Understanding API Attributes and their impact on Drug ... · • Assumes there is a preference for simpler manufacturing routes • Builds on prior knowledge e.g. Hancock’s direct

Understanding API Attributes and their impact on Drug Product Manufacturing

Process SelectionData gathering from industry

AAPS Annual Meeting and Exposition 2016 Denver CO USA November 2016

Neil Dawson (Pfizer) Michael Leane (BMS) Gavin Reynolds (AstraZeneca) Kendal Pitt (GSK) APS Manufacturing System Working Group

2

bull A Pharmaceutical Sciences vision material attributes and the MCS

bull Importance of Understanding the attributes of our materials and how they impact processing or product performance ndash an evolution

bull Material Attributes ndash What types of attributes might we want to understand and why

bull Molecular attributes

bull Solid state attributes

bull Particle attributes

bull Surface attributes

bull Case study

bull Future perspective

bull Acknowledgements

Overview

3

Science of scale and

Computation Tools

MCS and Product Quality

Attributes

Design API

for DP

Material Attributes

Connecting material and drug product attributes

Ticehurst MD1 Marziano I2 J Pharm Pharmacol 2015 Jun67(6)782-802 Integration of active pharmaceutical ingredient solid form selection and particle engineering into drug product design

Materials Science Tetrahedron ndash A Useful Tool for Pharmaceutical Research and Development Calvin Sun et alJ Pharm Sci 98 1671 ndash 1687 2009

Data Structure ndash PropertyPerformanceConnectivity

6

bull Make it easier for the formulator to develop their formulation and productbull Assumes there is a preference for simpler manufacturing routesbull Builds on prior knowledge eg Hancockrsquos direct compression criteriabull Be able to perform prediction of drug product design using material attribute

data ndash ldquoBig Datardquo - ADDoPTbull Desire

bull Materials are stable and robustbull The manufacturing process is reproducible and robustbull The quality and performance of the product is through particle engineering or controls

bull Which attributes are the most useful crystal particle or bulk

Manufacturing Classification System

Particle Attributes

Blending Batch Size 800-12 Kg

CPS ProcessBatch Size 10 Kg

Inlet Temp 20 degCAir Volume 60 m3hr

Rotor Speed 1000 rpm

Spray Rate 50-25-15 gmin

API

EXCIPIENTS

Drying-OvenTemp 38-40 degC

Tap and Bulk DensityParticle Size and shape

Flow

Powder Rheometer

Segregation test

Wettability

Particle Size

Particle Shape

Porosity

Surface Area

Surface Roughness

Chemical mapping

Friability

Wettability

Moisture content

Screening-SievesParticle Size Range 106-400 microm

Particle Size

Particle ShapeBulk Density

Flow(Paediatrics)

Friability (basic meas)

Porosity

Surface Area

Surface RoughnessChemical mapping (once)

Wettability initial core

penetration

Dissolution profile

EXCIPIENTSWater 0600-0800 Kg

Microcrystalline Cellulose 0050 kg

Fluid Bed CoatingBatch Size 10Kg

Inlet Temp degCProduct Temp degC

Spray Rate gmin

Blending

Weight

Hardness

Friability

Wettability

Disintegration time

Dissolution profile

Tap Density tester

Shear cellFlowdex

Angle of repose Hausner ratio

compressibility index

FT4

Fluidized

Contact angle

In line FBRM

QICPIC

Moisture analyzer

Taste Mask Particle Size

Film coating layer

Disintegration time

Dissolution profile

QICPIC

SEM section view

Disintegration apparatus

Dissolution methods

QICPIC

Tap Density tester

Shear cell Flowdex

Friability tester

Mercury porosimetry

Gas absorption BET SSA

SEM section view AFM

Contact angle

Dissolution methods

Weight test

Hardness Tester

Friability tester

Contact angle

Disintegration tester

Dissolution methods

CompressionWeight

Tablet size

Compression force

Compression speed

Bulk Attributes Characterisation techniquesUnit Operations

API or Excipient AttributesSolid state formParticle SizeParticle shapeSpecific surface areaPowder FlowCrystallinityMelt onsetHygroscopicitySurface energySurface roughnessSolubilityProcess impurities hellip

API amp DP ndash Characterisation Map

Composition Process Selection

Attributes of API

- Solubility - Flow - PSD

Quality Target

Product Profile

- Dose- Dosage form- hellip

Choice of processBatch Continuous

DGWGDC

Choice of excipients

Drug Loading

MCS

Other considerationsbull Speed of developmentbull No of API lotsbull Business drivers

Equant Lath

(blade)

Needle

(acicular)

Flake

Plate

(tabular)

Columnar

(prismatic)

Equant Lath

(blade)

Needle

(acicular)

Flake

Plate

(tabular)

Columnar

(prismatic)

bull Particle size and crystal form are wellunderstood regarding API manufacturability

bull Changes in particle shape can impact materialsmanufacturability

Case Study 1

1 Recrystallisation of ibuprofen

2 Materials characterisation

3 Mechanical characterisation

What role does particle shape play in drug product manufacture

Hooper D Compaction Simulation Forum 2016 Boston and APS Pharm Sci 2016 Glasgow

Ascorbic acid Ibuprofen Aspartame

Celecoxib Sodium chloride Ibuprofen

Pharmaceutical Particle Shapes

bull It is much easier to treat all particles as approximating spheresbull Laser diffraction particle sizing instruments do thisbull Sieve analysis does thisbull Our theories of dissolution (eg Fickrsquos law) do thisbull Discrete element models used for powder processing simulations

usually do thisbull Other Computational modelsbull And so onhellip

bull But this creates several problemshellipbull Surface area to volume can change significantly with aspect ratiobull Surface contacts are highly dependent on shapebull Particles are anisotropic

Particle Shape Challenges

Problem 1

httpcommonswikimediaorgwikiFileComparison_of_surface_area_vs_volume_of_shapessvg

ShapeSAV SAV of sphere

Sphere 100

Icosahedron 106

Dodecahedron 110

Octahedron 118

Cube 124

Tetrahedron 150

Surface area to volume can be very important

Problem 2Surface contacts are very different for spheresbull Real particles donrsquot rollbull Sphere donrsquot interlockbull Spheres have no preferred orientation

For example There is only one contact type for spheres In contrast there are at least seven types of contact for discscylinders

Hancock B Yu W Gordon Conference on Preclinical Form and Formulation Waterville Valley NH June 7-12 2015 ldquoAPI Particle ShapeThe Cinderella Quality Attributerdquo

Problem 3

Monoclinic acetaminophen surface hardness anisotropy (Duncan-Hewitt et al 1994)


Real particles are anisotropic

bull Particle shape has traditionally been a very challenging property to measurebull Static image analysis is slow

bull And it is difficult to express particle shape using a single numberbull How do we capture symmetry elongation flatness

smoothness curvature etc

bull Many ways to represent the full data set

bull However this is beginning to changehellip

In addition hellip


bull Morphologies (shape) are reported in the Cambridge Crystallographic Database (CSD)

bull Dinesh Vatvani has analyzed the CSD for particle shape trendsbull This indicates that more than 56

of all drug-like crystals have an aspect ratio of greater than 07

bull This analysis does not support the commonly held view that many APIs are comprised of needle-like particles

16

prisms29

blocks27

plates18

needles14

other12

Morphology statistics of all organic single component crystals

Publicly available data


bull Particle shape data generated over several years at multiple sites were collected together and analyzed as a population

Data Analysisbull 14 databases 2007-2013 bull Data Selection Strategies

bull One analysis selected for each sample

bull 10th 50th 90th percentile values of aspect ratio convexity sphericitywere reported

bull Final data 1591 samplesbull 1309 API samplesbull 282 excipient samples

Analysis of Pfizer in-house data


00 00 00 01 0524

55

197

659

60

0

10

20

30

40

50

60

70

Pe

rce

nta

ge

S50

00 00 00 00 01 00 10

72

477440

0

10

20

30

40

50

60

70

Pe

rce

nta

ge

C50

00 00 03 1953

195

299

389

23 19

0

10

20

30

40

50

60

70

Pe

rce

nta

ge

AR50

All 1591 samples (1309 API amp 282 excipients)

(volume weighted)

Typical (median) samples (gt65) have- an aspect ratio of 06 to 08- a circularity of 08 to 10- a sphericity of 08 to 09

That is they are somewhat elongated and have slight surface roughness

Falling between the theoretical distributions of a cube and a square plate

Shape factor distributions


J Bunyan N Shankland and D Sheen AIChE Symp Ser 1991 44ndash57

Solvent Hexane Toluene Acetonitrile Ethanol Methanol

Expected

Shape

Actual

Shape

Yield 77 83 67 49 60

Comments

Increasing solvent polarity

Increasing shape regularity

Case study 1 ndash Lab-scale Ibuprofen


Comparison 1 Ethanol and Hexane

PSD = similar

Shape = different

Tabletability = similar

Sticking = different

Particle size volume distribution measured by laser diffraction

Case study 1


NEEDLES

1 Large scale recrystallisation using hexane and ethanol2 Quantitative size and shape characterisation

3 Mechanical Characterisation

000002004006008010012014016018020022024026028030032034036038040042044046048050052054056058060062064066068070072074076

Aspect

ratio

40 60 80 100 200 400 600 800 1000 2000Particle size microm

BatchIBUHEXL01IBUETHL01IBUETHL01IBUHEXL01

Particle size

Asp

ect

rati

o

CUBES

000

005

010

015

020

025

030

035

040

045

050

055

060

065

070

075

080

085

090

095

100

105

110

115

120

125

130

135

140

145

150

155

160

165

170

175

180

185

190

195

De

nsity d

istr

ibu

tio

n q

3

2 4 6 8 10 20 40 60 80 100 200 400 600 800 1000 2000

particle size microm

Batch

IBUHEXL01

IBUETHL01

IBUETHL01

IBUHEXL01

QICPIC volume distributions

ldquoLargerrdquo scale Ibuprofen


bull There is a scarcity of data in the literature indicating the typical shape of pharmaceutical particles and the impact on flowbull Fewer than 10 papers

bull Our preliminary pooled analysis suggest thatbull Most pharmaceutical particles (~70) have a median aspect ratio of between

06-08 (not needles)bull The majority of particles have smooth surfacesbull Aspect ratio is a discriminating particle shape parameterbull Compared to APIs excipients are more equant

bull Given the importance of particle shape to the quality and performance of pharmaceutical products we should be making much greater efforts to measure understand and control itbull Influence on powder flow

Summary of Case study 1


bull We need to develop target material attribute ranges for a given manufacturing process route

bull Then assess new materials against those ranges for selection of processing route

bull Prospective parameter range proposed for a direct compression process

Case study 2 ndash Process Route Selection

Hancock et al cited in MCS whitepaper

Can the optimal API attributes be developed for other processing routes

apprentice

apprentice

apprentice

apprentice

(FFC)

Knowledge management Material Property DatabasesldquoBig Datardquo and also data from computational tools

bull APIs (red) tend to be above this limit (have a higher fracture tendency) while blends (blue) and granulations (green) tend to be below the limit regardless of absolute density

Absolute density vs brittle fracture index

Target true

density

Target BFI

bull APIs (red) tend to have a poorer flow than the target flow for a DC formulation

bull APIs with poorer flow tend to have a greater fracture tendency

bull Blends (blue) and granulations (green) tend to have low fracture tendencies regardless of flow

Flow function coefficient vs Brittle fracture index

Target FFC

Target BFI

bull For APIs as the effective angle of internal friction increases the fracture tendency increases

bull Active blends and granulations fall within range and have a low fracture tendency despite increases in the effective angle of internal friction

Effective angle of internal friction vs Brittle fracture index

Target effective angle of internal friction

Target BFI

bull For APIs as the D[v01]

increases the tensile strength decreases The same trend is not observed for active blends or granulations

bull The majority of APIs are below the target range for DC

bull The API can impact tensile strength but does not make a difference when included as part of a formulationbull Drug Loading

Particle Size D[v01] vs Tensile strength

Target D[v01]

Target tensile strength


Attributes of API


Quality Target

Product Profile



DGWGDC


Drug Loading

MCS


bull Visualisation and comparison of API material attributes data against target ranges for a given manufacturing route(s) can enable risk-based decisions on the selection of the appropriate routebull Support computational modelling approaches

bull Can we deconvolute company based preferences on manufacturing process to science driven risk-based decisionsbull Company preferences vs materials attributes driving

process selection

Case study 2 conclusions

bull Can we develop attribute ranges for different manufacturing process (eg DC WG DG and so on)bull Do these need to be related to dosebull Do we know what attributes to develop ranges on

bull How can we improve data stewardship data sharing trending and knowledge management systems to enable us to develop meaningful target attribute ranges

bull Can we engineer the attributes of an API to make it suitable for a simpler processing route

Future perspective

Conrad Davies

Martyn Ticehurst

Tiffany Lai

Bruno Hancock

Weili Yu

Debbie Hooper

Fiona Clarke

Alan Carmody

Bob Docherty

Alastair Coupe

Tim Lukas

Lourdes Contreras

Acknowledgements

Questions

neildawsonpfizercom

Michael Leane (BMS)

Gavin Reynolds (AstraZeneca)

Kendal Pitt (GSK)

Iris Zieglar (Corden Pharma)

APS Manufacturing System Working Group

Backup

Dynamic Indentation Hardness Refers to the resistance of a particle to irreversibly deform when stressed As DIH decreases bonding contact area between particles increases Permanent deformation plastic deformation and ductility are additional terms used to describe the ability of a material to irreversibly deform In general indentation hardness is proportional to permanent deformation stress (eg a material with a high permanent deformation stress will have high DIH)

Tensile Strength Indicates the stress required to separate compacted material in a tensile mode A high tensile strength indicates a high likelihood of bond survival during tablet decompression and the probable formation of tablets with a high crushing strength Tensile strength is determined using a transverse crushing strength test

Brittleness Particle bonds are stressed in tension during tablet decompression The BFI indicates how this stress is relieved - by plastic deformation of the bonding region or by bond fracture within or between particles The plastic mechanism is preferred A high BFI indicates that compacted material is more likely to fail by brittle fracture The BFI is calculated from tensile strength values

Worst Case Bonding Index The degree that particle bonds have survived (not ruptured) during tablet decompression The bonding indices are determined from the relative magnitudes of the ductility and tensile strength parameters

Compression Stress The stress required to compress a compact to a solid fraction of 085 Moderate values of compression stress are desirable toenable interparticulate bond formation and maintain compact integrity

Viscoelasticity A measure of the effect of speed on mechanical properties It is calculated as the ratio of the dynamic hardness to the quasi-static hardness A large value for the viscoelastic number indicates that the mechanical properties are very sensitive to speed (ie viscoelastic)

Flowability (UFN) A normalized in-house parameter that describes powder flow under normal compressive loads The UFN indicates the potential for achieving acceptable tablet weight variation during tableting It is calculated from the effective angle of internal friction which is measured using a simple shear cell

Flowability (FFC) Describes how powder will flow under normal compressive loads on a shear cell (eg Schulze Ring Shear Tester) It is calculated from the ratio of the major consolidating stress to the unconfined yield strength of the powder

Cohesivity A measure of a powderrsquos tendency to agglomerate and resist flow High cohesivity can be problematic during low shear blending and powder transfer operations It is measured using a powder avalanche tester

Derived from the Hiestand compact testing method Determined using the compaction simulator Determined using the Schulze Shear Cell (RST)

Backup

Convexity reflects

particle surface

roughness Smoother

particles have higher

convexity

Feret_MIN

Feret_MAX

Aspect ratio reflects elongation of

particles Needle-shaped particles

have low aspect ratio spherical

particles have an aspect ratio of 1

Sphericity reflects the combination of particle

elongation and surface roughness

Shape parameters ndash Determined dynamic image analysis

Confirmation of mechanical properties

ldquoCommercial tablets are well represented by a sample with a solid fraction of ~085rdquo Bruno C

Hancock PhD Basic Principles of Materials Science with Application to Pharmaceutical Materials 48th AAPS Annual Arden Conference Pharmaceutical Materials Science and Engineering March 4-6 2013 Rockville

2

bull A Pharmaceutical Sciences vision material attributes and the MCS

bull Importance of Understanding the attributes of our materials and how they impact processing or product performance ndash an evolution

bull Material Attributes ndash What types of attributes might we want to understand and why

bull Molecular attributes

bull Solid state attributes

bull Particle attributes

bull Surface attributes

bull Case study

bull Future perspective

bull Acknowledgements

Overview

3


Computation Tools


Attributes

Design API

for DP

Material Attributes





6






Particle Attributes






API

EXCIPIENTS



Flow

Powder Rheometer

Segregation test

Wettability

Particle Size

Particle Shape

Porosity

Surface Area

Surface Roughness

Chemical mapping

Friability

Wettability

Moisture content


Particle Size


Flow(Paediatrics)


Porosity

Surface Area



penetration

Dissolution profile





Spray Rate gmin

Blending

Weight

Hardness

Friability

Wettability

Disintegration time

Dissolution profile

Tap Density tester

Shear cellFlowdex



FT4

Fluidized

Contact angle

In line FBRM

QICPIC

Moisture analyzer


Film coating layer

Disintegration time

Dissolution profile

QICPIC

SEM section view


Dissolution methods

QICPIC

Tap Density tester

Shear cell Flowdex

Friability tester

Mercury porosimetry



Contact angle

Dissolution methods

Weight test

Hardness Tester

Friability tester

Contact angle


Dissolution methods

CompressionWeight

Tablet size

Compression force

Compression speed





Attributes of API


Quality Target

Product Profile



DGWGDC


Drug Loading

MCS


Equant Lath

(blade)

Needle

(acicular)

Flake

Plate

(tabular)

Columnar

(prismatic)

Equant Lath

(blade)

Needle

(acicular)

Flake

Plate

(tabular)

Columnar

(prismatic)



Case Study 1













Problem 1



Sphere 100

Icosahedron 106

Dodecahedron 110

Octahedron 118

Cube 124

Tetrahedron 150





Problem 3









In addition hellip






16

prisms29

blocks27

plates18

needles14

other12











00 00 00 01 0524

55

197

659

60

0

10

20

30

40

50

60

70

Pe

rce

nta

ge

S50

00 00 00 00 01 00 10

72

477440

0

10

20

30

40

50

60

70

Pe

rce

nta

ge

C50

00 00 03 1953

195

299

389

23 19

0

10

20

30

40

50

60

70

Pe

rce

nta

ge

AR50


(volume weighted)








Expected

Shape

Actual

Shape

Yield 77 83 67 49 60

Comments






PSD = similar

Shape = different




Case study 1


NEEDLES



000002004006008010012014016018020022024026028030032034036038040042044046048050052054056058060062064066068070072074076

Aspect

ratio



Particle size

Asp

ect

rati

o

CUBES

000

005

010

015

020

025

030

035

040

045

050

055

060

065

070

075

080

085

090

095

100

105

110

115

120

125

130

135

140

145

150

155

160

165

170

175

180

185

190

195

De

nsity d

istr

ibu

tio

n q

3

2 4 6 8 10 20 40 60 80 100 200 400 600 800 1000 2000


Batch

IBUHEXL01

IBUETHL01

IBUETHL01

IBUHEXL01
















apprentice

apprentice

apprentice

apprentice

(FFC)




Target true

density

Target BFI





Target FFC

Target BFI





Target BFI






Target D[v01]



Attributes of API


Quality Target

Product Profile



DGWGDC


Drug Loading

MCS




process selection





Future perspective

Conrad Davies

Martyn Ticehurst

Tiffany Lai

Bruno Hancock

Weili Yu

Debbie Hooper

Fiona Clarke

Alan Carmody

Bob Docherty

Alastair Coupe

Tim Lukas

Lourdes Contreras

Acknowledgements

Questions

neildawsonpfizercom

Michael Leane (BMS)


Kendal Pitt (GSK)



Backup











Backup

Convexity reflects

particle surface

roughness Smoother


convexity

Feret_MIN

Feret_MAX











3


Computation Tools


Attributes

Design API

for DP

Material Attributes





6






Particle Attributes






API

EXCIPIENTS



Flow

Powder Rheometer

Segregation test

Wettability

Particle Size

Particle Shape

Porosity

Surface Area

Surface Roughness

Chemical mapping

Friability

Wettability

Moisture content


Particle Size


Flow(Paediatrics)


Porosity

Surface Area



penetration

Dissolution profile





Spray Rate gmin

Blending

Weight

Hardness

Friability

Wettability

Disintegration time

Dissolution profile

Tap Density tester

Shear cellFlowdex



FT4

Fluidized

Contact angle

In line FBRM

QICPIC

Moisture analyzer


Film coating layer

Disintegration time

Dissolution profile

QICPIC

SEM section view


Dissolution methods

QICPIC

Tap Density tester

Shear cell Flowdex

Friability tester

Mercury porosimetry



Contact angle

Dissolution methods

Weight test

Hardness Tester

Friability tester

Contact angle


Dissolution methods

CompressionWeight

Tablet size

Compression force

Compression speed





Attributes of API


Quality Target

Product Profile



DGWGDC


Drug Loading

MCS


Equant Lath

(blade)

Needle

(acicular)

Flake

Plate

(tabular)

Columnar

(prismatic)

Equant Lath

(blade)

Needle

(acicular)

Flake

Plate

(tabular)

Columnar

(prismatic)



Case Study 1













Problem 1



Sphere 100

Icosahedron 106

Dodecahedron 110

Octahedron 118

Cube 124

Tetrahedron 150





Problem 3









In addition hellip






16

prisms29

blocks27

plates18

needles14

other12











00 00 00 01 0524

55

197

659

60

0

10

20

30

40

50

60

70

Pe

rce

nta

ge

S50

00 00 00 00 01 00 10

72

477440

0

10

20

30

40

50

60

70

Pe

rce

nta

ge

C50

00 00 03 1953

195

299

389

23 19

0

10

20

30

40

50

60

70

Pe

rce

nta

ge

AR50


(volume weighted)








Expected

Shape

Actual

Shape

Yield 77 83 67 49 60

Comments






PSD = similar

Shape = different




Case study 1


NEEDLES



000002004006008010012014016018020022024026028030032034036038040042044046048050052054056058060062064066068070072074076

Aspect

ratio



Particle size

Asp

ect

rati

o

CUBES

000

005

010

015

020

025

030

035

040

045

050

055

060

065

070

075

080

085

090

095

100

105

110

115

120

125

130

135

140

145

150

155

160

165

170

175

180

185

190

195

De

nsity d

istr

ibu

tio

n q

3

2 4 6 8 10 20 40 60 80 100 200 400 600 800 1000 2000


Batch

IBUHEXL01

IBUETHL01

IBUETHL01

IBUHEXL01
















apprentice

apprentice

apprentice

apprentice

(FFC)




Target true

density

Target BFI





Target FFC

Target BFI





Target BFI






Target D[v01]



Attributes of API


Quality Target

Product Profile



DGWGDC


Drug Loading

MCS




process selection





Future perspective

Conrad Davies

Martyn Ticehurst

Tiffany Lai

Bruno Hancock

Weili Yu

Debbie Hooper

Fiona Clarke

Alan Carmody

Bob Docherty

Alastair Coupe

Tim Lukas

Lourdes Contreras

Acknowledgements

Questions

neildawsonpfizercom

Michael Leane (BMS)


Kendal Pitt (GSK)



Backup











Backup

Convexity reflects

particle surface

roughness Smoother


convexity

Feret_MIN

Feret_MAX













6






Particle Attributes






API

EXCIPIENTS



Flow

Powder Rheometer

Segregation test

Wettability

Particle Size

Particle Shape

Porosity

Surface Area

Surface Roughness

Chemical mapping

Friability

Wettability

Moisture content


Particle Size


Flow(Paediatrics)


Porosity

Surface Area



penetration

Dissolution profile





Spray Rate gmin

Blending

Weight

Hardness

Friability

Wettability

Disintegration time

Dissolution profile

Tap Density tester

Shear cellFlowdex



FT4

Fluidized

Contact angle

In line FBRM

QICPIC

Moisture analyzer


Film coating layer

Disintegration time

Dissolution profile

QICPIC

SEM section view


Dissolution methods

QICPIC

Tap Density tester

Shear cell Flowdex

Friability tester

Mercury porosimetry



Contact angle

Dissolution methods

Weight test

Hardness Tester

Friability tester

Contact angle


Dissolution methods

CompressionWeight

Tablet size

Compression force

Compression speed





Attributes of API


Quality Target

Product Profile



DGWGDC


Drug Loading

MCS


Equant Lath

(blade)

Needle

(acicular)

Flake

Plate

(tabular)

Columnar

(prismatic)

Equant Lath

(blade)

Needle

(acicular)

Flake

Plate

(tabular)

Columnar

(prismatic)



Case Study 1













Problem 1



Sphere 100

Icosahedron 106

Dodecahedron 110

Octahedron 118

Cube 124

Tetrahedron 150





Problem 3









In addition hellip






16

prisms29

blocks27

plates18

needles14

other12











00 00 00 01 0524

55

197

659

60

0

10

20

30

40

50

60

70

Pe

rce

nta

ge

S50

00 00 00 00 01 00 10

72

477440

0

10

20

30

40

50

60

70

Pe

rce

nta

ge

C50

00 00 03 1953

195

299

389

23 19

0

10

20

30

40

50

60

70

Pe

rce

nta

ge

AR50


(volume weighted)








Expected

Shape

Actual

Shape

Yield 77 83 67 49 60

Comments






PSD = similar

Shape = different




Case study 1


NEEDLES



000002004006008010012014016018020022024026028030032034036038040042044046048050052054056058060062064066068070072074076

Aspect

ratio



Particle size

Asp

ect

rati

o

CUBES

000

005

010

015

020

025

030

035

040

045

050

055

060

065

070

075

080

085

090

095

100

105

110

115

120

125

130

135

140

145

150

155

160

165

170

175

180

185

190

195

De

nsity d

istr

ibu

tio

n q

3

2 4 6 8 10 20 40 60 80 100 200 400 600 800 1000 2000


Batch

IBUHEXL01

IBUETHL01

IBUETHL01

IBUHEXL01
















apprentice

apprentice

apprentice

apprentice

(FFC)




Target true

density

Target BFI





Target FFC

Target BFI





Target BFI






Target D[v01]



Attributes of API


Quality Target

Product Profile



DGWGDC


Drug Loading

MCS




process selection





Future perspective

Conrad Davies

Martyn Ticehurst

Tiffany Lai

Bruno Hancock

Weili Yu

Debbie Hooper

Fiona Clarke

Alan Carmody

Bob Docherty

Alastair Coupe

Tim Lukas

Lourdes Contreras

Acknowledgements

Questions

neildawsonpfizercom

Michael Leane (BMS)


Kendal Pitt (GSK)



Backup











Backup

Convexity reflects

particle surface

roughness Smoother


convexity

Feret_MIN

Feret_MAX











6






Particle Attributes






API

EXCIPIENTS



Flow

Powder Rheometer

Segregation test

Wettability

Particle Size

Particle Shape

Porosity

Surface Area

Surface Roughness

Chemical mapping

Friability

Wettability

Moisture content


Particle Size


Flow(Paediatrics)


Porosity

Surface Area



penetration

Dissolution profile





Spray Rate gmin

Blending

Weight

Hardness

Friability

Wettability

Disintegration time

Dissolution profile

Tap Density tester

Shear cellFlowdex



FT4

Fluidized

Contact angle

In line FBRM

QICPIC

Moisture analyzer


Film coating layer

Disintegration time

Dissolution profile

QICPIC

SEM section view


Dissolution methods

QICPIC

Tap Density tester

Shear cell Flowdex

Friability tester

Mercury porosimetry



Contact angle

Dissolution methods

Weight test

Hardness Tester

Friability tester

Contact angle


Dissolution methods

CompressionWeight

Tablet size

Compression force

Compression speed





Attributes of API


Quality Target

Product Profile



DGWGDC


Drug Loading

MCS


Equant Lath

(blade)

Needle

(acicular)

Flake

Plate

(tabular)

Columnar

(prismatic)

Equant Lath

(blade)

Needle

(acicular)

Flake

Plate

(tabular)

Columnar

(prismatic)



Case Study 1













Problem 1



Sphere 100

Icosahedron 106

Dodecahedron 110

Octahedron 118

Cube 124

Tetrahedron 150





Problem 3









In addition hellip






16

prisms29

blocks27

plates18

needles14

other12











00 00 00 01 0524

55

197

659

60

0

10

20

30

40

50

60

70

Pe

rce

nta

ge

S50

00 00 00 00 01 00 10

72

477440

0

10

20

30

40

50

60

70

Pe

rce

nta

ge

C50

00 00 03 1953

195

299

389

23 19

0

10

20

30

40

50

60

70

Pe

rce

nta

ge

AR50


(volume weighted)








Expected

Shape

Actual

Shape

Yield 77 83 67 49 60

Comments






PSD = similar

Shape = different




Case study 1


NEEDLES



000002004006008010012014016018020022024026028030032034036038040042044046048050052054056058060062064066068070072074076

Aspect

ratio



Particle size

Asp

ect

rati

o

CUBES

000

005

010

015

020

025

030

035

040

045

050

055

060

065

070

075

080

085

090

095

100

105

110

115

120

125

130

135

140

145

150

155

160

165

170

175

180

185

190

195

De

nsity d

istr

ibu

tio

n q

3

2 4 6 8 10 20 40 60 80 100 200 400 600 800 1000 2000


Batch

IBUHEXL01

IBUETHL01

IBUETHL01

IBUHEXL01
















apprentice

apprentice

apprentice

apprentice

(FFC)




Target true

density

Target BFI





Target FFC

Target BFI





Target BFI






Target D[v01]



Attributes of API


Quality Target

Product Profile



DGWGDC


Drug Loading

MCS




process selection





Future perspective

Conrad Davies

Martyn Ticehurst

Tiffany Lai

Bruno Hancock

Weili Yu

Debbie Hooper

Fiona Clarke

Alan Carmody

Bob Docherty

Alastair Coupe

Tim Lukas

Lourdes Contreras

Acknowledgements

Questions

neildawsonpfizercom

Michael Leane (BMS)


Kendal Pitt (GSK)



Backup











Backup

Convexity reflects

particle surface

roughness Smoother


convexity

Feret_MIN

Feret_MAX











Particle Attributes






API

EXCIPIENTS



Flow

Powder Rheometer

Segregation test

Wettability

Particle Size

Particle Shape

Porosity

Surface Area

Surface Roughness

Chemical mapping

Friability

Wettability

Moisture content


Particle Size


Flow(Paediatrics)


Porosity

Surface Area



penetration

Dissolution profile





Spray Rate gmin

Blending

Weight

Hardness

Friability

Wettability

Disintegration time

Dissolution profile

Tap Density tester

Shear cellFlowdex



FT4

Fluidized

Contact angle

In line FBRM

QICPIC

Moisture analyzer


Film coating layer

Disintegration time

Dissolution profile

QICPIC

SEM section view


Dissolution methods

QICPIC

Tap Density tester

Shear cell Flowdex

Friability tester

Mercury porosimetry



Contact angle

Dissolution methods

Weight test

Hardness Tester

Friability tester

Contact angle


Dissolution methods

CompressionWeight

Tablet size

Compression force

Compression speed





Attributes of API


Quality Target

Product Profile



DGWGDC


Drug Loading

MCS


Equant Lath

(blade)

Needle

(acicular)

Flake

Plate

(tabular)

Columnar

(prismatic)

Equant Lath

(blade)

Needle

(acicular)

Flake

Plate

(tabular)

Columnar

(prismatic)



Case Study 1













Problem 1



Sphere 100

Icosahedron 106

Dodecahedron 110

Octahedron 118

Cube 124

Tetrahedron 150





Problem 3









In addition hellip






16

prisms29

blocks27

plates18

needles14

other12











00 00 00 01 0524

55

197

659

60

0

10

20

30

40

50

60

70

Pe

rce

nta

ge

S50

00 00 00 00 01 00 10

72

477440

0

10

20

30

40

50

60

70

Pe

rce

nta

ge

C50

00 00 03 1953

195

299

389

23 19

0

10

20

30

40

50

60

70

Pe

rce

nta

ge

AR50


(volume weighted)








Expected

Shape

Actual

Shape

Yield 77 83 67 49 60

Comments






PSD = similar

Shape = different




Case study 1


NEEDLES



000002004006008010012014016018020022024026028030032034036038040042044046048050052054056058060062064066068070072074076

Aspect

ratio



Particle size

Asp

ect

rati

o

CUBES

000

005

010

015

020

025

030

035

040

045

050

055

060

065

070

075

080

085

090

095

100

105

110

115

120

125

130

135

140

145

150

155

160

165

170

175

180

185

190

195

De

nsity d

istr

ibu

tio

n q

3

2 4 6 8 10 20 40 60 80 100 200 400 600 800 1000 2000


Batch

IBUHEXL01

IBUETHL01

IBUETHL01

IBUHEXL01
















apprentice

apprentice

apprentice

apprentice

(FFC)




Target true

density

Target BFI





Target FFC

Target BFI





Target BFI






Target D[v01]



Attributes of API


Quality Target

Product Profile



DGWGDC


Drug Loading

MCS




process selection





Future perspective

Conrad Davies

Martyn Ticehurst

Tiffany Lai

Bruno Hancock

Weili Yu

Debbie Hooper

Fiona Clarke

Alan Carmody

Bob Docherty

Alastair Coupe

Tim Lukas

Lourdes Contreras

Acknowledgements

Questions

neildawsonpfizercom

Michael Leane (BMS)


Kendal Pitt (GSK)



Backup











Backup

Convexity reflects

particle surface

roughness Smoother


convexity

Feret_MIN

Feret_MAX












Attributes of API


Quality Target

Product Profile



DGWGDC


Drug Loading

MCS


Equant Lath

(blade)

Needle

(acicular)

Flake

Plate

(tabular)

Columnar

(prismatic)

Equant Lath

(blade)

Needle

(acicular)

Flake

Plate

(tabular)

Columnar

(prismatic)



Case Study 1













Problem 1



Sphere 100

Icosahedron 106

Dodecahedron 110

Octahedron 118

Cube 124

Tetrahedron 150





Problem 3









In addition hellip






16

prisms29

blocks27

plates18

needles14

other12











00 00 00 01 0524

55

197

659

60

0

10

20

30

40

50

60

70

Pe

rce

nta

ge

S50

00 00 00 00 01 00 10

72

477440

0

10

20

30

40

50

60

70

Pe

rce

nta

ge

C50

00 00 03 1953

195

299

389

23 19

0

10

20

30

40

50

60

70

Pe

rce

nta

ge

AR50


(volume weighted)








Expected

Shape

Actual

Shape

Yield 77 83 67 49 60

Comments






PSD = similar

Shape = different




Case study 1


NEEDLES



000002004006008010012014016018020022024026028030032034036038040042044046048050052054056058060062064066068070072074076

Aspect

ratio



Particle size

Asp

ect

rati

o

CUBES

000

005

010

015

020

025

030

035

040

045

050

055

060

065

070

075

080

085

090

095

100

105

110

115

120

125

130

135

140

145

150

155

160

165

170

175

180

185

190

195

De

nsity d

istr

ibu

tio

n q

3

2 4 6 8 10 20 40 60 80 100 200 400 600 800 1000 2000


Batch

IBUHEXL01

IBUETHL01

IBUETHL01

IBUHEXL01
















apprentice

apprentice

apprentice

apprentice

(FFC)




Target true

density

Target BFI





Target FFC

Target BFI





Target BFI






Target D[v01]



Attributes of API


Quality Target

Product Profile



DGWGDC


Drug Loading

MCS




process selection





Future perspective

Conrad Davies

Martyn Ticehurst

Tiffany Lai

Bruno Hancock

Weili Yu

Debbie Hooper

Fiona Clarke

Alan Carmody

Bob Docherty

Alastair Coupe

Tim Lukas

Lourdes Contreras

Acknowledgements

Questions

neildawsonpfizercom

Michael Leane (BMS)


Kendal Pitt (GSK)



Backup











Backup

Convexity reflects

particle surface

roughness Smoother


convexity

Feret_MIN

Feret_MAX











Equant Lath

(blade)

Needle

(acicular)

Flake

Plate

(tabular)

Columnar

(prismatic)

Equant Lath

(blade)

Needle

(acicular)

Flake

Plate

(tabular)

Columnar

(prismatic)



Case Study 1













Problem 1



Sphere 100

Icosahedron 106

Dodecahedron 110

Octahedron 118

Cube 124

Tetrahedron 150





Problem 3









In addition hellip






16

prisms29

blocks27

plates18

needles14

other12











00 00 00 01 0524

55

197

659

60

0

10

20

30

40

50

60

70

Pe

rce

nta

ge

S50

00 00 00 00 01 00 10

72

477440

0

10

20

30

40

50

60

70

Pe

rce

nta

ge

C50

00 00 03 1953

195

299

389

23 19

0

10

20

30

40

50

60

70

Pe

rce

nta

ge

AR50


(volume weighted)








Expected

Shape

Actual

Shape

Yield 77 83 67 49 60

Comments






PSD = similar

Shape = different




Case study 1


NEEDLES



000002004006008010012014016018020022024026028030032034036038040042044046048050052054056058060062064066068070072074076

Aspect

ratio



Particle size

Asp

ect

rati

o

CUBES

000

005

010

015

020

025

030

035

040

045

050

055

060

065

070

075

080

085

090

095

100

105

110

115

120

125

130

135

140

145

150

155

160

165

170

175

180

185

190

195

De

nsity d

istr

ibu

tio

n q

3

2 4 6 8 10 20 40 60 80 100 200 400 600 800 1000 2000


Batch

IBUHEXL01

IBUETHL01

IBUETHL01

IBUHEXL01
















apprentice

apprentice

apprentice

apprentice

(FFC)




Target true

density

Target BFI





Target FFC

Target BFI





Target BFI






Target D[v01]



Attributes of API


Quality Target

Product Profile



DGWGDC


Drug Loading

MCS




process selection





Future perspective

Conrad Davies

Martyn Ticehurst

Tiffany Lai

Bruno Hancock

Weili Yu

Debbie Hooper

Fiona Clarke

Alan Carmody

Bob Docherty

Alastair Coupe

Tim Lukas

Lourdes Contreras

Acknowledgements

Questions

neildawsonpfizercom

Michael Leane (BMS)


Kendal Pitt (GSK)



Backup











Backup

Convexity reflects

particle surface

roughness Smoother


convexity

Feret_MIN

Feret_MAX


















Problem 1



Sphere 100

Icosahedron 106

Dodecahedron 110

Octahedron 118

Cube 124

Tetrahedron 150





Problem 3









In addition hellip






16

prisms29

blocks27

plates18

needles14

other12











00 00 00 01 0524

55

197

659

60

0

10

20

30

40

50

60

70

Pe

rce

nta

ge

S50

00 00 00 00 01 00 10

72

477440

0

10

20

30

40

50

60

70

Pe

rce

nta

ge

C50

00 00 03 1953

195

299

389

23 19

0

10

20

30

40

50

60

70

Pe

rce

nta

ge

AR50


(volume weighted)








Expected

Shape

Actual

Shape

Yield 77 83 67 49 60

Comments






PSD = similar

Shape = different




Case study 1


NEEDLES



000002004006008010012014016018020022024026028030032034036038040042044046048050052054056058060062064066068070072074076

Aspect

ratio



Particle size

Asp

ect

rati

o

CUBES

000

005

010

015

020

025

030

035

040

045

050

055

060

065

070

075

080

085

090

095

100

105

110

115

120

125

130

135

140

145

150

155

160

165

170

175

180

185

190

195

De

nsity d

istr

ibu

tio

n q

3

2 4 6 8 10 20 40 60 80 100 200 400 600 800 1000 2000


Batch

IBUHEXL01

IBUETHL01

IBUETHL01

IBUHEXL01
















apprentice

apprentice

apprentice

apprentice

(FFC)




Target true

density

Target BFI





Target FFC

Target BFI





Target BFI






Target D[v01]



Attributes of API


Quality Target

Product Profile



DGWGDC


Drug Loading

MCS




process selection





Future perspective

Conrad Davies

Martyn Ticehurst

Tiffany Lai

Bruno Hancock

Weili Yu

Debbie Hooper

Fiona Clarke

Alan Carmody

Bob Docherty

Alastair Coupe

Tim Lukas

Lourdes Contreras

Acknowledgements

Questions

neildawsonpfizercom

Michael Leane (BMS)


Kendal Pitt (GSK)



Backup











Backup

Convexity reflects

particle surface

roughness Smoother


convexity

Feret_MIN

Feret_MAX















Problem 1



Sphere 100

Icosahedron 106

Dodecahedron 110

Octahedron 118

Cube 124

Tetrahedron 150





Problem 3









In addition hellip






16

prisms29

blocks27

plates18

needles14

other12











00 00 00 01 0524

55

197

659

60

0

10

20

30

40

50

60

70

Pe

rce

nta

ge

S50

00 00 00 00 01 00 10

72

477440

0

10

20

30

40

50

60

70

Pe

rce

nta

ge

C50

00 00 03 1953

195

299

389

23 19

0

10

20

30

40

50

60

70

Pe

rce

nta

ge

AR50


(volume weighted)








Expected

Shape

Actual

Shape

Yield 77 83 67 49 60

Comments






PSD = similar

Shape = different




Case study 1


NEEDLES



000002004006008010012014016018020022024026028030032034036038040042044046048050052054056058060062064066068070072074076

Aspect

ratio



Particle size

Asp

ect

rati

o

CUBES

000

005

010

015

020

025

030

035

040

045

050

055

060

065

070

075

080

085

090

095

100

105

110

115

120

125

130

135

140

145

150

155

160

165

170

175

180

185

190

195

De

nsity d

istr

ibu

tio

n q

3

2 4 6 8 10 20 40 60 80 100 200 400 600 800 1000 2000


Batch

IBUHEXL01

IBUETHL01

IBUETHL01

IBUHEXL01
















apprentice

apprentice

apprentice

apprentice

(FFC)




Target true

density

Target BFI





Target FFC

Target BFI





Target BFI






Target D[v01]



Attributes of API


Quality Target

Product Profile



DGWGDC


Drug Loading

MCS




process selection





Future perspective

Conrad Davies

Martyn Ticehurst

Tiffany Lai

Bruno Hancock

Weili Yu

Debbie Hooper

Fiona Clarke

Alan Carmody

Bob Docherty

Alastair Coupe

Tim Lukas

Lourdes Contreras

Acknowledgements

Questions

neildawsonpfizercom

Michael Leane (BMS)


Kendal Pitt (GSK)



Backup











Backup

Convexity reflects

particle surface

roughness Smoother


convexity

Feret_MIN

Feret_MAX











Problem 1



Sphere 100

Icosahedron 106

Dodecahedron 110

Octahedron 118

Cube 124

Tetrahedron 150





Problem 3









In addition hellip






16

prisms29

blocks27

plates18

needles14

other12











00 00 00 01 0524

55

197

659

60

0

10

20

30

40

50

60

70

Pe

rce

nta

ge

S50

00 00 00 00 01 00 10

72

477440

0

10

20

30

40

50

60

70

Pe

rce

nta

ge

C50

00 00 03 1953

195

299

389

23 19

0

10

20

30

40

50

60

70

Pe

rce

nta

ge

AR50


(volume weighted)








Expected

Shape

Actual

Shape

Yield 77 83 67 49 60

Comments






PSD = similar

Shape = different




Case study 1


NEEDLES



000002004006008010012014016018020022024026028030032034036038040042044046048050052054056058060062064066068070072074076

Aspect

ratio



Particle size

Asp

ect

rati

o

CUBES

000

005

010

015

020

025

030

035

040

045

050

055

060

065

070

075

080

085

090

095

100

105

110

115

120

125

130

135

140

145

150

155

160

165

170

175

180

185

190

195

De

nsity d

istr

ibu

tio

n q

3

2 4 6 8 10 20 40 60 80 100 200 400 600 800 1000 2000


Batch

IBUHEXL01

IBUETHL01

IBUETHL01

IBUHEXL01
















apprentice

apprentice

apprentice

apprentice

(FFC)




Target true

density

Target BFI





Target FFC

Target BFI





Target BFI






Target D[v01]



Attributes of API


Quality Target

Product Profile



DGWGDC


Drug Loading

MCS




process selection





Future perspective

Conrad Davies

Martyn Ticehurst

Tiffany Lai

Bruno Hancock

Weili Yu

Debbie Hooper

Fiona Clarke

Alan Carmody

Bob Docherty

Alastair Coupe

Tim Lukas

Lourdes Contreras

Acknowledgements

Questions

neildawsonpfizercom

Michael Leane (BMS)


Kendal Pitt (GSK)



Backup











Backup

Convexity reflects

particle surface

roughness Smoother


convexity

Feret_MIN

Feret_MAX














Problem 3









In addition hellip






16

prisms29

blocks27

plates18

needles14

other12











00 00 00 01 0524

55

197

659

60

0

10

20

30

40

50

60

70

Pe

rce

nta

ge

S50

00 00 00 00 01 00 10

72

477440

0

10

20

30

40

50

60

70

Pe

rce

nta

ge

C50

00 00 03 1953

195

299

389

23 19

0

10

20

30

40

50

60

70

Pe

rce

nta

ge

AR50


(volume weighted)








Expected

Shape

Actual

Shape

Yield 77 83 67 49 60

Comments






PSD = similar

Shape = different




Case study 1


NEEDLES



000002004006008010012014016018020022024026028030032034036038040042044046048050052054056058060062064066068070072074076

Aspect

ratio



Particle size

Asp

ect

rati

o

CUBES

000

005

010

015

020

025

030

035

040

045

050

055

060

065

070

075

080

085

090

095

100

105

110

115

120

125

130

135

140

145

150

155

160

165

170

175

180

185

190

195

De

nsity d

istr

ibu

tio

n q

3

2 4 6 8 10 20 40 60 80 100 200 400 600 800 1000 2000


Batch

IBUHEXL01

IBUETHL01

IBUETHL01

IBUHEXL01
















apprentice

apprentice

apprentice

apprentice

(FFC)




Target true

density

Target BFI





Target FFC

Target BFI





Target BFI






Target D[v01]



Attributes of API


Quality Target

Product Profile



DGWGDC


Drug Loading

MCS




process selection





Future perspective

Conrad Davies

Martyn Ticehurst

Tiffany Lai

Bruno Hancock

Weili Yu

Debbie Hooper

Fiona Clarke

Alan Carmody

Bob Docherty

Alastair Coupe

Tim Lukas

Lourdes Contreras

Acknowledgements

Questions

neildawsonpfizercom

Michael Leane (BMS)


Kendal Pitt (GSK)



Backup











Backup

Convexity reflects

particle surface

roughness Smoother


convexity

Feret_MIN

Feret_MAX











Problem 3









In addition hellip






16

prisms29

blocks27

plates18

needles14

other12











00 00 00 01 0524

55

197

659

60

0

10

20

30

40

50

60

70

Pe

rce

nta

ge

S50

00 00 00 00 01 00 10

72

477440

0

10

20

30

40

50

60

70

Pe

rce

nta

ge

C50

00 00 03 1953

195

299

389

23 19

0

10

20

30

40

50

60

70

Pe

rce

nta

ge

AR50


(volume weighted)








Expected

Shape

Actual

Shape

Yield 77 83 67 49 60

Comments






PSD = similar

Shape = different




Case study 1


NEEDLES



000002004006008010012014016018020022024026028030032034036038040042044046048050052054056058060062064066068070072074076

Aspect

ratio



Particle size

Asp

ect

rati

o

CUBES

000

005

010

015

020

025

030

035

040

045

050

055

060

065

070

075

080

085

090

095

100

105

110

115

120

125

130

135

140

145

150

155

160

165

170

175

180

185

190

195

De

nsity d

istr

ibu

tio

n q

3

2 4 6 8 10 20 40 60 80 100 200 400 600 800 1000 2000


Batch

IBUHEXL01

IBUETHL01

IBUETHL01

IBUHEXL01
















apprentice

apprentice

apprentice

apprentice

(FFC)




Target true

density

Target BFI





Target FFC

Target BFI





Target BFI






Target D[v01]



Attributes of API


Quality Target

Product Profile



DGWGDC


Drug Loading

MCS




process selection





Future perspective

Conrad Davies

Martyn Ticehurst

Tiffany Lai

Bruno Hancock

Weili Yu

Debbie Hooper

Fiona Clarke

Alan Carmody

Bob Docherty

Alastair Coupe

Tim Lukas

Lourdes Contreras

Acknowledgements

Questions

neildawsonpfizercom

Michael Leane (BMS)


Kendal Pitt (GSK)



Backup











Backup

Convexity reflects

particle surface

roughness Smoother


convexity

Feret_MIN

Feret_MAX
















In addition hellip






16

prisms29

blocks27

plates18

needles14

other12











00 00 00 01 0524

55

197

659

60

0

10

20

30

40

50

60

70

Pe

rce

nta

ge

S50

00 00 00 00 01 00 10

72

477440

0

10

20

30

40

50

60

70

Pe

rce

nta

ge

C50

00 00 03 1953

195

299

389

23 19

0

10

20

30

40

50

60

70

Pe

rce

nta

ge

AR50


(volume weighted)








Expected

Shape

Actual

Shape

Yield 77 83 67 49 60

Comments






PSD = similar

Shape = different




Case study 1


NEEDLES



000002004006008010012014016018020022024026028030032034036038040042044046048050052054056058060062064066068070072074076

Aspect

ratio



Particle size

Asp

ect

rati

o

CUBES

000

005

010

015

020

025

030

035

040

045

050

055

060

065

070

075

080

085

090

095

100

105

110

115

120

125

130

135

140

145

150

155

160

165

170

175

180

185

190

195

De

nsity d

istr

ibu

tio

n q

3

2 4 6 8 10 20 40 60 80 100 200 400 600 800 1000 2000


Batch

IBUHEXL01

IBUETHL01

IBUETHL01

IBUHEXL01
















apprentice

apprentice

apprentice

apprentice

(FFC)




Target true

density

Target BFI





Target FFC

Target BFI





Target BFI






Target D[v01]



Attributes of API


Quality Target

Product Profile



DGWGDC


Drug Loading

MCS




process selection





Future perspective

Conrad Davies

Martyn Ticehurst

Tiffany Lai

Bruno Hancock

Weili Yu

Debbie Hooper

Fiona Clarke

Alan Carmody

Bob Docherty

Alastair Coupe

Tim Lukas

Lourdes Contreras

Acknowledgements

Questions

neildawsonpfizercom

Michael Leane (BMS)


Kendal Pitt (GSK)



Backup











Backup

Convexity reflects

particle surface

roughness Smoother


convexity

Feret_MIN

Feret_MAX















16

prisms29

blocks27

plates18

needles14

other12











00 00 00 01 0524

55

197

659

60

0

10

20

30

40

50

60

70

Pe

rce

nta

ge

S50

00 00 00 00 01 00 10

72

477440

0

10

20

30

40

50

60

70

Pe

rce

nta

ge

C50

00 00 03 1953

195

299

389

23 19

0

10

20

30

40

50

60

70

Pe

rce

nta

ge

AR50


(volume weighted)








Expected

Shape

Actual

Shape

Yield 77 83 67 49 60

Comments






PSD = similar

Shape = different




Case study 1


NEEDLES



000002004006008010012014016018020022024026028030032034036038040042044046048050052054056058060062064066068070072074076

Aspect

ratio



Particle size

Asp

ect

rati

o

CUBES

000

005

010

015

020

025

030

035

040

045

050

055

060

065

070

075

080

085

090

095

100

105

110

115

120

125

130

135

140

145

150

155

160

165

170

175

180

185

190

195

De

nsity d

istr

ibu

tio

n q

3

2 4 6 8 10 20 40 60 80 100 200 400 600 800 1000 2000


Batch

IBUHEXL01

IBUETHL01

IBUETHL01

IBUHEXL01
















apprentice

apprentice

apprentice

apprentice

(FFC)




Target true

density

Target BFI





Target FFC

Target BFI





Target BFI






Target D[v01]



Attributes of API


Quality Target

Product Profile



DGWGDC


Drug Loading

MCS




process selection





Future perspective

Conrad Davies

Martyn Ticehurst

Tiffany Lai

Bruno Hancock

Weili Yu

Debbie Hooper

Fiona Clarke

Alan Carmody

Bob Docherty

Alastair Coupe

Tim Lukas

Lourdes Contreras

Acknowledgements

Questions

neildawsonpfizercom

Michael Leane (BMS)


Kendal Pitt (GSK)



Backup











Backup

Convexity reflects

particle surface

roughness Smoother


convexity

Feret_MIN

Feret_MAX


















00 00 00 01 0524

55

197

659

60

0

10

20

30

40

50

60

70

Pe

rce

nta

ge

S50

00 00 00 00 01 00 10

72

477440

0

10

20

30

40

50

60

70

Pe

rce

nta

ge

C50

00 00 03 1953

195

299

389

23 19

0

10

20

30

40

50

60

70

Pe

rce

nta

ge

AR50


(volume weighted)








Expected

Shape

Actual

Shape

Yield 77 83 67 49 60

Comments






PSD = similar

Shape = different




Case study 1


NEEDLES



000002004006008010012014016018020022024026028030032034036038040042044046048050052054056058060062064066068070072074076

Aspect

ratio



Particle size

Asp

ect

rati

o

CUBES

000

005

010

015

020

025

030

035

040

045

050

055

060

065

070

075

080

085

090

095

100

105

110

115

120

125

130

135

140

145

150

155

160

165

170

175

180

185

190

195

De

nsity d

istr

ibu

tio

n q

3

2 4 6 8 10 20 40 60 80 100 200 400 600 800 1000 2000


Batch

IBUHEXL01

IBUETHL01

IBUETHL01

IBUHEXL01
















apprentice

apprentice

apprentice

apprentice

(FFC)




Target true

density

Target BFI





Target FFC

Target BFI





Target BFI






Target D[v01]



Attributes of API


Quality Target

Product Profile



DGWGDC


Drug Loading

MCS




process selection





Future perspective

Conrad Davies

Martyn Ticehurst

Tiffany Lai

Bruno Hancock

Weili Yu

Debbie Hooper

Fiona Clarke

Alan Carmody

Bob Docherty

Alastair Coupe

Tim Lukas

Lourdes Contreras

Acknowledgements

Questions

neildawsonpfizercom

Michael Leane (BMS)


Kendal Pitt (GSK)



Backup











Backup

Convexity reflects

particle surface

roughness Smoother


convexity

Feret_MIN

Feret_MAX











00 00 00 01 0524

55

197

659

60

0

10

20

30

40

50

60

70

Pe

rce

nta

ge

S50

00 00 00 00 01 00 10

72

477440

0

10

20

30

40

50

60

70

Pe

rce

nta

ge

C50

00 00 03 1953

195

299

389

23 19

0

10

20

30

40

50

60

70

Pe

rce

nta

ge

AR50


(volume weighted)








Expected

Shape

Actual

Shape

Yield 77 83 67 49 60

Comments






PSD = similar

Shape = different




Case study 1


NEEDLES



000002004006008010012014016018020022024026028030032034036038040042044046048050052054056058060062064066068070072074076

Aspect

ratio



Particle size

Asp

ect

rati

o

CUBES

000

005

010

015

020

025

030

035

040

045

050

055

060

065

070

075

080

085

090

095

100

105

110

115

120

125

130

135

140

145

150

155

160

165

170

175

180

185

190

195

De

nsity d

istr

ibu

tio

n q

3

2 4 6 8 10 20 40 60 80 100 200 400 600 800 1000 2000


Batch

IBUHEXL01

IBUETHL01

IBUETHL01

IBUHEXL01
















apprentice

apprentice

apprentice

apprentice

(FFC)




Target true

density

Target BFI





Target FFC

Target BFI





Target BFI






Target D[v01]



Attributes of API


Quality Target

Product Profile



DGWGDC


Drug Loading

MCS




process selection





Future perspective

Conrad Davies

Martyn Ticehurst

Tiffany Lai

Bruno Hancock

Weili Yu

Debbie Hooper

Fiona Clarke

Alan Carmody

Bob Docherty

Alastair Coupe

Tim Lukas

Lourdes Contreras

Acknowledgements

Questions

neildawsonpfizercom

Michael Leane (BMS)


Kendal Pitt (GSK)



Backup











Backup

Convexity reflects

particle surface

roughness Smoother


convexity

Feret_MIN

Feret_MAX













Expected

Shape

Actual

Shape

Yield 77 83 67 49 60

Comments






PSD = similar

Shape = different




Case study 1


NEEDLES



000002004006008010012014016018020022024026028030032034036038040042044046048050052054056058060062064066068070072074076

Aspect

ratio



Particle size

Asp

ect

rati

o

CUBES

000

005

010

015

020

025

030

035

040

045

050

055

060

065

070

075

080

085

090

095

100

105

110

115

120

125

130

135

140

145

150

155

160

165

170

175

180

185

190

195

De

nsity d

istr

ibu

tio

n q

3

2 4 6 8 10 20 40 60 80 100 200 400 600 800 1000 2000


Batch

IBUHEXL01

IBUETHL01

IBUETHL01

IBUHEXL01
















apprentice

apprentice

apprentice

apprentice

(FFC)




Target true

density

Target BFI





Target FFC

Target BFI





Target BFI






Target D[v01]



Attributes of API


Quality Target

Product Profile



DGWGDC


Drug Loading

MCS




process selection





Future perspective

Conrad Davies

Martyn Ticehurst

Tiffany Lai

Bruno Hancock

Weili Yu

Debbie Hooper

Fiona Clarke

Alan Carmody

Bob Docherty

Alastair Coupe

Tim Lukas

Lourdes Contreras

Acknowledgements

Questions

neildawsonpfizercom

Michael Leane (BMS)


Kendal Pitt (GSK)



Backup











Backup

Convexity reflects

particle surface

roughness Smoother


convexity

Feret_MIN

Feret_MAX












PSD = similar

Shape = different




Case study 1


NEEDLES



000002004006008010012014016018020022024026028030032034036038040042044046048050052054056058060062064066068070072074076

Aspect

ratio



Particle size

Asp

ect

rati

o

CUBES

000

005

010

015

020

025

030

035

040

045

050

055

060

065

070

075

080

085

090

095

100

105

110

115

120

125

130

135

140

145

150

155

160

165

170

175

180

185

190

195

De

nsity d

istr

ibu

tio

n q

3

2 4 6 8 10 20 40 60 80 100 200 400 600 800 1000 2000


Batch

IBUHEXL01

IBUETHL01

IBUETHL01

IBUHEXL01
















apprentice

apprentice

apprentice

apprentice

(FFC)




Target true

density

Target BFI





Target FFC

Target BFI





Target BFI






Target D[v01]



Attributes of API


Quality Target

Product Profile



DGWGDC


Drug Loading

MCS




process selection





Future perspective

Conrad Davies

Martyn Ticehurst

Tiffany Lai

Bruno Hancock

Weili Yu

Debbie Hooper

Fiona Clarke

Alan Carmody

Bob Docherty

Alastair Coupe

Tim Lukas

Lourdes Contreras

Acknowledgements

Questions

neildawsonpfizercom

Michael Leane (BMS)


Kendal Pitt (GSK)



Backup











Backup

Convexity reflects

particle surface

roughness Smoother


convexity

Feret_MIN

Feret_MAX











NEEDLES



000002004006008010012014016018020022024026028030032034036038040042044046048050052054056058060062064066068070072074076

Aspect

ratio



Particle size

Asp

ect

rati

o

CUBES

000

005

010

015

020

025

030

035

040

045

050

055

060

065

070

075

080

085

090

095

100

105

110

115

120

125

130

135

140

145

150

155

160

165

170

175

180

185

190

195

De

nsity d

istr

ibu

tio

n q

3

2 4 6 8 10 20 40 60 80 100 200 400 600 800 1000 2000


Batch

IBUHEXL01

IBUETHL01

IBUETHL01

IBUHEXL01
















apprentice

apprentice

apprentice

apprentice

(FFC)




Target true

density

Target BFI





Target FFC

Target BFI





Target BFI






Target D[v01]



Attributes of API


Quality Target

Product Profile



DGWGDC


Drug Loading

MCS




process selection





Future perspective

Conrad Davies

Martyn Ticehurst

Tiffany Lai

Bruno Hancock

Weili Yu

Debbie Hooper

Fiona Clarke

Alan Carmody

Bob Docherty

Alastair Coupe

Tim Lukas

Lourdes Contreras

Acknowledgements

Questions

neildawsonpfizercom

Michael Leane (BMS)


Kendal Pitt (GSK)



Backup











Backup

Convexity reflects

particle surface

roughness Smoother


convexity

Feret_MIN

Feret_MAX























apprentice

apprentice

apprentice

apprentice

(FFC)




Target true

density

Target BFI





Target FFC

Target BFI





Target BFI






Target D[v01]



Attributes of API


Quality Target

Product Profile



DGWGDC


Drug Loading

MCS




process selection





Future perspective

Conrad Davies

Martyn Ticehurst

Tiffany Lai

Bruno Hancock

Weili Yu

Debbie Hooper

Fiona Clarke

Alan Carmody

Bob Docherty

Alastair Coupe

Tim Lukas

Lourdes Contreras

Acknowledgements

Questions

neildawsonpfizercom

Michael Leane (BMS)


Kendal Pitt (GSK)



Backup











Backup

Convexity reflects

particle surface

roughness Smoother


convexity

Feret_MIN

Feret_MAX

















apprentice

apprentice

apprentice

apprentice

(FFC)




Target true

density

Target BFI





Target FFC

Target BFI





Target BFI






Target D[v01]



Attributes of API


Quality Target

Product Profile



DGWGDC


Drug Loading

MCS




process selection





Future perspective

Conrad Davies

Martyn Ticehurst

Tiffany Lai

Bruno Hancock

Weili Yu

Debbie Hooper

Fiona Clarke

Alan Carmody

Bob Docherty

Alastair Coupe

Tim Lukas

Lourdes Contreras

Acknowledgements

Questions

neildawsonpfizercom

Michael Leane (BMS)


Kendal Pitt (GSK)



Backup











Backup

Convexity reflects

particle surface

roughness Smoother


convexity

Feret_MIN

Feret_MAX












apprentice

apprentice

apprentice

apprentice

(FFC)




Target true

density

Target BFI





Target FFC

Target BFI





Target BFI






Target D[v01]



Attributes of API


Quality Target

Product Profile



DGWGDC


Drug Loading

MCS




process selection





Future perspective

Conrad Davies

Martyn Ticehurst

Tiffany Lai

Bruno Hancock

Weili Yu

Debbie Hooper

Fiona Clarke

Alan Carmody

Bob Docherty

Alastair Coupe

Tim Lukas

Lourdes Contreras

Acknowledgements

Questions

neildawsonpfizercom

Michael Leane (BMS)


Kendal Pitt (GSK)



Backup











Backup

Convexity reflects

particle surface

roughness Smoother


convexity

Feret_MIN

Feret_MAX













Target true

density

Target BFI





Target FFC

Target BFI





Target BFI






Target D[v01]



Attributes of API


Quality Target

Product Profile



DGWGDC


Drug Loading

MCS




process selection





Future perspective

Conrad Davies

Martyn Ticehurst

Tiffany Lai

Bruno Hancock

Weili Yu

Debbie Hooper

Fiona Clarke

Alan Carmody

Bob Docherty

Alastair Coupe

Tim Lukas

Lourdes Contreras

Acknowledgements

Questions

neildawsonpfizercom

Michael Leane (BMS)


Kendal Pitt (GSK)



Backup











Backup

Convexity reflects

particle surface

roughness Smoother


convexity

Feret_MIN

Feret_MAX















Target FFC

Target BFI





Target BFI






Target D[v01]



Attributes of API


Quality Target

Product Profile



DGWGDC


Drug Loading

MCS




process selection





Future perspective

Conrad Davies

Martyn Ticehurst

Tiffany Lai

Bruno Hancock

Weili Yu

Debbie Hooper

Fiona Clarke

Alan Carmody

Bob Docherty

Alastair Coupe

Tim Lukas

Lourdes Contreras

Acknowledgements

Questions

neildawsonpfizercom

Michael Leane (BMS)


Kendal Pitt (GSK)



Backup











Backup

Convexity reflects

particle surface

roughness Smoother


convexity

Feret_MIN

Feret_MAX















Target BFI






Target D[v01]



Attributes of API


Quality Target

Product Profile



DGWGDC


Drug Loading

MCS




process selection





Future perspective

Conrad Davies

Martyn Ticehurst

Tiffany Lai

Bruno Hancock

Weili Yu

Debbie Hooper

Fiona Clarke

Alan Carmody

Bob Docherty

Alastair Coupe

Tim Lukas

Lourdes Contreras

Acknowledgements

Questions

neildawsonpfizercom

Michael Leane (BMS)


Kendal Pitt (GSK)



Backup











Backup

Convexity reflects

particle surface

roughness Smoother


convexity

Feret_MIN

Feret_MAX
















Target D[v01]



Attributes of API


Quality Target

Product Profile



DGWGDC


Drug Loading

MCS




process selection





Future perspective

Conrad Davies

Martyn Ticehurst

Tiffany Lai

Bruno Hancock

Weili Yu

Debbie Hooper

Fiona Clarke

Alan Carmody

Bob Docherty

Alastair Coupe

Tim Lukas

Lourdes Contreras

Acknowledgements

Questions

neildawsonpfizercom

Michael Leane (BMS)


Kendal Pitt (GSK)



Backup











Backup

Convexity reflects

particle surface

roughness Smoother


convexity

Feret_MIN

Feret_MAX












Attributes of API


Quality Target

Product Profile



DGWGDC


Drug Loading

MCS




process selection





Future perspective

Conrad Davies

Martyn Ticehurst

Tiffany Lai

Bruno Hancock

Weili Yu

Debbie Hooper

Fiona Clarke

Alan Carmody

Bob Docherty

Alastair Coupe

Tim Lukas

Lourdes Contreras

Acknowledgements

Questions

neildawsonpfizercom

Michael Leane (BMS)


Kendal Pitt (GSK)



Backup











Backup

Convexity reflects

particle surface

roughness Smoother


convexity

Feret_MIN

Feret_MAX













process selection





Future perspective

Conrad Davies

Martyn Ticehurst

Tiffany Lai

Bruno Hancock

Weili Yu

Debbie Hooper

Fiona Clarke

Alan Carmody

Bob Docherty

Alastair Coupe

Tim Lukas

Lourdes Contreras

Acknowledgements

Questions

neildawsonpfizercom

Michael Leane (BMS)


Kendal Pitt (GSK)



Backup











Backup

Convexity reflects

particle surface

roughness Smoother


convexity

Feret_MIN

Feret_MAX














Future perspective

Conrad Davies

Martyn Ticehurst

Tiffany Lai

Bruno Hancock

Weili Yu

Debbie Hooper

Fiona Clarke

Alan Carmody

Bob Docherty

Alastair Coupe

Tim Lukas

Lourdes Contreras

Acknowledgements

Questions

neildawsonpfizercom

Michael Leane (BMS)


Kendal Pitt (GSK)



Backup











Backup

Convexity reflects

particle surface

roughness Smoother


convexity

Feret_MIN

Feret_MAX











Conrad Davies

Martyn Ticehurst

Tiffany Lai

Bruno Hancock

Weili Yu

Debbie Hooper

Fiona Clarke

Alan Carmody

Bob Docherty

Alastair Coupe

Tim Lukas

Lourdes Contreras

Acknowledgements

Questions

neildawsonpfizercom

Michael Leane (BMS)


Kendal Pitt (GSK)



Backup











Backup

Convexity reflects

particle surface

roughness Smoother


convexity

Feret_MIN

Feret_MAX











Questions

neildawsonpfizercom

Michael Leane (BMS)


Kendal Pitt (GSK)



Backup











Backup

Convexity reflects

particle surface

roughness Smoother


convexity

Feret_MIN

Feret_MAX











Backup











Backup

Convexity reflects

particle surface

roughness Smoother


convexity

Feret_MIN

Feret_MAX





















Backup

Convexity reflects

particle surface

roughness Smoother


convexity

Feret_MIN

Feret_MAX











Convexity reflects

particle surface

roughness Smoother


convexity

Feret_MIN

Feret_MAX














Documents

Understanding API Attributes and their impact on Drug ... · • Assumes there is a preference for simpler manufacturing routes • Builds on prior knowledge e.g. Hancock’s direct