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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].trieste.it

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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( )B. Campisi et al.r Chemometrics and Intelligent Laboratory Systems 48 1999 597062

.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