an automated control strategy for wurster coating to
TRANSCRIPT
©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, [email protected], +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
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met
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)
PSD - S2
Dv10
Dv50
Dv90
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Co
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ickn
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)
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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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
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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.