http://informahealthcare.com/phd ISSN: 1083-7450 (print), 1097-9867 (electronic) Pharm Dev Technol, Early Online: 1–7 ! 2014 Informa Healthcare USA, Inc. DOI: 10.3109/10837450.2014.979942

RESEARCH ARTICLE

Amphotericin B-loaded polymeric nanoparticles: formulation optimization by factorial design Pharmaceutical Development and Technology Downloaded from informahealthcare.com by Monash University on 11/26/14 For personal use only.

Talita Cristina Moreira Moraes Carraro, Najeh Maissar Khalil, and Rubiana Mara Mainardes Department of Pharmacy, Universidade Estadual do Centro-Oeste, Guarapuava, PR, Brazil

Abstract

Keywords

In this study, PLGA or PLGA-PEG blend nanoparticles were developed loading amphotericin B (AmB), an antifungal agent broadly used in therapy. A 22  31 factorial experimental design was conducted to indicate an optimal formulation of nanoparticles containing AmB and demonstrate the influence of the interactions of components on the mean particle size and drug encapsulation efficiency. The independent variables analyzed were polymer amount (two levels) and organic phase (three factors in one level). The parameters methanol as cosolvent and higher polymer amount originated from the higher AmB encapsulation, but with the larger particle size. The selected optimized parameters were set as the lower polymer amount and ethyl acetate as cosolvent in organic phase, for both PLGA and PLGA-PEG nanoparticles. These parameters originated from nanoparticles with the size of 189.5 ± 90 nm and 169 ± 6.9 nm and AmB encapsulation efficiency of 94.0 ± 1.3% and 92.8 ± 2.9% for PLGA and PLGA-PEG nanoparticles, respectively. Additionally, these formulations showed a narrow size distribution indicating homogeneity in the particle size. PLGA and PLGA-PEG nanoparticles are potential carrier for AmB delivery and the factorial design presented an important tool in optimizing nanoparticles formulations.

Antifungal, encapsulation efficiency, nanoencapsulation, particle size, polydispersity index

Introduction The application of nanotechnology in various areas, particularly in medicine, is growing evolution and explores the peculiarity of the physicochemical properties of materials in submicrometric size1,2. Polymeric nanoparticles have been the most studied nanoscale structures for drug delivery due to the ability to load a wide variety of molecules3–5. Nanoparticles present high versatility due to the possibility of modulation of its characteristics, such as particle size, morphology and superficial charge by introducing chemicals6,7. These properties are often investigated and improved to meet important pharmacological parameters, such as controlled drug release, increased biodistribution and targeting to a specific site8–10. Size is the main property that characterizes the nanoparticles and consists of a relevant parameter in drug carriers systems, which influence cellular and tissue uptake11. The small diameter of the nanoparticles entails numerous pharmacokinetic advantages compared to other systems that have larger dimensions, such as increased capacity to cross biological barriers12 and the large surface area provides higher adhesion and interaction with biological components6. The particle size may be adjusted from the method of nanoparticles obtaining considering the experimental parameters involved, such as temperature, solvent evaporation rate, volume and composition of the phases, surfactant concentration, polymer and drug concentration and intensity and

Address for correspondence: Rubiana Mara Mainardes, Department of Pharmacy, Universidade Estadual do Centro-Oeste, Rua Simea˜o Camargo Varela de Sa´ 03, 85040-080 Guarapuava, PR, Brazil. Tel: +55 (42) 36298160. Fax: +55 (42) 36298135. E-mail: [email protected]

History Received 5 June 2014 Revised 13 August 2014 Accepted 20 October 2014 Published online 11 November 2014

duration of sonication10,11. The modulation of these characteristics is the object of numerous studies and have the aim to improve the drug performance. Amphotericin B (AmB) is a broad-spectrum antifungal drug with high efficacy being widely used in therapy of the systemic fungal infections13. Besides action against fungi, AmB has shown effective response in cases of leishmaniasis, a parasitic disease that consists of an epidemiological problem in many developing countries14. Although there are in the market, for decades, conventional AmB formulations based on micellar and colloidal dispersions, liposomes and lipid complexes15,16, these formulations show problems related to AmB toxicity and reduced efficacy. These problems are probably related to the lack of molecular homogeneity of AmB in conventional systems17–19 and to a strong interaction with the lipids present in the formulation affecting the drug release from the structure, low stability in physiological medium, low oral availability, with the need of intravenous administration20,21. Nanoparticles are promising alternative for the carrying of AmB because they can improve the drug solubility, promote the formation of drug monomers and increase its distribution in biological tissues22,23. Thus, the development of nanoparticles with reduced diameter and high AmB encapsulation efficiency is ideal and the determination of an optimum formulation depends upon the study of the variables involved in obtaining it. A mathematical factorial design allows multiple components (variables) of the formulation to change simultaneously, allowing analysis of the effect of the variable or a variable level in the final characteristics of nanoparticles to optimize the formulation. The use of a factorial design requires minimum experiments and provides maximum information24. The advantage in using all possible combinations of the variables in a factorial design can

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identify situations, in which the effect of one factor depends on the level of the other factor25. Thus, the objective of this study was to establish an optimal formulation of PLGA and PLGA-PEG nanoparticles containing AmB through the application of a factorial study and demonstrate the interactions and possible influences of formulation components on the properties of the mean particle size and AmB encapsulation efficiency.

Materials and methods

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Materials Amphotericin B (AmB), polyvinyl alcohol (PVA, MW 31 kDa, 88% hydrolyzed), polyethylene glycol (PEG, MW 10 kDa) and poly (D,L-lactide-co-glycolide) (PLGA, 50:50, MW 40–75 kDa) were acquired from Sigma-AldrichÕ (St. Louis, MO). Analytical reagents employed: dimethyl sulfoxide, chloroform, ethyl acetate were acquired from BiotecÕ (Curitiba, Brazil), dichloromethane from FmaiaÕ (Sa˜o Paulo, Brazil) and methanol HPLC grade from J.T. BakerÕ (Boston, MA). Ultra-purified water was obtained in Milli-Q Plus system (MilliporeÕ , Bedford, MA). Experimental factorial design In this work, a 22  31 factorial design was conducted to assess the influence of two different parameters on the properties of polymeric nanoparticles containing AmB. The individual effects of each parameter and whether there was interaction between parameters was verified. The PLGA and PLGA-PEG polymer blend was set at two levels and three organic solvents (chloroform, ethyl acetate and methanol) were set at one level and comprised the independent variables. Table 1 summarizes the established factorial design. The dependent variables were the mean particle size and the drug entrapment efficiency. The design required a total of 12 experiments and each formulation was developed in triplicate (n ¼ 3). The other parameters involved in the synthesis of polymeric nanoparticles containing AmB were kept constant to avoid fluctuations. Statistical data of the dependent variables obtained were subjected to the analysis of variance (ANOVA) with scrolling degrees of freedom, followed by Tukey’s test, and were considered significant when p50.05. Statistical calculations were performed using STATISTICA 7.0 (Stafsoft, Inc., Tulsa, OK) software and Tukey’s test was carried out with the assistance of Equation (1). pffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffi ð1Þ LSD ¼ q MQR=n where LSD is the smallest difference between averages that must be taken as statistically significant; MQR is the residual mean square of the ANOVA; n is the number of repetitions of each formulation and q is the tabulated value at an established significance level. Table 1. Independent variables of the 22  31 factorial design employed in the development of polymeric nanoparticles containing amphotericin B. Levels Variables PLGA (mg) PLGA-PEG (mg:mg) Chloroform (mL) Methanol (mL) Ethyl acetate (mL) a

Coded units

1

2

P PPEG Ca Ma Ea

50 50:10 800 800 800

100 100:20 800 800 800

Variables C, M and E are mutually excluding.

Preparation of AmB-loaded nanoparticles Nanoparticles were obtained by an emulsion solvent evaporation technique26. First, AmB (5 mg) was dissolved in 200 mL of dimethyl sulfoxide (DMSO) and it was added to 800 mL of the organic solvent. Solvents varied among chloroform, ethyl acetate or methanol, according to the formulation since these components were mutually excluding. PLGA (50 or 100 mg) or PLGA-PEG blend (50:10 or 100:20, w/w) was accurately weighted and dissolved in dichloromethane (DCM). These two organic phases were joined. Then, the final organic phase was poured into an aqueous phase consisting of 10 mL of PVA (1%, w/v) and sonicated (UniqueÕ Ultrasonic Mixing, Indaiatuba, Brazil) at room temperature for 5 min, to produce an oil-in-water (O/W) emulsion. This sub-micronized emulsion was subjected to evaporation under vacuum at 37  C (MarconiÕ – Ma 120), for nanoparticles solidification. Nanoparticles formed were isolated by ultracentrifugation (19 975  g at 25  C for 30 min – CientecÕ CT-15000R Ultracentrifuge, Piracicaba, Brazil) and washed twice with ultrapure water. The precipitate was suspended in 5% sucrose and freeze-dried. Additional details regarding the method used in this study are in deposited patent in Instituto Nacional de Pesquisa Tecnolo´gica (INPI) in Brazil (PI 1107205-9 A2)27 and are protected according to the Brazilian regulatory agency. Particle size analysis Mean particle size, size distribution and polydispersity index (PI) of the nanoparticles were measured by dynamic light scattering (BIC 90 plus, Brookhaven Instruments Corporation, Holtsville, NY). Freshly prepared samples were dispersed in ultrapure water and measurements were performed at a scattering angle of 90 at 25  C. Each sample was measured 10 times and the values were used in the calculations of factorial design and production of response surface curves. The mean diameter and PI were recorded as mean ± standard deviation. Determination of AmB encapsulation efficiency The amount of drug incorporated in PLGA and PLGA-PEG nanoparticles was determined indirectly. An aliquot of the supernatant containing the free drug was collected immediately after ultracentrifugation in the process of production of the nanoparticles, and it was diluted in methanol (1:10 – v/v). The samples were analyzed by HPLC (Waters 2695 Alliance HPLC system, Milford, MA). The mobile phase consisted of acetonitrile, methanol and water mixture (40:50:10, v/v/v) at a flow rate of 1 mL/min, in an isocratic mode28. The injection volume was 20 mL and photodiode array detector was operated at 408 nm. The samples were measured in triplicate. The encapsulation efficiency (EE) of AmB in nanoparticles was calculated according to Equation (2). EE% ¼ ðAinitial  Afree Þ=Ainitial  100

ð2Þ

where, Ainitial is the amount of AmB initially added to the formulation and Afree is the concentration of the unencapsulated drug quantified in the supernatant after ultracentrifugation. The values of the EE were used in the calculations of factorial design and production of response surface curves.

Results and discussion The choice of the adequate organic solvent and the concentration of the polymers employed in the factorial design were based on preliminary preformulation assays and further based on the study of Rao and Geckeler10, which states that the concentration of polymer and solvent used in the formulation directly affect the

Optimization of amphotericin B polymeric nanoparticles

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DOI: 10.3109/10837450.2014.979942

final properties of polymeric nanoparticles prepared by the solvent evaporation method. The drug model to be nanoencapsulated was also previously chosen considering its therapeutic use in conventional pharmaceutical forms, as well as their poor pharmacokinetic characteristics that could be improved through a nanoencapsulation. In this particular case, defining the adequate composition of the organic phase containing AmB and PLGA was not a simple issue, because these compounds present distinct solubility characteristics. PLGA is very soluble in DCM, an ideal solvent for emulsification methods, due to its water immiscibility, while AmB is not soluble in DCM, but it is very soluble in DMSO. The DMSO, due to its high viscosity and low volatility, is not a suitable solvent for emulsification methods but it was used in the lowest volume possible (0.2 mL). When DCM and DMSO phases were joined, it was not obtained in a homogenous phase, the alternative chosen was an cosolvent for AmB to obtain a homogeneous organic phase composed of drug and polymer. The cosolvent should present sufficient hydrophobic character to provide the organic phase immiscible with an aqueous phase to avoid the premature precipitation of the polymer upon contact with the aqueous phase. Thus, chloroform, methanol or ethyl acetate was eligible as cosolvent, and the factorial design was used to improve the experimental efficiency with a minimum of experiments. The PLGA and PLGA-PEG were chosen to obtain an optimal conventional and long circulating nanoparticle formulations containing AmB, respectively. To achieve an optimal nanoparticle formulation containing AmB, a 22  31 factorial design was developed to give information about the influence of dependent variables on particle size and EE of AmB in PLGA or PLGA-PEG nanoparticles. The factorial protocol resulted in 12 preparations of the AmB nanoparticles considering the possible combinations of the independent variables in the factorial planning as described in the section ‘‘Experimental factorial design’’. The data were divided into two groups for analysis: formulations of PLGA (F1– F6) and formulations of PLGA-PEG (F7–F12). Particle size analysis Particle size is an important parameter to be determined in nanoparticles-based drug delivery systems because it influences the biopharmaceutical and pharmacokinetics properties of the drug6,24. The homogeneity of size in a formulation is indicative of stability and some behaviors of the system can be foreseen with

more security29. It is known that the physicochemical properties of nanoparticles significantly influence their biological profile6. The characteristics of the 12 nanoparticles preparation and the effects of the dependent variables on mean particle size and PI of PLGA (F1–F6) and PLGA-PEG (F7–F12) nanoparticles containing AmB are shown in Table 2. The low values of PI show that it was obtained monodisperse nanoparticles populations, in both polymeric compositions. The mean size of PLGA and PLGA-PEG nanoparticles followed the same pattern according to independent variables. It is observed that the addition of PEG did not generate significant difference between groups (confirmed with Student’s t-test not shown,  ¼ 0.05%). Influence of formulation parameters on mean particle size of PLGA nanoparticles containing AmB The mean particle size of the six PLGA formulations (F1–F6) is shown in Table 2 and varied from 181.4 ± 12.4 nm (F6) to 705.5 ± 235.8 nm (F5), whereas PI ranged from 0.12 ± 0.02 nm (F6) to 0.32 ± 0.03 nm (F5). To evaluate this great variation in particle size from the influence of each variable as well as a possible interaction between the variables, an analysis of variance (ANOVA) was conducted with deployment degrees of freedom (Table 3) to check whether there is a significant difference between the data or if the variability occurred randomly. As indicated by the F-value in Table 3, the statistical significance occurred not only for isolated factors – (S) and (P) – but for the interaction between them – (S  P). It means the particle size is directly affected by the combination of polymer

Table 3. Analysis of the mean size of PLGA nanoparticles by ANOVA statistical test. Factors and their interactions

Degrees of freedom

SQa

MSb

F value

Solvent (S) Polymer (P) Interation (S  P) Treatment Residue Total

2 1 2 5 12 17

362 133.643 82 377.405 123 350.804 567 861.852 122 168.233 690 030.085

181 066.821 82 377.405 61 675.402 – 10 180.686 –

17.785* 8.091* 6.058* – – –

a

Sum of squares; bMean squares. *Statistically significant p50.05 (F calculated4F tabulated).

Table 2. Components of the formulations resulting from the 22  31 factorial design, used in the development of polymeric nanoparticles containing AmB and experimental results of mean particles size and PI expressed as mean ± standard deviation (SD) (n ¼ 3).

Formulation F1 F2 F3 F4 F5 F6 F7 F8 F9 F10 F11 F12 a

Polymer (mg) PLGA 50 50 50 100 100 100 PLGA:PEG 50:10 50:10 50:10 100:20 100:20 100:20

Solvent (mL) C

a

b

Dependent variables Mean particle size (nm) ± SD

PId ± SD

– – 800 – – 800

255.2 ± 57.5 338.2 ± 22.1 189.5 ± 90.0 301.8 ± 38.1 705.5 ± 235.8 181.4 ± 12.4

0.14 ± 0.04 0.20 ± 0.02 0.16 ± 0.07 0.15 ± 0.03 0.32 ± 0.03 0.12 ± 0.02

– – 800 – – 800

300.5 ± 42.4 337.8 ± 86.0 169.8 ± 6.9 406.3 ± 94.7 644.5 ± 209.4 172.1 ± 2.6

0.13 ± 0.01 0.22 ± 0.07 0.18 ± 0.03 0.19 ± 0.05 0.31 ± 0.02 0.12 ± 0.01

M

E

800 – – 800 – –

– 800 – – 800 –

800 – – 800 – –

– 800 – – 800 –

Chloroform; bMethanol; cEthyl acetate; dPolydispersity index.

3

c

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amount and organic solvent employed. Due to its interaction, an ANOVA was conducted with deployment degrees of freedom (data not shown), and the results showed that when chloroform or ethyl acetate was used, the concentration of PLGA tested (50 or 100 mg) did not show significant interaction (p40.05). It was found that methanol showed strong interaction with PLGA in order to influence the mean diameter of the nanoparticles. This strong interaction may be the reason for the larger sizes of the formulations F2 and F5 (Table 2) that have employed this solvent. Another study investigating the effect of the methanol in the diameter of a nanotech formulation concluded that the addition of this solvent resulted in an increased particle size30. Another statistical study of the deployment of interaction (S  P) was performed to obtain the polymer concentration that promotes interaction with the solvent. It was observed (data not shown) that 50 mg of PLGA did not present effect on particle size while 100 mg of the polymer produced significant effect (p50.05). In this sense, we applied Tukey’s test (data not shown) for obtaining the least significant difference (LSD) among formulations F4, F5 and F6 (100 mg PLGA). It was found no LSD between F4 (chloroform) and F6 (ethyl acetate) (p40.05), and significant difference between F4 and F5 (methanol) and with F5 and F6 (p50.05). Despite the presence of a strong interaction of 100 mg of PLGA with methanol (F5), this was not a favorable interaction since it produced particles with larger diameters. Three-dimensional surface plots were used to demonstrate the relationship and interaction between variables on the mean size of PLGA nanoparticles (Figure 1A). The particle size decreases when the cosolvent in the organic phase was chloroform or ethyl acetate. The PLGA amount did not influence the particle size when these two solvents were used. A pronounced slope in the plot was observed when methanol was employed due to its contribution to increase the particle size, mainly with 100 mg of PLGA. This fact corroborated the affirmation that methanol combined mainly with higher polymer concentration, caused a negative interaction producing larger particles size. This result corroborates other studies29,31 that found the smaller particles sizes when ethyl acetate was applied as a solvent in an organic phase of emulsion. A suitable explanation concerns the characteristic of fast solvent partition into the aqueous phase of the emulsion due to its partial miscibility in water. This partition is accompanied by rapid polymer precipitation producing small particles; however, often this characteristic can decrease the drug entrapment31. Influence of formulation parameters on mean particle size of PLGA-PEG nanoparticles containing AmB The mean particle sizes of the six PLGA-PEG formulations (F7–F12) varied from 169.8 ± 6.9 nm (F9) to 644.5 ± 209.4 nm (F11), whereas PI ranged from 0.12 ± 0.008 (F12) to 0.31 ± 0.02 (F11). As occurred with PLGA nanoparticles, the size variation among the formulations was large. To evaluate this variation in particles size from the influence of each variable or interaction between the variables, an ANOVA was conducted, according to Table 4. There was no statistically significant interaction identified between the factors (p40.05), where the effects of the polymer and solvent were only observed in isolation. It means that the effect of the variable alone is stronger than the interaction occurred. Three-dimensional surface plots were used to demonstrate the relationship and interaction between variables on the mean size of PLGA-PEG nanoparticles (Figure 1C). The nanoparticles formulated with ethyl acetate presented the smaller

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mean size, followed by those obtained with chloroform, and the larger values of mean diameter were observed with methanol as cosolvent. Tukey’s test was realized to define the LSD between paired formulations, and the results (data not shown) pointed as F11 (methanol), the only formulation different from other formulations (p50.05), demonstrating again the strong influence of methanol on the mean particle size, as occurred with PLGA nanoparticles. This strong interaction of methanol is considered negative because larger particle sizes were obtained. Determination of AmB encapsulation efficiency in PLGA and PLGA-PEG nanoparticles The encapsulation efficiency (EE) of AmB in nanoparticles was determined indirectly and the results of 12 formulations are presented in Table 5. The EE of AmB in nanoparticles was high in almost all formulations. Six formulations presented EE values over 90%. The high rates of AmB entrapped in nanoparticles constitute a significant result for the present study, demonstrating that the nanoencapsulation method was efficient. In formulations containing only the polymer PLGA, the EE varied from 71.2 ± 20.3% (F1) to 95.2 ± 2.5% (F5). For nanoparticles PLA-PEG-based, the EE varied from 62.8 ± 1.5% (F10) to 97.1 ± 0.8% (F11). According to Table 5, addition of PEG did not alter significantly the EE (confirmed with Student’s t-test not shown,  ¼ 0.05%). Probably, the more superficial association of PEG in PLGA matrix contributed to not affect the drug loading. Other observation is that the variations in EE reported to PLGA formulations were followed by those containing PLGA-PEG in the presence of same formulation parameters. Influence of formulation parameters on encapsulation efficiency of AmB in PLGA nanoparticles (F1–F6) To evaluate the influence of the independent variables on EE of AmB in PLGA nanoparticles, an ANOVA with deployment degrees of freedom was proceeded (Table 6). The results showed that only the solvents have an effect on the amount of drug encapsulated. Three-dimensional surface graphics was used to demonstrate the relationship and interaction between variables on the response (AmB EE) (Figure 1B). It can be observed that chloroform presents the lower AmB EE compared to ethyl acetate and methanol as cosolvent. Tukey’s test was performed to calculate the LSD between the paired formulations (F1–F6). The results (not shown) showed no difference among the formulations (p40.05), since the minimal significant difference was 28.23. Thus, all values of EE of AmB in PLGA formulations are considered equal to one another, and all are superior to those in the literature. In a study by Van de Ven et al. [30], PLGA nanoparticles loaded with AmB were considered excellent with EE greater than 50%, and in the present study ranged from 71.2 to 95.5%. Influence of formulations parameters on encapsulation efficiency of AmB in PLGA-PEG formulations (F7–F12) The ANOVA applied to the data of EE of AmB in PLGA-PEG nanoparticles is described in Table 7. The solvent alone and the interaction between solvent and polymer amount significantly influenced the EE of the AmB in nanoparticles. Three-dimensional surface graphics was used to demonstrate the relationship and interaction between variables on the AmB EE (Figure 1D). As demonstrated by PLGA nanoparticles, chloroform presents the lower EE compared to ethyl acetate and methanol as cosolvent. Tukey’s test demonstrated the significant difference (p50.05) on mean size of chloroform-based

Optimization of amphotericin B polymeric nanoparticles

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5

Figure 1. Surface response plot according of the variables polymer amount and solvent on the: (A) – mean particle size of PLGA nanoparticles; (B) – encapsulation efficiency of AmB in PLGA nanoparticles; (C) – mean particle size of PLGA-PEG nanoparticles and (D) – encapsulation efficiency of AmB in PLGA-PEG nanoparticles.

Table 4. Analysis of the mean size of PLGA-PEG nanoparticles by the ANOVA statistical test. Factors and their interactions Solvent (S) Polymer (P) Interaction (S  P) Treatment Residue Total

Degrees of freedom

SQa

MSb

F value

2 1 2 5 12 17

309 585.191 86 057.175 71 869.458 46 7511.820 124 098.970 591 610.791

154 792.595 86 057.175 35 934.729 – 10 341.580 –

14.968* 8.321* 3.475NS – – –

Sum of squares; bMean squares; NSnot significant. *Statistically significant p50.05 (F calculated4F tabulated).

result indicates that methanol was directly associated with higher percentages of AmB encapsulation. The strong influence of methanol combined with a polymer was again demonstrated by this analysis. In order to continue with the choice of the optimal formulation, it was necessary to establish the amount of the polymer combined with cosolvent methanol that produced greater interaction and influenced the AmB EE. Through statistical analysis (data not shown), it was found that both polymer concentrations employed – 100:20 and 50:10 (mg:mg) – have produced significant effect combined with methanol.

a

formulations compared to formulations composed of methanol or ethyl acetate, that were similar in size (p40.05). To elucidate the interaction of polymer and solvent, other statistical analysis was performed with the aim of finding which solvent interacts with PLGA-PEG blend. The results showed (data not shown) that only the interaction with the methanol produced significant effect. This

Indication of optimum formulation The variables involved in obtaining nanoparticles are very important in the final characteristic of the particles and should be established according to the intended biological purpose. In general, we support that small diameter and narrow size distribution combined with high-drug loading are important characteristics to achieve the desired therapeutic effect. Considering the development of PLGA nanoparticles, we observed that the formulation presenting higher encapsulation

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Table 5. Components of the formulations factorial design, used in the development containing AmB and experimental results (EE) of AmB expressed as mean ± standard

Formulation

Polymer (mg) PLGA

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F1 F2 F3 F4 F5 F6 F7 F8 F9 F10 F11 F12

50 50 50 100 100 100 PLGA:PEG 50:10 50:10 50:10 100:20 100:20 100:20

resulting from the 22  31 of polymeric nanoparticles of encapsulation efficiency deviation (SD) (n ¼ 3).

Solvent (mL) Mb

Ec

Dependent variable EE (%) ± SD

800 – – 800 – –

– 800 – – 800 –

– – 800 – – 800

71.2 ± 20.3 95.5 ± 0.9 94.0 ± 1.3 73.2 ± 14.4 95.3 ± 2.5 91.6 ± 2.3

800 – – 800 – –

– 800 – – 800 –

– – 800 – – 800

63.9 ± 5.4 84.7 ± 3.2 92.8 ± 2.9 62.8 ± 1.5 97.1 ± 0.8 89.3 ± 4.0

C

a

a

Chloroform; bMethanol; cEthyl acetate.

Table 6. Analysis of the encapsulation efficiency of AmB in PLGA nanoparticles by the ANOVA statistical test. Source of variation Solvent (S) Polymer (P) Interation (S  P) Treatment Residue Total

Degrees of freedom

SQa

MSb

F value

2 1 2 5 12 17

1923.013 0.023 11.666 1934.702 1272.009 3206.711

961.506 0.023 5.833 – 106.001 –

17.785* 8.091NS 6.058NS – – –

a Sum of squares; bMean squares; NSnot significant. *Statistically significant p50.05 (F calculated4F tabulated).

Table 7. Analysis of the encapsulation efficiency of AmB in PLGA-PEG nanoparticles by the ANOVA statistical test. Factors and their interactions Solvent (S) Polymer (P) Interaction (S  P) Treatment Residue Total

Considering PLGA-PEG nanoparticles, a similar profile to PLGA nanoparticles occurred. Methanol-based formulation presented high-drug loading but the larger mean size excludes the formulations F8 and F11. The mean size of formulations was not affected when chloroform was substituted by ethyl acetate but the encapsulation efficiency was affected. Thus, F9 and F12 were considered ideal; and due to F9 use, the lower polymer amount was chosen as the optimal formulation. The results showed similarity of the PLGA and PLGA-PEG formulations elected as great. It is believed that the partial miscibility of ethyl acetate and its tendency to form small particles contribute to the choice of ethyl acetate as cosolvent in nanoparticulate formulations33. The optimal formulations (F3 and F9) present around 200 nm as mean size but due to different surface characteristics that can be applied for different purposes. Considering the perspective of a possible in vivo study with these nanoparticles, we can consider them potential in the treatment of systemic fungal infections and leishmaniasis. Nanoparticles with diameters larger than 200 nm accumulate in the spleen and liver, where they are processed by the mononuclear phagocytic systems cells. Because macrophages are the sites of Leishmania infections in vertebrates, nanostructured systems represent successful alternatives because when their surfaces are not functionalized (as the PLGA nanoparticles containing AmB), nanoparticles are rapidly opsonized and taken up by macrophages. After phagocytosis, the drug is released from the nanostructure and can bind with the membrane of the parasite and interact with sterols to form channels, causing the extravasation of important ions and subsequent cell damage34. While the functionalized PLGAPEG blend nanoparticles containing AmB represent potential to treat systemic fungal infections. Particles containing PEG on the surface are able to persist longer in the bloodstream owing to the steric effect over plasmatic opsonins, which avoid premature phagocytosis7–9. The size and surface characteristics of the nanoparticles governs their pharmacokinetics, and the ability to modulate these parameters became the polymeric nanoparticles versatile drug delivery systems.

Conclusion

Degrees of freedom

SQa

MSb

F value

2 1 2 5 12 17

3054.874 31.469 221.634 3307.977 134.772 3442.749

1527.437 31.469 110.817 – 11.231 –

136.002* 2.802 NS 9.867* – – –

a Sum of squares; bMean squares; NSnot significant. *Statistically significant p50.05 (F calculated4F tabulated).

efficiency also presented the larger particles size (p50.05) (formulations employing methanol as cosolvent). It has been reported that the encapsulation efficiency of the nanoparticles increases with the increment of their diameter32. Thus, since the mean diameters of methanol-based formulations were larger, the formulations F2 and F5 were excluded the choice of optimal formulation. The mean size and encapsulation efficiency were not significantly affected when ethyl acetate was substituted by chloroform (p40.05) or when polymer amount was changed using these solvents. Thus, we considered the lower toxicity of ethyl acetate compared to chloroform10, to elect the formulations F3 and F6 as ideal. Considering that F3 used the lower amount of polymer, it can be classified as the optimal formulation of PLGA containing AmB.

Factorial analysis allowed the verification of the effect of different components of the formulation simultaneously on nanoparticles features, based on a limited number of experiments. Our results indicated that the proper choice of the organic phase is a key factor in determining the mean size and AmB loading in PLGA and PLGA-PEG nanoparticles. Optimal parameters were obtained for nanoparticles composed of ethyl acetate as cosolvent for AmB and PLGA. The factorial design is an important tool in optimizing polymeric nanoparticle formulations in order to modulate their physicochemical characteristics.

Declaration of interest The authors state no conflict of interest in this study. The authors thank to CNPq (process 476071/2009-7 and 478020/2012-0) and Fundac¸a˜o Arauca´ria (conv. 176/2012) for financial support.

References 1. Parveen S, Misra R, Sahoo SK. Nanoparticles: a boon to drug delivery, therapeutics, diagnostics and imaging. Nanomedicine 2012;8:147–166. 2. Kumari A, Yadav SK, Yadav SC. Biodegradable polymeric nanoparticles based drug delivery systems. Colloids Surf B 2010; 75:1–18.

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DOI: 10.3109/10837450.2014.979942

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