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1 Artificial Cells, Nanomedicine, and Biotechnology An International Journal ISSN: (Print) X (Online) Journal homepage: Preparation, characterization, and optimization of altretamine-loaded solid lipid nanoparticles using Box-Behnken design and response surface methodology Bina Gidwani & Amber Vyas To cite this article: Bina Gidwani & Amber Vyas (16) Preparation, characterization, and optimization of altretamine-loaded solid lipid nanoparticles using Box-Behnken design and response surface methodology, Artificial Cells, Nanomedicine, and Biotechnology, 44:, , DOI: 1.319/ To link to this article: Published online: 3 Nov 14. Submit your article to this journal Article views: 133 View Crossmark data Citing articles: 6 View citing articles Full Terms & Conditions of access and use can be found at

2 Artificial Cells, Nanomedicine, and Biotechnology, 16; 44: Copyright 14 Informa Healthcare USA, Inc. ISSN: print / X online DOI: 1.319/ Preparation, characterization, and optimization of altretamine-loaded solid lipid nanoparticles using Box-Behnken design and response surface methodology Bina Gidwani & Amber Vyas University Institute of Pharmacy, Pt. Ravi Shankar Shukla University, Raipur, Chhattisgarh, India Abstract The objective of the present study was to prepare solid lipid nanoparticles (SLNs) of altretamine (ALT) by the hot homogenization and ultrasonication method. The study was conducted using the Box-Behnken design (BBD), with a 3 3 design and a total of 17 experimental runs, performed in combination with response surface methodology (RSM). The SLNs were evaluated for mean particle size, entrapment efficiency, and drug-loading. The optimized formulation, with a desirability factor of.9, was selected and characterized. In vitro release studies showed a biphasic release pattern from the SLNs for up to 4 h. The results of % EE ( ), %DL (1.15.6), and mean diameter of (1.6.1) nm, were very close to the predicted values. Keywords: altretamine, Box-Behnken design, optimization, response surface methodology, solid lipid nanoparticles Introduction ALT is a synthetic, cytotoxic, alkylating, and antineoplastic agent, used in the treatment of advanced ovarian cancer (Hansen and Hughes 1991). The common name for altretamine is hexamethylmelamine (HMM). It is a white-colored, crystalline powder, having small molecules with a molecular weight of 1. It is practically insoluble in water, and has poor oral bioavailability because it undergoes firstpass metabolism (Thigpen 1987). It is an FDA-approved drug, used either alone or in combination with other drugs (cisplatin, cyclophosphamide, doxorubicin, fluorouracil and melphalan), for the treatment of refractory ovarian cancer (Damia and Dincalci 1995, Wiernik 199, Gidwani and Vyas 13). Due to its poor aqueous solubility and high permeability, it is categorized as a class II drug, according to the Biopharmaceutical Classification System (BCS). This means that its absorption is limited by its poor solubility. The BCS is based on the absorption of drug through the intestinal membrane, which is proportional to its concentration at the membrane surface, being the in vivo dissolution (Amidon et al. 1995). The therapeutic activity of ALT is limited, due to its poor solubility and dose-dependent toxicity (including gastrointestinal, hematologic, and neurotoxicity) (Hansen and Hughes 1991). Various approaches have been utilized to develop suitable formulations of altretamine; for instance, Hariharan and Price (), incorporated ALT into microspheres using poly lactic acid (PLA) as a carrier. Later, Sun et al. (1), prepared liposomes of ALT using sodium deoxycholate as a carrier, for oral delivery. Furthermore, Kreilgard et al. (1975) prepared a complex of HMM with gentisic acid species, to enhance apparent solubility and make it suitable for parenteral delivery. However, no significant results were achieved. The pharmacokinetic properties are often inappropriate or may be hampered, due to poor drug solubility. Moreover, it is very difficult to formulate oral dosage for such drugs. Oral bioavailability of ALT can be improved through its lymphatic delivery. Lymphatic delivery is mostly the preferred method to avoid first pass metabolism in peroral delivery (Driscoll ). The transport of the drug through the lymphatic system reduces the hepatic first-pass effect and improves bioavailability, because the intestinal lymph vessels drain directly into the thoracic duct, followed by the venous blood, thus by-passing the portal circulation (Porter and Charman 1, Khan et al. 13). For this purpose, the lipid-based approach is used, and the drug ALT is incorporated into the lipid nanoparticles (SLNs). However, recent methods, using cyclodextrin-based nanocarriers, are gaining significant importance in the delivery of hydrophobic drugs. They provide a synergistic or additive effect (Gidwani and Vyas 14). SLNs came into existence in the early 199s, and were advantageous compared to liposomes, emulsions, and polymeric nanoparticles. These lipid-based nanoparticles are made of solid lipid, which means lipids that remain solid at room temperature and body temperature. They are Correspondence: Dr. Amber Vyas, Assistant Professor, University Institute of Pharmacy, Pt. Ravi Shankar Shukla University, Raipur, Chhattisgarh, India. Mobile: , Office: ambervyas@gmail.com (Received August 14; revised 4 September 14; accepted 8 September 14) 571

3 57 B. Gidwani & A. Vyas stabilized by the use of suitable surfactants, and can be delivered through the oral, topical, and parenteral routes (Wong et al. 7). Moreover, there is no issue regarding the status of regulatory acceptance. SLNs are superior compared to polymeric and colloidal carriers, in terms of biocompatibility, stability, and scalability. Moreover, it is possible to achieve desired drug release, that is, controlled or sustained, through SLNs (Wolfgang and Karsten 1). The drug release from SLNs can be modulated in order to optimize their blood levels. The unique advantages of SLNs are the absence of biotoxicity, avoidance of organic solvents, drug stability, high drug payload, and incorporation of both lipophilic and hydrophilic drugs. However, research into SLNs, conducted over the last 15 years, has been focused mostly on the pharmaceutical applications, including formulation, development and drug delivery (Muller et al. 11). All these features make SLNs promising and potential carriers for the optimized delivery of drugs with poor physicochemical properties. The effect of process parameters and the physicochemical properties of SLNs, like particle size, surface morphology, drug release, etc., can be improved by optimization (Das et al. 11). The procedure of optimization involves systematic formulation of designs to minimize the number of trials, and analyzes the response surfaces in order to realize the effects of causal factors and to obtain the appropriate formulations with the target goals (Zhang et al. 1). Statistical models are extensively used in diversified areas, especially in pharmaceuticals, to strengthen the art of drug formulation. Most of the hydrophobic drugs, like enalapril maleate (Singh et al. 11), simvastatin (Gambhire et al. 11), lutein (Liu and Wu 1), sildenafil citrate (Ghasemian et al. 13), etc., are optimized using factorial design and RSM. The objective of the present study was to develop a mathematical model using a BBD, combined with RSM, in order to deduce the adequate conditions to prepare SLNs with the desired optimized characteristics. This will help to determine the relationship between the selected independent variables and their responses. The variables selected are fitted in a three-factor, three-level BBD, with three replicates. In the present work, the role of SLNs in oral delivery of ALT has been examined. Compritol 888 ATO, Poloxamer- 188, and soya lecithin, were selected as the solid lipid, surfactant, and cosurfactant respectively. SLNs loaded with ALT were prepared by the hot homogenization and ultrasonication method, and were optimized using 3 3 factorial response surface methodologies. They were further characterized for their mean particle size, loading parameters, entrapment efficiency, in vitro drug release behavior, and morphology. Experimental Materials ALT was obtained as a gift sample from Spectra Lab. (Hyderabad, India). Poloxamer 188 was supplied by Signet Chemical Corporation Pvt. Ltd. (Mumbai, India). Soya lecithin was purchased from HiMedia Lab Pvt. Ltd. (Mumbai, India). Compritol 888 ATO and Precirol ATO 5 were kind gifts from Gattefosse (France). Glycerol monostearate(gms), stearic acid (SA), cetyl alcohol (CA), and cetostearyl alcohol (CSA), were purchased from Loba Chemie Pvt. Ltd. (Mumbai, India). Purified water from an ultra-pure water system (Synergy UV water purification system, India) was used throughout the study. All other chemicals and solvents used in the study were of analytical grade. Partitioning behavior of drug (ALT) in lipids The choice of lipid was done on the basis of the solubility and partitioning behavior of ALT in different lipids. The partitioning behavior of ALT was tested in five different lipids, viz., Compritol 888 ATO, Precirol ATO 5, GMS, SA, CA, and CSA. An accurate weight of 5 mg of Altretamine was dispersed in a mixture of melted lipid (approx. g) and hot distilled water ( ml). This mixture was shaken for 3 min at 8 C in a hot water bath, and then centrifuged at 5 rpm for 1 min. The aqueous phase obtained was filtered through a membrane filter (pore size.45 mm). The concentration of ALT in water was determined by UV spectroscopy (UV 17-Spectrophotometer, Shimadzu, Japan) (Shailesh et al. 1). Preparation of SLNs SLNs loaded with ALT were prepared by the hot homogenization-ultrasonication method (Alex et al. 11). Briefly, ALT (1 mg) was dissolved in melted lipid (Compritol 888 ATO), and this solution was then dissolved in ml acetone, maintained at 75 C. The aqueous phase consisted of the surfactant (Poloxamer-188) and co-surfactant (soya lecithin), dissolved in ml of distilled water to obtain a % solution, which was heated up to 8 C in a beaker. When a clear homogeneous lipid phase was obtained, the hot aqueous phase solution was added to the lipid phase maintained at 75 C, and homogenized at 15, RPM for 1 min using a high-speed homogenizer, UltraTurrax T1 (T-1 basic ULTRA-TURRAX - IKA, Germany). The resulting preemulsion was then ultrasonicated using a probe sonicator (Frontline Sonicator) at 5 W for 5 min. Later, the mixture was poured into cold water (1 4 C) and stirred with a magnetic stirrer. The SLNs were allowed to recrystallize until room temperature, and then diluted up to 1 ml with deionized water, yielding an ALT-SLN dispersion. Determination of optimal concentration of surfactant The stability of lipid nanoparticles depends on the balance of emulsifiers/surfactants at the oil-water interface, and the nature of the oil phase (Martini and Herrera 8). For this, preliminary screening experiments were used to choose the ingredients for the production of SLNs, before the optimization of formulations in this study. All the ingredients screened in this study are generally recognized as safe (GRAS). Briefly, ALT (5 mg) was dissolved in 1 mg melted lipid (Compritol ATO 888), and the solution was then dissolved in ml acetone. The surfactant was added to the aqueous dispersion of the cosurfactant at concentrations of 1%, %, 3%, 4% and 5% (w/v). The most suitable concentration of the surfactant was estimated from particle size, entrapment efficiency (EE), and drug loading (DL) of the prepared SLNs (Song et al. 6).

4 Optimization of altretamine-loaded SLNs 573 Experimental design and statistical analysis Mathematical models based on statistics are extensively used to design the formulation of lipid-based nanoparticles; therefore, it is essential to investigate the independent variables which can affect the properties of SLNs in the desired formulation (Lewis et al. 6). In this study, all the data are reported as mean standard deviation (n 3). A 3-level experimental factorial design-response surface methodology was used to study the effect of different variables on formulation properties like mean particle size (PS) (Y 1 ), percentage drug loading (% DL) (Y ), and entrapment efficiency (% EE) (Y 3 ) of the prepared SLNs. Independent variables used in the study include drug concentration (X 1 ), lipid concentration (X ), and surfactant concentration ( ). In order to optimize the ALT SLNs, equations based on mathematical modeling were derived by computer simulation programming with Design-Expert 9.. The models were presented as three-dimensional (3D) response-surface graphs. Three dimensional surface plots were used to establish the relationship between independent variables and dependent variables (response). The best-fitted models for statistical analysis were considered significant, with probability limits of the P value.5. Predicted R value and one-way ANOVA were estimated to confirm the best fit of the model. The desirability function of particle size was at the minimum level, and that of entrapment efficiency and drug loading was at the maximum level, to optimize formulations (Zhang et al. 1). Design-Expert software (version 9..1; Stat-Ease, trial version) was utilized for statistical analysis and graph plotting. Evaluation of SLNs Determination of mean particle size, PDI and zeta-potential of ALT-SLNs The mean particle size (z-average) of the SLNs, and the poly dispersity index (PDI), as measures of the width of particle size distribution, were determined by dynamic light scattering (DLS), using a Zetasizer (Nano ZS 9, Malvern Instruments, UK) at 5 C with a scattering angle of 9 o (Venkateswarlu and Manjunath 4). The SLN formulations were diluted with double-distilled water to weaken opalescence before measurement. The surface charge was estimated by measuring the zeta potential of SLNs based on the Helmholtz-Smoluchowski equation, using the same equipment at 5 C, with an electric field strength of 3 V/cm. ph x = EM 4 e Where z is the zeta potential, EM is the elecrophoretic mobility, h is the viscosity of the dispersion medium and e is the dielectric constant. Determination of %EE and %DL of ALT-SLNs The percentage of EE and DL were evaluated by determining the amount of free ALT in the aqueous solution of surfactant & cosurfactant, which was separated by using the cooling centrifuge. The free (unentrapped) drug in the SLNs dispersion was sedimented by controlled centrifugation (Remi Instruments Ltd., Mumbai, India) at 1, rpm, 4 C, for min, and the SLN dispersion was decanted without disturbing (Lamprecht et al. ). The concentration of ALT in the aqueous phase was determined using a UV visible spectrophotometer (UV 17, Shimadzu, Japan) at l max 6 nm. The %EE and %DL were calculated by the following equations: EntrappedDrug EE 1 Entrappeddrug Unentrappeddrug Similarly, % drug loading (DL) was determined as the percentage of drug/lipid ratio. Differential scanning calorimetry The DSC studies of pure drug (ALT), Compritol 888, physical mixture (1/1), and freeze dried ALT-SLNs of the optimized batch were performed using a Shimadzu DSC-6 Systems (Shimadzu, Kyoto, Japan) equipped with a computerized data station TA-5WS/PC. The thermal behavior of samples was studied by heating in a sealed flat bottom aluminum pan, using an empty sealed pan as reference, over the temperature range of 3 C, at a rate of 1 C/min and under a constant flow of dry nitrogen (Venkateswarlu and Manjunath 4, Rahman et al. 1). Transmission electron microscopy study The surface morphology and shape of optimized SLNs loaded with ALT was analyzed by Transmission Electron Microscopy (TEM) (model TECNAI Kv TEM, Fei Electron Optics, Japan). The optimized ALT-SLN aqueous dispersion was placed on copper grids coated with carbon film and stained with negative stain using a drop of % (w/w) aqueous solution of phosphotungstic acid for 3 s. Excess staining solution was washed out by filter paper, leaving a thin aqueous film on the surface. After staining, samples were dried at room temperature for 1 min to carry out investigations. The magnification for the TEM images was 15X (Zhang et al. 1, Liu and Wu 1). In vitro release of ALT from SLNs The in vitro release study of ALT from the optimized SLNs was performed by using a modified Franz diffusion cell. The dialysis membrane (molecular weight cutoff 1, Da) was kept in double-distilled water for 4 h before mounting it on the diffusion cell. The ALT-SLN aqueous dispersion ( ml) was placed in the donor compartment, and the receptor compartment was filled with dissolution medium (ph 6.8 phosphate buffer), and the temperature was maintained at 3 C by continuous stirring at 1 rpm. At regular time intervals, aliquots of samples (.1 ml) were withdrawn from the receptor compartment and an exact volume of dissolution medium was added in the same compartment, to maintain constant volume throughout the study. The amount of ALT released was analyzed by UV-visible spectrophotometer (Shimadzu, UV 17, Japan) (Paliwal et al. 9). The release kinetics profile from the optimal ALT-SLNs was fitted on zero order, first order, Higuchi model, Korsmeyer-Peppas model and Hixson-Crowell model (Costa and Sousa Lobo 1, Dash et al. 1).

5 574 B. Gidwani & A. Vyas Stability studies The storage stability studies of the optimized ALT-SLN dispersion was performed as per the ICH guidelines. For this, 1 ml of ALT-SLN dispersion with the drug of concentration mg/ml was taken into glass vials and stored at two different temperature conditions, 4 C and 5 C, for a period of 3 months. The stability study was confirmed by evaluating the effect on particle size, zeta potential, and percentage entrapment efficiency in the SLN dispersion. The sampling was done with a frequency of 1 month. Results and discussion Partitioning behaviour of ALT in lipids The concentration of ALT in the aqueous phase was determined from the standard curve prepared in methanol. The partition coefficients obtained were , , , and for Compritol 888 ATO, Precirol ATO 5, glycerol monostearate, stearic acid, cetyl alcohol and cetostearyl alcohol, respectively. On the basis of results obtained, it is clear that the maximum solubility of ALT is in Compritol 888 ATO, more than in other lipids. Thus, Compritol 888 ATO is used as the lipid phase for the entire study. Preparation of SLNs loaded with ALT The hot homogenization-ultrasonication method is the easiest and most feasible method for the production of SLNs in laboratories. Compritol 888 ATO, Poloxamer 188, and soya lecithin, were used as lipid, surfactant, and co-surfactant respectively. The homogenization speed was optimized at 15, rpm for 1 min and the sonication time was 5 min at 5W. Optimization of surfactant concentration The surfactants tested in this study include Tween, Tween 8, Span 6 and Pluronic F68 (Poloxamer 188). Poloxamer 188 is a non-ionic emulsifier which offers added steric stabilization effects to lipid nanoparticles, by avoiding aggregation of the nanoparticles in the system. In addition, it is a good surfactant for oral delivery, as it can easily manipulate the p-glycoprotein efflux system and cytochrome P-45, thereby improving the oral absorption of hydrophobic drugs. Therefore, Poloxamer 188 was used as surfactant in the study, and its concentration was optimized. On the basis of preliminary studies, the surfactant concentrations ranging from 1% to 5% (w/v) were selected for evaluating the effect of surfactant concentration on mean particle size, percentage of drug EE and DL. A % concentration of surfactant (w/v) led to the smallest particle size, highest % EE and %DL. At this concentration, the mean particle size, %EE and %DL of SLNs were , and , respectively. So, from the above results, a % concentration of Poloxamer 188 as the surfactant was determined to be the optimum. Optimization of formulas The BB factorial design, in combination with RSM, is an effective approach for investigating maximum variables at different levels, with a limited number of experimental runs (Zhang et al. 9). In the present study, the threefactor, three-level factorial design is used with three different responses. The variables used in the study were selected on the basis of preliminary experiments. From the results of optimization, the three dependent values ranged from 96.4% 81.4% for EE, 1.48%.16% for DL, and nm 8.16 nm for PS. It was seen that the quadratic model was best-fitted for all the responses studied, that is, mean particle size, %EE and %DL. The quadratic equations generated for different responses are as under: EE (%) X X X 1 X 1.8X X.8X X 3.6 DL (%).18.15X 1.19X.16.3X 1 X.36X 1.15X 9X 1.1X.3 PS (nm) X 1.X X 1 X 3.7X 1 1.1X 14.6X X 13.8 Where, X 1, X and represent the coded values of the drug concentration, lipid concentration and surfactant/ cosurfactant ratio, respectively. The positive sign of values of a factor in the above quadratic equation indicates a synergistic effect or an enhancement of that particular response, and vice versa. The lack-of-fit was not significant in all the cases, at 95% confidence level. However, all the remaining parameters were significant, at P.5 or lower. Table I shows that the average experimental values of the three batches of SLNs prepared using the optimized range were very close to the predicted values. The results showed low percentage bias, suggesting that the optimized formulation was consistent and reasonable. Response-surface plots According to the surface response methodology using the BB design, a total of 17 experimental runs were performed for three levels and three factors. The detailed summary of experimental runs using dependent and independent variables is shown in Table II. The three-dimensional (3D) response- surface plots generated by the Design-Expert software (trial version 9..1.) was used to study the pattern of interaction between variables. The values of various statistical parameters like multiple correlation coefficients (R ), F-value, P-value, coefficient of variation, standard deviation, predicted residual sum of squares, and mean square values, are shown in Table III. The response-fitting results indicated that optimized SLNs with high % EE, high % of DL, and small mean particle size, were obtained at the ALT (drug) concentration of.99%, lipid concentration of.16%, and surfactant/cosurfactant ratio of.43 [coded Table I. Comparison of the observed and predicted values in the SLN prepared under predicted optimum conditions. Response Variable Predicted values Observed values % Bias * Entrapment Efficiency (%) Drug Loading (%) Particle Size (nm) * Bias was calculated as (predicted value observed value)/predicted value 1%.

6 Optimization of altretamine-loaded SLNs 575 Table II. Independent variables, along with their coded and actual values, along with their respective responses for different batches of ALT-SLNs. Coded value variables Actual value variables Response values Std. Run X1 X X3 X1 X X3 % EE %DL PS (nm) ZP (mv) PDI : : : : : : : : : : : : : : : : : values] respectively. The graphs show that with an increase in the concentration of lipid, the mean particle size and % EE increased, with a decrease in % DL. Particle size significantly increased with increase in drug concentration, but decreased at concentrations above.75%. The values of zeta potential in all the cases were more than mv, indicating good stability of the SLN dispersion. However, in case of the surfactant/cosurfactant ratio, an optimum concentration ratio (1/1) led to minimum particle size and maximum % EE and % DL. The response-surface graphs showed that all the selected dependent variables were statistically significant. The surface plot showing the effect of different variables on the particle size of ALT- SLNs is shown in Figure 1A. Particle size, zeta potential and PDI of ALT-loaded SLNs The values for particle size, zeta potential and PDI of the ALT-loaded SLNs are depicted in Table II. The mean particle sizes, PDI values and zeta potential of all the 17 formulations were in the range of nm,.1.41 and mv, respectively. Effect of drug concentration on particle size, zeta potential, and PDI There was remarkable and significant difference in the particle size of SLNs prepared with different drug concentrations (DC) ranging from.5 to.75%. Initially, the particle size significantly increased with an increase in DC, but later on, at higher values of DC (above.75%), the particle size decreased. The values of PDI also increased with higher DC, this may be due to the presence of a higher amount of drug. The zeta potentials of all the 17 SLN dispersions indicated good stability of the SLN dispersions. Thus, in the present study,.5% DC was selected for the final preparation of the ALT-loaded SLN dispersion. Effect of lipid concentration on the particle size, PDI and zeta potential The concentration of lipid also produced significant effect on the particle size, zeta potential, and PDI of the SLN dispersion, as it solubilized the drug present in the formulation. The response-surface plot Figure 1A shows that with the increase in lipid concentration from 1% to 3%, the mean particle size increased in all the cases. The SLN dispersions which contained the highest amount of lipid, comparatively, showed the largest particle size, greater than that of the dispersions with low lipid content. The zeta potential also varied with increase in lipid content. The particle size increased on increasing the lipid concentration (LC). Efficient reduction of particle size was observed in dilute dispersions containing low lipid, due to uniform distribution of sonication energy. In most of the literature, it is reported that increasing the LC leads to larger sized particles with broader particle size distribution (Shailesh et al. 1, Alex et al. 11). Table III. Summary of the results of regression analysis for responses, and analysis of variance for particle size, EE and DL. Parameters DF SS MS F P value R SD C. V % % Entrapment efficiency Model SIGNIFICANT Residual Total % Drug loading Model SIGNIFICANT Residual 7.75 Total Mean particle size (nm) Model SIGNIFICANT Residual Total

7 576 B. Gidwani & A. Vyas (A) Design-Expert Software Factor Coding: Actual PS (nm) Design points above predicted value Design points below predicted value X1 = A: Drug Conc X = B: Lipid Conc Actual Factor C: Surfactant Cosurfactant Ratio = PS (nm) B: Lipid Conc A: Drug Co (B) Design-Expert Software Factor Coding: Actual EE (%) Design points above predicted value Design points below predicted value X1 = A: Drug Conc X = B: Lipid Conc 1 Actual Factor C: Surfactant Cosurfactant Ratio = 95 9 EE (%) B: Lipid Conc A: Drug Conc Figure 1. Surface plots showing the effect of variables on (A) particle size (B) percentage of entrapment efficiency and (C) percentage of drug loading. Effect of Surfactant and Cosurfactant ratio on the particle size, PDI and zeta potential The surfactant/cosurfactant ratios produced a predominant effect on the particle size and the stability of the ALT- SLN dispersion. A higher ratio led to a reduction in surface tension, prevented the agglomeration of particles, and led to a decrease in particle size. This decrease in particle size with increase in the ratio was seen up to the optimal ratio (1/1), and after that, further increase in the surfactant concentration produced an inverse effect, i.e., an increase in the mean

8 Optimization of altretamine-loaded SLNs 577 (C) Design-Expert Software Factor Coding: Actual DL (%) Design points above predicted value Design points below predicted value X1 = A: Drug Conc X = B: Lipid Conc Actual Factor C: Surfactant Cosurfactant Ratio = DL (%) B: Lipid Conc Figure 1. (Continued) A: Drug particle size at a constant amount of lipid and the increase of cosurfactant concentration, and vice versa. This shows that increase in surfactant and cosurfactant concentrations at optimal level reduces the particle size significantly. However, no considerable effect was observed in the PDI, with the changes in ratio of surfactant and cosurfactant. The zeta potential is an indication of the stability of a colloidal dispersion through electrostatic repulsion between the charged particles. The observed values of zeta potential indicate that the surface charge of the almost all the particles was negative. Moreover, there was no significant correlation between the zeta potential and surfactant/cosurfactant ratio. Percentage entrapment efficiency and percentage drug loading of ALT-loaded SLNs The entrapment efficiency (EE) and drug loading (DL) percentages of all the 17 SLN dispersions were in the range of % and % respectively. The percentage of EE and the DL capacity were significantly affected by the nature of the drug (ALT). Table II shows the percentage of EE and percentage of DL of all the SLN dispersions loaded with ALT. Effect of drug concentration on percentage of EE and percentage of DL The percentage of EE significantly decreased with an increase in DC. This is because the EE directly depends on the amount of drug added (i.e., DC). As lipids also have certain drug-loading capacity, the addition of excess of drug led to an increase in the amount of unencapsulated drug (i.e., decrease in EE). The percentage of drug loading increased with higher DC, up to.75%, after which the amount of drug added was beyond the encapsulation capacity of the lipid used. Effect of surfactant to cosurfactant ratio on percentage of EE and DL The ratio of surfactant concentration to cosurfactant concentration played a vital role in the solubility of drug. High percentage of EE and DL were obtained with the optimum surfactant/cosurfactant concentration ratio (1/1), compared to the other two ratios (1/, 1/3). Responsesurface plots showing the effect of different variables on the percentage of EE and DL are shown in [Figure 1B and C]. Effect of lipid concentration on Percentage EE and DL The lipid concentration produced a significant effect on the percentage of EE and DL. The response-surface curve showed that an increase in LC led to a higher percentage of EE. This is because of the fact that more amount of lipid was available for dissolving the drug (ALT). As estimated, the EE increased with the increase in LC. This is because a higher amount of lipid was available for drug encapsulation at high LC, which led to higher EE, with significantly remarkable increase in particle size. In case of low LC, the opposite behavior was observed. However, the percentage of DL significantly decreased with increasing LC. This was because of the fixed DC used during formulation. Based on these observations, LC of %w/v was selected as the value achieving the best results.

9 578 B. Gidwani & A. Vyas Figure. (A) DSC thermogram of altretamine (ALT), Compritol ATO 888, physical mixture and ALT-SLNs. (B) TEM image of optimized ALT-SLNs. DSC studies The DSC thermogram of the drug (ALT), lipid (Compritol ATO 888), physical mixture (1/1), and SLNs loaded with ALT, is shown in Figure A. The melting point of ALT is in the range of C. The DSC thermogram of ALT and Compritol ATO 888 showed a sharp endothermic peak at 173 C and 75 C respectively. The physical mixture showed no significant change in the position of endothermic peaks. Thus, there was no chemical interaction between ALT and Compritol ATO 888. SLNs containing ALT showed two endothermic peaks in the DSC thermogram, one at 71 C for lipid, and another at around 155 C for mannitol (cryoprotectant used in lyophilization of SLNs), but no peak was observed for ALT. The peak of Compritol 888 ATO in the SLN-formulation showed a shift towards lower temperature. This may be due to a reduction in particle size and increase in surface area, leading to a decrease in melting enthalpy, as compared with heat flow through larger crystals, which requires more time. This result suggests that ALT was entrapped in SLNs and existed in the amorphous state. Transmission electron microscopy On the basis of optimization, the shape and surface morphology of the selected SLN dispersion containing ALT was determined by TEM. TEM was done to study the particle size and shape of the samples. The results of TEM showed smooth spherical surface morphology of the SLNs. This confirmed that the particles were spherical in shape, with narrow size distribution, and no aggregation. The TEM image is shown in Figure B. In vitro drug release studies The cumulative percentage drug release of optimized ALT-SLN dispersion was determined for 4 h. The in vitro release profile of the optimized ALT-SLN suspension in 6.8 ph phosphate buffer at 37 C is shown in Figure 3. From the graph, it is clear that the suspension of ALT released almost 97.51%.4% of the drug at the end of 4 h, while the SLNs loaded with ALT released 69.19% % of drug after 4 h. The drug release pattern of SLNs loaded with ALT was bi-phasic in nature; with an initial burst release followed by sustained drug release. The initial burst release of about 45% was observed within the first h, and after that, the release rate was sustained. This burst release may be due to the presence of the free drug ALT in the external phase and on the surface of the SLNs. The initial burst release rate was also affected by the change in concentration of lipid and surfactant in the external phase. When the concentration of lipid increased, the initial burst release rate decreased, due to the higher concentration of drug present in the inner core. Meanwhile, as the surfactant concentration increased, the initial burst release rate also increased, due to an increase in the drug s solubility in the external phase. This bi-phasic release pattern (burst release) can be used to improve the penetration of the drug, and sustained release can provide a drug supply for a prolonged time period. The release kinetics from the optimized formulation of ALT-SLNs was compared to different kinetic models. Results showed that the model was best fitted with data in the Higuchi equation (R.986). This model explains the drug release from an insoluble matrix, time-dependently based on Fickian diffusion (Higuchi 1963). The release Cumulative % drug release ALT-Suspension ALT-SLNs dispersion 1 3 Time in hours Figure 3. Release profile of the optimized ALT-SLN dispersion in 6.8 ph phosphate buffer at 37 C.

10 Optimization of altretamine-loaded SLNs 579 constant was computed from the slope of the suitable plots, and the regression coefficient was determined (Table IV). Moreover, the values obtained proved that next to the Higuchi model, the best linearity was followed by the first-order kinetics model (R.914). Stability studies Stability studies of optimised SLNs under two storage conditions were carried out, and the changes in particle size, zeta potential, and EE were evaluated as the prime parameters. The results showed that there was a slight increase in the particle size during the three months of storage at 4 C and 5 C, from nm to nm and nm respectively. However, in case of zeta potential, no significant changes were observed after three months of storage. The percentage of EE of the optimised SLNs was found to be 93.1%.68% and after 3-monthstorage at 4 C and 5 C, it was found to be 91.5%.7% and 89.7%.11% respectively. Figure 4A shows the changes in particle size against storage time. The particle sizes of SLNs increased significantly during the storage periods at 4 C, whereas the particle sizes increased slowly when stored at 5 C. This indicates that the drug ALT can remain within the SLNs for a sufficient time. In addition, the percentage of EE also decreased significantly during storage at 4 C [Figure 4B]. Thus, the optimized ALT-SLNs was found to be stable at two different storage temperature conditions for 3 months. Conclusion The hot homogenization-sonication method was employed to prepare the ALT-loaded SLNs. The SLNs were optimized using the Box-Behnken factorial design response surface methodology. The experimental values of the prepared SLNs under the optimum conditions were mostly close to the predicted values. ALT-SLNs, under the optimized conditions, gave rise to an % EE of ( ), percentage of DL of ( ) %, and mean diameter of (1 1.8) nm. The results of the in vitro drug release studies demonstrated significantly low release of ALT (69.19 %) from ALT-SLNs, as compared to the dispersion of pure drug (97.51%). These results indicate that the SLNs can be potentially exploited as a carrier system, with an initial dose and sustained release when therapeutically desired. Thus, RSM is a suitable tool for optimization of the drug-loaded SLNs to achieve a biphasic release pattern. Further, parameters can be optimized by systemic approach for therapeutic effect of these SLNs in vivo. Therefore, future studies about the structure, size, efficacy, and toxicity of solid lipid nanoparticles loaded with ALT is crucial. Table IV. Model-fit for optimized ALT-SLNs. Models Slope R Zero order First order Higuchi Hixson- Crowell Korsmeyer-Peppas (A) 14 Particle size in nm (B) 1 % Entrapment Efficiency Acknowledgement The authors are thankful to the Director, University Institute of Pharmacy, Pt Ravi Shankar Shukla University Raipur, Chhattisgarh, for providing necessary infrastructural facilities. The authors are also thankful to CCOST/MRP/1 Endt. No. 196 and UGC-MRP F. No. 4-76/13 (SR), for providing financial assistance relating to this work. One of the authors (BG) is thankful to UGC-BSR for JRF, Department of Science and Technology (DST-FIST). Declaration of interest The authors report no declarations of interest. The authors alone are responsible for the content and writing of the paper. References Storage time in months at 5 C at 4 C at 5 C at 4 C storage time in months Figure 4. (A) Effect of storage conditions on the particle size of SLNs. (B) Effect of storage conditions on the percentage of entrapment efficiency of SLNs. Alex MR, Chacko AJ, Jose S, Souto EB. 11. Lopinavir loaded solid lipid nanoparticles (SLN) for intestinal lymphatic targeting. Eur J Pharm Sci. 4: Amidon GL, Lennernas H, Shah VP, Crison JR A theoretical basis for a biopharmaceutical drug classification: the correlation in vitro drug product dissolution and in vivo bioavailability. Pharm Res. 1:413 4.

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