Optimization of linalool-loaded solid lipid nanoparticles

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dation, Ministry of Science and Education (FCT/MEC) through national funds, and co-financed by FEDER, under the Partnership Agreement PT2020.

XII Spanish-Portuguese Conference on Controlled Drug Delivery, University of Coimbra, 2018

Optimization of linalool-loaded solid lipid nanoparticles (SLNs) by experimental factorial design Pereira, I.

1,5*

2

1,5

2

3,4

1,5

, Zielińska , A. , Santos, A.C. , Nowak, I. , Silva, A.M. , Souto, E.B.

1

Faculty of Pharmacy, University of Coimbra, Coimbra, Portugal 2 Faculty of Chemistry, Adam Mickiewicz University in Poznań, Poland 3 University of Trás-os-Montes and Alto Douro, Portugal 4 Centre for Research and Technology of Agro-Environmental and Biological Sciences, Vila Real, Portugal 5 REQUIMTE/LAQV, Group of Pharmaceutical Technology, Faculty of Pharmacy, University of Coimbra, Portugal *e-mail: [email protected]

Introduction

Results

Linalool (C10H18O), also known as 3,7-dimethyl-1,6-octadien-3-ol, is an acyclic monoterpene tertiary alcohol found in essential oils and teas that has several bioactivities, in particular, anti-oxidative properties [1]. According to recent studies oral administration of linalool has a hypocholesterolemic effect since it reduces body weight in mice and decreases cholesterol levels through the reduction of mRNA and protein expression of sterol regulatory element binding protein-2 (SREBP-2) and the suppression of the transcription of 3-hydroxy-3-methylglutaryl-coenzyme A reductase (HMGCR) [2]. Recently, the encapsulation of linalool in solid lipid nanoparticles (SLN) has demonstrated to overcome its physicochemical limitations (poor water-solubility and volatility) improving its therapeutic efficacy [3,4]. The present study aims to development and optimize linalool-loaded SLN (Lin-SLN) by 22 experimental factorial design.

Table 2. 22 full factorial design for the development of linalool-loaded SLN and the respective response parameters.

INDEPENDENT VARIABLES

DEPENDENT VARIABLES

SLN (no.)

Imwitor® 900 K

Kolliphor® P 188

Mean Particle Size (nm) ± SD

Mean PI (arb. units) ± SD

Mean ZP (mV) ± SD

1

−1

−1

459.3 ± 231.8

0.470 ± 0.080

0.010 ± 0.054

2

+1

−1

343.6 ± 47.7

0.384 ± 0.014

−0.056 ± 0.034

3

−1

+1

94.8 ± 0.8

0.106 ± 0.017

0.128 ± 0.182

4

+1

+1

283.0 ± 6.0

0.333 ± 0.031

−0.016 ± 0.220

5

0

0

258.3 ± 0.5

0.241 ± 0.017

−0.030 ± 0.047

6

0

0

239.6 ± 9.8

0.299 ± 0.034

0.030 ± 0.180

7

0

0

236.5 ± 3.1

0.286 ± 0.035

−0.033 ± 0.104

Materials and Methods

Table 3. ANOVA statistical analysis.

MEAN PARTICLE SIZE

Preparation of SLN Evaluated factors and their interactions

SLN dispersions were produced by Hot High Pressure Homogenization (HPH) technique (EmulsiFlex-C3, Avestin, Portugal)

PI

ZP

p-value

(1) Imwitor® 900 K

0.424129

0.206850

0.059430

(2) Kolliphor® P 188

0.012376

0.017973

0.111496

1 by 2

0.030558

0.037746

0.350187

Figure 1. High Pressure Homogenizer EmulsiFlex-C3.

Physicochemical characterization The dependent variables were the mean particle size, polydispersity index (PI) analysed by dynamic light scattering (DLS) and zeta potential (ZP) analysed by electrophoretic light scattering (Zetasizer Nano ZS, Malvern Instruments, UK, respectively). Experimental factorial design ® The influence of the independent variables, surfactant (Kolliphor P 188) and lipid concentrations (Imwitor® 900 K) on Lin-SLN was evaluated 2 by a 2 factorial design composed of 2 variables which were set at 2levels each (−1 and +1) (Table 1).The central point was replicated three times for estimating the experimental error and it was represented by (0). The data were analyzed by STATISTICA 7.0 (Stafsoft Inc., USA) software. The SLN dispersions were randomly produced and in order to identify the significance of the effects and interactions between them an analysis of variance, ANOVA statistical test, was performed for each response parameter. A p-value < 0.05 was consider statistically significant.

A

A

B

B

C

C

Table 1. Initial 2-level full factorial design, providing the lower (−1), upper (+1) and (0) central point level values for each variable.

LEVELS FACTORS

−1

0 (central point)

+1

Imwitor® 900 K

2 % (w/v)

4 % (w/v)

8 % (w/v)

Kolliphor® P 188

1.25 % (w/v)

2.5 % (w/v)

5 % (w/v)

Figure 2. Pareto charts showing the effect of the concentration variation of the solid lipid (1), surfactant (2) and the interaction of both (1by2) on the SLN dispersions (A) mean particle size, (B) PI and (C) ZP.

Figure 3. 3D surface response chart showing the influence of the two factors (surfactant and lipid concentrations) on the (A) mean particle size, (B) PI and (C) ZP of SLN dispersions.

Conclusions The concentration of surfactant as well as the interaction between the different concentrations of lipid and surfactant has a statistically significant effect (p-value < 0.05) on the particle size and PI. Experimental factorial design has been successfully employed to develop an optimal SLN dispersion composed of 1 % (w/v) linalool, 2 % (w/v) of Imwitor® 900 K and 5 % (w/v) of Kolliphor® P 188 that is currently being studied by in vitro assays. AKNOWLEDGMENTS: The authors would like to acknowledge the financial support received through the projects M-ERANET/0004/2015 and UID/QUI/50006/2013, from the Portuguese Science and Technology Foundation, Ministry of Science and Education (FCT/MEC) through national funds, and co-financed by FEDER, under the Partnership Agreement PT2020. The authors also acknowledge FCT for the scholarship SFRH/BD/109261/2015. References: [1] A. El Asbahani, Int. J. Pharm, 2015, 483, 220. [2] S.Y. Cho, FEBS Lett, 2011, 585, 3289. [3] H.D. Han, Mol. Cancer Ther., 2016, 15, 618. [4] F. Shi, Pharm. Biol., 2016, 54, 2320.

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