multivariate design for the evaluation of lipid and surfactant composition effect for optimisation...

European Journal of Pharmaceutical Sciences 45 (2012) 613–623

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European Journal of Pharmaceutical Sciences

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Multivariate design for the evaluation of lipid and surfactant composition effectfor optimisation of lipid nanoparticles

Susana Martins a,b,⇑, Ingunn Tho b,2, Eliana Souto c,3, Domingos Ferreira a,1, Martin Brandl b,d,2,4

a Laboratory of Pharmaceutical Technology/Centre of Research in Pharmaceutical Sciences (LTF/CICF), Faculty of Pharmacy, University of Porto, Rua Aníbal CunhaNo. 164, 4050-047 Porto, Portugalb Department of Pharmacy, University of Tromsoe, N-9037 Tromsoe, Norwayc Faculty of Health Sciences, University of Fernando Pessoa, Rua Carlos da Maia, 296, 4200-150 Porto, Portugald Department of Physics, Chemistry and Pharmacy, University of Southern Denmark, Campusvej 55, DK-5230 Odense M, Denmark

a r t i c l e i n f o

Article history:Received 14 October 2011Received in revised form 13 December 2011Accepted 29 December 2011Available online 10 January 2012

Keywords:Lipid nanoparticles (LN)Experimental design (DoE)Multivariate analysisLipidsSurfactantsPhoton correlation spectroscopy (PCS)

0928-0987/$ - see front matter � 2012 Elsevier B.V. Adoi:10.1016/j.ejps.2011.12.015

⇑ Corresponding author at: Laboratory of PharmacResearch in Pharmaceutical Sciences (LTF/CICF), FaculPorto, Rua Aníbal Cunha No. 164, 4050-047 Porto, Portfax: +351 222 003 977.

E-mail address: [email protected] (S. Martin1 Tel.: +351 222 078 900; fax: +351 222 003 977.2 Tel.: +47 77 64 61 50; fax: +47 77 64 61 51.3 Tel.: +351 225 074 630; fax: +351 225 074 637.4 Tel.: +45 6550 2525; fax: +45 6615 8760.

a b s t r a c t

Physicochemical properties of lipid nanoparticles (LN), such as size, size distribution and surface charge,have a major influence both, on in vitro stability and delivery of the incorporated drug in vivo. With thepurpose of understanding how these properties are influenced by variations of LN composition (e.g. lipidand surfactant type and concentration) 22 factorial designs with centre point were applied for severaltypes of lipids and surfactants in the present study. Tested factors and levels were the type and concen-tration of lipid (cetyl palmitate, Dynasan 114 and Witepsol E85) at the concentrations of 5%, 10% and 15%,in combination with type and concentration of surfactant (polysorbate 20, 40, 60 and 80 and poloxamer188 and 407) at concentrations of 0.8%, 1.2% and 2.0%. Responses measured within the design space werethe mean size and polydispersity index (photon correlation spectroscopy), content of microparticles(optical single particle sizing), macroscopic appearance, pH and zeta potential on the day of production,1 and 2 years after production. Multivariate evaluation and modelling were performed starting with aprincipal component analysis (PCA) and followed by partial least square regression analysis (PLS) toassess both qualitative and quantitative influence of the investigated factors in the LN. Our study showedthat both, lipid and surfactant concentration and the type of surfactant are crucial parameters for the par-ticle size of the LN prepared by high pressure homogenisation (HPH). For LN stability during 2 years both,lipid and surfactant types and concentrations were identified as the most relevant parameters. Amongthe surfactants most suitable for producing LN with small sizes were the polysorbates and the lipid yield-ing best storage stability was cetyl palmitate. Furthermore, the models allowed the prediction of themean size of LN that could be achieved with a certain lipid/surfactant combination and concentration.The obtained results are considered useful for future design of stable LN formulations without the needof extensive empirical testing of formulation parameters within the given HPH technology.

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1. Introduction

Lipid nanoparticles (LN) have been widely investigated aspromising drug delivery systems because they combine the advan-tages of liposomes, emulsions and polymeric nanoparticles butavoid theirs drawbacks; they are a good alternative to emulsionsin terms of more easily controlling drug release, and to polymeric

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s).

nanoparticles in terms of lower toxicity (Joshi and Müller, 2009;Wissing et al., 2004). In comparison with PLA or PLGA (Food andDrug Administration (FDA) approved polymers), which showed50% viability at 0.2% of polymeric nanoparticles, LN (Compritol, ce-tyl palmitate) were 20 times less toxic on human granulocytes andHL 60 cells (Müller and Olbrich, 1999). Additionally, tripalmitinand stearic acid based LN (0.1–0.25%) (Miglietta et al., 2000) andCompritol and Dynasan 114 based LN (0.015–1.5%) (Müller et al.,1997), showed non-toxic behaviour towards HL60 and MCF-7 orHL60 cells, respectively.

LN are colloidal (submicron) particles, with mean sizes between50 and 1000 nm, made of a biocompatible and biodegradable(solid) lipid core and surfactant forming the outer shell.

Targeting drugs directly to the diseased organ or tissue will im-prove the drug therapeutic effect and reduce the side effects. Oneof the organs that represent a major challenge in drug targeting

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Table 1Experimental design: investigated factors and levels for 22 factorial designs withcentre point.

Level

Low Centre High

VariablesLipid conc. [% (w/w)] 5 10 15Surfactant conc. [% (w/w)] 0.8 1.2 2Category variablesType of lipid CP, D14, WE85Type of surfactant P20, P40, P60, P80, PL188, PL407

CP: cetyl palmitate; D14: Dynasan 114; WE85: Witepsol E85; P20, P40, P60 andP80: polysorbate 20, 40, 60 and 80; PL188 and PL407: poloxamer 188 and 407.

614 S. Martins et al. / European Journal of Pharmaceutical Sciences 45 (2012) 613–623

is the brain due to the presence of a restrictive blood–brain barrier(BBB) that prevents the passage of numerous drugs from the bloodto the cerebral tissue. Among the strategies for drug delivery to thebrain (Alam et al., 2010), colloidal delivery systems, namely LN(Bondi et al., 2010; Kaur et al., 2008), seem to be the most promis-ing because of their capability to deliver drugs effectively to thebrain without changing the BBB properties. The above mentionedcharacteristics make LN a very promising drug delivery system.

During manufacturing of LN by high pressure homogenisation(HPH), numerous parameters may have an influence on the physico-chemical properties of LN. Consequently, it is crucial to gain a com-plete perception of how both, manufacturing conditions as well asformulation design influence LN characteristics and especially howthese characteristics are influenced by possible interactions betweenvariables in the manufacturing process. Despite that effects of somevariables on the physicochemical properties have been studied ear-lier (Malzert-Freon et al., 2010a,b; Rahman et al., 2010; Schubertand Muller-Goymann, 2003; Zhang et al., 2009), a systematic inves-tigation of the influence of formulation variables on the LN propertiesproduced by HPH has not yet been properly performed.

During the past few years the concept of ‘‘Quality by Design’’(QbD) has been implemented in pharmaceutical development(ICH, 2009). The focus of this concept is that quality should be builtinto a product with a thorough understanding of the product andprocess by which it is developed and manufactured along with aknowledge of the risks involved in manufacturing the productand how best to avoid those risks. The design space defines a mul-tidimensional space, including combinations and interaction of for-mulation variables and process parameters, which is established togive product quality assurance (ICH, 2009). In order to establish thedesign space, thorough screenings are performed utilising designof experiments (DoE). Screening by use of DoE is a rational wayof evaluating the effect of numerous formulation variables andprocess variables by a limited number of experiments. The combi-nation of variables and their interactions that provides an endproduct, which are within the desirable/acceptable quality require-ments defined the design space. By optimisation of multivariatemodels (e.g. regression models) it is possible to deduce significantmain effects, their interactions and linearity. Response surfaces arefrequently used to visualise how a model describes the designspace investigated.

Principal component analysis (PCA) is a multivariate statisticalprojection technique that is commonly used to look for trends or‘‘latent variables’’ in data matrices. PCA is a powerful tool andmay even be used in the case of imbalanced designs or designswhere combinations are missing for various reasons.

Nowadays, numerous regression methods for data analysis arebeing used among them one of the most accepted is partial leastsquares regression (PLS). PLS has turned out to be the standardfor multivariate analysis, due to the quality of the prediction mod-els, ease of implementation, and availability of commercial soft-ware (Goicoechea et al., 2005; Hiorth et al., 2006; Tho et al.,2002). PLS regression has already been used to study the influenceof LN composition, but the systems and parameters investigatedwere different (Malzert-Freon et al., 2010a,b).

In the present work, as the lipid component, the triglyceride tri-myristin (C14, Dynasan 114) was selected. Additionally, WitepsolE85a hard fat-type with a high emulsifying capacity, were alsoused. Furthermore, the cetyl palmitate wax (Cutina), a mixture ofC14–C18 esters of lauric, myristic, palmitic and stearic acids (‘‘Ce-tyl esters wax’’), was also introduced in this study as a model wax,because of its faster in vitro degradation and lower in vivo toxicity(Lukowski et al., 2000; Weyhers et al., 2006) as compared to otherwaxes.

Several different types of surfactants (polysorbate 20, 40, 60and 80 and poloxamer 188 and 407) were employed. Considering

that our final goal is brain delivery, surfactants being approvedas pharmaceutical excipients, and known as sensitizers of drug-resistant cancers to anticancer drugs and/or transport-enhancersof drugs across the BBB were selected (Batrakova and Kabanov,2008; Petri et al., 2007; Wilson et al., 2008).

In a pilot-study (Martins et al., 2011) we have through a univar-iate approach, i.e. one by one studied the influence of selected for-mulation components on both, mean LN particle size andcontamination with micron particles and identified the followingkey design parameters: the type of lipid and surfactant as well astheirs concentrations influence initial mean particle size and poly-dispersity as well as colloidal stability of the LN dispersions. Thesefindings were used for defining the working space of the currentstudy. The results should to be applied during future formulationdevelopment studies, where inter-dependencies between theinfluence factors should be studied in a multi-variate approach.

The main aim of the current study thus was to systematicallyevaluate all relevant combinations of lipids and surfactants both,qualitatively (composition) and quantitatively (concentration) interms of their influence on the key quality characteristic of LN,namely mean particle size, polydispersity and stability using mul-tivariate design to reduce the number of experiments and to con-struct models of prediction.

2. Materials and methods

2.1. Materials

The selected glycerides: Dynasan 114 (D14) (trimyristin),Witepsol E85 (WE85) were offered from Sasol Germany GmbH(Witten, Germany). The wax Cutina (CP) (cetyl palmitate) was ob-tained by Apotekproduksjon AS (Oslo, Norway). The surfactants se-lected: polysorbate 20 (P20), 40 (P40), 60 (P60) and 80 (P80) wereprovided by Merck (KgaA, Darmstadt, Germany). Poloxamer 188and 407 (PL188 and PL407) were supplied by BASF (Ludwigshafen,Germany). Purified water was of MilliQ�-quality.

2.2. Experimental design

The basic experimental design consists of a set of 22 factorialdesigns where three lipids (cetyl palmitate, Dynasan 114, WitepsolE85) are investigated in combination with six different surfactants(polysorbate 20, 40, 60 and 80, poloxamer 188 and 407). For eachcombination of lipid and surfactant a 22 factorial design (with cen-tre point) testing different concentration of the two variables wascarried out. The investigated factors and their levels are presentedin Table 1. In total, 90 formulations were studied.

2.3. Annotation of formulation

A unique code was selected for identifying the quantitativecomposition of all formulations; it consists of an abbreviation for

S. Martins et al. / European Journal of Pharmaceutical Sciences 45 (2012) 613–623 615

the lipid type and a subscript for the lipid concentration in percentplus an abbreviation for the surfactant with a subscript for the sur-factant concentration. CP5P802 for example means: CP for cetylpalmitate, subscript 5 for 5% lipid, P80 for polysorbate 80, subscript2 for 2% surfactant.

2.4. Methods

2.4.1. Production of lipid nanoparticlesFor LN production, the procedure described in (Martins et al.,

2011) was used. In brief: the lipid phase was melted at approxi-mately 5–10 �C above its melting point followed by the additionof an aqueous surfactant solution heated to the same temperature.A coarse emulsion was formed by treating the blend for 1 min withan ultra-turrax T25 (IKA�-Werke GmbH & Co. KG, Staufen, Ger-many) at 8000 rpm. The produced hot emulsion was then sub-jected to three cycles at 50 MPa in a temperature controlledMicronLab 40 high pressure homogeniser (APV DeutschlandGmbH, Unna, Germany). After HPH the resulting hot oil-in-water(o/w) nanoemulsion was cooled down to room temperature allow-ing the inner oil phase to solidify and forming LN dispersed in anaqueous phase.

The HPH process parameters were selected based on a 22

screening of a selected formulation (D1410P801.2) where HPH cy-cles (three, seven cycles) and pressure (50, 100 MPa) were investi-gated. A centre point (five cycles, 50 MPa) and an additional point(five cycles, 75 MPa) were included. The mean particle size and thepolydispersity of the resulting LN were the evaluated responses inthe process parameter screening.

2.4.2. Assessment of particle size and size distributionThe average hydrodynamic diameter in volume and polydisper-

sity index (PI) of submicron LN were analysed by PCS (Nicompmodel 370, PSS Nicomp Sta Barbara, CA) as described by Frantzenet al. (2003). The PI was used as a measure for the broadness of thesize distribution. Briefly, the LN samples where diluted with water(refractive index of 1.333) until a count rate of 250–350 kHz in or-der to eliminate multiple scattering. In all the measurements v2

was smaller than three, indicating a monomodal (Gaussian) distri-bution; the amount of data collected in the first channel was higherthan 1000 K and the baseline adjustment was smaller than 0.03%.Supplementary optical single particle sizing (OSPS) was used to de-tect any particles in the micrometer range or aggregates of LN asdescribed in Martins et al. (2011). The Accusizer (PSS-Nicomp,Sta Barbara, CA) combines single particle light extinction measure-ment with light scattering measuring particles from 500 nm up to400 lm. Samples were step-wise diluted with particle-free (fil-tered MilliQ) water until consecutive dilutions yielded a propor-tional drop in total particle counts. OSPS yields a number-weighted distribution. The number of particles in the micro rangewas evaluated; number of microparticles >1 lm per mL and num-ber of microparticles >5 lm per mL. One has to bear in mind, how-ever, that the vast majority of LN is outside the measuring range ofOSPS.

2.4.3. pHpH measurements were performed using a Metrohm 744 pH

Meter (Metrohm, Herisau, Switzerland). The potentiometer wasintroduced in the samples vials and the pH was assessed at roomtemperature. pH was measured on the day of production and after1 and 2 years of storage.

2.4.4. Zeta potentialThe electrophoretic mobility, zeta potential, was measured by

combining laser Doppler velocimetry and phase analysis light scat-tering (PALS) using a Zetasizer Nano ZS (Malvern, Worcestershire,

UK). The samples were diluted with MilliQ-water having a conduc-tivity adjusted to 50 lS/cm by dropwise addition of 0.9% (m/v)NaCl solution.

2.4.5. Assessment of storage stabilityAll LN samples were stored in closed containers at room tem-

perature for a period of at least 1 year; most samples have cur-rently also reached 2 years of storage. All samples wereexamined on the day of production, after 1 year of storage and ifpossible also after 2 years of storage. In order to parameterise stor-age stability a coded stability parameter was introduced. All sam-ples that showed macroscopic signs of phase separation, creaming,solidification or gelation after storage were denoted 0 as for insta-ble samples. All samples that appeared to be stable by visualinspection (i.e. fluid, without phase separation, creaming or anysign of solidification or gelation) were subjected to further analysis.Most of these samples were measurable (with parameters in therange stated above) and thus given the code 1 as for stable sam-ples. In the case of irregularities during PCS analysis, such as bimo-dal distribution or difficulties to fit models to the measuredsamples, the sample was classified as unstable. We may thus un-der-estimate storage stability rather than over-estimate.

The indicators of storage stability were mean LN particle size, PI,number of microparticles larger than 1 lm per mL (OSPS) and pH-values after storage in comparison to the day of production. Alsothe absolute change in pH during storage was evaluated.

2.4.6. Multivariate analysisAll multivariate analysis and modelling were performed using

The Unscrambler� 9.7 and The Unscrambler� X (Camo, Norway).Principal component analysis (PCA) and partial least square regres-sion analysis (PLS) were employed to assess qualitative and quan-titative effects of the investigated factors on parameters related tostorage stability. Prior to analysis, the variation of each variablewas scaled to unit variance (1/SD). The models were calculatedusing systematic cross-validation. The Unscrambler uses Jack-knif-ing to estimate the uncertainty of the regression coefficients of PLS(Martens and Martens, 2000), which for most practical reasonsresembles a 0.05. A more detailed explanation of PCA and PLSmethods can be found in (Esbensen, 2006).

PLS-models were calculated for freshly prepared LN sampleswith respect to mean particle size, PI, number of microparticles lar-ger than 1 lm per mL (OSPS) and pH for the full matrix and foreach lipid and surfactant separately. In addition, for prediction pur-poses PLS models were calculated for each set of combination lipid/surfactant with respect to mean LN size of freshly prepared LNsamples. Further, PLS models were calculated for the indicatorsof storage stability after 1 and 2 years.

3. Results

The results from the physicochemical characterisation of allproduced LN are depicted in Table A.1 (Supplementary data). Themain purpose of Table A.1 is to provide the reader with all theraw data; the results will be further discussed based on multivar-iate evaluation of the data. Table A.1 as such will not be furtherdiscussed.

3.1. Lipid nanoparticles characteristics at day of production

3.1.1. Influence of HPH parametersFirstly, we investigated if HPH parameters (number of HPH cy-

cles and pressure) different from the ones used during our pilot-study (three cycles at 50 MPa) would influence mean particle sizesof the produced LN. For screening a factorial design 22 with centre

Fig. 1. Regression coefficients from PLS of mean size (A), polydispersity index (PI)(B) and the number of microparticles higher than 1 lm (C) of lipid nanoparticles(LN) on the production day (90 samples). CP: cetyl palmitate; D14: Dynasan 114;WE85: Witepsol E85; P20, P40, P60 and P80: polysorbate 20, 40, 60 and 80; PL188and PL407: poloxamer 188 and 407.


point was performed with one randomly selected formulation(D1410P801.2).

The mean particle sizes of all LN samples prepared using the dif-ferent process parameters (three, five and seven HPH cycles com-bined with a pressure of 50, 75 and 100 MPa, respectively) werefound to be similar, around 190 ± 70 nm (data not shown). Theinvestigation revealed no correlations between the tested parame-ters and mean particle diameter. There is no statistical evidencethat a high number of cycles and/or high pressure produce smallerparticles for the tested set-up. Furthermore, the mean size homo-geneity (PI) was 0.114 ± 0.05 and the LN with lower PI(0.084 ± 0.03) were produced when the number of cycles wasthree. Therefore the previously used number of cycles (three cy-cles) and pressure (50 MPa) were kept for the preparation of parti-cles. They represent rather mild conditions and will hopefully notaffect the excipients or drug stability. These parameters are in thesame order of magnitude as used in other studies (Liedtke et al.,2000).

3.1.2. Influence of formulation parameters on particle size and sizehomogeneity (PI)

After selecting HPH conditions, a systematic evaluation wasperformed to assess any influence of the formulations variableson the LN. PLS analyses were performed to determine the effectof each variable (three lipids and six surfactants, lipid and surfac-tant concentrations) on the characteristics of the LN in terms ofmean size, PI and number of microparticles larger than 1 lm permL. Fig. 1 shows the regression coefficient from PLS of mean size(Fig. 1A), PI (Fig. 1B) and number of microparticles larger than1 lm per mL (Fig. 1C) of all formulations variables. Large regres-sion coefficients indicate strong influences; positive regressioncoefficients indicate a direct correlation between the variable andhigh mean particle size, high PI or high number of microparticleslarger than 1 lm per mL, whereas negative coefficients indicatean inverse correlation, i.e. low size, low PI and low number ofmicroparticles larger than 1 lm per mL.

The model of Fig. 1A (mean size) explains 90% of Y-variance ontwo components. From Fig. 1A it is clear that the mean size of theparticles is mostly determined by the lipid concentration (directcorrelation), and the surfactant concentration (inverse correlation).This means that a low concentration of lipid and a high concentra-tion of surfactant were the factors in favour of LN with the smallestmean particle size.

When comparing the influence of the type of surfactant, thechoice of surfactant in all cases (except polysorbate 40) showedsignificant effect on mean particle size; smaller mean particleswere found to correlate with polysorbates, while the poloxamerscorrelated with larger particle sizes, General trends are that poly-sorbates produce LN with lower mean size, than the poloxamers.Especially poloxamer 407 produces larger LN.

In contrast, the choice of lipid was found not to correlate withmean particle size at the day of production. No major differencescould be identified between the lipids cetyl palmitate, Dynasan114 and Witepsol E85 with respect to mean size in the overallanalysis. This does not necessarily mean that the choice of lipidis not important, but in the overall analysis as presented inFig. 1A the effects could even each other out.

On the production day the size distribution (PI) was assessed togive information about the homogeneity of the LN sizes. PI valuesfor LN varied from 0.08 to 0.26. The PLS-model of PI (Fig. 1B) onlyexplains 37% of the variation in Y-variance, nevertheless certaininfluence factors can be identified, which appear to be of impor-tance for low PI (i.e. narrow size distribution): as for mean size, li-pid concentration appears to have a (significant) influence (highlipid concentration promoting low PI). The surfactant concentra-tion appears to show inverted (but not significant) influence. In

terms of type of lipid, cetyl palmitate-based LN (meanPI = 0.12 ± 0.02) tended most to low PIs, while Witepsol E85-basedLN (mean PI = 0.16 ± 0.05) tended most to high PIs. The surfactantthat showed an influence in terms of more heterogeneous particleswas poloxamer 188 (mean PI = 0.16 ± 0.03).

OSPS was used for determining the count of microparticles lar-ger than 1 lm per mL and measure their sizes in the LN formula-tions to detect the presence of any microparticles larger than5 lm, since the presence of large microparticles will prohibit theuse of these formulations for i.v. administration. The model ofthe number of microparticles larger than 1 lm per mL (Fig. 1C) ex-plains 42% of Y-variance on two components. The low degree of ex-


plained variance is caused by the lack of OSPS data for several ofthe formulations. However, some general trends can be recogni-sed: neither lipid nor surfactant concentrations appear to influencethe occurrence of microparticles. Cetyl palmitate appears to corre-late with higher numbers of microparticles (larger than 1 lm permL) and Witepsol E85 with lower numbers of such particles. Itshould be mentioned, however, that no particles bigger than5 lm were detected in any of the cetyl palmitate-based formula-tions but in a few formulations of Witepsol E85. These resultsmeans that despite the higher counts of microparticles in the cetylpalmitate based LN in comparison with Witepsol based LN, thesemicroparticles have smaller mean sizes than the Witepsol E85based LN. In relation to the surfactants, polysorbates seemed toyield formulations with lower amounts of microparticles thanthe poloxamers, but none of the surfactants showed significantregression coefficients at p < 0.05 (Fig. 1C).

Taken together, the above results revealed, that a low concen-tration of lipid and a high concentration of surfactant were the fac-tors in favour of LN with small mean particle size, yet high PI. Thehigh PI values that we are referring to are indeed still small PI val-ues, since the mean PI values were around 0.16 ± 0.05, which isconsidered still an monodisperse system (PI < 0.25). Interestinglythe type of lipid did not appear to correlate with mean particle size,while cetyl palmitate was found in favour of low PIs, but highmicroparticle-counts and for Witepsol E85 it was the other wayround. The polysorbates were the surfactants that produced LNwith the smallest mean particle size, the lowest PI and the lowestnumber of microparticles larger than 1 lm per mL.

The bi-plot from a PCA of mean size, PI and number of micro-particles larger than 1 lm per mL shows the LN formulations inrelation to the three parameters (Fig. 2). The LN formulations lo-cated furtherest away from each of the response parameters inthe bi-plot have a small size, a low PI and a low number of micro-particles larger than 1 lm per mL, respectively. The most suitableLN formulations are those, which are low in all responses; someof these are highlighted in the ellipse in the bi-plot. These formu-lations can be identified as: CP5P800.8, CP5P602, CP5P802,CP5PL4072, CP15P602, D1415P602, WE855P200.8 and WE855P800.8.

Since mean size is an important characteristic of LN, and PI andnumber of microparticles larger than 1 lm per mL are withinacceptable limits for most combinations in the design, the influ-ence parameters on this response was further investigated bybreaking down the matrix (Fig. 1A) and model the individual lipids(Table 2) and individual surfactants (Table 3). In the overall analy-sis significant interaction were identified between some of the fac-

Fig. 2. PCA bi-plot of mean particle size, polydispersity index (PI) and number ofmicroparticles larger than 1 lm per mL.

tors (e.g. [lipid]% � [surf], see Fig. 1A). This should be interpreted asan indication that there are important interactions between thevariables, but exactly which factors should be further investigated.Tables 2 and 3 summarises the characteristics of optimised PLSmodels for each of the lipids and each of the surfactants, respec-tively. The size of the regression coefficient quantifies the influenceof the variable on the mean particle size. Significant regressioncoefficients are marked in bold. Satisfying models were achievedfor all three lipids with R2 of the prediction model between0.8269 (Witepsol E85) and 0.9242 (cetyl palmitate) (Table 2); R2

of the calibration model are, as always, higher.Judging from the value of the coefficients in Table 2, the lipid

concentration is more important for determining size in WitepsolE85 than in Dynasan 114 (higher coefficient), whereas for Dynasan114 the surfactant concentrations is the most important factor.Polysorbate 20 and 60 show significant ability to reduce the parti-cle size for all three lipids and the poloxamer 407 to increase thesize in all lipids.

Poloxamer 407 has the largest influence on particle size (interms of larger particles), when combined with cetyl palmitateand Dynasan 114, as compared to the other surfactants. For Witep-sol E85 the poloxamer 188 showed a similar behaviour. Polysor-bate 60 has the largest influence in reducing nanoparticles size,when combined with Witepsol E85. For Dynasan 114 the polysor-bate 20 showed similar behaviour. In the model for cetyl palmitatea significant interaction was also identified (Table 2).

Table 3 confirms the finding suggested above (Fig. 1A), i.e. nooverall difference between the three lipids with respect to the abil-ity to produce particles in the evaluated size range (cetyl palmi-tate-based LN: 131–391 nm; Dynasan 114-based LN: 80–405 nm;Witepsol E85: 83–374 nm), in combination with the evaluated sur-factants. Significant interactions were found between the surfac-tants concentrations of polysorbate 40 and 60 and the lipidconcentration and between poloxamer 188 concentration and theconcentration of the lipids Dynasan 114 and Witepsol E85(Table 3).

Since the surfactants may interact slightly differently with eachof the lipids, individual models were optimised for each lipid andsurfactant combination. The separate PLS models constructed aresuited for predictions within the design space and particles witha given mean size can be produced by selecting the appropriateconcentrations of lipid and surfactant reading from the plots.Fig. 3 shows surface plots of PLS models of the three lipids in com-bination with polysorbate 20 (A–C), 60 (D–F) and 80 (G–I) and pol-oxamer 407 (J–L). By comparing the surface plots of the differentlipids combinations giving the same mean LN size can be identi-fied. As an example; particles of mean size 150 nm stabilised withpolysorbate 80 (Fig. 3G–I) are obtained for cetyl palmitate (Fig. 3G)for combination of 5–9% lipid and 1.4–2%surfactant, whereas thesame mean size would be obtained for Dynasan 114 (Fig. 3H) bya different combination of lipid and surfactant concentrations (5–13% lipid and 1.3–2% polysorbate 80) or as finally for WitepsolE85 (Fig. 3I) with lipid concentrations (between 5% and 10% anda concentration between 1% and 2% of polysorbate 80). Anotherexample; for all the lipid/surfactant combinations if the appropri-ated concentrations of lipid and surfactants are selected LN smallerthan 200 nm can be produced. For cetyl palmitate stabilised withpolysorbate 20, 60 and 80 those concentrations can be found inthe dark green and blue areas. For poloxamers 407 the suitablearea will be the blue.

3.1.3. Influence of formulation parameters on pHSince the pH of a formulation intended for i.v. administration is

important and could also interfere with the excipients or drug sta-bilities, the influence of the excipients constitution on the LN pHwas assessed on the day of production as well as after storage.

Table 2PLS models of lipids separately; effect on size of lipid nanoparticles (LN) on theproduction day (bold numbers indicate significant regression coefficients).

Variables CP D14 WE85

Lipid concentration [% (w/w)] 0.666 0.553 0.721Surfactant type

P20 �0.073 �0.118 �0.128P40 �0.024 �0.031 �0.056P60 �0.095 �0.091 �0.144P80 �0.086 �0.111 �0.062PL188 0.026 0.068 0.276PL407 0.253 0.284 0.115

Surfactant concentration [% (w/w)] �0.600 �0.701 �0.465Lipid conc. � surfactant conc. �0.281 �0.028 �0.170No. of PC 2 2 2BOW 3.3070 3.1893 2.1476RMSEP 18.4145 26.2222 35.1297R2 (prediction) 0.9242 0.9072 0.8269

CP: cetyl palmitate; D14: Dynasan 114; WE85: Witepsol E85; P20, P40, P60 andP80: polysorbate 20, 40, 60 and 80; PL188 and PL407: poloxamer 188 and 407.


Generally, the lipid and surfactant concentrations tested werenot found to correlate with the pH at the day of production, butthe type of lipid and type of surfactant did (Fig. A.1 Supplementarydata). The lipid cetyl palmitate contributed to a lower pH of the LNformulations compared to the other lipids. Witepsol E85 contrib-uted to formulations with higher pH values (max value measuredto 7.4). From the regression coefficients it was found that formula-tions with polysorbate 40 contributed to reduced pH of the freshlyprepared LN. Slightly acidic pH was measured in combination ofthese surfactants with all lipids; values down to pH 4–5, despitethe pH of the surfactants be between 5.0 and 8.0 for polysorbates(5% w/v aqueous solution) and pH between 5.0 and 7.5 for polox-amers (2.5% w/v aqueous solution) (supplier information).

3.2. Lipid nanoparticles storage stability

3.2.1. Coded storage stability parameterA systematic evaluation of the best formulation design in terms

of storage stability was also performed to screen for any trend. Thestorage stability was evaluated 1 and 2 years after production.Storage stability was evaluated as a coded parameter (stable 1,instable 0). One year after production 67 samples of the originalset of 90 samples were stable. Most of the instable samples werefound for the lipid Witepsol E85; 12 of the 30 formulations basedon Witepsol E85 were not stable for 1 year. It is worth noticing thatnone of the combinations 15% lipid Witepsol E85 and 0.8% surfac-tant was stable after 1 year. When it comes to Dynasan 114, eightof the 30 formulations were not stable for 1 year; also for this lipid

Table 3PLS models of surfactants separately; effect on size of lipid nanoparticles (LN) on the prod

Variables P20 P40

Lipid concentration [% (w/w)] 0.654 0.638Lipid type

CP 0.099 0.038D14 �0.014 0.015WE85 �0.086 �0.053

Surfactant concentration [% (w/w)] �0.652 �0.671Lipid conc. � surfactant conc. – �0.274D14 � surfactant conc. [%] – –WE85 � lipid conc. [%] – –No. of PC 2 2BOW 3.4032 2.8425RMSEP 30.7243 35.3437R2 (prediction) 0.7626 0.8249

CP: cetyl palmitate; D14: Dynasan 114; WE85: Witepsol E85; P20, P40, P60 and P80: p

the combination 15% lipid and 0.8% surfactant seems to be themost vulnerable. For cetyl palmitate only three samples were notstable for 1 year.

Fig. 4 shows the regression coefficients from PLS models of thestorage stability parameter. A positive regression coefficient indi-cates stability of formulation, whereas a negative coefficient indi-cates the negative influence of the variable on the stability of theformulation. The factors influencing the 1 year storage stability(Fig. 4) are type of lipid, lipid concentration and concentration ofsurfactant. Low lipid concentration and high surfactant concentra-tion were the factors providing stability of the particles. Cetyl pal-mitate is the lipid that produced most stable formulations. On theother hand, Witepsol E85 is the lipid that produced least stable for-mulations. The type of surfactant seems to be less important forthe stability of the formulations during 1 year. The major trendsare the same after 2 years stability (Fig. A.2 Supplementary data).

3.2.2. Influence of formulation parameters on particle size and sizehomogeneity upon storage

Fig. 5 shows the regression coefficients of the PLS model of theLN mean particle size (A) and PI (B) after 1 year of storage. The re-sults confirm that formulations containing, higher amount of lipid,lower amount of surfactant and the lipid Witepsol E85 contributedto larger mean particle size after 1 year (Fig. 5A). In relation to thesurfactants, the polysorbate 80 was the surfactant that showed thehighest tendency to maintain smaller particles size after 1 year.

The mean PI of LN samples 1 year after production (Fig. 5B) washigher than the values obtained on the day of production (exceptfor poloxamer 188). The LN produced with Witepsol E85 as the li-pid that shows a higher polydispersity as compared to cetyl palmi-tate and Dynasan 114. A high lipid concentration and lowsurfactant concentration were again favouring a low PI, e.g. a morenarrow size distribution.

3.2.3. Influence of formulation parameters on pH changesThe alterations in pH values after 1 year was assessed to iden-

tify which LN constituents lead to lower or higher pH changes dur-ing storage (Fig. A.3 A Supplementary data).

During storage pH was found to drop several pH units, at themost 3–4 pH units during the first year of storage. Most formula-tions showed acidic pH (between 3 and 4) after 1 year of storage.A trend of high pH changes occurred in formulations with Dynasan114 whereas cetyl palmitate show low pH changes during storage.Polysorbate 20 were the surfactant that showed more neutral pHat preparation, but it seems that this surfactant actually experiencethe largest pH change during storage. A minor drop in pH was seenalso the second year (for those samples investigated) (data notshown). One year after production the lipid and surfactant concen-

uction day (bold numbers indicate significant regression coefficients).

P60 P80 PL188 PL407

0.683 0.700 0.669 0.735

0.057 0.036 �0.126 0.0270.029 �0.031 �0.068 0.122�0.085 �0.005 0.194 �0.149�0.648 �0.636 �0.555 �0.583�0.246 – – –– – �0.196 –– – 0.248 –2 2 3 22.6956 2.6000 3.2565 3.225629.2097 35.8494 30.7336 29.83550.8608 0.8054 0.8363 0.8709

olysorbate 20, 40, 60 and 80; PL188 and PL407: poloxamer 188 and 407.

Fig. 3. Surface plot from PLS model of the surfactants polysorbate 20 (P20) (A, B and C), 60 (P60) (D, E and F), 80 (P80) (G, H and I) and poloxamer 407 (PL407) (J, K and L) withthe lipids cetyl palmitate (CP) (A, D, G J), Dynasan 114 (D14) (B, E, H and K) and Witepsol E85 (WE85) (C, F, I and L). CP + P20: R2cal: 0.98, R2pred: 0.54, explained Y-variance98%, two PC; CP + P60: R2cal: 0.98, R2pred: 0.67, explained Y-variance 98%, two PC; CP + P80: R2cal: 0.96, R2pred: 0.42, explained Y-variance 96%, two PC; CP + PL407: R2cal:0.99, R2pred: 0.82, explained Y-variance 99%, two PC; D14 + P20: R2cal: 0.95, R2pred: 0.88, explained Y-variance 95%, two PC; D14 + P60: R2cal: 0.96, R2pred: 0.91, explained Y-variance 96%, two PC; D14 + P80: R2 cal: 0.95, R2pred: 0.89, explained Y-variance 95%, two PC; D14 + PL407: R2cal: 0.99, R2pred: 0.96, explained Y-variance 99%, two PC;WE85 + P20: R2cal: 0.91, R2pred: 0.58, explained Y-variance 91%, one PC; WE85 + P60: R2cal: 0.99, R2pred:0.61, explained Y-variance 99%, two PC; WE85 + P80: R2cal: 0.97,R2pred: 0.93, explained Y-variance 97%, two PC; WE85 + PL407: R2cal: 0.91, R2pred: 0.61, explained Y-variance 91%, one PC.


trations tested were not found to correlate with the pH, but thetype of lipid and type of surfactant did (Fig. A.3 B supplementarydata). The lipid Dynasan 114 contributed to a lower pH of the LNformulations compared to the other lipids. Witepsol E85 contrib-uted to formulations with higher pH values. From the regressioncoefficients it was found that formulations with polysorbate 40and 60 contributed to reduced pH of LN storage during 1 year. Pol-oxamers contributed to formulations with higher pH values.

3.2.4. Correlation between the responsesThe PCA allows interpretation of latent structures in the data

matrix, and the projections of the PCA of the full set-up of 90experiments on the first three components are depicting in Fig. 6.

The components explain 33%, 23% and 17% of the variation in thedata, respectively, i.e. 73% of the variation is explained using threePCs. The mean size, pH and zeta potential are positively correlatedon PC1, and they are inversely correlated to the storage stability for1 year and microparticles >1 lm per mL, i.e. low size, low pH andlow zeta potential are correlated to high storage stability.

PI has a very low value on PC1. On PC2 the mean size is posi-tively correlated to the number of microparticles larger than1 lm per mL. The most important phenomena are described onthe first PC in a PCA, the next most on PC2 etc. The fact that allthe responses are located in the outer circle in the plot (Fig. 6A),indicating high degree of explanation on the investigated compo-nents (here PC1 and PC2). Responses located closer to origin, or

Fig. 4. Regression coefficients of PLS models of the coded storage stabilityparameter after 1 year. CP: cetyl palmitate; D14: Dynasan 114; WE85: WitepsolE85; P20, P40, P60 and P80: polysorbate 20, 40, 60 and 80; PL188 and PL407:poloxamer 188 and 407.

Fig. 5. Regression coefficients from PLS model of lipid nanoparticles (LN) (A) meanparticle size after 1 year storage and (B) polidispersity index (PI) after 1 yearstorage. CP: cetyl palmitate; D14: Dynasan 114; WE85: Witepsol E85; P20, P40, P60and P80: polysorbate 20, 40, 60 and 80; PL188 and PL407: poloxamer 188 and 407.

Fig. 6. (A) PCA correlation loadings PC1 vs PC2 and (B) PCA correlation loadings PC1vs PC3. PI: polydispersity index.


in the inner circle, are less explained by the components and thusthe correlation will be weaker. On the third component PI is posi-tively correlated to size and inversely correlated to storage stability

(Fig. 6B), i.e. low PI and low size are correlated to high storagestability.

4. Discussion

In a previous study (Martins et al., 2011) we have identifiedsome key influence factors and seen some major trends in termsof what influences mean particle size (lipid concentration, surfac-tant concentration and type of surfactant). This has been investi-gated by looking into potential influence factors one by one. Incontrast, the current study systematically investigates all qualita-tive and quantitative formulation design aspects. This allows toscreen for any trend including minor trends we may have detectedearlier as well as for interactions between factors.

It is well-known that the particle size and size distribution arethe most significant parameters for the evaluation of the stabilityof colloidal systems. The reason for selecting LN of less than200 nm for the purpose of brain delivery, is based on the literature:particles sizing in the range of 200–500 nm has been reported aspotentially more long-circulating whereas particles smaller than100 nm are eliminated by renal excretion, and particles larger than500 nm can be rapidly taken up by the MPS cells (Gaumet et al.,2008). Thus, LN in the size range of 100–200 nm is expected tohave increased circulation time, and hence an increase in the timefor which the LN remains in contact with BBB and can be uptakenby the endothelial cells or can release the drug to be taken up bythe brain.

For those reasons, the particle size parameters have been eval-uated on the day of production of the LN, 1 and 2 years after pro-duction. Generally, it was observed that LN with mean sizebellow 200 nm and with a narrow size distribution were obtainedby combining appropriate amounts of lipids with surfactants. Fur-


thermore, the smallest particles were usually seen with low con-centration of lipid (5%) and high concentrations of surfactants(2%). The lipid concentration had a positive influence in the meansize of the LN, i.e. larger LN at higher concentrations. This wasprobably due to the fact that higher concentrations of lipid in-creased the viscosity of the inner phase (lipid phase), which af-fected the shearing capacity of homogeniser and stirrer. Thesurfactant concentration revealed a negative influence on themean size of the LN, i.e. smaller LN at higher concentrations. Thiswas mainly due to the fact that higher amounts of surfactants al-low better stabilisation of the smaller droplets (higher surfacearea) formed during the homogenisation, and thus preventingthe coalescence into bigger droplets. Furthermore, the presenceof surfactants on the LN surface reduces surface tension betweenthe lipid and water and facilitates the solid particle formation dur-ing the cooling phase of LN production.

Despite the concentration of lipid as large influence on the par-ticle size on the day of production the type of lipid seems to be lessimportant.

When it comes to the surfactants, the polysorbates were gener-ally more suitable in stabilising and producing smaller LN in com-bination with the lipids tested than the poloxamers. Taking inconsideration the different molecular weights of the surfactants(MW of polysorbate 20 � 1128 < polysorbate 40 � 1284 < polysor-bate 80 � 1310 < polysorbate 60 � 1312 < poloxamer188 � 8400 < poloxamer 407 � 12600) it is evident that for thesame amount of surfactant (0.8–2% w/w) in the formulation, thenumber of surfactant molecules will be lower for the poloxamersthan for the polysorbates, and this amount of the molecules wasprobably not able to stabilise the LN as well as the polysorbates.Furthermore, the higher MW of the poloxamer should be the mainreason for increase in LN size seen for these surfactants; the pres-ence of the larger molecule of poloxamer on the LN surface shouldcontribute to a larger LN size. Also the highest size increase wasseen for the poloxamer with the highest molecular weight (polox-amer 407), which is consistent with the explanation.

Accordingly, it can be concluded that polysorbates were moreefficient in stabilising LN in combination with the lipids testedthan poloxamers, which is also in agreement with previous studiesdescribed in literature (Abdelbary and Fahmy, 2009; Scholer et al.,2001).

PI represents the quality of the dispersion. PI values 60.1 reflectan excellent monodispersity and high quality of the LN dispersions,values 60.25 indicate a monodisperse size distribution whereasvalues higher than 0.25 to values close to one reflect a higher het-erogeneity and reduced quality of the samples. Generally, the LNproduced had a PI 60.25 indicating homogeneous particle size dis-tribution. The lipid Witepsol E85 and the surfactant poloxamer 188seem to yield more heterogeneous LN. These two excipients pro-duced LN less stable and more heterogeneous in terms of size prob-ably due to the lower stability of the glycerides and high molecularweight of the poloxamer 188 referred previously in this study. Onthe other hand, cetyl palmitate and polysorbate 80 were the excip-ients that produced LN with more homogenous sizes, which alsocan be explained by the stability of the wax and the low molecularweight of the surfactant.

The number of microparticles larger than 1 lm per mL washigher for cetyl palmitate and poloxamers, and lower for WitepsolE85. Generally, there were not detected microparticles larger than5 lm, which make these formulations compatible with i.v. admin-istration. Only in few formulations with Witepsol E85 microparti-cles larger than 5 lm were detected which is probably related withthe lower stability of the glycerides. Despite that Witepsol E85 pro-duces a smaller number of microparticles, these microparticlesprobably are agglomerated in large microparticles, which couldbe very dangerous if injected by i.v. administration. The lower

number microparticles (larger than 1 lm per mL) observed, couldbe a result of an agglomeration of smaller nano/microparticles intomicroparticles (>5 lm).

We have seen above that the three lipids behave similar withrespect to size of the LN on the day of production. In order to selectbetween the three lipids, the storage stability is one factor that willbe of high relevance. PLS models revealed that the factors influenc-ing storage stability are type of lipid, lipid concentration and con-centration of surfactant. Low lipid concentration and highsurfactant concentration were the factors providing good stabilityof the particles.

The universal knowledge is that appropriate LN are producedwithin the design space. LN with mean size bellow 200 nm couldbe reached for all lipids/surfactants combinations. In order to ob-tain LN with a desired mean particle size, the appropriate concen-tration of combination of the particular lipid and surfactant couldbe optimised using the predictive models constructed.

Despite the finding that the type of lipid seems to be less impor-tant on the particle size on the day of production, enormous differ-ences of LN stability could be detected depending on the lipid used.Cetyl palmitate did not produce the smallest particles but the par-ticles were more stable than the Witepsol E85. This finding is inagreement with literature where cetyl palmitate-based LN are re-ported to be more stable than glycerides-based LN (Jenning andGohla, 2000). The large size increase during storage as seen forWitepsol E85-based LN, is probably related to particle growth oragglomeration during storage and can be seen as a confirmationof the lower storage stability described above for Witepsol E85.The same trend was found after 2 years of storage, though fewersamples were remaining or data were not available at the time ofevaluation. In addition to that, Witepsol E85 was the lipid with lar-ger microparticles, which confirmed once more the lower stabilityof this lipid.

According to the PLS models to produce small and stable LN acombination of low lipid and high surfactant concentrationsshould be used. Furthermore, cetyl palmitate and polysorbatesshould be preferred as lipid and surfactants, respectively. With re-spect to formulation of LN particles with the goal of brain deliverysmall and homogenous size and slight negative or neutral zeta po-tential are important selection criteria. Since polysorbates areamong the surfactants, which are known as transport-enhancersof drugs across the BBB (Kreuter, 2004; Kreuter et al., 1997), for-mulations of cetyl palmitate and polysorbate, e.g. polysorbate 80,are considered as highly interesting.

The pH stability of LN is another key point for the preparation andthe application of such systems. The pH values measured in thisstudy are compatible with i.v. administration. Despite the low pHvalues of the LN 1 year after the production, the LN were found tobe stable. This is also in agreement with the literature where theLN are reported to be stable in the range of pH 2–8 (Shahgaldianet al., 2003). The low pH of the system could be taken advantage offor the incorporation of drugs that are stable or in the active formin those ranges of pH (e.g. camptothecin). Incorporation of drugswith specific pH requirements can otherwise be achieved by usinga buffer with pH around the stability of the drug. For example, forcamptothecin, a buffer with a pH around five where almost all thecamptothecin is on the active lactone form (Saetern et al., 2005)could be applied. The lipid that revealed higher stability also interms of pH change was once again cetyl palmitate; the lipid showedlower change in pH during the storage. Also the polysorbates re-vealed a lower change during the storage compared to the poloxa-mers. Once more the same trend of stability was verified.

The PCA correlation loadings (Fig. 6) revealed that low zeta po-tential is correlated to high storage stability. This was probably dueto the fact that LN perfectly covered by non-ionic surfactants tendto be more stable despite the lower zeta potential associated to


such particles. The stability of these LN were due to a steric stabi-lisation instead an electrostatic stabilisation. Furthermore, cover-ing the LN surface with non-ionic surfactants as known todecrease the mobility of the LN leading to lower zeta potential val-ues (Santander-Ortega et al., 2006).

Differential scanning calorimetry analyses were performed toconfirm the physical state and polymorphism of the LN whichcan also be an indicator of LN stability (data not shown). The find-ings were in agreement with our earlier report (Martins et al.,2011), where the more stable LN formulations were the cetyl pal-mitate and Dynasan 114 due to the fact that no polymorphicchanges were detected during storage. Since Dynasan 114 pro-duced supercooled melts, the cetyl palmitate seems to be the bestlipid to produce stable LN.

The models developed, will be helpful for new formulations,since PLS models could be applied for reducing the number ofexperiments which might present an economical advantage anda time-saver.

5. Conclusion

Employing experimental design and multivariate evaluation hasbeen shown to be helpful to identify important factors in the man-ufacture and optimisation of LN produced by the HPH method. Inour study, LN with a mean particle size lower than 200 nm witha (close-to-) monodisperse size distribution and good storage sta-bility were targeted and obtained within the design space.

Acceptable models have been constructed describing the influ-ence of compositional variations on the size and stability duringstorage of LN. A quantitative correlation between the lipids andsurfactants and theirs concentrations and the mean size and stabil-ity of LN has been established. The models calculated allow to con-trol and optimise easily those LN characteristics within the designspace. The mathematical analysis clearly revealed that the influ-ence of the composition parameters is central for determiningthe particle size of the LN prepared by the HPH method. Bearingin mind that the LN should have sizes smaller than 200 nm andbe stable for as long as possible, the results of this work revealedthat to reach that purpose low concentrations of lipid should becombined with high concentrations of surfactants. Furthermore,cetyl palmitate seems the lipid more appropriated and the polysor-bates the surfactants more appropriated for achieving the requiredLN characteristics.

The models calculated are suitable for predictions purposes andcan be used to optimise particle size and size distribution, by defin-ing the appropriate combinations of lipid and surfactant and theirsconcentrations to reach the target size properties. From the estab-lished design space, promising formulations to specific targets, e.g.brain delivery, can be selected for further studies.

Ultimately, the multivariate design methodology has clearlyshown its usefulness in this optimisation process and may confera time-saver and an economical advantage for further LNdevelopment.

Appendix A. Supplementary data

Supplementary data associated with this article can be found, inthe online version, at doi:10.1016/j.ejps.2011.12.015.

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