

Based on good experimental data

Process Knowledge


Acknowledgments53

Guillem Molas Andrew Bird

Peter Clark

Joe Hannon

Victor Nicolau

Cristina Renau

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