investigation into the inherent variability of pharmaceutical excipients

Investigation into the inherent variability of pharmaceutical

excipients

The International Pharmaceutical Excipients Council (IPEC) defines excipients as,

“Substances, other than the Active Pharmaceutical Ingredient (API) in finished dosage form,

which have been appropriately evaluated for safety and are included in a drug delivery

system to either aid the processing or to aid manufacture, protect, support, enhance stability,

bioavailability or patient acceptability, assist in product identification, or enhance any other

attributes of the overall safety and effectiveness of the drug delivery system during storage or

use.”

Although they do not produce medical effect, excipients have been proven to be essential in

both biopharmaceutical and technical aspects. Due to their important roles, interchangeability

and uniformity of excipients are necessary in order to achieve consistent quality in the

finished products. All raw materials, including excipients, must be standardized to the

acceptance of the regulatory authorities. However, there are examples of excipients meeting

the monograph standard but performing differently during processing and in the final dosage

form1.

Many factors can contribute to the batch-to-batch variability within a source or

between sources, such as differences in raw material, manufacturing process, storage

conditions and transportation. Despite the multiple sources of variability, it has been

suggested that the pharmacopoeia is primarily based on the verification of identity, purity and

chemical stability with only limited testing on particle and powder physical properties, which

can affect an excipients’ functionality. As a result, certificate of analysis of excipients may

not provide sufficient confidence of equivalency between vendors or batches. In fact,

excipients’ functionality may not only depend on their intrinsic properties but also their

applications and formulation details. Therefore, it is controversial to include functionality or

physical testing related to functional properties in the monograph due to the many different

ways that an excipient can be used23. Nevertheless, a better understanding of the properties of

excipients and their relationships to the functionalities can help formulators to select

appropriate excipient and validate manufacturing process accordingly, hence improve process

control and move towards more controlled products with consistent quality in line with the

FDA’s Quality by Design initiative.

In fact, powders’ processibility and functionality can be influenced by many variables

including physical and mechanical properties and environmental effects. It is well known that

particle size, shape, and size distribution can impact on flow and compaction. Moisture

content and electrostatic effects can also influence flow depending on the material and the

environment, including humidity and storage conditions. Mechanical properties, for example

elasticity, plasticity, hardness, brittleness, can also have a profound effect on powders’

tabletting properties4.

The aim of this paper is to investigate batch-to-batch, vendor-to-vendor variations of

excipients and their impact on processing by studying the physical and function-related

properties of the excipients, including particle size, specific surface area, surface energy,

relative flowability, compressibility, compactibility and disintegration. Three commonly used

excipients received from the major suppliers were chosen for characterisation, including

lactose anhydrous, microcrystalline cellulose (MCC) and croscarmellose sodium (CCS). The

three Avicel grades of MCC, PH101, PH102 and PH200, which were noted as having

different particle sizes and processing behaviours, were also characterised and functionally

compared.

Lactose anhydrous

Lactose is a naturally occurring disaccharide, which is composed of one galactose and one

glucose moiety bonded through a glycosidic link. Due to the presence of an asymmetric

carbon, lactose can exist in two isomeric forms, a or b, which have different properties.

Anhydrous lactose typically contains 70-80% anhydrous b- lactose and 20-30% anhydrous a-

lactose. It occurs in white crystalline form with no water of hydration and is usually produced

by roller drying a lactose solution, which is then milled and sieved to the desired size. It is

widely used in direct compression tabletting process and employed as a capsule or tablet filler

and binder, especially in moisture sensitive compound where low moisture content is

desirable5. The popularity of lactose as an excipient can be contributed by its cost,

availability, bland taste, low hygroscopicity, good compatibility with other ingredients,

excellent stability and water solubility6.

Microcrystalline cellulose

Microcrystalline cellulose is derived from a naturally occurring polymer, a-cellulose,

obtained as a pulp from fibrous plant materials. It is comprised of linear chains formed by

glucose units joining together by a 1-4 b glycosidic bonds. It is widely used as tablet

filler/binder in both granulation and direction compression. In addition to its low chemical

reactivity, high plasticity and ease of availability, it also possesses some lubricant and

disintegrant properties. MCC is manufactured by spray drying the aqueous slurry resulting

from the acid-catalysed hydrolysis of cellulose and the commercial grades are produced by

manipulating the spray drying process to vary the moisture content and the degree of

agglomeration, hence particle sizeError: Reference source not found. MCC is available in

many different grades with differing particle size, shape and moisture content to meet the

needs of the formulators. For example, higher PH grades, which are reported as having larger

nominal particle sizes, have been developed to provide better flow properties than the

conventional grades. Today, MCC products can be obtained from a range of suppliers in

different countries. However, differences in the wood used as raw materials and

manufacturing processes can attribute significantly to the source and batch variations.

Manufacturer dependent and/or batch-wise variations have been reported in a number of

molecular-related properties7,8 (such as crystallinity, lignin content, etc.), powder properties9,10

(such as particle size, surface area, etc.) and tabletting behaviourError: Reference source not

found,11 (such as crushing strength, friability, etc.). Moreover, tablet characteristics, including

drug dissolution rate12 and drug absorption13, were also found to be variable in formulations

produced of MCC from different sources.

Croscarmellose sodium

Croscarmellose sodium is a cross- linked polymer made up of carboxymethyl cellulose

sodium. It is insoluble in water but highly absorbent and therefore, it possesses good water

wicking and swelling properties without losing its’ fibrous integrity. These enable its role

working as a superdisintegrant at concentrations up to 5% in the production of oral

formulations by direct compression or wet granulation. Multiple sources of croscarmellose

sodium are now available, however unlike MCC neither batch-to-batch nor vendor-to-vendor

variation has been studied extensively in the reason of its utility in only small fraction

(normally 2-3%) within a formulation.

Materials

The following batches of excipients were used in this study:

Lactose anhydrousVendor Origin Grade Batch number

DMV- Fonterra Excipients GmbH & Co. Germany

SuperTab 21AN104212771042817310444667

Borculo Domo NetherlandsLactopress 250

632751633379631678

Kerry BioscienceUSA

Lactose anhydrous NF

DT

1320010016132001001713200100211320019937

Microcrystalline celluloseVendor Origin Grade Batch number

FMC Ireland

Avicel PH-10160811C60727C

Avicel PH- 102

70745C70709C70728C

XN07818924P208819026

Avicel PH-200M0913CM0728C

JRS Pharma Germany Vivapur 102

5610291512561029100956102905065610290203561028804556102916125610285531

Croscarmellose sodiumVendor Origin Grade Batch number

DMV Netherlands PrimelloseFN00023FN0000510417292

JRS Pharma Germany Vivasol

3201083113320108316732010820803201082082320108310432010842193201091027

FMC Ireland Ac-Di-SolT0937CT0731CT0850C

Methods

Specific surface area

Micromeritics Gemini 2390A surface area analyser was employed to measure the SSAs of

the samples. The saturation pressure was needed to calculate the surface area and so was

measured at the beginning of the tests each day. Prior to the surface area measurement, the

samples were weighed accurately into sample tubes and outgassed overnight at 1000C to

remove any gases and vapours that might have become physically absorbed onto the sample

surfaces. The weight of lactose, MCC and CCS tested were 3g, 1g and 1.5g respectively.

After degassing, the samples were allowed to cool to room temperature with flowing nitrogen

and were then weighed prior to analysis. The specific surface area (m2/g) was determined by

the physical absorption resulting from the van der waals forces between nitrogen and the

absorbent surface of test samples using Brunauer, Emmett and Teller (BET) theory. The

samples were evacuated at a rate of 500mmHg/min for 5 minutes and equilibrated for 5 minutes.

Multipoint measurements (5 points) over the range of 0.06-0.02 were performed and triplicate

results were generated for each sample.

Particle size

Samples were tested on the Malvern Morphologi G3 image based particle size analyser. They

were dispersed using compressed air and allowed to settle on the glass plate which was then

analysed with appropriate magnification lens. The settings for each class of excipient tested

are detailed in table. Both geometric (volume-based) and arithmetic (number-based) particle

size distribution were produced and triplicate results were generated for each sample. Some

filtering settings, by means of convexity or circularity, were employed to remove any

partially imaged and/or overlapping particles. An intensity filter, whereas the primary

particles could be isolated through inversion of the intensity cut-off setting for the

agglomerates, was also employed for the study of agglomeration in MCC samples.

Material Dry air pressure

(bar)

Magnification lens Morphological filters applied (limits)

Lactose anhydrous

1.010X and 5X

(3.5-420 micron resolution range)

Convexity (<0.9)

MCC 4.010X


Whole population: Circularity (<0.45)

Primary particles: Convexity (<0.8) + Intensity mean (>40)

Agglomerates: Intensity mean (<40)

CCS 0.810X


Convexity (<0.85)

Surface energy

Inverse gas chromatography (IGC) was employed to characterise the surface energy, which is

often divided into polar (gP) and dispersive (gD) components. The samples were packed into

30cm (3cm inside diameter) silonised glass columns, plugged at either end by silonised glass

wool. They were conditioned at 30oC (303K), 0% RH, 10sccm for three hours prior to

analysis. The dispersive surface energy analysis was conducted by injecting a range of

hydrocarbon probes; decane, nonane, octane, heptane, and hexane at 0.04p/po. The specific

polar energy analysis was conducted by injecting a range of polar probes; acetone,

acetonitrile, ethyl acetate, and ethanol at 0.04p/po. The column dead time was determined

using an inert probe (methane at 0.04p/po). The acidic (ka) and basic (Kd) polar interaction

were also determined for each sample. All samples were analysed in triplicate.

Flow behaviours

Angle of Repose

Samples were tested on the Geldart Mark4 Angle of Repose tester. Due to its free flowing

and stable behaviour, spray-dried mannitol was employed for calibration each day prior to the

test. Temperature and relative humidity were also recorded. 100g of each sample was poured

slowly onto the upper part of the chute using a small metal scoop. A motor was employed to

generate a minimum degree of vibration required to aid the samples to slide down the upper

chute and feed into the metal hopper. As the powder left the lower chute and reached the L-

shaped plastic base unit, a semi-cone with well-defined, sharp apex was formed. An

electrostatic eliminator was also employed when the sample showed signs of static-like

behaviour such as sticking to the lower chute and blocking the passage to flow. The height

(h) of the semi-cone was read off and the average radius (r) of the base was determined by

taking readings at 7 positions. (Fig) The samples were recovered after the test and triplicate

results were generated for each of them. The AOR value was calculated from the following

equation:

AOR = tan-1(h/r)

Compressibility and compatibility

The compression study was conducted on the Stylcam compression simulator 100R serial no.

0507104 (Medelpharm, France) which was fitted with 11.28mm round flat face plan punches

using coupling plate no. 7. The tablets were made in ‘one tablet’ mode using hand filling of

the die by individually weighing each sample to the desired value (table). Before filling the

die, the punches were lubricated using magnesium state slurry (2% magnesium stearate in

acetone) which was painted on by hand. Each sample was compressed to a target solid

fraction of 0.85. Literature true density values were used to determine the target tablet

thickness (table). Six compacts were produced for each sample and once ejected, the accurate

weight and thickness of each tablet was measured using an analytical balance and digital

calipers. Tablet hardness was measured using a Schleuniger hardness tester HT1. Heckel

analysis using an ‘in-die’ method was employed to obtain the yield pressure (Py) of the

particles. The strength of the compact, i.e. tensile strength, was calculated using the following

equation:

Tensile strength (MPa) = 2P/ (πDt)

Where P= Hardness (N)

t= tablet thickness (mm)

D= diameter of tablet (mm)

MaterialLiterature true density (g/cm3)

Desired tablet thickness (mm)

Tablet Weight (mg)

Lactose anhydrous 1.589 2.96 400

MCC 1.59 2.96 300

CCS 1.543 3.05 400

Dicalcium phosphate 2.389 1.97 400

Disintegration

Tablets produced of pure dicalcium phosphate, 5% and 0.1% croscarmellose sodium were

tested on disintegration immediately after the compression using the Stylcam compression

simulator. Disintegration was conducted according to the USP method without disc at

37±1oC. Beaker filled with approximately 800ml distilled water was placed into a

disintegration bath ZT502 (Copley, Copley Scientific Limited, Nottingham, UK). Six

compacts were placed into the basket and moved in and out of the beaker at constant vertical

motion until all of them had disintegrated. Disintegration time for each of the tablet was

recorded.

Statistical tests

All statistical analyses were conducted using Minitab software (Minitab 15, Minitab Corp.,

PA). One-way analysis of variance (ANOCA) was carried out on the results. Interval plots

were constructed to show differences between and/or within vendors. A p-value of less than

0.05 was considered to be significant.

Results and Discussion

Lactose anhydrous

Physical properties


The specific surface area (SSA) results appear to indicate notable differences between the

three vendors, with the highest SSA results found for the four Kerry batches and the lowest

for the three DMV batches. Vendor batch-to-batch variability for both DMV and Borculo

Domo materials was observed to be low whilst Kerry batches were noticeably more variable,

with two lots (batch ref: 1320010021, 1320009937) having higher SSAs and the other two

(batch ref: 1320010016, 1320009937) being lower (Figure X).

Particle size

The particle size data (Table X) was observed to show similar trends in variability between

the batches and / or vendors as the SSA data, however no direct correlation between particle

size and SSA was noted. This is probably due to the wide particle size distribution of lactose

anhydrous. As the magnification lens used for particle size analysis had a resolution range of

3.5 to 400 microns, any particles small than 3.5 micron were outside the scope of analysis.

However, the BET surface area measurements are especially sensitive to these fine particles

with smaller particles having larger surface areas. Hence, the results may indicate that the

three vendors contain similar numbers of coarse particles but different levels of fines, which

contribute to the differences in the SSA data.

Lactose anhydrous

vendor Batch reference

SSA m²/g

Geometric particle size (mm)

Arithmetic particle size (mm)

Dv10 Dv50 Dv90 Dn10 Dn50 Dn90

DMV

10421277 0.40(0.00)

23.0(2.5)

116.8(14.8)

282.3(14.0)

1.4(0.1)

4.4(0.4)

11.8(0.6)

10428173 0.39(0.01)

34.0(8.5)

130.4(16.8)

265.7(15.6)

1.3(0.0)

3.6(0.1)

9.8(1.0)

10444667 0.41(0.01)

33.5(7.1)

158.7(2.0)

309.5(23.7)

1.5(0.1)

4.2(0.6)

10.6(1.0)

Borculo Domo

632751 0.48(0.00)

28.8(4.5)

114.3(5.5)

248.4(52.8)

1.4(0.0)

4.1(0.3)

11.8(1.3)

633379 0.48(0.01)

26.5(1.6)

103.2(16.4)

260.8(86.2)

1.6(0.1)

5.0(0.1)

14.0(0.5)

631678 0.48(0.01)

25.7(1.5)

110.8(10.4)

255.3(34.6)

1.3(0.1)

3.8(1.0)

12.4(1.1)

Kerry Bioscience

1320010016 0.50(0.00)

26.5(2.9)

164.0(25.1)

394.5(59.3)

1.4(0.2)

4.2(1.1)

12.8(1.1)

1320010017 0.50(0.01)

29.5(3.9)

185.9(11.0)

356.8(47.6)

1.3(0.1)

5.5(0.3)

15.6(0.3)

1320010021 0.54(0.00)

31.3(2.7)

154.5(18.6)

327.5(3.7)

1.3(0.0)

4.2(1.1)

16.1(1.3)

1320019937 0.55(0.01)

26.8(0.8)

118.8(17.6)

285.3(14.4)

1.5(0.0)

4.4(0.6)

19.5(0.7)

Surface energy

Similarly to the SSA data, the dispersive surface energy (DSE) results indicate that the three

vendors are significantly different from each others. The three DMV batches were observed

to have noticeably higher DSEs than Borculo Domo while the lowest DSEs were for Kerry

(with the exception of batch ref: 1320010021). Batch-to-batch variability of DMV and

Borculo Domo was low but the Kerry batches were again found to be more variable with two

lots (batch ref: 1320010021, 1320009937) were measured with higher DSEs and the other

two (batch ref: 1320010016, 1320009937) being lower. The reason for the differences in

DSEs of the materials is not fully understood, however, it may be explained by slight

differences in mechanically induced amorphous content on the surface of the samples, which

can be created during milling. Newell et al.14 reported that a small fraction (0.7%) of

amorphous materials present on the surface of lactose can have a significant effect to the

surface energies due to the very low hydrocarbon probe concentration (0.04p/po) used in the

test.

A slight variation in the polar surface energies between the three vendors was also reported.

The variations in the heats of absorption for DMV and Borculo Domo batches was found to

have a relationship with the measured SSA, probably due to varied availability of crystalline

faces for probe interaction due to milling. The highest inter-batch variability was again for

Kerry; however this material did not show the above relationship. The variations in surface

energetics could be hypothesized to be due to the small changes in crystallinity or surface

purity15.

Lactose anhydrousvendor Batch

referenceDSE

(mJ/m2)Free energy of absorption (J/mol)

Acetone Acetonitrile Ethanol Ethyl acetate

DMV

10421277 46.02(0.22) 8970.0 11924.1 13266.2 11638.5

10428173 45.66(1.35) 8537.3 11918.0 13386.7 11635.5

10444667 45.66(0.56) 8624.9 11875.0 13262.7 11683.4

Borculo Domo

632751 43.19(0.54) 8245.0 11227.6 12385.5 10950.8

633379 43.31(0.54) 8340.3 11275.0 12492.4 11123.4

631678 43.11(0.68) 8231.6 11338.5 12483.6 11060.6

Kerry Bioscience

1320010016 39.81(0.44) 8616.1 11426.3 12945.4 10973.1

1320010017 39.68(1.78) 9005.6 11907.2 13896.2 11901.8

1320010021 44.02(0.81) 9173.1 12537.3 14375.5 12441.3

1320019937 42.51(0.83) 9308.7 12024.2 14045.9 11994.5

Function-related properties

Flowability

A minimum degree of vibration was needed to aid the flow of all the ten batches of lactose.

Despite the difference in the levels of fines and surface energies observed, the AOR results

show similar flow properties (AOR= 38-41o) in the ten batches of lactose, which would

indicate that the materials are on the borderline between free flowing and a cohesive powders

according to the manufacturer’s guide. This may indicate that the differences in particle sizes

are not significant enough to affect a change in powder flow properties. However, it is

important to note that the AOR tester is generally considered to be an unreliable tool for the

prediction of powder flowability due to the lack of sensitivity to distinguish between two

slightly different flowing materialsError: Reference source not found. Therefore, the results

obtained in the study may not be able to relate to the real life powder flow properties.

Compressibility and compactability

Inter-batch variability appears to be low for all the vendors in terms of both compressibility

and compactability. However, statistically significant differences between the suppliers were

observed.

The compressibility (yield pressure) data indicates that DMV and Borculo Domo batches

possessed similar particle deformability behaviour while the four Kerry batches required a

distinctly higher stress to initiate deformation. This could be resulted from a number of

factors, including the fines content. As reported earlier, SSA data suggested that the four

Kerry batches contained the highest levels of fines, which could accommodate and fill up the

air gaps between the coarse particles. This may lead to a reduction in particle rearrangement

during compression and therefore a greater pressure would be required to cause particle

deformation. Moreover, it is generally concluded that lactose deforms predominantly by

brittle fracture with some plastic deformation at the contact points. Therefore, a reduction in

particle size may also contribute to the increased yield pressure (Py) by reducing the amount

of fragmentation16.

In terms of compactability, Kerry batches produced harder tablets with greater tensile

strengths than DMV and Borculo Domo. Generally, the tensile strength increases with

increasing bonding capacity of the powder. It has been demonstrated that the presence of

small particles can fill up and reduce the void spaces, leading to the greater interparticulate

contact areas and hence producing higher compacted strengths within the tablets17. The

highest tensile strength of tablets produced of Kerry batches may be due to its increased fines

content.

Lactose anhydrousvendor Batch

referenceAOR

(o)Yield pressure (MPa) Tensile strength (MPa)

DMV

10421277 39±2 136.9±0.7 1.7±0.1

10428173 39±1 135.2±1.2 1.8±0.0

10444667 38±1 136.1±1.7 1.9±0.1

Borculo Domo

632751 39±1 133.2±1.9 1.6±0.1

633379 41±2 134.3±1.6 1.8±0.1

631678 40±1 130.4±2.1 1.4±0.1

Kerry Bioscience

1320010016 40±2 148.4±1.4 2.4±0.2

1320010017 39±1 144.7±1.5 2.4±0.2

1320010021 40±1 146.2±2.3 2.5±0.1

1320019937 40±2 144.0±2.1 2.5±0.2

Microcrystalline cellulose (MCC)

Physical properties

Particle size

PH-102

The geometric (volume based) and arithmetic (number based) particle size distributions for

the 12 batches of MCC PH-102 were also found to be indistinguishable and the two vendors

were noted to be statistically equivalent (P=0.659). However, the geometric distributions

were observed to be wide with a high degree of skewing to the fines end of the particle size

range. When the particle images were viewed, two distinct classes of particles, fine primary

particles and coarser agglomerates, were identified. This phenomenon was further analysed

on another two grades of Avicel, PH101 and PH200, and the degrees of agglomeration were

also compared.

Comparison of grades of Avicel (PH101, PH102 and PH200)

According to the manufacturer’s particle size data determined by using laser light diffraction,

the three Avicel grades, PH101, PH102 and PH200, are reported as having mean geometric

particle sizes of 50mm, 100mm and 180mm respectively. Using the Morphologi image-based

analyzer, the median geometric particle size (Dv50) results obtained were generally in

agreement with the manufacturer’s laser light diffraction data, indicating that both methods

were measuring comparable particle populations. The only exception is one of the PH200

batch (ref.: M0913C) which has a significantly lower Dv50 than the one (Dv50= 224mm) in

the certificate of analysis (COA). A Dv90 value of 403mm is noted on COA whilst no particle

greater than 400mm was identified in the three replicate samples. It is believed, and the trends

in the arithmetic size data appear to concur, that the very coarse (>400mm) particles, which

have significant weighting in the geometric data, are present in such low numbers that

relatively large sample sizes would be required to ensure detection. As such the Morphologi

is much less likely to ‘observe’ one of these large particles than a typical laser light scattering

method. Nevertheless, the geometric particle size data show clearly that the geometric

distributions shift to the coarse end of the scale as the Avicel ‘series’ ascends from PH101 to

PH200. The arithmetic distributions however do not show the equivalent shift in particle

population, indicating that the shift in the geometric data is not due to an overall increase in

size of all particles within the population. The distributions instead show very similar modes

but varied degrees of skewing towards the coarse end of the distribution which would appear

to indicate that the main population of particle remain unchanged although with the addition

of a small population of coarser agglomerates.

To further investigate this hypothesis, morphological filtering was used to differentiate

between the fine primary particles and the larger agglomerated particles. The filtered

geometric data was found to show two overlapping normal distributions (Fig).

Both distributions were found to be very repeatable for all PH102 batches tested, independent

of vendor. All grades of MCC show indistinguishable geometric particle size distributions for

the primary particle population (Fig, table), which contains approximately 97-99% of the

number of particles in the population. The grades of MCC were found to differ in the particle

size distribution for the agglomerated particles. The arithmetic particle size distribution (Fig)

indicates a higher degree of tailing to the coarse end of the distribution resulting in the

geometric particle size data (Fig, table) having a wider distribution in the higher PH grades.

Both findings can be related to the presence of a very small population of significantly larger

agglomerates in higher PH grades. The percentage of agglomerated particles in the whole

population was noted to be 0.6%-2.4% however an increase in numbers with the higher MCC

grades was not found to cause the differences in the geometric size data, but rather the width

of the geometric agglomerate distribution (table). Arithmetic span?!

This hypothesis is further supported by the scanning electron micrographs (SEM) (Fig.),

showing clearly the presence of fine primary particles in all three grades, but differences in

the sizes of agglomerates which are found only in small numbers. Particle sizes in PH200

appear to be distinctly separated into fine primary particles and very large agglomerates, with

the absence of middle-sized ones.

Geometric data is known to be sensitive to the presence of large particles as a cubic function

is applied to the particle diameters in order to obtain the equivalent spherical volumes and as

such a small change in the fraction of agglomerates could have a significant impact on the

particle size data obtained. This suggests that an increase in geometric mean particle size may

not necessarily mean a shift in size in the whole particle population as may be assumed. In

fact, the three Avicel grades investigated are generally very similar with the only differences

seen being the width of the distribution of the small population of large, agglomerated

particles within the population.


VendorMCC grade

Batch reference




FMC

Avicel PH101

60811C30.8(3.0)

71.2(6.3)

119.3(6.0)

1.3(0.2)

4.6(2.1)

21.9(7.4)

60727C33.6(3.7)

72.3(7.1)

124.1(15.7)

1.5(0.2)

5.4(1.7)

30.8(2.9)

Avicel PH102

70745C33.8(4.6)

86.1(23.5)

182.8(24.2)

1.2(0.1)

3.7(1.8)

19.9(11.3)

70709C43.5(5.1)

132.1(32.3)

255.2(39.7)

1.2(0.1)

3.3(1.2)

19.1(8.1)

70728C45.1(3.3)

128.0(30.8)

212.8(22.6)

1.2(0.0)

2.7(1.0)

18.5(15.9)

XN0781892439.1(3.2)

99.1(5.9)

209.6(26.5)

1.3(0.1)

4.9(0.9)

27.5(1.5)

P20881902641.7(2.7)

100.4(1.4)

197.8(12.9)

1.2(0.0)

3.1(0.1)

20.3(1.6)

Avicel PH200

M0913C40.9

(14.0)122.1(27.2)

261.7(25.4)

1.1(0.0)

2.3(0.5)

9.8(4.6)

M0728C55.6

(14.5)160.5(45.5)

306.0(88.0)

1.1(0.0)

2.2(0.3)

7.8(2.0)

JRS

Vivupur 102

561029151235.7(1.7)

83.6(13.3)

201.2(18.2)

1.2(0.0)

2.5(0.6)

15.6(11.5)

561029100944.7(2.9)

121.2(7.4)

223.3(19.8)

1.2(0.0)

2.5(0.3)

15.9(6.2)

561029050639.2(1.5)

98.7(8.4)

214.0(17.6)

1.2(0.0)

2.8(0.5)

24.6(8.5)

561029020341.7(1.1)

100.1(6.6)

219.9(22.2)

1.1(0.0)

2.5(0.2)

18.4(7.9)

561028804544.1(2.2)

122.9(25.2)

233.3(20.6)

1.1(0.0)

2.5(0.3)

19.7(3.7)

561029161244.8(2.8)

119.9(9.7)

229.2(7.7)

1.2(0.1)

2.8(1.2)

20.2(18.1)

561028553138.0(3.2)

92.5(12.4)

213.2(22.4)

1.2(0.1)

3.6(2.2)

23.0(7.2)

GradeBatch

reference

Dv50- primary particles

mm

Dv50- agglomerates

mm

PH10160811C 50.0 103.360727C 48.2 106.3

PH102

70745C 49.5 114.670709C 49.6 135.870728C 48.8 145.7

XN07818924 48.9 142.5P208819026 51.4 121.15610285531 50.8 155.85610288045 54.0 146.15610291512 47.6 143.15610291009 54.4 145.25610290506 54.6 146.15610290203 56.0 155.95610291612 53.0 155.0

PH200M0913C 47.1 176.5M0728C 48.4 219.6

GradeBatch

reference

Arithmetic span of the

agglomerates

Geometric span of the

agglomerates

PH10160811C 0.68 0.6860727C 0.83 0.91

PH102

70745C 0.88 0.9670709C 1.00 1.1770728C 1.07 1.17

XN07818924 0.84 1.06P208819026 0.92 1.165610285531 1.14 0.855610288045 1.14 1.025610291512 1.12 1.005610291009 1.05 1.065610290506 1.20 0.915610290203 1.06 0.965610291612 1.01 1.33

PH200M0913C 1.02 1.42M0728C 0.88 1.29

0.0

0.1

0.2

0.3

0.4

0.5

0.6

0.7

0.8

0.9

1 10 100 1000

%

CE Diameter (µm)

Volume transformation: CE Diameter (µm) smoothed over 60 points

Record 3: Avicel PH102 #70709C Record 67: Avicel PH102 #70709C Int>40Record 68: Avicel PH102 #70709C Int<40 Conv>0.8

Geometric frequency plot of Avicel PH102 batch 70709C (----), with separated to show overlapping agglomerated (----) and primary particle (----) distributions

0.0

0.1

0.2

0.3

0.4

0.5

0.6

0.7

0.8

0.9

1.0

1 10 100 1000

%

CE Diameter (µm)


Record 63: Avicel PH101 #60811C Int<40 Conv>0.8 Record 65: Avicel PH102 #P208819026 Int<40 Conv>0.8Record 68: Avicel PH102 #70709C Int<40 Conv>0.8 Record 72: Avicel PH200 #M0728C Int<40 Conv>0.8Record 80: Avicel ph101 #60806c Int<40 Conv>0.8 Record 96: Avicel PH200 #M0913C Int<40 Conv>0.8

Geometric frequency plot of primary particles within MCC PH101, PH102 and PH200 samples

0.0

0.2

0.4

0.6

0.8

1.0

1.2

10 100 1000

%

CE Diameter (µm)

CE Diameter (µm) smoothed over 60 points

Record 69: Avicel PH102 #70728C Int>40 Record 71: Avicel PH200 #M0728C Int>40Record 115: Avicel PH101 #60727C Int>40

Arithmetic frequency plot of agglomerated particles within MCC PH101 (----60727C), PH102 (----70728C) and PH200 (----M0728C) samples

0.0

0.1

0.2

0.3

0.4

0.5

0.6

0.7

0.8

0.9

1.0

10 100 1000

%

CE Diameter (µm)


Record 69: Avicel PH102 #70728C Int>40 Record 71: Avicel PH200 #M0728C Int>40Record 115: Avicel PH101 #60727C Int>40

Geometric frequency plot of agglomerated particles within MCC PH101 (----60727C), PH102 (----70728C) and PH200 (----M0728C) samples


Similarly to the particle size data, the SSA results for PH-102 indicate no noticeably

difference between the two vendors, although JRS batches were shown to have slightly

higher SSAs. Intra-vendor batch-to-batch variability was observed to be minimal. No

significant difference in the SSAs of PH-101, PH-102 and PH-200 was recorded.

Surface energy

The DSE results for the 12 batches of MCC appear to be variable with batch-to-batch

variations observed for both vendors. The reason for the surface heterogeneity within and/or

between suppliers is not fully understood, however the higher dispersive energy may be due

PH101- 60811C PH102- 70728C

PH200- M0728C PH200- M0728C

to differences in the availability of amorphous regions that are remnants from acid hydrolysis

of cellulose. The thermodynamically less stable amorphous materials have been suggested to

dominate the measured surface energy despite the fact that MCC is largely crystalline18.


vendor Grade Batch reference SSA (m²/g) DSE (mJ/m2)

FMC

Avicel PH-101

60811C 1.07(0.04)

N/A60727C 1.16

(0.01)

Avicel PH-102

70745C1.09

(0.02)61.99(0.39)

70709C1.16

(0.02)66.55(0.58)

70728C1.12

(0.01)60.91(2.10)

XN078189241.05

(0.02)58.78(0.56)

P2088190261.05

(0.02)67.83(0.38)

Avicel PH-200

M0913C 1.15(0.02)

N/AM0728C 1.20

(0.02)

JRS

Vivapur 102

56102915121.22

(0.00)60.63(0.20)

56102910091.26

(0.08)59.36(0.31)

56102905061.17

(0.02)59.71(0.41)

56102902031.12

(0.04)55.84(0.25)

56102880451.26

(0.05)64.56(0.75)

56102916121.25

(0.01)59.87(1.00)

56102855311.25

(0.01)60.48(0.40)


Flowability

All three grades of Avicel required a minimum degree of vibration to flow down the metal

hopper, however two batches of JRS 102 (batch ref: 5610285531, 5610288045) were unable

to form precise peaks as the powders accumulated due to static-like behaviour at the lower

chute, blocking the passage to flow. This phenomenon was only observed on a day of

relatively low humidity (RH=31%) but not observed when repeated on a day with a higher

relative humidity (RH=59%), and was much improved using a static eliminator. The cause

was believed to be electrostatic charge accumulation, which is dependent on various factors

including a powders intrinsic properties and environmental conditions. Nevertheless, further

investigation of this phenomonon was outside the scope of this report and so was not further

investigated.

The AOR results for PH102 show that FMC batches generally flowed slightly better than JRS

batches but no significant inter-batch variability was reported in both vendors. Despite the

presence in only small fractions within the samples, the agglomerates have shown to play a

significant role in terms of flow. It is generally accepted that larger particles flow better than

smaller ones and this is illustrated in the AOR study of Avicel, showing a rank order of flow

properties: PH101<PH102< PH200, which is in agreement with the literature results. Taylor

et al. observed a similar rank order but suggested that the relative difference was not as

discriminating as predicted based on the large difference in the reported mean geometric

particle sizes (50mm vs 100mm vs 180mm).

In fact, multiple studies have shown an improvement in flowability in line with increasing

geometric particle size. Instead of shifting the size of the whole particle population, the

difference in relative flowability may also be described by the hypothesized shift in only a

small sub-population of large agglomerates discussed earlier. Rather than behaving as a

glidant like silica dioxide, Jones19 suggested an improvement in flow properties in the

presence of larger ‘glidant’ particles by the mechanism of physically separating the finer

particles and hence reducing the cohesive bridging. Lahdenpää et al.20 observed that an

addition of only small amount of PH200 to PH101 can be sufficient to improve flow of the

latter.


The compressibility data indicates a lower batch-to-batch variability in Vivupur 102, which is

also noted to have a statistically higher yield pressure than Avicel PH102. No correlation

with the physical properties measured was observed. One hypothesis for this variation in

compressibility is the different degree of polymerizations which can be resulted from the

different wood types used for the production of MCC in the two suppliers21.

MCC is well-known to deform primarily by plastic deformation and its compressibility is

noted to be independent of particle size22. It is illustrated in the yield pressure results that no

significant difference was observed between the three Avicel grades.

Looking at the compactability of the tablets produced of PH102, no significant difference was

identified between FMC and JRS. However, inter-batch variability was noted for both

vendors.

In addition to plastic deformation, mechanical interlocking also plays a major role in the

compactability of MCC. It is expected that an increase in particle size can reduce the inter-

particulate bonding area and so produce weaker tablets23. However, this trend was not

observed in the three Avicel grades where the differences were generally insignificant such

that they could be considered of similar compactability. Comparable results were also

reported by Doelker et al., who observed identical compactability in unlubricated tablets

produced of the three Avicel grades, particularly when taken into account of inter-batch

variability24. This also correlates with the surface area findings and possibly supports the

hypothesis of similar sizes in the majority particle populations within the three grades.

Another possible explanation is that the large agglomerates, consisting of small MCC

primary particles, are weak enough to undergo fragmentation during compression, leading to

the increased binding points and therefore no difference in compatibility would be detected.

However, similar intrinsic compaction properties do not necessary mean similar

compactability in lubricated MCC. A greater lubricant sensitivity has been noted in higher

particle size grades, leading to the formation of tablets with lower tensile strengthsError:

Reference source not found,25


Vendor Grade Batch reference AORo Yield pressure (MPa)

Tensile strength (MPa)

FMC

Avicel PH-102

70745C 33±2 62.1(0.69)

8.11(0.15)

70709C 34±0 59.5(1.15)

7.74(0.38)

70728C 34±1 59.2(2.14)

7.71(0.28)

XN07818924 35±1 59.0(1.08)

7.30(0.12)

P208819026 35±0 61.2(0.61)

7.97(0.12)

JRS

Vivupur 1025610291512 36±0 64.8

(1.00)8.27

(0.20)5610291009 36±1 66.0

(1.31)7.45

(0.13)5610290506 36±1 65.7

(0.66)7.95

(0.23)5610290203 36±1 65.9

(0.42)7.35

(0.18)5610288045 36±0 66.2

(0.65)7.95

(0.18)5610291612 34±1 66.1

(0.85)8.37

(0.36)5610285531 37±1 65.8

(0.76)8.12

(0.15)

FMC

Avicel PH-101

60811C 37±3 63.5(0.62)

8.44(0.19)

60727C 37±1 65.3(0.51)

8.38(0.30)

Avicel PH-200

M0913C 30±2 67.2(0.81)

8.61(0.21)

M0728C 32±0 63.2(0.62)

8.03(0.24)


Physical properties


A notable difference in SSA results between the three vendors was reported with distinctly

higher SSAs for the three Ac-Di-Sol (FMC) batches and the lowest for Primellose (DMV).

Some degrees of batch-to-batch variations were observed in all three vendors but DMV

batches were generally more consistent.

Particle size

A statistically significant difference (P = 0.00?) in particle size distribution between the three

vendors was observed. An ascending order in the geometric and arithmetic particle sizes was

noted to be FMC<JRS<DMV, which correlates inversely with the SSA results. This

relationship is expected as the smaller the particles, the greater the expected surface area.

Intra-vendor batch-to-batch variability was however acceptable.


vendorBatch

referenceSSA m²/g




DMV

FN000230.33

(0.01)29.8(1.5)

67.7(3.7)

106.9(6.0)

5.6(0.5)

14.4(0.4)

44.1(1.6)

FN000050.33

(0.02)34.8(2.4)

62.2(1.2)

98.5(1.8)

3.3(1.1)

15.2(0.7)

50.9(2.9)

104172920.28

(0.01)39.9(1.9)

67.7(3.7)

106.9(6.0)

3.4(0.6)

17.6(2.8)

58.2(0.9)

JRS

32010831130.42

(0.01)27.0(2.5)

48.4(2.1)

67.4(3.3)

3.9(0.2)

11.1(0.3)

39.3(3.1)

32010831670.44

(0.01)29.1(0.8)

50.9(1.2)

71.7(0.7)

2.7(0.5)

9.5(1.0)

39.8(2.0)

32010820800.50

(0.01)25.7(3.1)

50.0(2.2)

71.4(1.1)

3.7(0.2)

10.4(0.7)

36.1(4.8)

32010820820.49

(0.01)29.2(1.8)

50.5(1.4)

71.3(1.6)

3.0(0.4)

10.1(0.5)

41.1(1.7)

32010831040.38

(0.01)29.5(1.3)

50.9(1.1)

70.5(1.4)

3.6(0.1)

10.9(0.5)

42.0(1.8)

32010842190.36

(0.01)29.1(0.3)

51.2(0.1)

71.1(0.9)

3.9(0.3)

11.3(1.1)

41.8(1.4)

32010910270.52

(0.02)30.1(1.1)

52.3(0.6)

72.8(1.1)

2.9(0.3)

10.2(0.6)

42.0(1.0)

FMC

T0937C0.66

(0.04)26.4(2.0)

45.1(0.7)

67.7(0.5)

2.3(0.1)

8.1(0.4)

34.6(3.0)

T0731C0.67

(0.01)25.2(0.2)

43.4(0.9)

66.0(1.4)

2.2(0.4)

7.4(1.3)

32.3(2.4)

T0850C0.58

(0.01)28.8(1.9)

47.5(0.4)

72.4(1.4)

2.1(0.1)

8.0(0.0)

38.1(2.7)

Surface energy

Batch-to-batch consistency was reported in both DMV and JRS batches. The three DMV

batches were observed to have significantly higher DSEs than JRS while the three FMC

batches were too variable that no pattern could be seen. Despite the variability, the results

show no correlation with the SSA or the particle size data.


Flowability

Although the three vendors were found to have statistically significant different particle size

and surface area, no notable difference between the batches and/ or vendors was noted in the

AOR data. Again, this might be due to the insensitivity of AOR tester as discussed

previously; or it might simply because the differences in the particle size were not significant

enough to cause a change in flowability.


Although croscarmellose sodium is usually used in low levels, typically less than 2%, as a

disintegrant in tablet production, its compressibility and compactability have been tested to

check for inter and intra vendor variability.

A software error was encountered doing the Heckel analysis which might be resulted from

the high compression forces (≈3000daN) required to produce 0.85 solid fraction

1 Reier, G.E., Excipient Standardization: User’s Viewpoint, Drug development and Industrial Pharmacy, 13(13), 2389-2407 (1987)2? Rios, M., Debating Excipient Functionality, Pharmaceutical Technology, 20063 Moreton, R.C., Excipient Functionality, Pharmaceutical Technology, 98-119 May 20044 Amidon, G.E., Physical and Mechanical Property Characterization of Powders, in: Physical Characterization of Pharmaceutical Solids (Drugs and the Pharmaceutical Sciences, Volume 70) Eds: Brittain, H.G. 1995 5 Handbook of Pharmaceutical Excipients, 5th Edition, Pharmaceutical Press, 20066 Guo, J., Lactose in Pharmaceutical Applications, Drug Del. Technol. 200414 Newell, H.E., Buckton, G., Butler, D.A., Thielmann, F., Williams, D.R., The use of inverse Phase Has Chromatography to Measure the Surface Energy of Crystalline, Amorphous, and Recently Milled Lactose, Pharmaceutical Research, Vol. 18, No. 5, 200115 Grimsey, I.M., Feeley, J.C., York, P., Analysis of the Surface Energy of Pharmaceutical Powders by Inverse Gas Chromatography, Journal of Pharmaceutical Sciences, Vol. 91, NO. 2 (2002)16 Roberts, R.J., Rowe, R. C., The effect of the relationship between punch velocity and particle size on the compaction behaviour of materials with varying deformation mechanisms, J, Pharm. Pharmacol. 38(8): 567-571 (1986)17 Rahmouni, M., Lenaerts, V., Massuelle, D., Doelker, E., Leroux, J., Influence of Physical Parameters and Lubricants on the Compaction Properties of Granulated and Non-granulated Cross-linked High Amylose Start, Chem. Pharm. Bull. 50(9) 1155-1162 (2002)18 Swaminathan, V., Cobb, J., Saracovan, I., Measurement of the surface energy of lubricated pharmaceutical powders by inverse gas chromatography, International Journal of Pharmaceutics 312: 158-165 (2006)19 Jones, T. M. Mechanism of flow improvement by addition of fine particles to bulk solids J Pharm. Sci. 1968; 57:2015-201620 Lahdenpää, E., Niskanen, M., Yliruusi, J. Crushing strength, disintegration time and weight variation of tablets compressed from three Avicel® PH grades and their mixtures, European Journal of Pharmaceutics and biopharmaceutics 43 (1997) 315-32221 Shlieout, G., Arnold, K. Muller, G. , Powder and Mechanical Properties of Microcrystalline Cellulose With Different Degrees of Polymerization, AAPS PharmaSciTech 2002; 3 (2)22 Celik, M. Overview of compaction data analysis techniques, Drug Dev. Ind. Pharm. 18 (1992) 767-810

croscarmellose tablets. Therefore, yield pressure (Py) values could not be measured

accurately.

The compactability results show a low degree of vendor batch-to-batch variability in FMC

and JRS batches. Surprisingly, the data obtained for DMV batches were variable despite the

reported consistency in the physical properties. Significant inter-vendor variability was also

noted. FMC batches produced much harder tablets than the other two suppliers, which might

be explained by the smallest particle size distributions measured. The finer the particles the

greater the inter-particulate areas and the stronger the tablets produced. Nevertheless,

croscarmellose sodium is normally incorporated at low level (2-3%) within a tablet and so it

would not be expected that these differences would be significant enough to impact the

compactability of a formulated blend.

Croscarmellose sodiumvendor Batch

referenceAORo Tensile strength

(MPa)DMV FN00023 31±2 2.7±0.2

FN00005 33±2 3.9±0.210417292 33±1 3.3±0.2

JRS 3201083113 34±0 2.8±0.13201083167 32±0 2.4±0.23201082080 32±1 2.9±0.13201082082 34±1 2.6±0.13201083104 34±2 2.8±0.23201084219 33±1 2.4±0.23201091027 33±1 3.2±0.1

FMCT0937C 33±0 6.9±0.3T0731C 32±1 6.7±0.2T0850C 35±1 6.9±0.2

Disintegration

Croscarmellose sodium works as a superdisintegrant mainly due to its water uptake and

swelling properties. Particle size is one of the most important parameters affecting the

swelling properties of a disintegrant. Larger particles are noted to be able to swell more

extensively compared to smaller particles, and hence a shorter disintegration time may be

expected26. Disintegration testing was not performed for all 13 bathces due to time

limitations, however one batch of Primellose from DMV (batch ref: 10417292) and Ac-Di-

Sol from FMC (batch ref: T0937C), which were noted to have particle sizes at the extremes

of the data set, were tested for disintegration. Tablets compressed from pure dicalcium

phosphate, which were used as a control, did not disintegrate at all (>1500s). This is expected

due to the fact that dicalcium phosphate is practically insoluble in water and so will not

disintegrate without the addition of disintegrant27. In the presence of 5% of croscarmellose

sodium, both vendors functioned equivalently and the tablets disintegrate completely in few

seconds as reported previouslyError: Reference source not found. No differentiation could be

observed even when the disintegrants were used in low level (0.1%). This would suggest that

26 Zhao, N., Augsburger, L.L., The Influence of Product Brand-to-Brand Variability on Superdisintegrant performance A Case Study with Crocarmellose Sodium, Pharmaceutical Development and Techonology, 11: 179-185, 200627 Bolhuis, G.K., Eissens, A.C., Zoestbergen, E., DC Calcium lactate, a new filler-binder for direct compaction of tablets, International Journal of Pharmaceutics, 221: 77-86 (2001)

the differences in particle size were not significant enough to cause a change in

disintegration. Similar observations by Zhao & AugsburgerError: Reference source not found

reported that Primellose and Ac-Di-Sol were functionally equivalent despite a significant

difference in particle sizes. They suggested that the ratio of basic to acidic substituents is also

important to disintegrant swelling apart from particle size. Despite having smaller particle

sizes, Ac-Di-Sol was reported with a high degree of basic substitution, leading to its’

increased hydrophilicity and hence swelling ability.

Conclusion

Although the commercially available excipients achieve compendial specifications, this does

not necessarily mean equivalent performance during processing and in the drug formulation.

This report has started the work on the investigation of excipients’ batch-to-batch and brand-

to-brand variability in terms of physical and functional-related properties. Some significant

batchwise (such as lactose from Kerry) and vendor-dependent (as seen in lactose and

croscarmellose) variations have been demonstrated. Despite of the inherent variability, the

significance of their effects during real life processing and on the final dosage from cannot be

confirmed and therefore, an advanced investigation, including testing on excipient-placebo or

excipient-API, is required. Moreover, a continuous study on other commonly used excipients,

for instance silicon dioxide and magnesium stearate, is also valuable to further enhance the

understanding of materials and hence the quality of the final products.

Acknowledgements

I would like to thank John Gamble for his constant help and support throughout the project.

Mike Tobyn is thanked for his advice on the study and especially on statistical analysis.

Mridul Majumder, from Pharmaceutical Ltd, UK, is also thanked for all the hard work on

IGC. I am very grateful to Helen Toale, Vivienne Gray, and Michael Leanne for their help

and contributions to this study.

Appendix

Morphologi G3 system28

The morphologi G3 particle characterization system is used to measure the morphological characteristics, including size and shape of the particles. Using the image analysis technique, the sample is scanned and digital images are produced. Each particle within the sample is individually measured and recorded, allowing particle count and foreign particle detection. In addition to the volume based distribution as reported by the ensemble particle sizing methods, the morphologic instrument is particularly useful for detecting the relatively small particles (fines) by the application of number-based resolution. Volume basis means that the contribution each particle makes is proportional to its volume, i.e. large particles dominate the distribution and sensitivity to small particles to small particles is reduced as their volume is so much smaller than the larger ones. Number basis means that the contribution each particle makes to the distribution is the same, i.e. a very fine particle has exactly the same ‘weighting’ as a very large one. Standard percentile readings, D0.1, D0.5 and D 0.9 are reported for both distributions. D0.1 is the size of particle below which 10% of the sample lies. D0.5 is the size in microns at which 50% of the sample is smaller and 50% is larger. D0.9 is the size of particle below which 90% of the sample lies.

As the particle is not a perfect sphere, there are many ways to describe particle size as a single number. In the Morphologi system, particle size is described as Circle Equivalent (CE) diameter. The 3D particle image is captured as a 2D image and converted to a circle of equivalent area to the 2D image. CE diameter is reported as the diameter of this circle.

Morphological filteringMorphological filtering was performed based on the following parameters:

Convexity - the perimeter of the convex hull of the object divided by its perimeterAn example is illustrated in figure and it is calculated as:

Convexity = (Perimeter of A+B)/ Perimeter of A.

It is a measure of how ‘spiky’ a particle is and is used to remove any partially imaged or overlapping particles.

28 Morphologi G3 User manual , Malvern Instruments Limited, 2008

Circularity- the ratio of the circumference of a circle equal to the object’s projected area to the perimeter of the object. It is calculated as:

Circularity = (2 X √( X Area))/ PerimeterA perfect circle has circularity of 1.0 while a very narrow elongated object has circularity close to 0. It is also employed in the study to exclude any partially imaged and overlapping particles in MCC.

Intensity Mean- the average of the pixel greyscale levels in the object and is calculated as:

Intensity mean = (∑ Ii)/ NWhere Ii Is the intensity value of pixel (i) and N is the total number of pixels in the particleAn intensity mean filter is employed for the characterisation of agglomeration in MCC samples. The more agglomerated the particle the higher the intensity mean.

Stylcam compression replicator

Compaction analysis can be performed using The Stylcam compression replicator (Medelpharm, France). Replicating the production tablet presses, tablet compression data can be obtained and hence the performance of the formulations at full scale production can be predicted. Having this information at early can allow formulator to improve the formulation, enhancing its performance and avoiding possible issues during production.

Heckel Analysis

Compression data (reduction in tablet porosity with applied pressure) is interpreted using Heckel analysis. The Heckel equation assumes that compression follows a first-order chemical reaction where the inter-particulate pores are the reactant whilst the densification is the product. The rate of change in density with respect to the applied pressure is therefore assumed to be directly proportional to the tablet porosity29. The equation is described as the followings:

In (1/ (1-E)) = kP + A

Where E is the tablet porosity and P is the applied pressure. A is a constant which is related to the particle rearrangement and fragmentation and k is the slope of the linear part of the relationship which represents the ability to densify by deformation.

The reciprocal of k is calculated to represent the yield pressure (PY) for the particles, which allows an interpretation of the mechanism of deformation. The PY is defined as ‘the stress at which particle plastic deformation is initiated’ (Aulton, 2002). This is the minimum pressure required to cause deformation of the material undergoing compression.

A typical Heckel profile30 is illustrated in Figure and three phases can be separated:

Phase I – initial curvature representing particle fragmentation and inter-particle motion

Phase II – linear portion over a substantial range of applied pressure. The PY is calculated from the gradient of this portion

Phase III –decompression stage where tablet height expands, representating the increased tablet porosity

29 Sonnergaard, J.M., A critical evaluation of the Heckel equation, International Journal of Pharmaceutics 193, 63-67 (1993)30 Comoglu, T, An overview of compaction equations, J. Fac. Pharm, Ankara, 36(2) 123-133, 2007

The Stylcam uses ‘in-die’ method for Heckel analysis. The ‘in-die’ method collects data during compaction of the powder and Heckel plots are automatically generated on the Stylcam for every compact, given that true density values and all relevant thickness, weight, diameter and hardness data has been entered. The Py is determined automatically by selecting a line of best fit at the Phase II linear section. Heckel equation is one of the most frequently used equations in pharmaceutical research for the prediction of tablet compression profile, however it is important to note that there is a certain amount of associated errors and limitations. It has been reported that the Heckel plot and the derived parameters are extremely sensitive to small errors in experimental conditions in variations such as true density values and so the results and conclusions should be carefully consideredError: Reference source not found.

References

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25 Jones, T.M. Mechanism of flow improvement by the addition of fine particles to bulk solids J. Pharm. Sci. 1968; 57: 2015-2016