Proposal for a Manufacturing
Classification System (MCS)
Michael Leane1, Kendal Pitt2, Kiren Vyas2, Stuart Charlton1, Gavin Reynolds3,
Richard Storey3, Conrad Davies4.
1
Bristol-Myers Squibb, Moreton, UK 2 ; GlaxoSmithKline, Ware, UK;
UK; 4 Pfizer, Sandwich, UK.
3
Astra Zeneca, Macclesfield,
With contributions from delegates to the Mat Sci / PEFDM seminar:
“BCS to MCS: Predictions From Materials Science to Manufacturing” EMCC,
Nottingham, May 2013.
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What do we need to get out of this
meeting?
• The story so far: Outcome from May seminar
and subsequent discussions.
• Outline description of a MCS based on
processing route.
• Discussion:
– How should we define the classes?
– How do we put materials into the different
classes?
– Future steps towards a white paper.
Why do we need an MCS?
• Current costs of failure are high.
• Regulators see importance of material properties in
QBD.
• Identify if API has desirable properties for drug product
development.
• Could provide a common understanding of risk.
• Defines what are the “right particles” and best process.
• Aid development and subsequent transfer to
manufacturing.
• Fits with QBD principles and potential of obtaining
regulatory relief on the development of dosage forms
by demonstrating that the properties of the ingoing API
and excipients are within established ranges for the
manufacturing process.
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MCS Based on Processing Route
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MCS Based on Processing Route
• Class I Direct compression. Class II: Dry Granulation,
Class III: Wet Granulation, Class IV: Specialised
Technologies Needed.
• Assumes there is a preference for simpler
manufacturing routes.
• Builds on prior knowledge e.g. Hancock’s direct
compression criteria could form the foundation of MCS
Class I. Data needed (from literature / sharing of noncompetitive data) to construct similar for the other
classes.
• Ultimate aim of prediction from previous experience.
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Class 1 Direct compression
• Assumes there is
a preference for
simpler
manufacturing
routes.
• Ultimate aim of
prediction from
previous
experience.
• Prior knowledge available. Do we agree with
Hancock’s criteria? Update needed?
Remaining Classes
• Class II: Roller compaction. Can we set similar
boundaries?
• Class III: Wet granulation. Can we set similar
boundaries?
• Class IV: Specialised manufacturing processes
reserved for materials which cannot be
processed using the first three conventional
routes. What would be included in this class?
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Example of a white paper
• It is proposed to draft a similar white paper
by 1H 2014. Would you like to take part?
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Risk Analysis
• Risk analysis score based on relevant API
properties and drug product target attributes
(link to TPP) . Overall score used to identify
appropriate manufacturing methods.
0
Not acceptable
10
Wet Granulate
25
Roller compaction
Dry Granulate
Wet Granulate
50
100+
Direct Compress
or
Direct Compress
1000
5000
Other Technology
15000
Examples:
Flow¹ x Drug loading
Bulk density x Tensile strength²
41 x 10 = 410
50 x 30 = 3333
55 x 30 = 10313
1.0 x 1.0
0.3 x 1.5
0.2 x 0.8
DC
DG
WG
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¹ Effective angle of internal friction; ² At ~0.85 solid fraction.
Risk Analysis
•Alternatively, the “risk factor” of various processes can be compared using
analytical testing or experiences during particle production processes e.g.
particle consistency, physical properties.
• Material X: low risk factor for all 3 selected manufacturing processes.
• Material Y: low risk factor for wet granulation, medium for dry granulation
and high for direct compression.
• Material Z: medium risk factor for wet granulation and high for dry
granulation and direct compression.
1.0
DC
DG
WG
DG
Z 3.0
2.0
Tested Parameter
Measured Value
4.0
5.0
6.0 X
RF= 10
RF= 10
RF = 5
RF = 5
RF = 5
7.0
RF=1
8.0
RF=5
RF=1
9.0 Y 10.0
RF=10
RF=5
RF=1
10
10
1
0.2
1
10
0.5
0
1
1000
100
10
0.5
10
2
0.1
3
Permeability
FFC (Blend)
Bulk Density (Blend)
FFC (granule)
Random Mix RSD
Dissolution / In-vivo
Flowability
Uniformity
Loss of Compressibility
Tensile strength
Low
Risk
Medium
Risk
High
Risk
1
Solubility
• Could use parallel
coordinates or spider
diagrams as a way of
plotting multiple attributes.
Failure
Mode
• These can be classified
based on key failure modes
(e.g. poor flow).
Failure
Effect
• Another option is to
consider physical properties
and ‘intermediate’ material
attributes as a way to map
new APIs and products.
200
Dv(0.9)
Failure Modes
Manufacturability
• Using this approach to plot data may help to identify appropriate
ranges/zones of L/M/H risk and therefore generate a common framework for
understanding risk associated with running a particular set of material
properties on a particular process.
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Example: Process guidance for high drug
loading using Rise time as API property.
Material
Rise time (s)
Avicel PH102
1.1
Hydrous Lactose 0.32
Drug Y
Drug Z
Drug X
Drug X Hydrate
0.21
0.41
0.39
0.45
Neat material
Compression
Excellent
Poor
Laminated :WG developed
Good: DC developed
Poor : WG developed
Good : DC developed
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Manufacturing Robustness
• Robustness being in terms of not impacting the dosage form
Quality Target Product Profile (QTPP) by secondary manufacturing
processing conditions. If any conditions below are not met then
particle is not robust
• Condition 1: Stability : API must be chemically (degradation)
and physically (form) stable during secondary manufacturing
transformations.
• Condition 2: Dissolution: API solubility, dose and permeability
are such that it is a DCS 1, 2A or 3. (i.e. absorption is not
solubility limited).
• Condition 3:
Content uniformity & particle size: Dose and
particle size distribution meet Rohr’s analysis.
• Condition 4: Content uniformity & segregation: Passes
segregation test
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Manufacturing Robustness
Data collected on prototype formulations
Property
Commercial Target
Tablet Product 1
Tablet Product 2
Tablet Product 3
Tablet Product 4
Tablet Product 5
Tablet Product 6
Tablet Product 7
Tablet Product 8
Tablet Product 9
Tensile
strength
(Mpa)
Solid
Fraction
>1.7
0.85 +/-0.05 Elegant
Ta bl et
a ppea ra nce
1.7
0.84 Variable
1.8 0.75 to 0.84
1.05
0.84 Logo eroded
1.8
0.69
2.2
0.83
2.2
0.85
2.2
0.9 Some filming
1.8
0.91 Infilling
2.3
0.9
Friability
(%)
Ejection
Shear
(Mpa)
Core tablet
Disintegration
time (Mins)
Outcome
<0.1
< 3 ngt 5
< 10 ngt 15
<0.1%
<0.1%
0.12
<0.1%
<0.1%
<0.1%
<0.1%
<0.1%
<0.1%
5
2
2
0.9
0.8
2.3
2.8
7
4
5 Capping
12 Investiagte variable density
12 Illegible& Dissolution failures
1 Formulation progressed
1 Formulation progressed
4 Formulation progressed
9 Tablet defects
12 Capping & Infilling
14 Dissolution failures
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Next Steps
• Gain agreement for preferred options.
• Gather and share data linking material attributes to process
selection.
• Generate ‘maps’ based on key failure modes for different
manufacturing routes across a diverse range of compounds
• Share and plot data (phys prop based, no compound info)
on ‘maps’.
• Publish as a consortium paper to provide a frame of
reference of level of risk vs process type. This will build
‘prior art’ and be a literature reference that can be used to
articulate risk in a regulatory submission.
• Gain alignment with pharmaceutical scientists in other
countries.
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