Transcript A QbD Approach to Process Development: Defining Critical

A QbD Approach to Process Development:
Defining Critical Quality Attributes and
Evaluating Criticality Across Scales
Carl A. Anderson, Duquesne University
NIPTE – Scientific Design of Pharmaceutical Products,
October 3rd, 2016
FDA White Oaks Campus, Silver Spring, MD
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Researchers and Acknowledgements
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FDA
 Sharmista Chatterjee, Ph.D.
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Duquesne University
 Graduate School of Pharmaceutical
Sciences
 Carl A. Anderson, Ph.D.
 Ira S. Buckner, Ph.D.
 James K. Drennen, Ph.D.
 Peter L.D. Wildfong, Ph.D., B.Sc.
 Yuxiang (Henry) Zhao
 Natasha Velez-Rodriguez
 Pradeep Valekar
 Purdue University
 Carl R. Wassgren
 Siddhartha Agarwal
 University of Connecticut
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Bohdi Chaudhuri, Ph.D.
Robin Bogner, Ph.D.
Kim Tran
Koyel Sen
 Acknowledgements
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James D. Lister, Ph.D.
Daniel Peng, Ph.D.
Jayanth Bhupasamudram
Maitraye Sen
Enhanced technical detail available at
poster session
 See: “A QbD Approach to
Process Development: …”
We are grateful to the National Institute for Pharmaceutical Technology and Education (NIPTE) and the U.S. Food
and Drug Administration (FDA) for providing funds for this research. This study was funded by the FDA Grant to
NIPTE titled "The Critical Path Manufacturing Sector Research Initiative (U01)"; Grant# 5U01FD004275
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Project Description 1
 The investigators propose a project that will provide an
example of QbD used for effective scale‐up of a complex
multistep manufacturing process of a high‐risk (narrow
therapeutic window) API, while investigating the criticality of
quality attributes (CQAs) and process parameters (CPPs)
across scales.
 Process
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High Shear Granulation and drying
Polymer film coating of granules
Blending
Compression
Project Description 2
 API – theophylline
 Narrow therapeutic window
 Facile formation of hydrate including solvent
mediated transformation1
 Formulation strategy
 Dosage form: tablet
 Controlled drug delivery: coated granules
1Rodriguez‐Hornedo,
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N., et al., Int J Pharm, 1992. 85: p. 149‐162
Yuen and Grant, International Journal of Pharmaceutics, 1996. 135(1‐2): p. 151‐16
Ticehurst et al., International Journal of Pharmaceutics, 2002. 247(1‐2): p. 1‐10
Airaksinen, S. et al., J Pharm Sci, 2003. 92: p. 516‐528
Experimental Plan
Product
Variability
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Optimum
granule
Granule
variant - low
Coated granule
variant - high
Optimum
coated granule
Coated granule
variant - low
Compression
Blend
variant - high
Optimum
blend
Blend
variant - low
Variability from processing conditions
Granule
variant - high
Blending
Variability from formulation and processing
conditions
Coating
Variability from formulation and processing
conditions
Variability from formulation and processing
conditions
Experimental Plan
Granulation
Acceptable
Quality
Risk Identification
for Granule
Coating
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Initial Risk Severity
Assessment
 Critical Quality Attributes
and severity ranking
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Content uniformity
Assay
Physical stability
Chemical stability/purity
Dissolution
Microbiology
Appearance
Tablet RTS
 Severity scale
1.
5
5
2
2.
3.
4
5
4
3
3
4.
5.
Has no appreciable consequences to
quality (change mid-process, change
next batch, root cause is well
understood)
Batch loss
Batch loss and
mild risk to patient
Between 3 and 5
Batch loss and
severe risk to patient
(potentially lethal)
Risk and Process Control
 Process control models within each unit operation
 Control
 Predict quality
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Analytical models to measure attributes during unit operation
Hierarchical models to use incoming material attributes.
Understand risk at a given scale
Understand additional risks that arise from scale-up
Critical Initial Process/Formulation
Optimization Characteristics
Granule size and
density to facilitate
coating
Granule coating to
control drug release
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Coated granules must
have appropriate
flowability for blending
and transfer
Coating on the granules
must stay intact during
compression
HSWG Scale Up
 Scale up equipment
 1 liter scale (Diosna mixer/granulator P 1-6)
 Current formulation
 Process development
 Next scale: 10 and 25 liter bowls (Diosna P Vac 10-60)
 Will use a regime map approach
 Described in Kayrak-Talay et al. (2013, Powder Technology, Vol. 240,
pp. 7 – 18)
 Empirical (little to no knowledge) =>
regime map (known parameters and processes) =>
mechanics-based (significant knowledge)
 Process
 Identify operating conditions at the lab scale
 Ensure that significant dimensionless parameters result in operation in
the same regime during scale-up
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Regime Maps
dimensionless spray flux
(rate at which drop area is generated /
rate at which fresh powder area appears)
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Growth Regime Map
granule deformation Stokes #
(impact pressure /
deformation strength)
dimensionless drop penetration time
(time for drop to penetrate/
bed circulation time)
Nucleation Regime Map
granule pore saturation
(fraction of granule volume filled with liquid)
Granule Coating
Process Model
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Granule Coating
Process Details
Material and
Environmental
Variability
FFC
Engineering Control
Analytical Control
Coating thickness / Moisture level
Deviation
Air Volume
Target
some delay
Atomization pressure
Spray rate
Inlet air temperature
Heater on/off
Set points
Flap position
Pump speed
Basic Control
FBC
FBC
Air Volume
Atomization pressure
Spray rate
Inlet air temperature
Adjustments
Measurement
Temperature / Air Volume
Deviation
Deviation
Target
nearly no delay
Dissolution / Flowability / Mechanical Properties
Target
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Process
FBC
long delay and RTRT model required
0.2
Before
Fluidization
0.15
After
Fluidization
0.1
0.05
0
0.25
50-100
100-150
150-200
200-250
250-300
300 - 350
350 - 400
400 - 450
450 - 500
500 - 550
550 - 600
600 - 650
650 - 700
700 - 750
750 - 800
800 - 850
850 - 900
900 - 950
Low-density granules
Labelled Particle Size: 355 – 710
Attrition Resistance: 96% ± 0.49% (n = 3)
0.25
Frequency
Granule Attrition
Example
Particle Size Distribution (Particle Number)
High-density granules
Labelled Particle Size: 355 – 710
Attrition Resistance: 98% ± 0.04% (n = 3)
Frequency
0.2
before
fluidization
After
Fluidization
0.15
0.1
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Particle Size Interval (μm)
900 - 950
850 - 900
800 - 850
750 - 800
700 - 750
650 - 700
600 - 650
550 - 600
500 - 550
450 - 500
400 - 450
350 - 400
300 - 350
250-300
200-250
150-200
100-150
0
50-100
0.05
Coating Thickness Determination
and Attrition Resistance
10% weight gain (batch 30-34, 355 - 710 μm)
20% weight gain (batch 25-29, 355 - 710 μm)
0.25
Before Coating
0.2
After Coating
0.15
0.1
0.05
0
0.2
Before Coating
After Coating
0.15
0.1
0.05
0
Particle Size (μm)
Attrition Resistance: 98.8% ± 0.01% (n = 3)
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Frequency (particle number)
Frequency (particle number)
0.25
Particle Size (μm)
Attrition Resistance: 95.0% ± 0.74% (n = 3)
Blending and Compaction
Formulation and Process Development
Is the formulation
manufacturable?
Define
formulation
requirements
in TPP
• Product Description:
Extended release tablet
• Formulation Goals: must
be manufacturable and
facilitate compression,
while avoiding any
disruption to the release
coating membrane.
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Identify initial
formulation
system
• Compatibility and
preliminary
feasibility studies
• Coated active
granules +
extragranular
excipients
Formulation
Development
Study
• Process feasibility
studies at small-scale
for unit operation
• Identification of
relevant formulation
variables
• Identification of
critical responses
Formulation
Optimization
Study
•Evaluate excipients
capabilities to meet
critical quality attributes
•Selecting final excipients
and optimal levels
Small-Scale Formulation
Development Study
 Evaluate the effect on blend stability (NIR)
 Brittle excipient
 MCC grades
MSSD
Mean Square Successive
Difference (MSSD)
0.0009
0.0008
0.0007
0.0006
0.0005
0.0004
0.0003
0.0002
0.0001
0
Lactose
Sorbitol
Mannitol
100:0%
MCC1:MCC2
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Developing Compaction
Process Understanding
Characterize Formulation
Components
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Intergranular Excipients
Coated Granules
•
Compressibility
Strength
Disintegration
Mean Yield Pressure/Indentation
Hardness
Strain rate sensitivity
Predict and Model
Optimum Formulation
Scale-up changes
DEFORMATION
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•
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Strength
Release
Tensile strength (MPa)
Compaction:
Process Understanding
6.0
Avicel PH 200
Sorbitol
Mannitol
Spray dried Lactose
5.0
4.0
3.0
2.0
1.0
0.0
0.5
0.6
0.7
0.8
Solid Fraction
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0.9
1
Release of API from
Coated Particle Systems
Coating Damaged – Faster release
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Matrix Formed – Slower release
Anticipated Outcomes
 Process models and/or analytical end-points
for individual unit operations at small scale
 First principle and empirical models
 Scale-up
 Adjustments to process models
 Sampling verification and robustness of analytical
end-points
 Prediction of final product CQAs throughout
process train
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