DEVELOPMENT, OPTIMIZATION AND ROBUSTNESS BY DESIGN

QUALITY BY DESIGN

Mayank

Global initiatives

1. ICH, Q8(R1) Pharmaceutical Development (Geneva, Switzerland, Nov. 10, 2005; Rev. 2008).

2. ICH, Q9 Quality Risk Management (Geneva, Switzerland, Nov. 9. 2005).

3. J. Agalloco et al., "FDA's Guidance for Industry: Process Validation: General Principles and Practices," presented at PDA, Jan. 14, 2009.

4. FDA, Draft Guidance for Industry—Process Validation: General Principles and Practices (Rockville, MD, Nov. 2008).

5. W. Charlton, T. Ingallinera, and D. Shive, "Validation of Clinical Manufacturing," and Validation Chapter, in Validation of Pharmaceutical Process, J. Agalloco and F. Carleton, eds. (Informa Healthcare, New York, 3rd ed., 2008), pp. 542–544.

References

Global initiatives

What is QbD?

Quality by design (QbD)

Product and process performance characteristics are scientifically designed to meet specific objectives, not merely empirically derived from performance of test batches

Focus during development

Critical Quality Attributes (CQA)

oCell viabilityoCell countoTitreoProduct characteristics (eg Glycocylation)oImpurity profile

Critical Process Parameter (CPP)oTemperatureopHoAgitationoDOoMedium compositionoOsmolarityoFeed typeoProcess type (eg Batch, fed batch or perfustion)

oOverall purityoType of impurity (eg HCP, endotoxins, DNA,)oYield

oColumn bed height and packing efficiencyoMedia selectivityoMedia particle sizeoDynamic capacityoBuffer conditions (eg pH, conductivity)oTemperatureoFlow rateoSample load

eg. USP DSP

Tools for successful implementation of QbD

Quality by design (QbD)

Team:

Analytical equipments

oOnline/Atlineo NIR detectorso Methanol sensorso CO2/O2 probeso Conductivity probeso Osmolarity probeso Turbidity sensoro Cell count/contamination analyzer

oEngineersoBiologistsoAnalystsoChemistsoIndustrial pharmacistoSatiations

oOfflineo HPLC/UPLC*o LC/MSo Ion analyzer*o C/N analyzero Gel doco Multi well plate readero ELISA

Powerful Statistical tools

Process flow:

Quality by design (QbD)

Screening

Optimization

Validation

Identification of significant parametersFinding parameter ranges

Finding interactions of parametersDefining models

Production

Identification of CPP

Continuous monitoring and development

Characterization range

Acceptable range

Operating range

Process design space

Set point

Identification of noise factorsProcess/ product Development:

RobustCost effectiveFeasible

Defining control strategies

Quality by design (QbD)

Defining Design space

Quality by design (QbD)

Defining Design space

Parameter Controllability

1 Easy

2 Difficult

Parameter Controllability

1 Difficult

2 Easy

Quality by design (QbD)

Defining Design space

Screening

Parameter selection

PhysicalChemicalRaw materialComponent/EquipmentProcess (time, type)EnvironmentalFacility

Categorical

Continuous

Screening

Level selection

Digging for a fossil

Parameter

Resp

onse

Screening

Fractional Factorial

No of exp Factors

A B

1 + -

2 - -

3 + +

4 - +

22

Screening

No of exp Factors Interaction

A B AB

1 + - -

2 - - +

3 + + +

4 - + -

22

Fractional Factorial

Screening

No of exp Factors

A B C=AB

1 + - -

2 - - +

3 + + +

4 - + -

23-1C=AB

C is confounding with AB

Fractional Factorial

Screening

No of exp Factors and interaction

A B=AC C=AB

1 + - -

2 - - +

3 + + +

4 - + -

23-1C=AB

C is confounding with ABB=AC

B is confounding with AC

Fractional Factorial

Screening

No of exp Factors and interaction

A=BC B=AC C=AB

1 + - -

2 - - +

3 + + +

4 - + -

23-1C=AB

C is confounding with ABB=AC

B is confounding with ACA=BC

A is confounding with BC

Fractional Factorial

Screening

Plackett Burman

2 level fractional factorial designsResolution III designEfficient estimationsInteractions between factors ignoredUsed in Matrix formMultiple of 4 not power of 2Saturated orthogonal array

Fractional Factorial

Screening

Plackett Burman

2/

)1()1(

N

yyEx

X1 X2 X3 X4 X5 X6 X7 X8 X9 X10 X11

1 1 1 1 1 1 1 1 1 1 1

1 1 1 -1 -1 -1 1 -1 -1 1 -1

1 1 -1 -1 -1 1 -1 -1 1 -1 1

1 -1 1 1 1 -1 -1 -1 1 -1 -1

1 -1 -1 1 -1 1 1 1 -1 -1 -1

1 -1 -1 -1 1 -1 -1 1 -1 1 1

-1 1 1 1 -1 -1 -1 1 -1 -1 1

-1 1 -1 1 1 1 -1 -1 -1 1 -1

-1 1 -1 -1 1 -1 1 1 1 -1 -1

-1 -1 1 -1 1 1 1 -1 -1 -1 1

-1 -1 1 -1 -1 1 -1 1 1 1 -1

-1 -1 -1 1 -1 -1 1 -1 1 1 1

Fractional Factorial

Matrix

Before deciding whether to build a response surface model, it is important to assess the adequacy of a linear model

The error term ε in the model is comprised of two parts:1. modeling error, (lack of fit, LOF)2. experimental error, (pure error, PE), which can be calculated from

replicate points

The lack of fit test helps us determine if the modeling error is significant different than the pure error

Screening

Lack of fit

Before deciding whether to build a response surface model, it is important to assess the adequacy of a linear model

The error term ε in the model is comprised of two parts:1. modeling error, (lack of fit, LOF)2. experimental error, (pure error, PE), which can be calculated from

replicate points

The lack of fit test helps us determine if the modeling error is significant different than the pure error

Screening

Lack of fit

Response surface methodology

Original System

x1

x2

1

DOE and Experiments

Black BoxedSystem

Input

1x

2x

Response

y

RS Model

Response surface methodology

RSM characteristics

Models are simple polynomials

Include terms for interaction and curvature

Coefficients are usually established by regression analysis with a computer program

Insignificant terms are discarded

Y = β0 constant + β1X1 + β2X2 main effects + β3X1

2 + β4X22 curvature

+ β5X1X2 interaction + ε error

Model equation for 2 factors

Y = β0 constant + β1X1 + β2X2 + β3X3 main effects + β11X1

2 + β22X22 + β33X3

2 curvature + β12X1X2 + β13X1X3 + β23X2X3 interactions + ε error

Model equation for 3 factors

Higher order interaction termsare not included

Response surface methodology

Central composite circumscribed (CCC)

Central composite inscribed (CCI)

Central composite face centered (CCF)

5 Levelsα (star point) are beyond levels

3 Levelsα (star point) are within levels (center)

5 Levelsα (star point) are within levelsScale down of CCC

Central composite design (CCD)

eg. 2 factor

Response surface methodology

3 factors

Total exp: 20Pattern X1 X2 X3+++ 1 1 1++− 1 1 -1+−+ 1 -1 1+−− 1 -1 -1−++ -1 1 1−+− -1 1 -1−−+ -1 -1 1−−− -1 -1 -10 0 0 00 0 0 00 0 0 00 0 0 00 0 0 00 0 0 0

00a 0 0 -1.68179

00A 0 01.68179

30a0 0 -1.68179 0

0A0 01.68179

3 0

A001.68179

3 0 0a00 -1.68179 0 0

++-

+++

--- +--

--+

-++

-+-

+-+

Central composite design (CCD)

Full factorial 8

Axial points 6

Center points 6

Central composite circumscribed (CCC)

Response surface methodology

Randomization:

To avoid effect of uncontrollable nuisance variablesPattern X1 X2 X3000 0 0 0

A001.68179

3 0 0++− 1 1 -100a 0 0 -1.68179−−+ -1 -1 1000 0 0 00a0 0 -1.68179 0−−− -1 -1 -1−+− -1 1 -1000 0 0 0+−+ 1 -1 1000 0 0 0000 0 0 0a00 -1.68179 0 0000 0 0 0+−− 1 -1 -1−++ -1 1 1

00A 0 01.68179

3

0A0 01.68179

3 0+++ 1 1 1

Central composite design (CCD)

++-

+++

--- +--

--+

-++

-+-

+-+

Central composite circumscribed (CCC)

Response surface methodology

To avoid effect of controllable nuisance variablesPattern X1 X2 X3 Block−−− -1 -1 -1 1−++ -1 1 1 1++− 1 1 -1 1+−+ 1 -1 1 1000 0 0 0 1000 0 0 0 1−+− -1 1 -1 2000 0 0 0 2−−+ -1 -1 1 2000 0 0 0 2+++ 1 1 1 2+−− 1 -1 -1 2000 0 0 0 3000 0 0 0 3a00 -1.63299 0 0 30a0 0 -1.63299 0 3

A001.63299

3 0 0 3

0A0 01.63299

3 0 300a 0 0 -1.63299 3

00A 0 01.63299

3 3

---

-++

++-

+-+

+++

--+

+--

-+-

Central composite design (CCD)

Blocking:Central composite circumscribed (CCC)

Response surface methodology

Box Behnen

12 experiments

eg. 3 factor

It is portion of 3k Factorial 3 levels of each factor is used Center points should be included It is possible to estimate main effects and second order terms Box-Behnken experiments are particularly useful if some boundary areas of the design region are infeasible, such as the extremes of the experiment region

Response surface methodology

* One third replicate is used for a 3k factorial design and one-half replicate is used for a 2k factorial design with the CCD for 5, 6 and 7 factors.

Comparison of RSM experiments

Robust process development

Who is better shooter?

BA

Robust process development

Goal post vs Taguchi view

LSL USL LSL USL

Robust process development

Reducing variation

Robust process development

Objective of robust process

Smaller-the-Better S/N Ratio = – 10 Log10 ( 1/n Yi2 )

e.g. defects, impurity, process time, cost

Larger-the-Better S/N Ratio = – 10 Log10 ( 1/n 1/Yi2 )

e.g. titre, yield, resolution, profit

Nominal-the-Best S/N Ratio = – 10 Log10 [ 1/n (YIDEAL- Yi ) 2 ]

e.g. target

Signal-to-Noise S/N Ratio = 10log[μ2/σ2]e.g. trade-off

Robust process development

Identification of Signal and noise

Parameters Plant Laboratory Agitation +++ +++ Feed rate +++ +++

Recipe +++ +++ Aeration ++ +++ Pressure ++ +++

pH ++ +++ Mass transfer + +++ Temperature + +++ Raw material + +++

Operator + +++ Scale up - ++

Environmental - -

What can be controlled in plant and laboratory

Signal:

Noise:

What can not be controlled in plant but in laboratory

eg: Fermentation

Robust process development

Developing robust process

To find a signal settings in presence of noise that minimize response variation while adjusting of keeping the process on target

Signal:

Noise:

Taguchi approach

Inner array

Outer array

OA IA

A B C - - - + + - ++

1 + + +

2 + + -

3 + - +

4 + - -

5 - + +

6 - + -

7 - - +

8 - - -