Scale up optimization using simulation experimentsScale up optimization using simulation experiments

Scale up optimization Scale up optimization

using simulation using simulation

experimentsexperiments

M. Bentolila, R.S. Kenett, S. Malca, R. Novoa, M. Hasson, B.N. YoskovichM. Bentolila, R.S. Kenett, S. Malca, R. Novoa, M. Hasson, B.N. YoskovichM. Bentolila, R.S. Kenett, S. Malca, R. Novoa, M. Hasson, B.N. YoskovichM. Bentolila, R.S. Kenett, S. Malca, R. Novoa, M. Hasson, B.N. Yoskovich

Scale up optimization using simulation experimentsScale up optimization using simulation experiments

Perrigo IL StructurePerrigo IL

Pharma Int’l. Consumer Prod

Finance Info. Systems

LogisticsBusiness

Development

Human Resources

DanAgis Agis Invest

Neca Careline NaturalFormula

Perrigo Israel Pharmaceuticals

Ltd.

ChemAgis Ltd.

ChemAgis Israel

ChemAgis USA

ChemAgis Germany(GmbH)

Zibo Xinhua-PerrigoPharma JV

Pharma IL

Perrigo NY

Scale up optimization using simulation experimentsScale up optimization using simulation experiments

R & D Organization Chart

Chemagis’ personnel constantly strive to develop newtechnologies and processes that meet the stringent scientific and regulatory demands and challenges to support today's global markets.

Scale up optimization using simulation experimentsScale up optimization using simulation experiments

Our ProductsGeneric API – Active Pharmaceutical Ingredients

production: production: 30 products py 30 products py production: production: 30 products py 30 products py

Examples ofr our products:

• Pentoxifylline Vasodilator

• Pramipexole Dihydrochloride Anti Parkinsonian

• Rocuronium Bromide Neuromuscular blocker

• Temozolomide Antineoplastic, alkylating agent

• Terbinafine Hydrochloride Antidermatophyte (fungal infections

of the nails)

• Tramadol Hydrochloride Analgesic

• Zonisamide Antiepileptic

Scale up optimization using simulation experimentsScale up optimization using simulation experiments

Scale Up MethodologyScale Up Methodology(J.M. Berty. CEP, 1979)(J.M. Berty. CEP, 1979)

Scale Up MethodologyScale Up Methodology(J.M. Berty. CEP, 1979)(J.M. Berty. CEP, 1979)

Scale up optimization using simulation experimentsScale up optimization using simulation experiments

Scale Up MethodologyScale Up Methodology(J.M. Berty. CEP, 1979)(J.M. Berty. CEP, 1979)

Scale Up MethodologyScale Up Methodology(J.M. Berty. CEP, 1979)(J.M. Berty. CEP, 1979)

Scale up optimization using simulation experimentsScale up optimization using simulation experiments

Before 2001Before 2001Before 2001Before 2001

2001-20052001-20052001-20052001-2005

2005 - 2005 - 2005 - 2005 -

AdvancedAdvancedAdvancedAdvanced

poorpoorpoorpoor

combinedcombinedcombinedcombined

Scale up optimization using simulation experimentsScale up optimization using simulation experiments

Before 2001Before 2001Before 2001Before 2001

The scale-up process The scale-up process columnarcolumnar

didn’t use any simulation toolsdidn’t use any simulation tools

The scale-up process The scale-up process columnarcolumnar

didn’t use any simulation toolsdidn’t use any simulation tools

Scale up optimization using simulation experimentsScale up optimization using simulation experiments

2001 - 20052001 - 20052001 - 20052001 - 2005Visimix and DynochemVisimix and DynochemVisimix and DynochemVisimix and Dynochem

DOE – Design Of ExperimentsDOE – Design Of Experiments

Scale up optimization using simulation experimentsScale up optimization using simulation experiments

2005 - 2005 - 2005 - 2005 -

Visimix DynochemVisimix DynochemVisimix DynochemVisimix Dynochem

Scale up optimization using simulation experimentsScale up optimization using simulation experiments

• VisiMix – Mixing simulation and calculation software. Mathematical modeling of mixing phenomena. Calculation of average and local characteristics of mixing flow and distribution of concentration. Simulation and calculation of real “non perfect” mixing.

• DynoChem – Chemical dynamic simulation software. Fitting if chemical reaction models. Prediction of scale-up conditions. Optimization of laboratory and production results. Equipment characterization. Shows effects of scale dependent physical phenomena (mixing, heat transfer, mass transfer).

Dynochem can be used for simulation of reactions performed in homogenous environment. When mixing is not ideal and the solution is not homogenous VisiMix is used for finding the required mixing conditions.

The programs used for Modeling simulation and optimization:

Scale up optimization using simulation experimentsScale up optimization using simulation experiments

Scale up optimization using simulation experimentsScale up optimization using simulation experiments

TITIme T To market reduction via S Statistical

IInformation M Management Project No. : Project No. : GRD1 – 2000 - 25724GRD1 – 2000 - 25724

INTRASOFT (GR), London School of Economics (UK), POLITECNICO DI TORINO (IT), Centre National de la Recherche Scientifique CNRS (F), BLUE Engineering

Group (IT), EASi Europe (D), KPA Ltd (IL), SNECMA (F), Israel Aircraft Industries Ltd IAI (IL)

INTRASOFT (GR), London School of Economics (UK), POLITECNICO DI TORINO (IT), Centre National de la Recherche Scientifique CNRS (F), BLUE Engineering

Group (IT), EASi Europe (D), KPA Ltd (IL), SNECMA (F), Israel Aircraft Industries Ltd IAI (IL)

Scale up optimization using simulation experimentsScale up optimization using simulation experiments

TEMO production ProcessTEMO production Process

1.1. Crude TEMO production - The crude production step Crude TEMO production - The crude production step contains two main operations – the reaction and the contains two main operations – the reaction and the precipitation.precipitation.

2.2. Crystallization – This is the main purification step of the Crystallization – This is the main purification step of the process.process.

The reaction is described at these equations:The reaction is described at these equations:

Scale up optimization using simulation experimentsScale up optimization using simulation experiments

Optimal reaction time Optimal reaction time yields maximum yields maximum amount of TEMO and minimum amount of amount of TEMO and minimum amount of impurities (maximum yield).impurities (maximum yield).

Scale up optimization using simulation experimentsScale up optimization using simulation experiments

Model Fitting, Optimization and SimulationModel Fitting, Optimization and Simulationusing Visimix and Dynochemusing Visimix and Dynochem

Scale up optimization using simulation experimentsScale up optimization using simulation experiments

Model Fitting, Optimization and SimulationModel Fitting, Optimization and Simulation

Where: Where:

K – Reaction Constant (m3/mol.s) and (1/s) K – Reaction Constant (m3/mol.s) and (1/s)

EEaa – Reaction Activation Energy (kJ/mol) – Reaction Activation Energy (kJ/mol)

KKLaLa – Mass transfer coefficient (1/s) – Mass transfer coefficient (1/s)

Where: Where:

K – Reaction Constant (m3/mol.s) and (1/s) K – Reaction Constant (m3/mol.s) and (1/s)

EEaa – Reaction Activation Energy (kJ/mol) – Reaction Activation Energy (kJ/mol)

KKLaLa – Mass transfer coefficient (1/s) – Mass transfer coefficient (1/s)

Scale up optimization using simulation experimentsScale up optimization using simulation experiments

optimization

8 TEMO batches were produced at RC1 scale at the production conditions set according to the Visimix and DynoChem simulation and optimization results.

The required impurities level is N.M.T 0.15% (for each impurity) at the final product.

Impurity levels at production:

At the end of crude step the impurity levels are higher then spec – We can’t skip crystallizationAt the end of crude step the impurity levels are higher then spec – We can’t skip crystallization

Scale up optimization using simulation experimentsScale up optimization using simulation experiments

The experimental arrayThe experimental arrayThe experimental arrayThe experimental array(Simulation experiments)(Simulation experiments)(Simulation experiments)(Simulation experiments)

Scale up optimization using simulation experimentsScale up optimization using simulation experiments

Process parameters vs. constraintsProcess parameters vs. constraintsProcess parameters vs. constraintsProcess parameters vs. constraints

Case Target Function Yield EOR [hr] Stirrer [rpm] TEMP [0C]

AHigh demand to the product and the reactor. Yield →max, EOR≤8

98.1 8 546 26

BThe price of the product equals 10 times the value of reactor availability. (10∙yield-EOR)→max

98.4 8.3 623 25

C

High demand of the product with low availability of reactors. One hour of available reactor equals 10 times yield. (1∙yield-10∙EOR)→max

95.2 1.5 483 39

DHigh availability of reactors, High cost of impurity purification. (10∙yield-10∙IMAM-1∙EOR)→max

98.9 14.4 637 20

Scale up optimization using simulation experimentsScale up optimization using simulation experiments

Non ideal stirring – non homogeneity

• When DynoChem simulation does not match experimental data we should suspect a stirring problem and non homogeneity conditions in the reaction solution.

• VisiMix software is used in order to find the required stirring characteristics.

• New conditions are applied on experiments before fitting a model at DynoChem software .

For example: The product XXXX is produced at a solid liquid reaction.

The main reaction at this process is:

BBCM + TA + POCA →XXXX

POCA reagent properties:• Solid• High particle size: mean=735m• High density: 2300 kg/m3

suspension must be achieved in order to fit a DynoChem model to the reaction.

VisiMix was used for suspension calculation.

Scale up optimization using simulation experimentsScale up optimization using simulation experiments

Non ideal stirring – non homogeneity

• Before performance of scale up experiments VisiMix simulation was used to check suspension at different Mini Pilot Reactors:

Reactor 7603 7605 7605 7607 Volume, L 10 25 25 50 RPM 500 (Max) 400 500 (Max) 150 (Max)

Main Characteristic

Liquid – Solid Mixing

Solid suspension quality

Complete suspension is questionable.

Partial settling of solid phase may

occur.

Complete suspension is

expected.

Complete suspension is

expected.

Complete suspension is questionable.

Partial settling of solid phase may

occur. Max. degree of non uniformity of solid

distribution

AXIAL, % 22.3 10.3 29.1 132 RADIAL, % 65.7 34.3 76.3 90.8

Not all Mini Pilot reactor are capable of full suspension of POCA.

Not all Mini Pilot reactor are capable of full suspension of POCA.

Scale up optimization using simulation experimentsScale up optimization using simulation experiments

Financial aspectsFinancial aspects

Scale up optimization using simulation experimentsScale up optimization using simulation experiments

Adjusting the EOR Adjusting the EOR time according to the time according to the financial optimization financial optimization saves about 4% of the saves about 4% of the material.material.

Scale up optimization using simulation experimentsScale up optimization using simulation experiments

Summary and ConclusionsSummary and Conclusions

• As part of the continuous professionals' formation policy of our company ours engineers and operative personal have to permanent learn about causes and consequences of changes present in scale-up, scale-down challenge .

• Using software package like VisiMix and Dynochem orient the eng during the process development to the best results.

• Scale-up (or down) is a very complex enterprise and, for to arrive an acceptable results, needs to be faced by an interdisciplinary team-work of:

– Technicians– Chemical process ENG – Chemists– Mathematical statistics experts, – Computation ENG– And others

As a result of the team-work we arrive at the desired result and at the same time every participant and their collaborators update his knowledge in a large spectrum of related sciences and arts.

Scale up optimization using simulation experimentsScale up optimization using simulation experiments

Thank you for your attentionThank you for your attention