INTRODUCTION:

Enormous increase in electrical impulses occurs in one focal locus of the brain and/or the entire brain, leading to partial or generalized seizures, this condition is known as epilepsy. Epilepsy is a non-communicable central nervous system (CNS) disorder. Abnormal and drastic neuronal excitation may lead to physical and mental benign ailments and serious co-morbidities. More than 60 million people in the world are affected with this disorder [1]. Despite the developments that happened in the treatment of this disorder, the quality of life of patients suffering from epilepsy remains poor. Drug resistance and recurrence of epilepsy after reduction of medication are the major hurdles in the treatment [2]. Most anti-epileptic drugs (AED) are administered orally or intravenously.

Up to 40% of patients develop drug resistance at later stages of treatment [3,4], resulting in uncontrolled seizures, a higher risk of brain damage and increased mortality rates [5]. Patients experience emotional and behavioral changes, seizures, convulsions, muscular spasms, depression and, in some cases, unconsciousness [6].

Drug-resistant epilepsy is a formidable health issue. Drugs for epilepsy suffer from poor bioavailability and eventually become ineffective over the course of treatment due to drug resistance [7]. Epilepsy treatment is often complicated due to the inability of available AEDs to cross the adjunctive blood brain barrier (BBB), which could be overcome through appropriate drug delivery systems. The ideal system would provide localized and controlled release of AEDs to targeted sites in the brain to help reduce drugassociated toxicities and enhance the efficacy of the drugs. Several strategies for the effective delivery of AEDs have been reported in the scientific literature.

Nanotechnology-based systems appear to be a promising and innovative development. Several nanostructure drug delivery carriers have recently been reported as an effective CNS delivery systems to overcome the problem of AED elimination at the BBB and result in increased persistence of drugs [7]. Nanotechnology-based medicine (nano-medicine) refers to the surface property characterization and design of nano-carriers for various medicinal strategies [8,9]. Therapeutic agents are embedded into or coated onto nano-carriers, small colloidal or compact structural platforms ranging in size from a 1 to 1000 nm [2,10]. These nano-platforms (NPs) readily interact with the cellular environment at the molecular level to produce the desired physiological response. Nanotechnology-based AEDs have recently garnered attention because of their ability to cross the BBB, improved selectivity and potential for sustained drug delivery to the brain [11]. The size, molecular weight, co-polymer ratio, mechanism of erosion and surface charge are important factors when considering the effectiveness of NPs. For example, the size of the NPs is a very important determinant for its efficiency in crossing the BBB; NPs ranging from 35 to 64 nm easily access most neural tissues [12]. Size-specific NPs synthesis could be achieved through different preparation methods. As a result of the reduction in the sizes of NPs, the nano-carrier system presents a large surface area that can carry large dosages of drugs, efficiently decrease the peripheral toxicity of drugs, and provide adequate delivery of drugs to their targets [7]. The surface charge of NPs is also an important factor in determining their efficiency in brain targeting. It has been reported in the literature that neutral and mildly negatively charged NPs are more effective than positively charged NPs. On the other hand, positively charged NPs are able to make immediate alterations in the BBB (albeit for shorter durations) and are later eliminated by the reticulo-endothelial system (RES) [13, 14].

Solid Lipid Nanoparticles (SLNs) are physiological lipid-based delivery systems that offer physical stability, protection of labile drugs from degradation, ease of preparation and lower toxicity due to their unique properties, including their small size, large surface area, high drug loading and phase interaction at the interfaces; thus, they have the potential to improve the efficacy of pharmaceuticals, nutraceuticals and other materials [15]. SLNs have been developed as an alternative delivery system to the existing traditional carriers, such as liposomes and polymeric NPs. SLNs are new-generation lipid emulsions where the liquid-lipid has been substituted with a solid-lipid [29]. Recently, SLNs have made substantial progress in targeted drug-delivery against various disorders such as cancer and neurodegenerative diseases, including epilepsy [15-19]

The main aim of the current research work is to develop solid lipid Nanoparticles of felbamte (antiepileptic drug) using placket and burman design of experiments.

Felbamate is a PEGylated phenylcarbamate derivative that acts as an antagonist of NMDA receptors. It is used as an anticonvulsant, primarily for the treatment of seizures in severe refractory epilepsy. It is slightly soluble in water with the logP value of 0.56 [20-25].

MATERIALS AND METHODS:

Materials:

Felbamate was received as gift sample from Aurobindo Pharma as gift sample. Stearic acid (Arjun Industries, India), Poloxamer 407 (Signet, Mumbai), Polysorbate 80 (Sisco Research Laboratories, Chennai), Chloroform and Methanol (Rankem, Chennai), Dialysis Membrane 50 – LA 387 (Himedia, Mumbai) were purchased from the local market. All the reagents used were of analytical grade.

METHODS:

Optimization of formulation by Plackett-Burman experimental design:

The development of Felbamate slid lipid nanoparticle process includes, many preparation variables appear to have a noticeable influence on the formulation characteristics (average particle size, span, surface area and poly dispersity index). The formulation can be optimized with proper optimized preparation variables. Evaluating the effect of many preparation variables usually requires many experiments, which are often expensive and time consuming. Consequently, prudent to minimize the total number of experiments done in the optimization process, without sacrificing the quality of prepared Felbamate solid lipid nanoparticles. The process and formulation can be understood by using numerous statistical designs of experiments. However, we have preferred Plackett-Burman design (PBD), which has been frequently used for screening the large number of factors and identifying the critical one in a minimal number of runs with good degree of accuracy. In PBD, the main effect of each variable was calculated as

Exi= 2 (ΣHxi − ΣLxi) / N

Where, Exi is the particular variable main effect, ΣHxi is the summation of response value at the higher level, ΣLxi is the summation of response value at the lower level and N is the number of trials. A main effect figure with a negative sign indicates an inverse relationship between that particular variable, while a positive sign indicates the effect that favors the optimization. The linear equation of Plackett-Burman design is as follows.

Y= b0 + b1 X1 + b2 X2 + b3 X3 + b4 X4 + b5 X5 +···+ bnXn

Where, Y is the response, b0 is the constant and b1, b2...bn are the coefficient of variables X1,

X2...Xn (representing the effect of each variable ordered within −1, +1).

Optimization and statistical analysis:

Based on preliminary experiments and on literature, the influence of independent variable parameters selected were lipid (X₁), surfactant (X₂), and co-surfactant concentration (X₃), aqueous phase volume (X₄), magnetic stirrer rate (X₅), probe sonication duration (X₆), volume of beaker used for sonication (X₇), volume of cold aqueous phase (X₈) on the dependent variable such as average particle surface area (Y₁) of the formulated Felbamate loaded solid lipid nanoparticles (Table 1) was studied.Other parameters, i.e., magnetic stirrer rate and probe sonicator duration were not having a significant impact on particle size and their levels were kept constant for all the experiments. Magnetic stirrer rate has an impact on particle size and was included in the design.

Table 1:Optimization process parameters at lower and higher levels

Factor	Process Parameter	Levels
Factor	Process Parameter	Lower	Lower
X₁	Lipid concentration (mg)	50	150
X₂	Surfactant Concentration (mg)	25	75
X₃	Co-surfactant Concentration (mL)	0.25	0.75
X₄	Volume of aqueous phase (mL)	5	15
X₅	Magnetic Stirrer rate (rpm)	200	400
X₆	Probe sonicator duration (min)	10	30
X₇	Volume of beaker used probe sonication (mL)	125	250
X₈	Volume of clod aqueous phase (mL)	30	50

Twelve experimental runs (Table 2) involving 10 process parameters at higher and lower levels were generated using Design-Expert^® (Version 7.1.5; Stat-Ease, Inc. USA).

Table 2: Scheme for the fabrication of Felbamate solid lipid nanoparticles according to Plackett-Burman experimental design

Run	X₁	X₂	X₃	X₄	X₅	X₆	X₇	X₈
FSN01	150.00	75.00	0.25	5.00	200.00	30.00	125.00	50.00
FSN02	50.00	75.00	0.25	15.00	400.00	10.00	250.00	50.00
FSN03	50.00	75.00	0.75	15.00	200.00	10.00	125.00	50.00
FSN04	50.00	25.00	0.25	15.00	200.00	30.00	250.00	30.00
FSN05	150.00	25.00	0.25	5.00	400.00	10.00	250.00	50.00
FSN06	150.00	75.00	0.75	5.00	200.00	10.00	250.00	30.00
FSN07	150.00	25.00	0.75	15.00	200.00	30.00	250.00	50.00
FSN08	50.00	25.00	0.25	5.00	200.00	10.00	125.00	30.00
FSN09	150.00	25.00	0.75	15.00	400.00	10.00	125.00	30.00
FSN10	150.00	75.00	0.25	15.00	400.00	30.00	125.00	30.00
FSN11	50.00	75.00	0.75	5.00	400.00	30.00	250.00	30.00
FSN12	50.00	25.00	0.75	5.00	400.00	30.00	125.00	50.00

Fabrication of Felbamate solid lipid nanoparticle:

The nanoscale solid lipid particles which contain the drug Felbamate was fabricated from oil-in-water microemulsion technique. In this method, lipid phase containing stearic acid was melted at 69-70°C and the drug was dissolved in the melted lipid. Then the lipid phase was added drop wise into the aqueous phase containing Polaxamer-407 as surfactant and Polysorbate-80 as a co-surfactant, which heated at the same temperature. A transparent, thermodynamically stable o/w microemulsion was obtained under magnetic stirrer at 400 rpm for 15min. This resulting o/w microemulsion was dispersed into cold aqueous medium under probe sonicator for 30 min to solidify the nanoparticles in a volume ratio of 1:1 hot microemulsion to cold water. The fabricated Felbamate loaded nanoscale solid lipid particles were freeze dried on a lyophilizer at -40°C temperature and operating pressure 0.4 bar. The dried powder was stored indesiccators till the analysis.

Determination of Particle Surface Area:

Prepared solid lipid nanoparticles were maintained at room temperature for 30 days, which were characterized for particle surface area using Malvern Zetasizer Nano ZS (Malvern, UK). About 1 ml of prepared solid lipid nanoparticles were diluted appropriatelyusing distilled water, which was then taken individually in a zeta cell and measured the average particle size and polydispersity index. The experiments were performed in triplicate.

RESULTS AND DISCUSSION:

Development of solid lipid Nano particulate drug delivery system using sonication approach:

Felbamate solid lipid nanoparticles were prepared using micro-emulsion method. During preparation, addition of organic phase containing polymer in to the aqueous phase results in rapid miscibility of organic solvent in to aqueous phase leading to increase in the polarity of organic solvent, which in turn decreases the solubility of polymer and initiate nucleation of polymer. However, sonication process inhibits the nucleation of polymer at the initial stage. The cationic nature of polymer provides higher zeta potential to the formed nanoparticles and develops an electrostatic force and keeps the nanoparticles in Brownian motion, which inhibits the further growth of polymeric nanoparticles resulting in the formation colloidal nanoformulation. Brownian motion of nanoparticles overcomes the Van der Waals force of attraction and gravitational force resulting in the prevention of aggregation and sedimentation of nanoparticles.

Prepared nanoparticles were characterized for distribution width, mean particle size, surface area, span, and uniformity using laser particle size analyser. However, these characterization parameters depends on process parameters such as organic solvent, polymer concentration, percentage of organic solvent, volume of aqueous phase, concentration, temperature generated during sonication process, sonication duration and drug concentration. Hence, a step-by-step optimization was carried out to evaluate the effect of these process parameters on prepared polymeric nanoparticles and the particle size spectrum of optimization batch (FSN01 to FSN12). The experiments were performed in triplicate and characterization results were expressed as mean ± standard deviation and student t test (GraphPad Prism software; version 6.0) was used to evaluate the significance of difference. The differences were considered signiﬁcant if P value <0.05 and non-signiﬁcant if P value >0.05.

Table 3: Characterization of prepared SLN’s

Run	Average Particle Surface Area (nm)
FSN01	17.1
FSN02	47.6
FSN03	50.8
FSN04	34.6
FSN05	14.8
FSN06	30.1
FSN07	14.5
FSN08	32.5
FSN09	15.6
FSN10	21.5
FSN11	51.9
FSN12	46.1

Effect of Formulation Variables on Particle Surface Area:

The effect could be explained by following linear model equation Y1, [Surface area=+31.43-12.54 *A+5.11 *B+3.49*C-0.82* D+1.49 *E-0.47 *F+0.82 *G]. Where Y1 is the predicted response of surface area and A, B, C, D, E, F and G are the coded values of concentration of lipid, concentration of surfactant, contration of co-surfactant, volume of aqueous phase, magentic stirrer rate, probe sonicator duration and volume of beaker used probe sonication respectively with their co-effeiceint. F-value of the model 202.56 implies that the model is not significant and the Values of “Prob > F” less than 0.0500 indicate model terms are significant. The predicted R2 (0.9944) and adjusted R2 (0.8985) values implied a good correlation between the obtained and predicted value and those of the fitted models. The value of the coefficient of variation (CV) is 4.13% and indicates the precision and reliability of the model. The independent variable of volume of beaker used for sonication (X7) does not have significant influence on surface area.

CONCLUSION:

Solid lipid nanoparticles were prepared using microemulsion method by stirring and sonication approach. Plackett-Burman factorial design was used to optimize the process parameters. Nanoparticles prepared were within an average particle size <100nm, PDI (i.e. uniformity <0.3). It was concluded from the study that the composition prepared with lipid concentration of 50 mg, surfactant concentration of 75mg, Co-surfactant concentration of 0.75ml, aqueous phase volume of 5ml, magnetic stirring speed of 400rpm, probe sonication duration 30 minutes, volume of beaker used for sonication 500ml and volume of cold aqueous phase 30 ml has shown the smallest particle surface area of 51.9m²/g.

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Received on 31.10.2019 Modified on 27.12.2019

Research J. Pharm. and Tech 2020; 13(9):4185-4189.

DOI: 10.5958/0974-360X.2020.00739.8