melt granulation articulo

7/28/2019 Melt Granulation Articulo

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.Chemometrics and Intelligent Laboratory Systems 48 1999 59 70

Melt granulation in a high shear mixer: optimization of mixtureand process variables using a combined experimental design

B. Campisi a,), D. Vojnovic b, D. Chicco b, R. Phan-Tan-Luu c

aDepartment of Economics and Commodity Science, Uniersity of Trieste, ia Valerio 6, I-34127 Trieste, Italy

bDepartment of Pharmaceutical Sciences, Uniersity of Trieste, P. le Europa 1, I-34127 Trieste, Italy

cLMRE, Centre de St. Jerome, Aenue Escadrille Normandie-Niemen, F-13397 Marseille Cedex 20, France

Received 7 January 1999; accepted 15 January 1999

Abstract

Melt granulation of a formulation of theophylline, containing lactose, microcrystalline cellulose and hydroxypropylmeth-

ylcellulose as excipients, was investigated in a 10 l high shear mixer as an alternative method to the wet granulation process,

using polyethylene glycol 6000 as melting binder. The experimentation was planned by combining mixture and factorial de-

signs in order to study the effect of two process variables, namely impeller speed and massing time, and of excipient mixture

composition on two characteristics of the granules. By the response surface methodology, it was possible to find the mixture

composition and the processing conditions leading to granulates with optimal granule characteristics. q 1999 Elsevier Sci-

ence B.V. All rights reserved.

Keywords: Melt granulation; High shear mixer; Mixture-process variable approach; Response surface methodology

1. Introduction

Melt granulation is an alternative technique to the

wet agglomeration process for the granulation of

pharmaceutical powders. In melt granulation, the ag-

gregation of the powder particles is promoted by a

low melting point binder, which is normally added to

the other components as a powder. Once in the molten

form, the binder acts like a granulating liquid. The

temperature of the mixture is risen to above the bindermelting point either by a heating jacket or by heat of

friction generated by the impeller blades, if the im-w xpeller speed is high enough 1 .

)

Corresponding author. Tel.: q39-040-6767031; Fax: q39-

040-6763215; E-mail: [email protected]

Melt granulation offers several advantages com-

pared to the conventional wet process. It is a good al-

ternative to wet granulation of water-sensitive mate-

rials, which require organic solvents for granulation.

Moreover, the wetting and drying phases are elimi-

nated, making the whole process less consuming inw xterms of energy and time 1 .

Melt granulation has been studied by several au-

thors, using different kinds of low-melting point ex-

cipients as binders: polyethylene glycols 3000, 6000and 8000, various types of waxes and stearic acidw x15 .

In this study, the melt granulation of a formula- .tion containing theophylline as a model drug was

investigated. Lactose, microcrystalline cellulose and

hydroxypropylmethylcellulose were used as excipi-

0169-7439r99r$ - see front matter q 1999 Elsevier Science B.V. All rights reserved. .P I I : S 0 1 6 9 - 7 4 3 9 9 9 0 0 0 0 8 - 8


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( )B. Campisi et al.r Chemometrics and Intelligent Laboratory Systems 48 1999 597060

ents and PEG 6000 as melting binder. The aim was

to study the effect of excipient proportions on some

product characteristics called response variables: the

geometric mean diameter of the granules and the

percentage of particles having a geometric mean di-

ameter smaller than 250 mm. As the effects of com-

ponent proportions on these properties were sup-

posed to be affected by the operating conditions, the

influence of two process parameters, i.e., impeller

speed and granulation time, was also investigated.

A combined experimental design was used for this

purpose, and a polynomial equation was estimated for

the description of each response variable as a func-

tion of both mixture and process variables. Further-

more, the process variable conditions were consid-

ered separately in order to display graphically the ef-

fect of each blending composition as well as the op-

timal mixtures that yielded the properties of interest.

2. Experimental

2.1. Experimental design

For the simultaneous analysis of the effects of ex-

cipient proportions and process parameters on the

granule characteristics, the process variables were in-

corporated into the mixture experiments. The experi-

Fig. 1. The augmented simplex-centroid design set up at each combination of the two process variables.


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( )B. Campisi et al.r Chemometrics and Intelligent Laboratory Systems 48 1999 5970 61

Table 1

Process variables, mixture components and response variables

Process variables Coded Original

units units

Impeller speed y1 300 rpm

q1 500 rpm

Massing time y1 10 min

q1 15 min

Original mixture Lower Upper . .components bound a bound bi i

.Lactose x 0.6 11Microcrystalline 0 0.4

.cellulose x2Hydroxypropylmethyl- 0 0.4

.cellulose x3

Response variables Units

.Geometric mean diameter h mm1 .Granules - 250 mm h %2

mental design was obtained by crossing a three-com-ponent mixture design simplex-centroid design aug-

. kmented with three interior points with a classical 2w xfactorial arrangement 68 . In general, the aug-

mented simplex-centroid design is recommend for

mixture experiments as this simplex-lattice arrange-

ment includes the design points to fit Scheffe poly-nomials from first-order model to the special cubic

.model inclusive and check points as well. In this

study, in addition to check points, the blend corre-sponding to the simplex centroid was replicated in

order to have a model independent measure of pure

error for testing the model adequacy. Including repli-

cates in the experimental design allows the partition .of the residual sum of squares SS into two com-E

.ponents: the one due to pure error SS and that duePE .to lack of fit SS . A test statistic based on theLOF

F-ratio can be used for testing the significance of the

null hypothesis about zero lack of fit of the model.

As shown in Fig. 1, by adopting an experimental

design like this, the blending properties of interest are

tested at all possible combinations of the extreme

levels of process variables. The development of a

textile formulation, the optimization of a sustained

release system, and the optimization of a wet granu-

lation process are some examples where such pro-

cess-mixture designs have been successfully appliedw x911 .

In order to fit a mathematical model for the de-

scription of the response variables as a function of .process variables and mixture components Table 1 ,

4the 3, 2 Scheffe quadratic polynomial for a three- ..component mixture Eq. 1 was multiplied by the

first-order model with interaction for the 2 2 factorial .. ..design Eq. 2 . In the resulting equation Eq. 3 ,

the 24 parameters to be estimated g j represent a bi j i 4 4where i g 1, 2, 3, 12, 13, 23 and j g 0, 1, 2, 12 :

y s b x q b x q b x q b x x1 1 2 2 3 3 12 1 2

q b x x q b x x , 1 .13 1 3 23 2 3

y s a q a z q a z q a z z , 2 .0 1 1 2 2 12 1 2

y s g0x q g0x q g0x q g0 x x1 1 2 2 3 3 12 1 2

q g0 x x q g0 x x13 1 3 23 2 31 1 1 1q g x q g x q g x q g x x1 1 2 2 3 3 12 1 2

1 1qg x x q g x x z13 1 3 23 2 3 1

2 2 2 2q g x q g x q g x q g x x1 1 2 2 3 3 12 1 2

2 2qg x x q g x x z13 1 3 23 2 3 2

12 12 12 12q g x q g x q g x q g x x1 1 2 2 3 3 12 1 2

12 12qg x x q g x x z z . 3 .13 1 3 23 2 3 1 2

In Fig. 2, the entire simplex region, representing a

three-component system at whose vertices the pure .components of the blend x lie, is presented. Basedi

on process and technological restrictions that arose inpreliminary trials, some constraints were placed on

Fig. 2. The experimental region defined by the constraints 0.6 F

x F1, 0 Fx F0.4 and 0 Fx F 0.4.1 2 3


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.the proportions of microcrystalline cellulose x and2 . ..hydroxypropylmethylcellulose x Eq. 4 . The re-3

sulting experimental region was rather a subregion

within the simplex, but still with a regular simplex

shape:

0.6 Fx F 1,1

0 Fx F 0.4,2

0 Fx F 0.4. 4 .3

In Fig. 2, the dots located along and inside the re-

gion of interest mark the mixture design points at

which the data were collected for fitting the polyno- .mial in Eq. 3 . Due to the restrictions on the compo-

.nent proportions, only lactose x was tested pure.1Indeed, the other two components represent binary

mixtures. The coordinates of each of the lattice points .x , corresponding to the mixture settings to bei

tested, are listed in Table 2. Mixture composition isreported in grams as well as in weight fraction so that

the real amounts of the excipients used are also given.

In Tables 3 and 4, the data refer to the ten blends

tested at each combination of the two process vari-

ables. Here, the mixture coordinates are reported af- .ter transformation of the original variables x toi

X .pseudocomponents x obtained with the follow-iing linear transformation:

xXs x y a rR , 5 . .i i i a

where a is the lower bound of the component i i si. q1, 2, . . . , q and R s 1 y a . Also the arrange-a is1 i

ment for the two process variables is reported in terms

of coded units calculated as follows:

z y max z q min z r2 . . . .i i iXz s , 6 .i max z q min z r2 . . .i i

. .where max z and min z are the high and lowi i .level of the variables z , respectively.i

These transformations are done to facilitate the in-terpretation of the regression coefficients and avoid

ill-conditioning of the matrix XX

X. In particular,

when restraints on mixture composition are consid-

ered, the ill-conditioning or collinearity between the

predictors often lead to an unstable least squares so-

lution. The model coefficients b , estimated by B si X .y1 XX X X y, are in fact poorly accurate, and one of

the possible solution is the transformation to pseudo-w xcomponents 12,13 .

2.2. Materials and methods

2.2.1. Materials .Lactose Pharmatose, 200 mesh and anhydrous

theophylline were purchased from Prodotti Gianni . Italy . Microcrystalline cellulose MC-Avicel PH

. 101 , hydroxypropylmethylcellulose Methocel E5. Premium , and polyethylene glycol PEG 6000, melt-

.ing point 60708C were obtained from Faravelli .Milano, Italy .

2.2.2. Equipment

The granulations were prepared in the 10-l

.laboratory scale Zanchetta Roto J high shear mixer,

Table 2 .The mixture composition expressed in grams and weight fractions x i

Trial no. Mixture Composition

Lactose Microcrystalline cellulose Hydroxypropylmethylcellulose

g x g x g x1 2 3

1 870 1 0 0 0 0

2 522 0.6 348 0.4 0 0

3 522 0.6 0 0 348 0.44 696 0.8 174 0.2 0 0

5 696 0.8 0 0 174 0.2

6 522 0.6 174 0.2 174 0.2

7 635.1 0.73 117.4 0.135 117.4 0.135

8 753.4 0.866 58.3 0.067 58.3 0.067

9 579.4 0.666 232.3 0.267 58.3 0.067

10 579.4 0.666 58.3 0.067 232.3 0.267


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Table 3 .Geometric mean diameter of the granules y1

X X .Trial no. Mixture composition Process variable settings z , z1 2X X X . . . .x x x y1, y1 y1, q1 q1, y1 q1, q11 2 3

1 1 0 0 292 327 309 373

2 0 1 0 374 462 375 478

3 0 0 1 546 726 555 628

4 0.5 0.5 0 396 475 335 4165 0.5 0 0.5 495 610 457 550

6 0 0.5 0.5 454 520 467 608

7 0.333 0.333 0.333 436, 410 549, 521 440, 417 528, 572

8 0.667 0.167 0.167 388 510 385 466

9 0.167 0.667 0.167 412 517 400 501

10 0.167 0.167 0.667 525 591 474 561

w xalready described in a previous article 14 . The

granulator was equipped with a heating jacket, which

supplied the heat required to melt the binder. .A vibrating apparatus Octagon 200, Endecotts

and a set of sieves 1250, 800, 630, 500, 400, 315,.250 and 200 mm were used for the granule charac-

terization.

2.2.3. Granulation manufacture

The total amount of theophylline and excipientmixture lactose, microcrystalline cellulose and hy-

.droxypropylmethylcellulose used in each experi-

ment was 1.5 kg. The amount of theophylline was

42% wrw of 1.5 kg, that is 630 g in each experi- .ment. The proportion of each excipient x in theimixture was varied according to the experimental ar-

rangement reported in Table 2. The amount of PEG

was calculated referring to the quantity of each ex-

cipient in the 1.5 kg mass, i.e., 18% of the weight oflactose and 61% of the weight of Avicel or Metho-

cel.

The granulation procedure was standardized on the

basis of preliminary trials, and the temperature of the

powders inside the bowl continuously recorded by a

thermoresistance probe fixed on the bowl lid and

dipped in the powder mass.

The excipients without PEG were first mixed,

while heating, at an impeller speed of 50 rpm, until

their temperature had reached 558C. The mixing was

interrupted in order to add the PEG, and then contin-ued for 3 min at 50 rpm and for other 3 min at 100

rpm. At this point, the PEG reached a molten state

Table 4

Percentage of particles having a geometric mean diameter smaller than 250 mmX X .Trial no. Mixture composition Process variable settings z , z1 2

X X X . . . .x x x y1, y1 y1, q1 q1, y1 q1, q11 2 3

1 1 0 0 47.36 36.09 51.34 27.00

2 0 1 0 15.86 12.83 23.44 12.00

3 0 0 1 3.59 1.51 4.73 2.074 0.5 0.5 0 15.67 9.17 36.23 13.86

5 0.5 0 0.5 6.36 2.05 6.67 1.94

6 0 0.5 0.5 8.30 8.91 9.84 2.45

7 0.333 0.333 0.333 12.98, 14.04 4.07, 5.58 11.47, 10.00 3.18, 1.20

8 0.667 0.167 0.167 18.60 12.18 25.41 8.23

9 0.167 0.667 0.167 15.76 7.59 19.35 7.05

10 0.167 0.167 0.667 6.68 2.87 3.21 3.83


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.the temperature was around 658C . During the sub-

sequent massing process, impeller speed and massing

time were applied according to the combined experi-

mental design. At the end of the granulation process,

the granules were cooled at room temperature by

spreading them out in thin layers on trays.

2.2.4. Granule characterization

The cooled granules were stored in well-closed

bags for 10 days, thereafter the geometric mean di- .ameter and the percentage in weight wrw of gran-

ules smaller than 250 mm were evaluated by sievew xanalysis, as described in a previous paper 15 .

3. Results and discussion

The mixture design and data processing, as well as

plots and contours surfaces here presented, were ob-w xtained using NEMRODW software 16 .

The experimental runs were carried out in a com-

pletely random order according to the combined de-

sign. As two replicates at the simplex centroid foreach combination of process variables design point

.7 were performed, the estimate of the variance due

to pure error was possible. Hence, the adequacy of the

fitted model could be checked by comparing the er-

ror component due to the model to that one due to

experimental error. For that purpose, a test procedure

was used to see whether to reject the null hypothesisabout the zero lack of fit or not the so-called LOF.test . The test statistic was the F-ratio given by the

.estimate of the variance due to lack of fit MSLOFand the estimate of the variance due to pure error .MS . In general, lack of fit of the model is sus-PEpected when the computed value of F is significant.

Table 5

Model coefficients estimated by least square method for geometric . mean diameter y , along with their estimated standard errors est.1

.S.E.X X X X

Mean z z z z est. S.E.1 2 1 2X

x 329.28 15.10 26.66 5.30 10.431X

x 424.41 1.42 47.79 2.43 10.432X

x 609.25 y26.79 57.18 y23.04 10.433X X

x x 121.51 y130.71 38.69 y42.04 47.241 2X X

x x 215.19 y59.12 43.47 5.00 47.241 3X X

x x y45.54 158.52 y7.26 110.27 47.242 3

Table 6

Model coefficients estimated by least square method for percent- .age of particles - 250 mm y , along with their estimated stan-2

.dard errors est. S.E.X X X X

Mean z z z z est. S.E.1 2 1 2X

x 39.78 y1.25 y8.84 y3.62 0.921X

x 16.30 1.58 y3.87 y1.94 0.922X

x 3.48 0.62 y0.93 0.07 0.923X Xx x y38.84 24.27 y4.20 y5.51 4.611 2

X Xx x y70.15 y2.35 11.78 6.14 4.611 3

X Xx x y6.97 y8.98 2.83 y2.74 4.612 3

X X Xx x x 27.03 y93.40 y21.94 57.86 3.031 2 3

In Tables 3 and 4, the geometric mean diameter of .the granules y and the percentage of granules with1

.a geometric mean diameter smaller than 250 mm y2are listed. The parameters of the combined model in

.Eq. 3 were estimated by fitting the 24-term polyno-mial to the experimental data here reported.

For the two variable responses, the estimated

residual variance was MS s 470.85 and MS s 6.19E Efor y and y , respectively. Using the replicates, the1 2experimental-error variance was estimated such as

MS s 490.6 with 4 df for y and MS s 1.18PE 1 PEwith 4 df for y . Having obtained the estimate of the2

.variance due to lack of fit MS s MS y MS ,LO F E PEbased on the LOF test for response y , the combined2

.model shown in Eq. 3 was augmented with four

terms, i.e., g0

x x x , g1

x x x , g2

x x x ,123 1 2 3 123 1 2 3 123 1 2 3and g12 x x x , referring to the term b x x x of123 1 2 3 123 1 2 3the special-cubic polynomial. In fact, the value of the

F-statistic, for testing the presence of lack of fit of .model in Eq. 3 , was Fs 0.95 with a P-value P (

0.59 for y and Fs 6.25 with a P-value P s 0.05 for1y , respectively. Since the F-statistic for the com-2bined 28-term model was Fs 3.27 with a P-value

P ( 0.11, this model was maintained. From the anal-

ysis of variance table, the R2 statistics for the two

combined models were computed and their values

were R2 s 0.97 with an R2 s 0.95 for y , and R2 sA 1

0.99 with an R2 s 0.97 for y , respectively. R2 , theA 2 Acoefficient of determination corrected for the number

of terms in the equation, should be always preferred

to R2 as it gives a more stable measure the model

adequacy.

Adopting a mixture-process variable approach al-

lows to understand not only how the granule charac-


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teristics studied depend on the component propor-

tions, but also on the level of the process variables

and how the effects of the component proportions on

the response may be influenced by the process vari-

able settings. As a matter of fact, in the combined

equation, the first six terms involve only the mixture

components, whereas the remaining eighteen terms

should give an estimation of the effect of the process

parameters on the blending property of each compo-

nent. Such effects can be evaluated considering the

. X Fig. 3. Geometric mean diameter y estimated according to the proportion of the mixture components x expressed in terms of pseudo-1 i. X X .components at each setting of the two process variables z , z in coded values.1 2


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coefficient estimates reported in Tables 5 and 6, along .with their estimated standard errors est. S.E. .

Another possible approach to analyse the effect of

process variables and mixture composition on the

granule characteristics is to consider the four process

variable combinations separately. In particular, when

a combined model is considered, the analysis of the

effect of process and mixture variables can be actu-

ally not so easy to be interpreted. On the contrary,

displaying graphically the variable responses under

.Fig. 4. Percentage of particles with a geometric mean diameter smaller than 250 mm y estimated according to the proportion of each2X . X X .mixture component x expressed in terms of pseudocomponents at each setting of the two process variables z , z in coded values.i 1 2


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study according to the proportion of component i

while holding fixed the relative proportions of the .other components Figs. 3 and 4 , or using contour

.plots Figs. 5 and 6 can make more easy the inter-

pretation of the influence of the process variables on

the mixture composition.

Each plot and contour surface displayed in Figs.

36 were obtained from the data collected at the cor-

responding arrangement of the two-level factors.

From the contour plots, it is evident that according to

process variable levels the fitted models are quite

different. In fact, as far as the response y is consid-1

X X .ered, the surfaces turned out to be planar for z , z1 2 . X X . .s y1, q1 and quadratic for z , z s q1, q1 ,1 2

. .y1, q1 , and y1, y1 . As regards response y ,2 X X . .the surfaces were quadratic for z , z s q1, q1 ,1 2

. X X . y1, q1 , and special cubic for z , z s y1,1 2. .y1 and y1, q1 .

Plots in Figs. 3 and 5 show that the pseudocom-

ponent xX , which consist of lactose and hydrox-3ypropylmethylcellulose, has the most significant ef-

fect on the response y for each of the process vari-1able setting. As from these plots the lactose seem to

have an opposite effect on the same response, the

.Fig. 5. Contour diagrams of the geometric mean diameter y at the four combinations of the process variables. The shaded areas represent1 .mixtures with acceptable properties according to the defined optimality criteria 300 mm Fy F500 mm .1


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Fig. 6. Contour diagrams of the response y at the four combinations of the process variables. The shaded areas represent mixtures with2 .acceptable properties according to the defined optimality criteria y F8% .2

augmentation of geometric mean diameter with in-

creasing xX

is likely due to the presence of hydrox-3ypropylmethylcellulose. Obviously, the interaction

between this excipient and PEG favours the granule

growth. This could be ascribed to an easier and more

uniform spreading of PEG on hydroxypropylmethyl-

cellulose granules compared to lactose ones. This

improved spreading of the binder on powder parti-

cles could as well account for the decrease in re-

sponse y observed with increasing xX

, as shown in2 3Figs. 4 and 6.

Using the contour plots, the optimal regions, which

represent mixtures yielding a finished product with

the desired characteristics at the four different oper-

ating conditions, were pointed out. In order to com-

pare the properties of the granules prepared by melt

granulation with those obtained by the wet processw x11 , the range between 300 and 500 mm was de-

fined as optimal for the geometric mean diameter,

whereas the percentage of particles smaller than 250

mm had to be as low as possible with a maximum

acceptable value of 8%. In Figs. 5 and 6, the shaded


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areas represent mixtures that are supposed to pro-

duce granules with optimal geometric mean diameter

and acceptable percentage of particles smaller than

250 mm, respectively. In addition, by overlapping the

so-obtained contour plots for the two responses, it

was possible to find the region wherein formulations

met both optimality criteria. In Fig. 7, the optimal re-

gions resulting from the overlapping of the two re-

sponse contour plots are displayed. It should be noted

that two are the optimum areas where are located

blends meeting the predefined optimality criteria ofboth granulate characteristics the darker shaded ar-

.eas . However, since these two optimum areas are

quite different in size, it should be preferred to iden-

tify an optimal formulation for the following process

variable arrangement: z s 500 rpm and z s 10 min.1 2In this subregion, an optimum formulation could be

X .such as x s 0.25, 0.10, 0.65 . The mixture compo-

sition is expressed in terms of pseudocomponents as

for the model fitted to the data, and it will corre-

spond in reality to a formulation containing 70% lac-

tose, 4% microcrystalline cellulose, and 26% hy-

droxypropylmethylcellulose.

Once identified an optimal blend, prediction inter-

vals for both responses might be given. Based on the

estimate of variance of prediction, computed such as

Fig. 7. Optimal regions defined by overlapping the two response contour plots displayed in Fig. 5 and Fig. 6.


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w .x X X . I 1 2est. var y x s x X X x s , the 95% confi-dence limits for the values of y and y for the opti-1 2mal formulation could be determined by y " D,i

w x w .x41r2where D s t est. var y x , f is thef, a r2number of degrees of freedom associated with the

sample estimates s2, and t is the t-value with f de-w xgrees of freedom at the ar2 level of significance 7 .

Therefore, the 95% confidence intervals on the two

responses for the optimal blend above mentioned

should become 482.5y 14.25 Fy F 482.5 q 14.251and 1.58 y 1.25 Fy F 1.58 q 1.25, respectively.2

In conclusion, the approach presented here has

undoubtedly made possible to improve knowledge

gained through previous investigation on melt granu-

lation. Combining mixture composition and process

variables using experimental design has proved to be

appropriate and effective, in particular, in finding

processing conditions and subregion yielding blend

formulations leading to a product with the character-istics required.

Acknowledgements

The authors wish to thank Zanchetta-Romaco

Group for supporting this research. It must be also

mentioned that this study has been made possible by

a fellowship from the Italian National Research .Council CNR to the first author for the Research

Project Quality optimization of products and pro-cesses using Experimental Design Methodology.

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