bruno hancock arden house final handouts

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Bruno C. Hancock - Pfizer Inc. 1

From Particles to Powders to Products: Predicting Properties

and Performance

Bruno C. Hancock


Acknowledgments• Glenn Carlson• Beth Langdon• Matt Mullarney• Dauda Ladipo• Weili Yu• Chris Sinko• Cindy Oksanen• Bill Ketterhagen• John Schelhorn• Sheri Shamblin

• Jim Prescott• James Elliott• Jennifer Sinclair• Carl Wassgren• Ken Morris• David Hedden

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Why do we characterize powders?

• To comply with regulatory requirements– ‘Checking the box’

• For quality control purposes– To ensure consistency from lot-to-lot– To ensure stability over time

• To benchmark their bulk properties– Relative ranking against other materials– Absolute measurements => comparison with theory

• To assess the impact of unit processes • To obtain material parameters for input into

predictive models of performance


Objective

• To predict the performance of drug products from the particle and bulk properties of the raw materials (API and excipients)

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Particle properties (x)• Size• Shape• Morphology• Densityetc

Bulk powder properties (y)• Porosity• Size distribution• Densityetc

y = f(x)

z = f(y)

Performance (z)• Flow• Segregation• Content uniformity• Compactionetc


Sample and particle size considerations

• A typical tablets ‘contains’ >>105 particles

• Typical tablet is 2 – 4 mm thick and 5 - 13 mm diameter•Typical particle = 0.01 - 0.10 mm diameter

10 - 1000 particles

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First principles ‘rules of thumb’

Shear

Compression A powder’s resistance to shear depends upon:- the number of inter-particle interactions- the strength of the interactions- the degree of consolidation (bulk density)- any compressive forces that exist

- low density - few weak interactions

-high density- many strong interactions


Initial considerations

• Which performance parameters are important?– Not the same for all products

• How best to quantify product performance?– Discriminating and meaningful performance tests are

needed

• How best to characterize raw material properties?– Functionality tests?– Ranking tests?– USP/NF? EP? JP?– Other?

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What should we be looking out for?• Inconsistency

– Purity (varies from lot-to-lot)– Particle size (varies from lot-to-lot)

• Heterogeneity– Size (broad particle size distribution)– Shape (wide variety of particle shapes)– Purity (low content uniformity of blend or granulation)

• Non-ideality– Shape (non-spherical)– Size distribution (deviates from log-normal)– Mechanical response (non-Hookean, viscoelastic)

• Obvious trends– To relate qualitatively to bulk performance– To develop ‘rules of thumb’– To support the development of predictive models


Functional relationships ( y=f(x) )

• Depend on– API type (e.g., small vs. large molecule, amorphous vs.

crystalline)– Dose– Manufacturing equipment available– Preferred processing pathway– Tolerable level of risk– Regulatory strategy

Therefore, custom relationships have to be developed for each situation and company

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How do I determine y=f(x)?

1. Standardize and simplify materials and processes2. Develop simple ‘fit for purpose’ methods to quantify

product performance and raw material properties3. Analyze data in multiple ways

– Look for qualitative trends in data– Capture existing knowledge from experts– Conduct quantitative data analyses (e.g., multivariate

analyses)4. Translate the trends into ‘rules of thumb’

Focus on • the biggest problem areas

– e.g., powder flow? segregation?• where the science is already well developed


‘Rules of thumb’ from the literature• API loadings <30% can be managed by smart excipient and

process selection• API loadings <1% will present content uniformity challenges

Physical properties are qualitatively additive in most cases• Molecular scale properties have less significance than

particle scale properties• Decreased particle sphericity decreases powder bulk density

and worsens powder flow• Smaller particles result in worse powder flow and increased

compact tensile strength• Broader particle size distributions correlate with worse

powder flow and increased segregation• Low powder bulk density is associated with poor powder flow• Drug product stability can be predicted from API stability and

excipient compatibility data

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Case study• Product profile

– API• Needle shaped particles, D(50)=25μm, Bmid80 = 5.0 (broad)• Bulk density = 0.10g/cc• Solubility >5mg/ml• Good API stability; incompatible with lactose

– Dug product (target properties)• IR tablet, dose = 250mg/tablet)• Stable for >2 years at room temperature, when no lactose used

• Environmental conditions– Most common problems encountered

• Poor flow in tablet press hopper, segregation during scale-up– Preferred manufacturing processes

• Direct compression, then dry granulation– Equipment available for use

• V-blenders, rotary tablet presses, Collette type high-shear mixers


Case study• Potential issues

– API is stable, in the absence of lactose– Solubility is high (BCS classification), which is good for an

IR tablet– Dose is high, therefore API properties will dominate– Particle size is small and shape is irregular, therefore flow

performance is likely to be poor– Powder bulk density is low, therefore flow performance is

likely to be poor– Segregation and compaction behavior is unknown

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Case study• Immediate actions

– Set appropriate API particle size targets• based on content uniformity models

– Apply meaningful flow tests to the prototype formulation• Known to be a likely problem area

– Evaluate segregation potential of prototype formulation– Understand compaction performance of the API &

prototype formulation• Is this a potential problem?• A rigorous final performance test is currently lacking

• Long term approach– Determine if API particles can be engineered to improve

performance


Predicting content uniformity of blends

• Content uniformity predictions can be made from theoretical models– E.g., Zhang & Johnson. Int.J.Pharm. (1997) 154 p179

• Based on statistical model of random mixing in a powder blend• Assumes perfect mixing and sampling

• Requires data for– Dose per unit– Particle size distribution of the API– API density

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Maximum volume median particle diameter (d50) predicted to pass USP Stage-I content uniformity criteria with 99% confidence

• As a function of dose (mg) and geometric standard deviation (sg).• An alternative estimation of the particle distribution width d90/d50 assumes a log-normal distribution and is calculated by (sg)1.28.

Rohrs et al, Journal of Pharmaceutical Sciences, Vol. 95, 1049–1059 (2006)


Predicting “flooding”• “Flooding” is defined as uncontrollable powder flow (similar

to liquid-type flow)• The smaller the mean particle size and density, the more

likely that a powder will show “flooding” behavior• According to Geldart & Williams (1985) these powders can

be identified using:

ρp0.934.dp

0.8 < 1

where dp = particle diameter & ρp = particle density

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Predicting weight uniformity of tablets

Variability in tablet weight increases as

powder flow deteriorates

(Data from Pfizer Inc.)

Poor flowGood flow


τ = σ tanφ + c

τ = Shear Stressσ = Normal Stressc = Cohesionφ = Angle of internal friction

σ

τ

Powder Bed

Powder friction

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Shear cell properties vs. tableting performance

Doelker, E., “Comparative compaction properties of various microcrystalline cellulose types and generic products”, Drug Development and Industrial Pharmacy, 19, 2399-2471 (1993).


Flow rate predictions

Flow rate

OutletsizeGravity

Bulkdensity

Particlesize

B and k are constants

For coarse powders:

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Hopper wall angle

Hopper outlet

diameter

Predicted flow rate

Particlediameter

Particledensity


ASTM fluidization tester

• Segregation tests can be conducted on representative blends made at a small scale

• For example, the ASTM fluidization segregation test requires ~80g of blend

TOP, MIDDLE & BOTTOM SAMPLES

TEST COLUMN

AIR INLET

EXPANSIONCHAMBER

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Fluidization segregation results

0

50

100

150

200

250

300

350

400

0 1 2 3 4 5 6 7 8 9 10 11 12 13 14 15 16 17

Sample

d50,

mic

ron

0.000

20.000

40.000

60.000

80.000

100.000

120.000

140.000

160.000

180.000

% L

C

SampleA

SampleC

SampleE

Sample C (ARD as % LC)

1 2 … 16

• Sample has a high segregation potential• Both particle size and assay vary markedly• Fines are API rich

• Test is reproducible (replicate tests give similar results)


Predicting segregation upon transfer• Using discrete element

method (DEM) computer simulations

• Every individual particle is considered and its movement (position & speed) calculated and tracked

• Very intensive calculations, so best suited to small numbers of particles (i.e., large particles or small volumes )

Ketterhagen et al., Powder Technol.(2007),

doi:10.1016/j.powtec.2007.06.023

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Predicting segregation upon transfer

Segregation results and snapshots of the hopper discharge showing the local normalized fines mass fraction

(A-ratio =2.76, xf=5%, ΦD=4.3, and θ=90°)


Modeling powder compaction• Using finite element

method (FEM) computer simulations

• System (the tablet) is treated as a series of finite elements (‘cells’) connected together to form the macroscopic sample

• Requires data on the physical and mechanical properties of the powder

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Initial powder fill Start of compression

End of compressionMiddle of compression

Relative density evolution during tablet compaction (Data from Tuhin Sinha, Pfizer Inc.)

Modeling powder compaction


Predicting powder compaction

Wu et al, Powder Technology 152 (2005) 107– 117

Tablet internal fracture

(‘capping’) Shear stress distributionfrom FEM model

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Importance of data-bases

• Ideally the results of all tests should be systematically recorded in an electronic database

• Comprehensive data for excipients and previous API and formulation samples under-lies any meaningful prediction of future formulation performance

• Data mining and historical trend analyses can be used to identify local formulation practices


Quantifying “normal” behavior(e.g., powder flow performance)

Ranking Range Count Percentpoor <4.5 103 15.6%

marginal 4.5-5.9 284 43.0%good 6.0-7.4 193 29.2%

excellent >=7.5 80 12.1%

Percent of Sample in Each Ranking Category

Sample Size 660Maximum -1.88Minumum 14.59Mean 5.78Median 5.65Standard Deviation 1.79

Summary Statistics

Poor Excellent

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Excellent

Poor

Bulk Drug ExcipientRanking Range

1 2Poor <4.5 77.8% (7) 27.6% (16)

Marginal 4.5-5.9 0% (0) 25.9% (15)Good 6.0-7.4 11.2% (1) 22.4% (13)

Excellent >=7.5 11.1% (1) 24.1% (14)

Placebo Blend Placebo GranulationBlend Granulation

4 5 6 710.0% (4) 21.8% (12) 14.5% (9) 13.5% (52)22.5% (9) 50.9% (28) 32.3% (20) 50.8% (195)

25.0% (10) 21.8% (12) 41.9% (26) 26.6% (102)42.5% (17) 5.6% (3) 11.3% (7) 9.1% (9)

Powder flow


Summary

• High quality pharmaceutical dosage forms can be rationally and rapidly designed based on the key powder property data

• This requires: – understanding of key scientific principles, and knowledge

of the literature– small scale material characterization tools– well tested empirical relationships – computational approaches (based on appropriate

physics)

bruno hancock arden house final handouts

Documents

hancock pfizer

lot particle size

particle size considerations

diametertypical particle

bulk performance

performance parameters

raw material properties

performance of drug