©2017, Innopharma Technology Ltd www.innopharmalabs.com/technology Page 1 of 4

An Automated Control Strategy for Wurster Coating to Reduce Dissolution Variability due to Raw Material Variations Chris O’Callaghan, Dr. Ian Jones - Innopharma Technology / Education

Dublin, Ireland, ocallaghanc@innopharmalabs.com, +353 1 695 0162

Introduction Extended or modified release formulations are

common within the pharmaceutical industry due to

their advantages for patient compliance and

efficacy. Drug-layered multiparticulates with a

functional coating which delays dissolution in the

body are a common dosage form for these

products, being delivered either in capsules, tablets

or as food additives in paediatric / geriatric

applications (Ratnaparkhi & Gupta, 2013). Bottom

spray fluid bed (Wurster) coating is commonly used

in multiple phases of their manufacture.

Control is typically achieved by spraying a fixed

quantity of release coating on the drug loaded

substrate as a means of achieving the desired

dissolution profile. This method does not guarantee

consistent dissolution performance however, as

coating thickness has been shown to be the critical

material attribute influencing dissolution rate (Ho,

et al., 2008) (Langer, 1993) (Patel, et al., 2017).

Variability can therefore be introduced by changes

in substrate particle size which affect the total

surface area, and therefore coating thickness.

In this work a control method is explored whereby

real-time size measurements of the in-process

particles are used to gauge the true thickness of the

release coating, and thereby determine when the

end of spraying has been reached.

Materials & Methods Cellets 500 supplied by Ingredient Pharm were used

as substrate material for coating. No API was used

as part of this development study. A 15% w/w

aqueous suspension of 80:20 Surelease : Opadry

both supplied by Colorcon inc. was used to coat the

particles.

Wurster coating was conducted in a Glatt GPCG2

lab-scale fluid bed system. A PAT (process analytical

technology) product container with additional

viewing ports was used to integrate the

Innopharma Technology Eyecon2 Particle Analyzer

for real-time measurement of the particle size

distribution inline. This data and all GPCG2 sensor

data was used by Innopharma Technology’s Smart

FBX fluid bed control system to control inputs on

the GPCG2 and automatically step through all

process phases ensuring that CPPs were kept within

the optimal processing ranges determined during

an earlier DoE.

Figure 1 - GPCG2 with Smart FBX HMI

Figure 2 - PAT product container with Eyecon2

Rather than controlling the spraying phase of the

process to deliver a fixed quantity of coating factor,

the Smart FBX controller was configured to monitor

particle size growth, and continue spraying until a

target growth had been achieved. This was

accomplished by inline measurement the Dv50 of

the fluidised pellets presented to the Eyecon2

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during the material pre-heating phase prior to the

start of spraying, and comparing Dv50s reported

throughout the spraying phase with this baseline

value to determine growth. For these experiments

the target Dv50 growth was 32.5µm – equating to a

coating thickness of 16.25µm. This value was

chosen as it was the approximate growth achieved

in prior experiments during which coating factor

had been added to reach a predicted 10% weight

gain.

To explore the effects of a variation in substrate

particle size on coating thickness / quantity of

coating factor required to reach a target growth the

Cellets 500 (approx. 500-710µm) were screened

with a 600µm sieve to create two populations of

marginally different sizes (approximately 67µm

difference in Dv50s). Both populations fall within

the material specification for Cellets 500, but can be

used to notionally represent a batch-to-batch

variation for this application.

Six batches in total were coated. Three batches of

the “larger” size pellets referred to as L1, L2 and L3;

and three batches of the smaller material, S1, S2

and S3.

Results & Discussion The Innopharma Technology Smart FBX

development system records all sensor and

setpoint data from throughout the process,

presenting a wide variety of values which can be

used to characterise and compare processes. In this

section a small subset of this data is examined in

several ways to determine the variability and likely

quality impacts of the variation in substrate sizes.

Eyecon2 Data

Figure 3 - Eyecon2 particle size data for batch S2

Figure 3 shows the Dv10, Dv50 and Dv90 data

produced by the Eyecon2 during batch S2. Spraying

took place between 16:05 and 18:22, during which

time a steady increase in particle size across all

three parameters can be seen. During the course of

each batch the Eyecon2 made approximately

500,000 particle measurements.

PSD Impact on Coating Thickness Before presenting results from the coating-

thickness driven control strategy it is important to

consider what effect the ~67µm variation in Dv50

would have had on product quality under a

traditional fixed-spray-quantity control regime. To

assess this we consider the measured particle

growth for a given spray quantity across all batches.

9% or 1050g of solution sprayed was chosen.

Figure 4 - Coating thickness at 9% w.g. vs. substrate starting size

In Figure 4 two groupings of points can clearly be

seen. Batches S1, S2, and S3 with initial size of 570-

575µm, and L1, L2, and L3 with initial size of

approximately 640µm. The coating thickness value

is derived from the difference in Dv50s between the

start of spraying and the point at which 1050g of

coating factor had been added, divided by two as

the Dv50 is a measure of diameter. It can be seen

from the figure that the coating thickness for the

smaller batches is approximately 3µm thinner than

with the larger pellets.

Impact on Dissolution To assess the influence this would have on end

product dissolution (assuming that 9% weight gain

had been the target for the process) we can refer to

an earlier work by the authors (Patel, et al., 2017)

in which a simple mathematical model was

constructed correlating coating thickness against

dissolution for the same Surelease / Opadry

coating, where applied to a chlorpheniramine

maleate drug-layered particle. Figure 5 shows the

variability we would expect to see for these

520

540

560

580

600

620

640

16:00:00 17:00:00 18:00:00 19:00:00 20:00:00

Dia

met

er (

µm

)

PSD - S2

Dv10

Dv50

Dv90

0

5

10

15

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560.00 580.00 600.00 620.00 640.00 660.00

Co

atin

g Th

ickn

ess

(um

)

Initial Dv50 (µm)

Coating Thickness at 9% w.g.

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functional coating thicknesses on a CPM-coated

bead.

Due to the relatively thin coatings and function of

Opadry as a pore former in this formulation, these

dissolution rates are representative of a relatively

fast extended / modified release product, such as

one to targeting a specific area of the GI tract. For a

slower release coating however we could imagine

similar variations in dissolution but over longer

timeframes. Variation in this case is >10% at the 30

minute dissolution time point, and 9% at 1 hour.

Figure 5 - Predicted CPM dissolution profiles for each batch at 9% weight gain

Variation in Required Coating Quantities Utilising the control strategy of spraying until a

target particle growth is reached resulted, as

expected, in variation in the total amount of coating

solution required by each batch. This can be linked

to the variation in particle size of the substrate, but

is also influenced by a range of other parameters

(Wesdyk, et al., 1990) (Lorck, et al., 1997).

Figure 6 - Total coating solution required per batch to reach coating thickness target

Figure 6 demonstrates a clear downward trend in

coating solution requirement from the “small” runs

(left-hand cluster) and “large” runs (right). This

behaviour is as expected due to the greater total

surface area present in the smaller particle size

batches, and demonstrates the control strategy’s

ability to effectively compensate for these

variations.

Predictive Model Results The benefit of basing these control decisions on PAT

measurements can be demonstrated by examining

the results of a simple prediction model relating

coating factor requirements to initial substrate

particle size. Figure 7 displays the results from

Figure 6 compared against the predicted quantities

for a range of starting Dv50s based on a calculation

of the ratio of final coating layer volume to

substrate volume.

Figure 7 - Coating factor required vs starting Dv50 for experimental batches and simple prediction model

While Figure 7 shows an agreement in overall trend

between the experimental and predicted results

there is significant variation. As each batch was

coated to a constant coating thickness this variation

in quantities sprayed can likely be attributed to

variations in other processing factors such as the

spray efficiency, substrate densities, particle mass

effects, PSD width, and any agglomeration or

attrition present in the process. As control decisions

have been made using real-time process

measurements to track the true particle growth

however, there is no need to develop complex

formulation-dependent empirical models to

calculate and compensate for these sources of

variability, as would be necessary if controlling

spray quantities based on an off-line raw material

size measurement only.

40%

50%

60%

70%

80%

90%

100%

0 60 120 180 240 300

Per

cen

tage

Dis

solv

ed

Dissolution Time (minutes)

Predicted Dissolution at 9% w.g.

Large 1 Large 2 Large 3Small 1 Small 2 Small 3

0

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600

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1200

1400

1600

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560.00 580.00 600.00 620.00 640.00 660.00

Am

t. S

pra

yed

(g)

Initial Dv50 (µm)

Total Coating Soln Added Per Batch

1000

1100

1200

1300

1400

1500

1600

560.00 580.00 600.00 620.00 640.00 660.00

Am

t. S

pra

yed

(g)

Initial Dv50 (um)

Experimental Batches vs Model

Experimental Results Volume Model

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Conclusions • With traditional process control methods

variation in substrate particle size impacts

coating thickness on a meaningful scale.

• Dissolution model results indicate that the

tested size difference of ~67µm in Dv50 would

have resulted in >10% variability in QC

dissolution test results where pellets were

coated to 9% weight gain.

• A control strategy utilizing real-time particle size

distribution measurement to calculate growth

has been shown to provide consistent coating

thickness results for varying substrate sizes.

• The Innopharma Technology Smart FBX system

with an Eyecon2 controlling a Glatt GPCG2

successfully executed multiple automated batch

process runs.

• The Innopharma Technology Eyecon2 enabled

real-time inline measurement of particle size

within the coating process with no requirement

for sampling

• Further work is planned to apply this control

methodology to an API-coated substrate and

validate the predicted lower variability in QC

dissolution testing.

References Ho, L. et al., 2008. Applications of terahertz pulsed

imaging to sustained-release tablet film coating

quality assessment and dissolution performance.

Journal of Controller Relese, pp. 79-87.

Langer, R., 1993. Polymer-Controlled Drug Delivery

Systems. Accounts of Chemical Research, pp. 537-

542.

Lorck, C. A., Grunenberg, P. C., Junger, H. & Laicher,

A., 1997. Influence of process parameters on

sustained-release theophylline pellets coated with

aqueous polymer dispersions and organic solvent-

based polymer solutions. European Journal of

Pharmaceutics & Biopharmaceutics, pp. 149-157.

Patel, P., Godek, E., O'Callaghan, C. & Jones, I.,

2017. Predicting Multiparticulate Dissolution in

Real Time for Modified- and Extended- Release

Formulations. Pharmaceutical Technology, pp. s20-

s25.

Ratnaparkhi, M. P. & Gupta, . J. P., 2013. Sustained

Release Oral Drug Delivery System - An Overview.

International Journal of Pharma Research & Review,

pp. 11-21.

Wesdyk, R. et al., 1990. The effect of size and mass

on the film thickness of beads coated in fluidized

bed equipment. International Journal of

Pharmaceutics, pp. 69-76.

Acknowledgements The authors thank Colorcon inc. for their supply of

the Surelease and Opadry materials used in the

coating solution, and Glatt GmbH / Ingredient

Pharm for the supply of Cellets. Further the authors

are very grateful for the support and advice of both

companies in the process development and process

parameter selection stages of the project.