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Colloids and Surfaces B: Biointerfaces 84 (2011) 117–130

Contents lists available at ScienceDirect

Colloids and Surfaces B: Biointerfaces

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he size of solid lipid nanoparticles: An interpretation from experimental design

arla Vitorinoa, Filomena A. Carvalhob, António J. Almeidac, João J. Sousaa, Alberto A.C.C. Paisd,∗

Center for Pharmaceutical Studies, Faculty of Pharmacy, University of Coimbra, Pólo das Ciências da Saúde, Azinhaga de Santa Comba, 3000-548 Coimbra, PortugalInstitute for Molecular Medicine, Faculty of Medicine, University of Lisbon, Lisbon 1649-028, PortugalResearch Institute for Medicines and Pharmaceutical Sciences (iMed.UL), Faculty of Pharmacy, University of Lisbon, Av. Prof. Gama Pinto, 1649-003 Lisbon, PortugalChemistry Department, University of Coimbra, 3004-535 Coimbra, Portugal

r t i c l e i n f o

rticle history:eceived 7 November 2010eceived in revised form1 December 2010ccepted 16 December 2010vailable online 24 December 2010

a b s t r a c t

This study aimed to investigate the role of different factors affecting the size of solid lipid nanoparticles(SLN), prepared by the emulsification–solvent evaporation method. A double factorial design was con-ducted so as to cover a wide range of sizes, highlighting zones with different behaviour with respect tochanges in the controlled variables: lipid concentration, solvent:lipid ratio and emulsifier concentration.

The solvent:lipid ratio constituted the main factor influencing particle size. Increasing the amount ofsolvent induced a decrease in the size. This was a general trend, essentially independent from solvent

eywords:olid lipid nanoparticlesarticle sizeactorial designimvastatin

and lipid type. The amount of emulsifier had a non-trivial impact on size, depending on whether systemswere located below, above or close to the optimal surface coverage. The amount of lipid had a limitedinfluence upon particle size, being more relevant for lower lipid concentrations. An optimal formulationwas selected for intermediate levels of the three variables. Sonication reduced both particle size andpolydispersity. These particles were also tested as drug carriers using simvastatin as a model of lipophilicdrug. SLN were able to entrap a high amount of simvastatin, with little effect upon size and zeta potential,

carrie

constituting a promising

. Introduction

Solid lipid nanoparticles (SLN) are systems of remark-ble technological relevance from a pharmaceutical perspective.hese particulate systems, with sizes typically in the range of0–1000 nm, are composed of biodegradable and biocompatibleolid lipids and stabilized by emulsifier(s). Their nature confers dis-inct advantages such as an excellent tolerability, physical stability,he ability to protect incorporated labile drugs from degradation,nd the possibility to modulate drug release and drug target-ng [1–6]. These colloidal carriers have shown to improve theioavailability of orally administered poorly soluble drugs, such asimvastatin, since the reduction of drug particles to the nanoscalencreases dissolution velocity and saturation solubility, leading tomproved in vivo drug performance [7–9]. For these reasons, SLN

ave emerged as a promising colloidal carrier system for drugelivery, allowing several routes of administration [10–11], suchs oral, parenteral, dermal, transdermal, ocular, pulmonary andectal [2,12–17]. The production of SLN can be carried out using dif-erent methods, including high shear homogenization, ultrasound,

∗ Corresponding author. Tel.: +351239854466; fax: +351239827703.E-mail address: pais@qui.uc.pt (A.A.C.C. Pais).

927-7765/$ – see front matter © 2010 Elsevier B.V. All rights reserved.oi:10.1016/j.colsurfb.2010.12.024

r for lipophilic drugs.© 2010 Elsevier B.V. All rights reserved.

high pressure homogenization, or via microemulsion and solventemulsification–evaporation techniques. Some of these can be com-bined in order to obtain stable particles, with suitable properties,such as particle size and size distribution [18–19]. These charac-teristics are crucial in SLN production, and result from both thepreparation method and, to a larger extent, the particle composi-tion [20].

In the present work, SLN production is studied using a modifiedsolvent emulsification–evaporation method, in which high shearhomogenization and ultrasound are also employed. The aim is tosystematically assess how different parameters, such as lipid typeand concentration, amount of solvent, emulsifier type and con-centration, influence the size of solid lipid nanoparticles. This hasbeen addressed using a factorial analysis, which allows not only toextract the maximum amount of information from the collecteddata, resorting to a limited number of experiments, but also toestablish the influence of multiple factors on the formulation prop-erties. The direct effect of each factor is studied, and the respectiveinteraction with other factors is also assessed in detail. Designsin different factor domains are additionally carried out, in orderto describe the system behaviour under different compositions.

A detailed physicochemical characterization of the systems and arationale for the observations are also provided. It should be notedthat the usefulness of experimental design in the formulation oflipid nanoparticles (see e.g., ref. [21]) is being increasingly acknowl-edged.

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As an example of practical application, SLN loaded with sim-astatin are prepared. Simvastatin, a potent inhibitor of cholesteroliosynthesis, is used as a model of a lipophilic, poorly water-solublerug and with low bioavailability, as a consequence of the intenseepatic first pass effect. It would thus benefit from the improvedontact with the intestine or the skin (oral and transdermal deliv-ry, respectively), in a nanoparticle formulation that promotes aigh degree of absorption with an extended delivery potential. Its

nfluence on particle size, zeta potential and entrapment efficiencyn the previously optimized formulation is also assessed.

. Materials and methods

.1. Materials

Glyceryl tripalmitate (tripalmitin, T8127, melting point 66 ◦C)as purchased from Sigma, Compritol® 888 ATO (glyceryl behen-

te, melting point: 71–74 ◦C), and Precirol® ATO 5 (glycerylalmitostearate, melting point: 53–56 ◦C) were kindly provided byattefossé (Saint-Priest, Cedex, France). Polyvinyl alcohol 87–89%ydrolyzed (PVA, typical MW 13.000-23.000) and polysorbate 80Tween® 80) were purchased from Sigma–Aldrich. Simvastatin wasindly provided by Labesfal – Laboratórios Almiro, S.A. (Santiago deesteiros, Portugal). All other chemicals were of analytical grade orquivalent. Water was purified (Millipore®) and filtered through a.22 �m nylon filter before use.

.2. Preparation of SLN

The SLN were prepared using a modification of the widely usedmulsification–solvent evaporation method described elsewhere19]. Briefly, the lipid was dissolved in dichloromethane (DCM)nd then added dropwise to 30 mL of emulsifier solution in a highhear homogenizer (Silverson, UK) at 12,300 rpm for 7 min. In thenal, optimized, method of preparation, the inner lipid phase wasdded dropwise to the external phase under ultra-sonication (40 W,min; Branson, Sonifier 250). The pre-dispersion obtained was

hen high-shear homogenized (12,300 rpm, 7 min). The dispersionbtained in both cases was then magnetically stirred at 200 rpm forh, in order to allow the solvent evaporation. In the drug loadedLN, the lipid and simvastatin were dissolved in DCM, and subjectedo the conditions described above.

.3. Photon correlation spectroscopy

Particle size was determined by photon correlation spec-roscopy (PCS) which yields the mean particle size (Z-average),nd the polydispersity index (PI), which measures the width ofhe size distribution. PCS was performed with Zetasizer Nano SMalvern Instruments, Malvern, UK) at a detection angle of 173◦,t 25 ◦C. Samples were suitably diluted in ultrapurified water.ach value was measured in triplicate. The results are shown asean ± standard deviation.

.4. Zeta potential

Zeta potential (ZP), which reflects the electric charge on the

article surface, is a useful parameter to predict the physical sta-ility of colloidal systems. ZP was determined using a Zetasizer000 (Malvern Instruments, Malvern, UK) at 25 ◦C. For the mea-urements, samples were diluted appropriately with ultrapurifiedater (pH ≈5.5).

: Biointerfaces 84 (2011) 117–130

2.5. Factorial design

An experimental design with a two-level, three-variable, 2k fullfactorial planning was performed for the optimization of the par-ticles composition. The experimental design and the polynomialmodels were solved resorting to the GNU Octave software [22],with specific programs developed by the authors. The mathemati-cal model

D = ˇ0 + ˇ1x1 + ˇ2x2 + ˇ3x3 + ˇ12x1x2

+ ˇ23x2x3 + ˇ13x1x3 + ε (1)

was applied in two different situations, to describe the princi-pal effects and interactions among the identified variables. Coded(−1, +1) levels were used for each independent variable, x1, x2, andx3, in which the −1 level corresponds to the lower value of eachvariable and +1 to the upper one. The choice of these limits wasbased on acceptable domains for each variable, considering a thera-peutic application, and the optimization procedure was carried outwithin these domains. In equation (1), ˇ0 is the arithmetic meanresponse, ˇ1–ˇ3 are the coefficients of the respective independentvariables and ˇ12, ˇ23 and ˇ13 their respective interaction terms;D, the mean particle size, is the dependent variable or response andε the error term. ANOVA and Student’s t-test were applied to testthe fitted models. Statistical analysis was considered significant ifthe p values were less than 0.05.

2.6. Laser diffractometry

Laser diffractometry (LD) was additionally performed in order todetect the presence of microparticles, using a laser diffraction parti-cle size analyzer (Beckman Coulter® LS 13,320, Miami, FL, USA) withpolarization intensity differential scattering (PIDS). The real refrac-tive index and the imaginary refractive index were set to 1.54 and0.1, respectively. The LD data were expressed using volume distri-butions, and given as diameter values corresponding to percentilesof 10%, 50%, and 90%. The span value is a statistical parameter use-ful to characterize the particle size distribution, and was calculatedusing reference [23]:

Span = (d90% − d10%)/d50%. (2)

A high value of span indicates a wide size distribution and a highpolydispersity.

2.7. Determination of residual PVA

The amount of residual PVA associated with the SLN surfacewas determined using a colourimetric method based on the for-mation of a coloured complex between adjacent hydroxyl groupsof PVA and an iodine molecule [24]. For this determination, onlya few formulations were selected. Briefly, a 250 �L volume of thenanosuspensions was ultrafiltered–centrifuged for 30 min throughAmicon® Ultra-4 centrifugal filter units (100 KDa MWCO, Millipore,Ireland) and 50 �L of the particles collected in the inner cham-ber were treated with 2 mL of 0.5 M NaOH for 15 min at 60 ◦C.Each sample was neutralized with 900 �L of 1 N HCl and the vol-ume was adjusted to 5 mL with distilled water. To each sample,

3 mL of a 0.65 M solution of boric acid, and 0.5 mL of a solution ofI2/KI (0.05/0.15 M) were added. The final volume was adjusted to10 mL with distilled water. Finally, the absorbance was measuredat 690 nm after 15 min incubation at room temperature. A standardcurve for PVA was prepared under identical conditions.

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.8. Differential scanning calorimetry (DSC)

The crystal structure was inferred from the thermal behaviourf the tripalmitin SLN using DSC equipment (Pyris 1 DSC,erkinElmer). Lyophilized SLN (1.5–2.5 mg) were placed in alu-inium pans and hermetically sealed. Empty pans were used as

eference. The individual components of SLN were also analyzeds raw materials. Each sample was heated from 10 to 80 ◦C, at0 ◦C/min, and then cooled down to 10 ◦C at the same rate. A phys-

cal mixture of the components was also analyzed as a control.

.9. Attenuated total reflectance infrared spectroscopy (ATR-FTIR)

In order to obtain information about the structure of the parti-les, ATR infrared spectra of the particles were recorded using anT–IR/FT–NIR spectrometer (Spectrum 400, PerkinElmer) with anTR accessory fitted with a Zn–Se crystal plate. Lyophilized sam-les were placed in the ATR device without previous treatment, andeasured using four scans for each spectrum, with a resolution ofcm−1 and a scan speed of 0.5. Spectra were collected between000 and 650 cm−1.

.10. Atomic force microscopy (AFM)

A NanoWizard II equipment (JPK Instruments, Germany)ounted on the top of an Axiovert 200 inverted microscope (Carl

eiss, Germany) was used for imaging and atomic force spec-roscopy experiments. The AFM head is equipped with a 15 �m-range linearized piezoelectric scanner and an infrared laser.he SLN optimized formulation was diluted in ultrapurified water1:100) and deposited on a poly-l-lysine treated glass slide. Afterpproximately 20 min, the preparation was washed with ultrapu-ified water and air-dried.

SLN imaging was performed in air by tapping mode. Oxidizedharpened silicon tips with a tip radius of 6 nm, resonant frequencyf about 60 kHz and a spring constant of 3 N/m were used formaging. Imaging parameters were adjusted to minimize the forcepplied on the scanning of the nanoparticles topography. This loworce is particularly useful to study soft or easily deformable sam-les. Imaging data were analyzed with the JPK image processing v.3JPK Instruments, Germany).

.11. Scanning electronic microscopy (SEM)

The SEM analysis was performed in order to investigate theorphological characteristics of the particles. Prior to analysis, the

ample was diluted with ultrapurified water, placed on a double-ide carbon tape mounted onto an aluminium stud, and dried in aesiccator. The sample was then sputter coated with gold in order toake it conducting. SEM images were recorded on a Jeol, JSM 5310,

Tokyo, Japan) scanning electron microscope, with an accelerationoltage of 25 kV.

.12. Fluorescence microscopy

The Nile red (NR) staining assay was used to confirm the SLNarticle size. A stock solution of NR 0.5 mg/mL in acetone was previ-usly prepared. Subsequently, a working solution was obtained bydding 0.05 mL of stock solution to 50 mL of a 75:25 glycerol–waterixture. A drop of the working solution was added to the parti-

les. The SLN were observed using an Olympus BX51 M microscope,quipped with a UplanFL N 100 × /1.30 oil-immersion objectiveens (∞/0.17/FN26.5) and a filter set type MNIBA3 (470–495 nmxcitation and 505 nm dichromatic mirror). Images were scannedo a computer through a video camera (Olympus digital camera

: Biointerfaces 84 (2011) 117–130 119

DP70) and analyzed with an image processor (Olympus DP Con-troller 2.1.1.176, Olympus DP Manager 2.1.1.158). All observationswere carried out at 25 ◦C.

2.13. Stability studies

The optimized SLN formulation (see below) was studied for sta-bility at 4 ◦C and 25 ◦C. PCS particle size and zeta potential valueswere monitored for a period of six months. The stability of theformulation was also assessed using the LUMiFuge (L.U.M. GmbH,Germany) stability analyzer, which consists of an analytical cen-trifugation system that measures the intensity of transmitted nearinfrared (NIR) light while the sample is being centrifuged. The sam-ple is measured over its full length, as a function of time. All dataare stored and displayed in real time as a function of radial position,thus enabling analysis at the micron level, signalling changes inthe dispersion characteristics. The separation behaviour of individ-ual samples can be compared and measured in detail by detectingchanges in light transmission at any part of the sample, or by fol-lowing the movement of any phase boundary [25]. The formulationstability was analyzed after 126 min of centrifugation, at an accel-eration of 2600 × g and 20 ◦C.

2.14. Entrapment efficiency and drug loading

The simvastatin entrapment efficiency, which corresponds tothe amount of drug that can be incorporated in the nanoparti-cles, either inside the particle or adsorbed at the respective surface,was determined indirectly by measuring the concentration of thefree drug in the aqueous phase of the nanoparticle dispersion.The ultrafiltration-centrifugation method was carried out throughcentrifugal filters (Amicon® Ultra-4, Millipore, Germany) with a100 kDa molecular weight cut-off. Briefly, 1 mL of SLN loaded withsimvastatin plus 1 mL of methanol were placed into the upperchamber of the centrifuge filter, which was centrifuged at 4000 × gfor 90 min at 4 ◦C. Methanol was added in order to dissolve crys-tals possibly present in the external phase of the SLN dispersion.The amount of free drug in the aqueous dispersion phase, collectedin the outer chamber of the centrifugal filter after separation, wasdetermined by high performance liquid chromatography (HPLC) asdescribed below. The entrapment efficiency (EE) and drug loading(DL) of SV nanoparticles were calculated according to the followingequations:

%EE = (Winitial drug − Wfree drug in suspension/Winitial drug) × 100, (3)

%DL = (Winitial drug − Wfree drug in suspension/Wnanoparticles) × 100, (4)

where Winitial drug is the amount of SV initially used for the assay,Wfree drug in suspension is the amount of free drug determined inthe aqueous phase after separation of the nanoparticles, andWnanoparticles is the weight of the vehicle loaded with SV.

2.15. HPLC determination of simvastatin

The HPLC analysis was carried out by a Shimadzu LC-2010CHTapparatus equipped with a quaternary pump, an autosamplerunit, and a L2450 UV/visible dual wavelength detector. A RP18(4.6 mm × 125 mm) Lichrospher® 100 analytical column (MerckKGaA, Germany) was used for the analysis. The mobile phase con-

sisted of a 90:10 (v/v) mixture of methanol/water pumped and aconstant flow rate of 0.8 mL/min was used for 5 min. The detectionwas carried out at 238 nm. An injection volume of 10 �L was usedfor all standards and samples. For these conditions, simvastatin iseluted at a retention time of 3.1 min.

120 C. Vitorino et al. / Colloids and Surfaces B: Biointerfaces 84 (2011) 117–130

Table 1Mean particle size (Z-average) of formulations containing Precirol® , Compritol® , and tripalmitin. Values are indicated for 5 mL DCM/250 mg lipid (ratio 1:50) and 1 mLDCM/100 mg lipid (ratio 1:100) preparations. A concentration of 1.25% (w/V) of the emulsifier was used.

Formulation Z-av. (nm) PI Formulation Z-av. (nm) PI Formulation Z-av. (nm) PI

Precirol® 1:100 754.1 0.746 Compritol® 1:100 1402.0 0.325 Tripalmitin 1:100 1642.0 0.878Tween®80 Tween®80 Tween®80Precirol® 1:100 1253.0 0.523 Compritol® 1:100 838.0 0.516 Tripalmitin 1:100 602.7 0.595PVA PVA PVAPrecirol® 5:250 403.5 0.820 Compritol® 5:250 1321.0 0.635 Tripalmitin 5:250 542.5 1.000Tween®80 Tween®80 Tween®80

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. Results and discussion

Before the experimental design was constructed, preliminaryormulation studies included the selection of the appropriate lipidhase and emulsifier type, and are described in what follows.

.1. Preliminary factor screening

The effect in particle size of three different lipids was studiedsing Precirol®, tripalmitin and Compritol® (Table 1). Tripalmitinas chosen due to the smaller particle size and narrow size distri-

ution of the resulting SLN.The choice of the emulsifier is of utmost importance in the

ptimization of any nanoparticle formulation. This componentontributes not only to control the particle size and stability ofhe dispersions, but also to control the crystallization behaviour,ncluding polymorphs [19,26]. The effect upon particle size of two

®

ifferent emulsifiers, polysorbate 80 (Tween 80) and polyvinyllcohol (PVA) was studied, indicating that PVA generally leads tomaller particles with narrower particle size distributions (Fig. 1),hich has prompted experiments to be carried out within a broader

mulsifier concentration range.

ig. 1. Mean size of particles and polydispersity indexes (PI) in formulations containingalues are indicated for 5 mL DCM/250 mg lipid (ratio 1:50) and 1 mL DCM/100 mg lipid (

8.8 0.402 Tripalmitin 5:250 253.0 0.314PVA

3.2. Factorial design

The preliminary results clearly indicate that this system is highlyinfluenced by the lipid type, emulsifier type and concentration. Itshould be noted that these are widely recognized as major factorsinfluencing the size of SLN [18]. In this design, the lipid and emul-sifier types were imposed (tripalmitin and PVA), as a consequenceof the preliminary results, constituting the lipid concentration thefirst design variable. The solvent:lipid ratio represents the secondvariable, and the emulsifier concentration the third one.

The choice of variables is a task of paramount importance,because it conditions both results and interpretation. The choicemust be based on the particular characteristics of the system. Thecurrent option is to use one composition variable, the lipid con-centration, that establishes a relationship between the inner phaseand the system as a whole and, implicitly, between the inner andexternal phases. The emulsifier concentration also uses the whole

system as reference, and the respective choice is based on the factthat the emulsifier is an interface component, which can be foundin both the inner and external phases. Finally, the influence of thesolvent is assessed through the solvent:lipid ratio, as selected vari-able. As such, it allows the characterization of the inner phase, and

Tween® 80 (A and C) and PVA (B and D), as a function of emulsifier concentration.ratio 1:100) preparations.

C. Vitorino et al. / Colloids and Surfaces B: Biointerfaces 84 (2011) 117–130 121

Table 2SLN composition for the different levels considered in the two 23 factorial designs. Key: L, lower level; M, middle level; H, higher level of lipid phase concentration. Composition:Weightlipid:VolumeDCM:Concentration(%w/V)PVA; Ratio: VolumeDCM:Weightlipid; PI: Polydispersity Index. Results include standard deviation (n = 3).

Formulation Composition (Wlipida:VDCM:CPVA) Ratio (VDCM:Wlipid) Particle Size (nm) PI Zeta potential (mV)

L1 100:1:0.5 1:100 788.6 ± 79.8 0.618 −17.9 ± 0.5L2 100:1:1.5 1:100 891.9 ± 63.8 0.707 −16.1 ± 0.4L3 100:2:0.5 1:50 286.5 ± 33.6 0.330 −17.5 ± 0.6L4 100:2:1.5 1:50 333.9 ± 13.0 0.367 −16.0 ± 0.5M1 250:2.5:0.5 1:100 260.8 ± 4.8 0.240 −26.2 ± 0.2M2 250:2.5:1 1:100 300.2 ± 14.4 0.385 −35.3 ± 0.6M3 250:2.5:1.5 1:100 437.2 ± 7.4 0.506 −26.0 ± 0.6M4 250:5:0.5 1:50 263.9 ± 3.8 0.145 −28.6 ± 0.1M5 250:5:1 1:50 226.3 ± 2.5 0.187 −36.2 ± 0.4M6 250:5:1.5 1:50 274.9 ± 2.7 0.295 −24.6 ± 0.9H1 500:5:0.5 1:100 300.3 ± 2.5 0.175 −19.3 ± 0.1H2 500:5:1.5 1:100 296.4 ± 0.8 0.307 −16.0 ± 0.3H3 500:10:0.5 1:50 380.9 ± 1.3 0.268 −18.3 ± 0.4

268.8 ± 2.7 0.226 −15.4 ± 0.6

mal values for lipid concentration (w/V) are, respectively, 0.33%, 0.83%, and 1.67% for theL

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Table 4Parameters of the response surfaces for size obtained from a 23 factorial planningin the indicated formulations and Student’s t-test analysis.

Formulations ˇ0 ˇ1 ˇ2 ˇ3 ˇ12 ˇ23 ˇ13

L1 to M6 442.2 −133.0 −152.4 42.3 112.6 −27.7 4.6Significance level 100.0 100.0 100.0 100.0 100.0 99.6 41.2

When the number of interaction terms increases, the analysisbecomes more complicated but proceeds along the same lines.

Table 4 gathers the values of the coefficients obtained for thelow/medium and medium/high designs, as well as the correspond-ing levels of confidence, while Fig. 2 displays the response surfaces

H4 500:10:1.5 1:50

a Note that, in this table, the total weight of the lipid is used for simplicity. The for, M and H levels (see also Table 3).

ocuses, implicitly, on the respective properties (the amount of lipidissolved per volume of organic solvent influences, for example, theiscosity, at least in the initial stages of the particle formation).

Table 2 describes the composition of SLN, prepared accordingo a double 23 factorial planning. It includes a low, a medium andhigh level of the lipid phase concentration, that are combined inlow/medium (L1 to M6) and a medium/high (M1 to L4) designs.ombining the information of the two designs, a general approachan be extracted from the first one, in which a wider particle sizeange is presented, while in the second one a fine tuning of theptimization can be performed. For the factorial designs, M2 and5 formulations were not included. These additional experimentsere carried out in order to complete the formulation optimizationrocess (see below). The lipid concentration, solvent:lipid ratio andmulsifier concentration are denoted respectively as x1, x2 and x3n equation (1), and the combined terms describe the respectiventeraction. Table 3 defines the coded values.

In order to evaluate the influence of each variable, and theespective combination in the response term, the polynomial coef-cients of equation (1) were determined. The higher the magnitudef each coefficient, the higher is the respective main effect on theystem. A positive coefficient sign indicates that an increase in thearameter level leads to an increase in particle size. Taking intoccount the interaction coefficients, the response must be ana-yzed in terms of how the variation of one factor modulates theffect of another factor. To illustrate this point, consider the coeffi-ient for variable 1, affected by the interaction with variable 2 (andeglecting other possible interaction terms),

ˇ1 + ˇ12x2)x1.

If we take ˇ12 > 0 and x2 < 0, three situations are possible,epending on the value of ˇ :

1

(i) for ˇ1 < 0, the interaction reinforces the negative effect of x1;(ii) for ˇ1 >0, and ˇ1 > |ˇ12x2|, the influence of x1 decreases;iii) for ˇ1 >0, and ˇ1 < |ˇ12x2|, the effect of x1 is reversed in signal.

able 3oding of factorial design variables.

Factorial design Coded level Lipid weight/solvent:lipidratio/emulsifierconcentration

L/M −1 100/1:100/0.5%+1 250/1:50/1.5%

M/H −1 250/1:100/0.5%+1 500/1:50/1.5%

t value 53.2 −16.0 −18.3 5.1 13.6 −3.3 0.6M1 to H4 310.4 1.2 −13.3 8.9 26.5 −34.2 −37.9Significance level 100.0 46.6 100.0 100.0 100.0 100.0 100.0t value 164.2 0.6 −7.0 4.7 14.0 −18.1 −20.1

A similar reasoning can be presented for x2 > 0, or in case ˇ12 < 0.

Fig. 2. Particle size response surfaces for the two more significant factors at eachfitted mathematical model. (A) Low/medium design; (B) medium/high design. Thevalue of the third, least significant, factor was set to zero.

122 C. Vitorino et al. / Colloids and Surfaces B: Biointerfaces 84 (2011) 117–130

Table 5ANOVA parameters for the characterization of the fitting in equation (1).

L1–M6 Degrees of freedom (DOF) Sum of squares (SS) Mean square (MS) F Significance p value

Total 23 1.38 × 106

Regression (REG) 6 1.35 × 106 2.25 × 105 151.89a 3.43 × 10−13

Residual 17 2.82 × 104 1656.20Lack of fit (LOF) 1 4488.10 4488.10 3.03b 0.10Pure error (PE) 16 2.37 × 104 1479.20

M1–H4 Degrees of freedom (DOF) Sum of squares (SS) Mean square (MS) F Significance p value

Total 23 8.71 × 104

Regression (REG) 6 8.56 × 104 1.43 × 104 989.79a 1.22 × 10−19

Residual 17 1457.60 85.74Lack of fit (LOF) 1 1226.90 1226.90 85.11b 8.33 × 10−8

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Pure error (PE) 16 230.66

a F1 = MSREG/MSPE.b F2 = MSLOF/MSPE.

s a function of the major factors for the two designs under consid-ration. To test whether the terms were statistically significant inhe regression model, t-tests were performed using a 95% (˛ = 0.05)evel of significance. The Student’s t-test analysis shows that thearameters are highly significant, with exception of the ˇ13 inter-ction coefficient for the low/medium design and the ˇ1 coefficientor the medium/high design. Additionally, ANOVA results are gath-red in Table 5, as a further characterization of the fittings.

Two Fisher tests were used to assess the validity of the modelstting. According to the F1 = MSREG/MSPE test, a ratio much largerhan 1 indicates a good correlation between the experimental and

redicted responses and that the model is adequate to describe theesponse variations. In turn, for F2 = MSLOF/MSPE test, the validityf the regression model is indicated by a ratio close to 1.

ANOVA analysis (Table 5) of the model fitting procedure for theow/medium design suggests that the estimated responses are well

ig. 3. Influence of the solvent:lipid ratio upon particle size. The size of the droplets pertafter evaporation, a lower lipid content in each droplet results in smaller particles. Viscos

14.42

described by the model, as evidenced by both F1 and F2 tests. Incontrast, for the medium/high design, there are statistically signifi-cant deviations from the mathematical model, as evidenced by theF2 test. However, according to the F1 test, the model itself is stillhighly significant and explains the major part of the deviations ofthe responses from their mean value [27].

The low/medium design results were analyzed as follows, focus-ing specifically in the model description and in the assessment ofthe importance of factors.

It is seen that the main parameter influencing mean particlesize is the lipid:solvent (dichloromethane) ratio, followed by the

lipid concentration and, to a lesser extent, the emulsifier concen-tration. It is also seen that, in this model, the isolated effect of eachparameter is stronger than the corresponding interactions.

In fact, the mean particle size depends on the lipid concentra-tion in the organic phase, since a higher lipid content results in a

ining to the internal phase is, in the initial stages, governed mainly by the solvent.ity effects also contribute to this result.

C. Vitorino et al. / Colloids and Surfaces B: Biointerfaces 84 (2011) 117–130 123

Fig. 4. Schematic representation of the influence of the emulsifier amount for situations in which the systems are above (L), close (M) and below (H) optimal surface coverage.( ticlesi , an inc face ai

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A) A large excess of emulsifier, relative to the lipid content, incorporates in the parn further growth. (B) For situations in the vicinity of the optimal surface coverageoncentrations, an increase in the emulsifier amount leads to an increase in the surn the literature.

ore viscous dispersed phase, and thus decreases the efficiencyf homogenization [19]. Naturally, a higher lipid content in eachnitial DCM droplet will also promote larger particles after solventvaporation, as represented in Fig. 3.

These effects are evident in the L formulations, when the ratio:100 is switched to 1:50, as clearly indicated in Fig. 2(A) byhe marked negative slope and the colour change of the surfaceesponse plot. The effect is also present, but less marked, for higheripid concentrations. A closer inspection of Table 1 suggests that thiss a general trend, present irrespectively of lipid- and surfactant-ypes.

Comparing the matching L and M formulations, the decreasen particle size originated by an increase in lipid content becomesvident (remember the negative signal in ˇ1). Note that, for theseatching formulations, the lipid:solvent ratio remains constant.arious studies have reported that increasing the lipid contentesults in larger particles and broader particle size distributions18,28–29]. However, this increase in the amount of lipid is usu-lly associated to an increase of its relative amount with respecto external phase, which makes it difficult to discriminate amongactors. Other authors have observed an increase in lipid concentra-ion associated to a concentration-dependent increase in particleize, but only when the lipid concentration exceeds a critical value21,30]. Below this value, an increase in lipid concentration hasnly a minor impact upon particle size. A similar observation isontained in the present results, in the case of moving between theand M formulations with a 1:50 solvent:lipid ratio. It is seen that

he slope is negligible, when compared to the opposite side of theesponse surface.

A closer inspection of Fig. 2(A) reveals a further trend. The high-st size obtained corresponds to a formulation with a low amountf inner phase. If this amount is increased, either by augmenting theipid content, the organic solvent content or both, the size markedly

iminishes. This may be ascribed to a deficient dispersion if themount of inner phase is too small. Another aspect is related to theolvent diffusion rate, and thus the rate of evaporation. A higherolvent content maintains the inner phase less viscous for a longerime, and therefore originates smaller particles. Similar findings

able 6article size determined by laser diffractometry.

Formulation d10% (�m) d50% (�m) d90% (�m) Span value

L1 0.405 4.113 18.31 4.353M5 0.113 0.325 0.979 2.665H1 0.236 0.396 1.039 2.028M5/S 0.112 0.255 0.458 1.357

, making them larger. This leads to bridging and, eventually, coalescence, resultingcrease in the amount of emulsifier produces a negligible effect. (C) For high lipid

rea and, thus, to a reduction in size. The latter is the behaviour commonly reported

have been obtained in previous works, although remaining largelyunexplained [31–32]. The above considerations allow conclud-ing that the characteristics of the pre-emulsion affect, to a largerdegree, the properties of the nanoparticles ultimately obtained.

Focusing now in the emulsifier effect at the L/M design, this isthe factor with the lowest importance for particle size, as indicatedby the relatively small coefficient. Its contribution becomes clearin the increase of particle size when comparing the L and M for-mulations with 0.5% and 1.5% of PVA, while keeping constant theother parameters. A possible explanation is that an excess of emul-sifier in solution for the L formulation leads to supersaturation ofthe emulsifier at the interface, induced by a diffusion process [20],resulting in an increase of particle size, as will be detailed below.

The second factorial planning is applied to the medium/highdesign (M1 to H4 formulations) and yielded the parameter valuespresented in the second half of Table 3. A representation of theresponse surface is given in Fig. 2(B).

In this design, the effect of the different parameters in particlesize is much lower than in the previous one. The solvent:lipid ratiois again the dominant factor, followed by the emulsifier and lipidconcentrations. Also, the interaction between the parameters hasa higher influence than the isolated factors.

Particle size tends to increase mainly for a lower solvent:lipidratio combined with a higher emulsifier concentration and, lessmarkedly, when a higher solvent:lipid ratio and lower emulsi-fier concentration are associated. The opposite conditions tend todecrease particle size. This behaviour is applicable irrespectivelyof the lipid concentration, i.e. both for the M and H formulations,although for different reasons. In the case of the H formulations, andsimilarly to what was explained above, increasing the inner phasefor a low amount of emulsifier may render this amount insufficientand results in an increase of particle size. On the other hand, anincrease in the emulsifier concentration from 0.5 to 1.5% tends to

reduce the size markedly for higher solvent:lipid ratios, althoughthe effect is less important for the lower ratios. This behaviour couldbe attributed simply to the need of a larger amount of surfactantwhen a higher amount of inner phase is present [33]. Regarding the

Table 7Percentage of PVA that remains associated to particles.

Formulation % Residual PVA

L1 47.8 ± 1.1M5 61.8 ± 3.1H1 70.0 ± 4.2M5/S 68.0 ± 0.3

124 C. Vitorino et al. / Colloids and Surfaces B: Biointerfaces 84 (2011) 117–130

Fig. 5. Particle size distribution profiles of L1, M5, H1 and sonicated formulations.

C. Vitorino et al. / Colloids and Surfaces B: Biointerfaces 84 (2011) 117–130 125

Mrtiot

timc

Fig. 6. SLN and tripalmitin thermograms.

level, the changes induced by the different compositions are lesselevant, with the exception of the M3 formulation. In this case,he presence of a higher number of emulsifier molecules tends toncrease particle size, probably due to a higher degree of depositionn its surface and because of bridging effects promoting aggrega-ion.

As stated above, the emulsifier concentration is, in relative

erms, the less significant parameter in the L/M design. However,t should be stressed that the respective effect is, in absolute value,

ore important in the M/H design. Overall, three distinct regionsan be identified: one in which the system is above the optimal

Fig. 8. C O stretching observ

Fig. 7. FTIR spectra of PVA, tripalmitin and some SLN formulations.

surface coverage, one in which it is close to this optimal value, andanother in which it is below that level [33]. The L formulations are inthe first region, and a direct correlation was observed between par-ticle size and emulsifier concentration. The same occurs for the Mformulations with a lower amount of solvent. The M4–M6 formula-

tions display an almost constant size, which means they are close tothe optimal surface coverage. Finally, the H formulations are belowthis optimal value, and particle size decreases with emulsifier con-centration. Fig. 4 summarizes, pictorially, the above considerations.

ed for all formulations.

126 C. Vitorino et al. / Colloids and Surfaces B: Biointerfaces 84 (2011) 117–130

pectiv

mHwattte

Fig. 9. AFM images: (A) error trace, (B) height trace and (C) 3D and res

Table 2 reveals that, according to the zeta potential measure-ents, the most stable formulations are in the M level. Both L andformulations are located above -20 mV. If this result is combinedith the overall output of the experimental design, taking into

ccount both the L/M and M/H designs, there is a clear indicationhat a close to optimal formulation is found for compositions inhe M level. Note that the intention is not to extrapolate beyondhe domain of the experiments: it was selected (see above) so as tostablish acceptable amounts in terms of the subsidiary substances,

e cross-section height profiles from the optimized formulation (M5/S).

namely emulsifier and organic solvent. The choices in the amountof lipid were, in contrast, more arbitrary.

In terms of selecting an ‘optimal formulation’, the M level wasfurther detailed with the introduction of M2 and M5 formula-

tions. The latter corresponds both to the smaller size and the morenegative value for the zeta potential. It is located in intermedi-ate value of the lipid amount, emulsifier concentration and DCMamount (albeit not solvent:lipid ratio, for which it is an extremevalue). In what follows, this is considered as the optimal for-

aces B: Biointerfaces 84 (2011) 117–130 127

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Table 8Molar faction (x) associated to � polymorphic form. For completeness, all formula-tions are included.

Formulation x� Formulation x�

L1 86.5 M5 65.2L2 57.2 M6 46.9L3 75.9 H1 59.1L4 61.4 H2 54.9M1 66.0 H3 68.5

assessed resorting to ATR-FTIR. The FTIR spectrum of pure PVAdisplays a broad peak around 3325 cm−1, corresponding to thestretching of the hydroxyl groups and peaks at 2923 and 2850 cm−1

due to C–H stretching. In what concerns the spectrum of pure

C. Vitorino et al. / Colloids and Surf

ulation, but others are also subjected to study for comparisonurposes.

.3. Optimization of the preparation method

It is known that the effect of ultrasound waves in a liquid macro-copic dispersion is generating cavitation bubbles, which tendo implode, and thus provide sufficient local energy to generateanometric-scaled droplets [20]. Thus, once selected the optimalomposition according to the factorial design results, a sonicationtep was introduced in the method of preparation, so as to furthereduce particle size. A final mean particle size of 209.1 ± 2.722 nm,ith a very small PI value of 0.094, and a zeta potential value of

31.6 ±3.3 mV were obtained. This formulation is denoted, in whatollows, as M5/S.

.4. Physicochemical characterization

.4.1. Additional size measurementsLaser diffractometry (LD) was used in order to discard the

resence of large aggregates in the optimized formulation. Thisechnique was also applied to other selected formulations (Table 6).

LD and PCS yield different results, with the former identify-ng larger aggregates in the L1 formulation, as a consequence ofhe wider size range allowed by the technique (0.040–2000 �m).owever, the results are in good agreement for the optimized for-ulations, M5 and M5/S. Note that in LD, an analysis by % volumeas carried out, which renders a higher contribution for larger par-

icles. The exclusive presence of nanoparticles is warranted by the90% value, which is lower than 458 nm for the M5/S formulation.ote, additionally, that the sonicated formulation presents the low-st span value, which indicates a narrower size distribution, againn agreement with the PCS findings and determined PI. The effectf the sonication step is also evident on the volume distributionrofile (Fig. 5), in which a small population with diameters largerhan 1 �m, present in the M5 formulation, is no longer visible afteronication.

.4.2. Determination of residual PVAThe results for the determination of residual PVA, i.e. the amount

f PVA which remains associated to the particles, are presentedn Table 7. For this determination, only one formulation of eachevel of the factorial design and the sonicated formulation wereelected. The results support the particles surface supersaturationhenomena suggested for the L formulations (high amount of PVA

n the external phase). The amount of PVA in the particles obviouslyncreases with the lipid concentration. M5 and M5/S formulationsorrespond to the intermediate state, being the residual PVA val-es for the M5/S formulation slightly higher than for M5, probablyecause of the wider available surface area, as a consequence of the

arger number of particles formed.

.4.3. Differential scanning calorimetryThe crystal structure of the lipid matrix, here inferred by the

hermal behaviour, is considered to influence the shape of SLN,s mentioned in previous studies [33]. DSC thermograms of tri-almitin SLN usually exhibit two polymorphic transitions, � to �’nd �’ to �, represented in the heating cycle as two exothermiceaks at 47 and 52 ◦C and three endothermic peaks at 43–44 ◦C,5 ◦C and 62–64 ◦C (Fig. 6) [34]. The latter peaks correspond to theelting of each form. In the thermograms, the transition �’ to �

s often absent, since its formation and conversion to � is likely toe too fast to be detected by the instrument. These transitions areelated with a shape change from spherical lipid crystals to nee-le or platelet-like particles, thereby increasing the hydrodynamicadius of SLN [33,35].

M2 47.4 H4 38.0M3 49.2 M5/S 39.0M4 51.6

The heating step was performed to investigate the effect of theemulsifier packing and concentration in the crystal structure and,consequently, in the size of the particles. As such, enthalpies foreach transition and the molar fractions (x�) of the polymorphicforms were calculated (Table 8). The molar fractions were obtainedusing the molar enthalpies from reference [36].

According to the results, in most cases, the � form is dominant,although there are a few exceptions. Also, there is a general ten-dency for a decrease in the amount of � form as the emulsifierconcentration increases from 0.5% to 1.5%. This behaviour is sup-ported by some findings of Bunjes et al. [37]. According to theseauthors, PVA seems to act by causing an immobilization of thetriglyceride molecules in the interfacial region, avoiding its struc-tural reorientation to � form. This effect is clearly exacerbated whenthe amount of emulsifier is increased. In some cases, however, forwhich the intermediate 1% concentration was also assessed, thebehaviour is slightly more complex. The highest content of the �form is found for formulations with lower amounts of lipid (L1and L3). Sonication promotes a clear increase in the content of the� form. The relationship between particle size and polymorphicforms is not trivial. Although in a significant number of cases, par-ticle size tend to increase with the amount of � form, it is possiblethat this only reflects a common emulsifier action on both size andpolymorphism [37]. This suggests a correlation between the lipidcrystal matrix and the emulsifier packing which could explain theincrease in mean particle size in the cases above mentioned.

3.4.4. Attenuated total reflectance infrared spectroscopyThe intermolecular interaction in the SLN systems was also

Fig. 10. SEM image from the optimized formulation (M5/S).

128 C. Vitorino et al. / Colloids and Surfaces B: Biointerfaces 84 (2011) 117–130

F tions.

taa(

cof

Fl

ig. 11. Fluorescence microscopy images depicting differences among SLN formula

ripalmitin, the peaks for the C–H stretching are visible at 2851nd 2919 cm−1, that corresponding the stretching of C O is foundt 1735 cm−1, while the stretching of C–O appears at 1473 cm−1

Fig. 7).In the SLN spectra, a broad peak is detected around 3425 cm1,

orresponding to the stretching of the hydroxyl groups of PVA. Mostf the remaining profile can be directly related to features arisingrom tripalmitin (Fig. 7).

ig. 12. Progression of transmission profiles of the M5/S formulation. The sequence of proast ones (for interpretation of the references to colour in this figure legend, the reader is

(A) L1 formulation; (B) H1 formulation; (C) M5 formulation; (D) M5/S formulation.

Overall, these results are extremely coherent, and are alsoconsistent with the previously discussed DSC data. For a fullcharacterization, results for all formulations are presented. Pure tri-

palmitin is essentially found in the more stable � crystalline form.Note that the features present in the spectra and related to both theC O and C–H stretching have been used to discriminate between�, �’ and � forms [36]. Focus was firstly directed to the former(Fig. 8). In these FTIR spectra, this region is characterized by two

files is shown from the bottom (red), for the first profiles, to the top (green) for thereferred to the web version of the article).

aces B: Biointerfaces 84 (2011) 117–130 129

wstrttttIottfjF

3

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C. Vitorino et al. / Colloids and Surf

ell separated peaks (at ca. 1728 and 1737 cm−1), while a smallhoulder is visible connected to that at 1734 cm−1. Variations inhe amount of PVA seem to strongly influence the spectra in thisegion. While in formulations containing lower amounts of PVA,his region is very similar to that found for pure tripalmitin, whenhe amount of PVA is increased, especially for the lower lipid con-ents, the two separated peaks evolve into a convoluted structurehat, in some cases, can better be described as a single, broad peak.t should be noted that this evolution clearly follows the one previ-usly extracted from the DSC results. As such, a lower presence ofhe � form gives rise to less discrete peaks, probably as a result fromhe coexistence of the different forms, originating slightly differentrequencies. This observation can be extended to formulations sub-ected to sonication. For these, DSC indicates a lower � content andTIR shows convoluted C O peaks.

.4.5. Atomic force microscopyAFM is a rapid, powerful and relatively non-invasive technique

hat can provide information on morphology, size and size distri-ution of these colloidal systems [38]. Given its relative simplicity,FM can be used for the technological control of size distributionrofiles. Fig. 9 shows the AFM images and the cross-section profilesrom the height and error images of the optimized SLN. Particleizes obtained using AFM images are similar to those establishedrom the PCS analysis. A thorough inspection of 3D image and theross-section profiles, in which the diameter of the particles corre-ponds to the width of the peak at the base of the graphic and theeight to the particles surface, indicates that particles present an

rregular and rough surface. Note that a slight increase in particleize may result from the deposition of the particles upon the glasslide. Also, the poly-l-lysine slide surface, conversely for instance toica, is detectable in the height profiles (see the initial and terminal

ones of the height curve).

.4.6. Scanning electronic microscopySEM analysis (Fig. 10) was also performed to complete the infor-

ation about particle size and morphology. The SEM image showedrelatively homogenous system which is consistent with the poly-ispersity index observed. The presence of the emulsifier, coveringhe particles surface, is evident, stressing the importance of thisarameter in the lipid packing and in the shape modelling.

.4.7. Fluorescence microscopyFluorescence images allow also inspecting SLN directly in the

uspension medium. The images confirm previous observations. Aseen in Fig. 11 (A–D), a reduction in particle size is evident from theand H formulations to the M ones. This illustrates the influencef the different compositions on the system. In the L1 formulation,arger particles are formed as a consequence of the low amount ofnner phase. For the H1 formulation, the number of particles tendso increase due to a higher lipid concentration, although with alightly larger particle size. Conversely, a well structured and homo-

eneous system can be observed in the M5 and M5/S formulations,or which a balance for the amount of three different componentsas found. This not only contributes to reduce the particle size, but

lso to increase the respective stability. Note that for formulation5/S there is additional difficulty in the observation of the parti-

able 9CS particle size and zeta potential of the optimized formulation for six months.

Storage conditions Particle size (nm) PI Zeta potential

After preparation 209.3 ± 2.7 0.094 −31.6 ± 3.36 months25 ◦C 217.1 ± 4.9 0.114 −20.1 ± 0.14 ◦C 218.1 ± 6.5 0.110 −31.5 ± 0.9

Fig. 13. Characterization of solid lipid nanoparticles loaded with increasingamounts of simvastatin (S), ranging from 5 to 17.5 mg. Key: (A) Particle size andpolydispersity index. (B) Zeta potential. (C) Entrapment efficiency and drug loading.(n = 3; mean ± S.D.).

cles, probably due to their smaller size. Larger aggregates are visiblein some of the formulations (panels A–B). This, again, corroboratesthe findings from PCS and LD.

3.4.8. Stability studies

The physicochemical stability of the optimized formulation

(M5/S) was evaluated at 4 and 25 ◦C for six months via PCS particlesize and zeta potential (Table 9).

Results reveal that the SLN size is not affected by storage temper-ature within the range tested and does not significantly change after

1 aces B

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30 C. Vitorino et al. / Colloids and Surf

months. The stability of the particles, indicated by the zeta poten-ial, remains essentially unaltered, once stored at low temperature4 ◦C).

The M5/S formulation stability was also assessed throughUMiFuge stability analyzer, which combines the centrifuge forceith near infrared transmission measurements. This technique

llows the separation process to be measured rapidly and thus givesfast and accurate means of evaluating nanoemulsion dispersion

tability [25].The graphic representation of the transmission as a function

f the local position reveals the corresponding transmission pro-le. From Fig. 12, it can be concluded that the M5/S formulation iselatively stable, due to the low transmission profiles, even afterubmitted at a high centrifuge force for 126 min. The evolution ofhe transmission profiles during the centrifugation allows detect-ng local alterations of particles concentration, which are reflecteds changes in light transmission. Zones of well mixed dispersionscatter and absorb the light, so transmission is low. In contrast,ny separation process (clarification) allows more light to pass andransmission rises.

.4.9. Drug-loaded SLNSimvastatin was encapsulated as a model drug, in order to assess

ts influence upon particle properties, such as particle size, zetaotential and entrapment efficiency.

According to the results, increasing the amount of drug in thearticles leads to an increase in the mean particle size and inhe polydispersity, and a decrease of the absolute value of theeta potential (Fig. 13(A–B)). In contrast, the entrapment efficiencyemains almost constant (Fig. 13(C)). Note also the existence ofwo distinct regions, respectively, 5–10 and 12.5–17.5 mg. Thencrease either in particle size or polydispersity associated withhe decrease of the zeta potential observed in the frontier ofhese two regions suggests matrix saturation with the accumula-ion of the drug at the particle surface. This also impacts on theeta potential, which becomes less negative and indicates somenstability.

. Conclusion

Factorial design enabled to successfully formulate solid lipidanoparticles with an optimized nanometric particle size, andood stability. A clear assessment of the importance of factorsas provided by this analysis. Solvent:lipid ratio represented

he main factor influencing particle size, and did not seem toepend on lipid or surfactant molecule. The amount of emulsi-er had a non-trivial impact upon size, depending on whetherystems were located below, above or close to the optimalurface coverage and, finally, the amount of lipid had a lim-ted influence upon particle size. The mathematical analysislearly showed that the influence of the composition param-ters is crucial for determining the particle size of the SLNrepared by the emulsification–solvent evaporation method devel-ped, and also facilitated the interpretation of the underlyinghysicochemical phenomena. The results were supported by theomplementary analyses by DSC and FTIR, which provided some

nsight in the molecular structure and arrangements, compati-le with rationales extracted from the experimental design, whileicroscopy techniques allowed direct visualization of particle size

nd morphology, corroborating size measurements by PCS andD.

[[[[

: Biointerfaces 84 (2011) 117–130

The SLN have also proved being an effective lipophilic drug car-rier. In the present experimental conditions, simvastatin loadingonly promoted minor changes upon SLN properties.

Finally, the design planning methodology has clearly shown itsusefulness in this optimization process, and this research consti-tuted a framework for the understanding of SLN formation.

Acknowledgements

CSV acknowledges financial support through grantSFRH/BD/41536/2007 from FCT (Fundacão para a Ciência eTecnologia, Portugal). We acknowledge Dr. Carmen Móran forfluorescence images, UCQfarma for making available ATR-FTIRspectrophotometer and L.U.M. GmbH, Germany (Dias de Sousa)for the availability of LUMiFuge centrifuge.

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