Quality By Design

Quality by Design
Development, optimization and robustness by Design
Mayank

Global initiatives

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 FDA, Draft Guidance for Industry—Process Validation: General Principles and Practices (Rockville, MD, Nov. 2008).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

Quality by design (QbD)
What is 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)
eg. USP
DSP
Cell viability
Cell count
Titre
Product characteristics (egGlycocylation)
Impurity profile
Overall purity
Type of impurity (eg HCP, endotoxins, DNA,)
Yield
Critical Process Parameter (CPP)
Column bed height and packing efficiency
Media selectivity
Media particle size
Dynamic capacity
Buffer conditions (eg pH, conductivity)
Temperature
Flow rate
Sample load
Temperature
pH
Agitation
DO
Medium composition
Osmolarity
Feed type
Process type (eg Batch, fed batch or perfustion)

Tools for successful implementation of QbD
Team:
Engineers
Biologists
Analysts
Chemists
Industrial pharmacist
Satiations
Analytical equipments
Online/Atline
NIR detectors
Methanol sensors
CO2/O2 probes
Conductivity probes
Osmolarity probes
Turbidity sensor
Cell count/contamination analyzer
Offline
HPLC/UPLC*
LC/MS
Ion analyzer*
C/N analyzer
Gel doc
Multi well plate reader
ELISA
Powerful Statistical tools

Process flow:
Screening
Characterization range
Identification of significant parameters
Acceptable range
Finding parameter ranges
Operating range
Optimization
Finding interactions of parameters
Defining models
Set point
Validation
Process design space
Identification of CPP
Identification of noise factors
Process/ product Development:
Robust
Cost effective
Feasible
Defining control strategies
Production
Continuous monitoring
and development

Defining Design space

Screening
Parameter selection
Physical
Chemical
Raw material
Component/Equipment
Process (time, type)
Environmental
Facility
Categorical
Continuous

Screening
Response
Level selection
Parameter
Digging for a fossil

Screening
Fractional Factorial
22

Screening
23-1
C=AB
C is confounding with AB

Screening
23-1
C=AB
B=AC
B is confounding with AC

Screening
23-1
C=AB
B=AC
B is confounding with AC
A=BC
A is confounding with BC

Screening
PlackettBurman
2 level fractional factorial designs
Resolution III design
Efficient estimations
Interactions between factors ignored
Used in Matrix form
Multiple of 4 not power of 2
Saturated orthogonal array

Screening
PlackettBurman
Matrix

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:
modeling error, (lack of fit, LOF)
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

DOE and Experiments
RS Model
1
x2
x1
Response surface methodology
Input
Response
Black Boxed
System
Original System

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
Model equation for 2 factors
Y = β0constant
+ β1X1 + β2X2 main effects
+ β3X12 + β4X22 curvature
+ β5X1X2 interaction
+ ε error
Model equation for 3 factors
Y = β0constant
+ β1X1 + β2X2 + β3X3 main effects
+ β11X12 + β22X22 + β33X32 curvature
+ β12X1X2 + β13X1X3 + β23X2X3 interactions
+ ε error
Higher order interaction terms
are not included

Central composite design (CCD)
eg. 2 factor
Central composite circumscribed (CCC)
5 Levels
α (star point) are beyond levels
Central composite face centered (CCF)
3 Levels
α (star point) are within levels (center)
Central composite inscribed (CCI)
5 Levels
α (star point) are within levels
Scale down of CCC

3 factors
Total exp: 20
Full factorial 8
Axial points 6
Center points 6
+++
-+-
---
+--
-++
++-
--+
+-+

Randomization:
To avoid effect of uncontrollable nuisance variables
+++
-+-
---
+--
-++
++-
--+
+-+

Blocking:
To avoid effect of controllable nuisance variables
-++
+++
+-+
--+
++-
-+-
+--
---

Box Behnen
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
eg. 3 factor
12 experiments

Comparison of RSM experiments
* 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.

Robust process development
Who is better shooter?
B
A

Goal post vs Taguchi view
LSL
USL
LSL
USL

Reducing variation

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-BestS/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

Identification of Signal and noise
eg: Fermentation
Signal:
What can be controlled in plant and laboratory
Noise:
What can not be controlled in plant but in laboratory

Developing robust process
To find a signal settings in presence of noise that minimize response variation while adjusting of keeping the process on target
Taguchi approach
Signal:
Inner array
Noise:
Outer array

Quality By Design

by realmayank on Jul 31, 2009

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