dynochem_webinar_novartis_flav_susanne_11jun2014

Development supported by process simulation

Flavien Susanne, Chemical Engineer

DynoChem Webinar 11th of June 2014

Application of QbD on a Continuous Process

Outline

Flavien Susanne / Dynochem Webinar 11th of June 2014

2

QbD The Right Time to Change

Why QbD for us in continuous process?

Our journey towards QbD...

Enabling process understanding for development and manufacturing

Build process robustness and reliability

Case study: Application to a multi-steps continuous process for PhII

Continuous process exist for century as

reported in The book De Re Metallica

published in 1556 representing 3 CSTRs with

gravity transfer system

Let’s move forward...

QbD and Design Space

Flavien Susanne / Dynochem Webinar 11th of June 2014 3

A Methodology for Good Development Practice

Process step

Process step

Process step

Process step

Continuous Process

Input

materials

Input

Process

Parameters

Monitoring of

the parameters

Product

pass or fail

Design Space

Process control

PAT

ICH concept of space design: multi-dimensional combination and interaction of input variables (e.g. material attributes) and process parameters that have been demonstrated to provide assurance of quality.

Goals: Deliver a robust and cost effective continuous process.

How can we achieve this? By reducing our product variability and increase our process acceptance.

Main Current Focus


Risk Assessment and Space Design Activities

Product

profile

CQAs

Risk

assessment

Design

space

Control

strategy

Continuous

Improvement

Target the product profile

Determine critical quality attibues (CQAs)

Link process parameters to CQAs and perform risk assessment

Develop a Design Space

Design and implement a control strategy

Manage product lifecycle

Target the product profile

Determine critical quality attibues (CQAs)

Link process parameters to CQAs and perform risk assessment

Develop a Design Space

Design an implement a control strategy

Manage product lifecycle

Main Current Focus


Risk Assessment and Space Design Activities

Product

profile

CQAs

Risk

assessment

Design

space

Control

strategy

Continuous

Improvement

Activities discussed in this talk

Continuous Synthesis


Process Description

This synthesis consists of three consecutive transformations to be conducted as a continuous process. Such Lithium exchange chemistry is known as being highly scale dependant. Continuous processing was seen as a solution to improve scale up reliability.

reactions

side reactions

Impurity 1

Sum mass loss

Impurity 2

Critical Quality Attribute


Objectives

Product

profile

CQAs

Risk

assessment

Design

space

Control

strategy

Continuous

Improvement

Definition: A Critical Quality Attribute (CQA) is a physical, chemical, biological, or microbiological property or characteristic that should be within an appropriate limit, range, or distribution to ensure the desired product quality. CQAs are generally associated with the drug substance, excipients, intermediates, and drug product.

High level of purity to meet specification of the product

Purity greater than 98% after full synthesis, work up and crystallisation. All impurity levels have to be below 0.1%

High yielding Yield greater than 60% for the complete sequence

High reliability of the process Minimise or suppress effect of scale up Minimise variability to less than 5-10%.

Enabling continuous processing has the purpose of minimising process variability and maximising process control. Significant yield and purity increase is seen as a bonus.

Q8(R1) Pharmaceutical Development Revision 1 :

http://www.fda.gov/downloads/RegulatoryInformation/Guidances/ucm128005.pdf


Critical Quality Attribute


What Do We Want to Control

The Impurity 1 level is critical to quality output. This impurity is generated by reprotonation of

Compound 4 and decompostion of Compound 5.

Mass loss is a significant issue to the overall yield and needs to be controlled.

One CQA may

dominate the picture Reactions

Side reactions

Impurity 1

Sum mass loss

Impurity 2

One KQA may

dominate the picture

Standard process development is conducted through a laboratory optimization exercise and process validation is done with Lab statistical (DoE) models.

Complex continuous processes are scale dependent. Therefore, the risk assessment needs to include the non-linear scale-up effect. DoE are rarely appropriate.

Risk Assessment in a QbD Approach


Mechanistic Thinking & Understanding

Product

profile

CQAs

Risk

assessment

Design

space

Control

strategy

Continuous

Improvement

Mechanistic

Thinking

Statistical

DoE

Critical Process

Parameters

Design

Space

Continuous

Process

Mechanistic

Model

Process

Understanding

For complex processing, mechanistic

modeling are put in place for process

development as well as process

validation.

“A mechanistic approach is justified whenever a basic

understanding of the system is essential to progress.”

George Box, Statistics for Experimenters, Wiley, 1978.

Standard process development is conducted through a laboratory optimization exercise and process validation is done with Lab statistical (DoE) models.

Complex continuous processes are scale dependent. Therefore, the risk assessment needs to include the non-linear scale-up effect. DoE are rarely appropriate.

.

Risk Assessment in a QbD Approach


Mechanistic Thinking & Understanding

Product

profile

CQAs

Risk

assessment

Design

space

Control

strategy

Continuous

Improvement

Mechanistic

Thinking

Statistical

DoE

Critical Process

Parameters

Design

Space

Continuous

Process

Mechanistic

Model

Process

Understanding

For complex processing, mechanistic

modeling are put in place for process

development as well as process

validation.

“A mechanistic approach is justified whenever a basic

understanding of the system is essential to progress.”

George Box, Statistics for Experimenters, Wiley, 1978.

Define an unifying reaction mechanism for the main transformations and the side product formations. The overall mechanism is postulated initially from literature searches and experimentally validated.

Methodology to Process Simulation

11

Process Understanding: Establish the Overall Process View

Reactions

Side reactions

Impurity 1

Sum mass loss

Impurity 2


Bulk liquid

Feed zone

Feed zone

Bulk liquid

Bulk liquid

UA

UA

UA

Solid

Solid

Solid

Solubility

kL

Solubility

kL

Solubility

kL

kinetic and physical rates



Graphical representation of a

Continuous Stirred Tank Reactor

What are the phases? Liquid and solid formation.

What are the components? Two solvents, starting material,

product and impurities.

What are the rates? Physical rates with mixing and mass

transfer. Kinetic rates for product and imputies formations.

Process Scheme


Process Variables

In our continuous process, what are the process variables?

Which ones are the Critical Process Parameters?

Bulk liquid

Bulk liquid

Feed zone

Feed zone

UA

SolidSolubility

kL



Bulk liquid

Feed zone

Feed zone

Bulk liquid

Bulk liquid

UA

UA

UA

Solid

Solid

Solid

Solubility

kL

Solubility

kL

Solubility

kL




Flow rate and

residence time

Feedstock composition - Stoichimetric ratio

- Dilution

Heat transfer Heating or cooling capacity

- Continuous reactor type

- Property of the media

Must consider

kinetic

In our continuous process, what are the process variables?

Which ones are the Critical Process Parameters?

Variation of concentrations and stoichiometry From substoichiometric to large excess Extremely important to include time as a variable of the process (residence time).

Process Development Methodology

13

Process Variables

process temperature

Variation of temperature From -50°C to rt

process temperature


Established platform enabling fast and reliable continuous process development


14

Experimental Aspect

Process understanding &

development platform

Dedicated laboratory and infrastructure

batch and continuous equipments

Specific tools for process understanding FT-IR, FBRM, calorimetry

Process simulation tools DynoChem kinetic modeling

Reaction followed by FT-IR, Heat Flow, off line

LC and NMR

Mass balance needs to be greater than 95%

Mettler Toledo RC1 batch experiments for

energy balance, calculation and reaction

profiling.

Flow experiments for really short residence

time end points.


Mechanistic modeling



15

Experimental Aspect








Starting material 1 + BuLi > intermediate + butane

intermediate + BuLi > Compound 4 + butane

Aryl lithium + BuLi = Compound 4- Li

+ + butane

Compound 4 + Starting material 1 > Impurity 1

Compound 4 + intermediate > Impurity 1

Compound 4 > Impurity 2



16

Experimental Aspect








Starting material 1 + BuLi > intermediate + butane

intermediate + BuLi > Compound 4 + butane

Aryl lithium + BuLi = Compound 4- Li

+ + butane

Compound 4 + Starting material 1 > Impurity 1

Compound 4 + intermediate > Impurity 1

Compound 4 > Impurity 2



Fitting of Kinetic Parameters

From the series of experiments, the kinetic parameters of the synthesis (main and side reactions) were fitted using DynoChem Algorithm (Simplex & Levenberg)

Main reactions defined as differential rate equations

-rSM = k1.exp[Ea/R*(1/T-1/T

0)]*CSM0

*(1-XSM)*CBuLi*(1-XBuLi)

T = T0 – DHrx/CpsolX*X

-rint = k1.exp[Ea/R*(1/T-1/T

0)]*Cint0

*(1-Xint)*CBuLi*(1-Xint)


Energy balance is critical

in a continuous process

Fast reactions. Only relative

ratio compared to mixing and

impurity formation is required




From the series of experiments, the kinetic parameters of the synthesis (main and side reactions) were fitted using DynoChem Algorithm (Simplex & Levenberg).

Side reaction and decomposition defined as differential rate equations.

-rprod = k1.exp[Ea/R*(1/T-1/T

0)]*[Cprod0

*(1-Xprod)]2*CBuLi*(1-XBuLi)


0)]*Cprod0

*(1-Xprod)

▼Experimental values

-- model prediction




From the series of experiments, the kinetic parameters of the synthesis (main and side reactions) were fitted using DynoChem Algorithm (Simplex & Levenberg).

Side reaction and decomposition defined as differential rate equations.

-rSM = k1.exp[Ea/R*(1/T-1/T

0)]*CSM0

*(1-XSM)*CBuLi*(1-XBuLi)


-rint = k1.exp[Ea/R*(1/T-1/T

0)]*Cint0

*(1-Xint)*CBuLi*(1-Xint)



0)]*[Cprod0

*(1-Xprod)]2*CBuLi*(1-XBuLi)


0)]*Cprod0

*(1-Xprod)

Acceptable prediction

of the chemistry

Design Space


How to use Process Simulation

Product

profile

CQAs

Risk

assessment

Design

space

Control

strategy

Continuous

Improvement

Design space definition: A design space can be defined in terms of ranges of input variables or parameters, or through more complex mathematical relationships. It is possible to define a design space as a time dependent function.

Design space will be defined using a mechanistic/kinetic model.

The design space is generated through process understanding

Q8(R1) Pharmaceutical Development Revision 1 :



Response Surface


Process Operation Step 1

The process temperature is a CPP, it is directly affected by:

The heat balance, energy input (DHr) – energy output (heat transfer)

Process flow rate, energy provided by unit of time

Characteristic of the reactor, CSTR Vs PFR

Response Surface



Impurity 1 below acceptance level Impurity 3 below acceptance level

The process flow rate is a CPP, it is directly affected by: Relative stoichiometry

Residence time

Response Surface



Yield above acceptance level

Overlapping multiple response surface

to construct design space

Process design

space

Response Surface



Process

design space

To account for the process simulation uncertainty (Sum of Square = deviation between

prediction and experimental values) as well as the response error.

To ensure CQAs within specification.

The design space has been reduced to 50% of its overall space.

“Buffer regions” to account for error

Compound 1 flow rate can deviate by +/-

6% from set point

BuLi 1 flow rate can deviate by +/- 6%

from set point

Temperature can deviate by +/- 5C from

set point

Response Surface


Process Operation Step1

The quality output from step 1 becomes

one of the variables of step 2

Process

design space

“Buffer regions” to account for error

Compound 1 flow rate can deviate by +/-

6% from set point

BuLi 1 flow rate can deviate by +/- 6%

from set point

Temperature can deviate by +/- 5C from

set point

To account the process simulation uncertainty (Sum of Square = deviation between prediction

and experimental values) as well as the response error.

To ensure CQAs within specification.

The design space has been reduced to 50% of its overall space.

Response Surface


Process Operation Step 2 and Following Steps

Process

design space

Process

design space

Consecutive design spaces of operation for each step have been defined.

It is critical to ensure that each design space is compatible to the next without restraining too much the potential of operation.

Process

design space

Step A

Step C

Step B

Overall design space of

operation of the process.

Response Surface


Process Simulation for Continuous Reactor

As all the reactions have been highly exothermic, a reactor system allowing fast and efficient control of the internal temperature was critical.

Continuous Stirred Tank Reactor was forseen with a split of the feed of BuLi. CSTRs was also accommodating the solid formation by avoiding clogging.

Various combinations of size and materials were insilico calculated to assess the compatibility with the process.

Vessel 1

Process Solvent

Temperature -20

Fouling factor 5000

Agitation properties

Impeller speed 500 rpm

Service Fluid 10 10

Temperature -35

Fouling factor 5000 1

Results summary

hi 2007.68 W/m2 K

hw 1908.71 W/m2 K

ho 1330.72 W/m2 K

U 563.864 W/m2 K

UA 0.746 W/K

duty -0.011 kW

cooling rate -87.699 C/min

Twall -24.21 C

UA intercept 0.13 W/K

UA(v) in straight side 112.77 W/L K

Add to Report

Development of the Scale-up Process


Based on the response surface the process conditions were defined Residence time

Process temperature Stoichiometry

Small footprint of 4m2

Process Control


Simple Pass/ Fail Strategy

Product

profile

CQAs

Risk

assessment

Design

space

Control

strategy

Continuous

Improvement

The process has been developed to accommodate reasonable process

deviation and still deliver quality matching specification.

The process has been developed to accommodate reasonable process

deviation and still deliver quality matching specification.

As the process is developed in CSTRs, deviation outside the operating range

could be acceptable if they can be buffered by the volume of the vessel.

A deviation below 10% for less than 0.75sec. is still acceptable

Process Control


Simple Pass/ Fail Strategy

Product

profile

CQAs

Risk

assessment

Design

space

Control

strategy

Continuous

Improvement

Deviation still below time specification

Conclusion


Process development was supported by process simulation and PAT.

The process transfer was enabled using QbD with process simulation.

As the first campaign using such methodology of process simulation and QbD, additional process checks were conducted to validate the prediction during Piloting.

• Compound 6 + 2 steps has been continuously produced during 1 week production.

The average yield to compound 6 (3 continuous chemical transformations) was 85%. The yield on the continuous process was increased by 31%.

Acknowledgement

32

CM Team Fransceco Venturoni

Benjamin Martin

Michel Aubry

Jutta Polenk

Berthold Schenkel

Fabio Lima

Joerg Sedelmeier

Mario Rentsch

Serbi Sevinc

Safety Lab Christoph Heuberger

Michael Schonhardt

LSC Carlo Jungo

Holger Scheidat

CHAD Isabelle Gallou

Thomas Kuhnle


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