5TH International Congress on Technology - Engineering & Science - Kuala Lumpur - Malaysia (2018-02-01)

Formulation And Evaluation Of Surface Functionalized Nanoparticles Enclosing Myelin Basic Protein For Treatment Of Multiple Sclerosis

Multiple sclerosis (MS) is a neurodegenerative, demyelinating disease where the body’s immune system attacks the central nervous system and leads to progressive disability [1-2]. Earlier, treatment was confined to symptomatic and acute treatment of relapses. New strategies focused on induction of long-term antigen-specific tolerance rather than broad-based immunosuppression is under investigation. However, even immunomodulators do not directly enhance the process of remyelination of damaged neurons [3]. Recent studies showed that administration of antigenic fragments of myelin basic protein (MBP) reduces the severity of the disease through a tolerance mechanism [4-6]. Utilization of nanotechnological approaches for the delivery of bio-drugs is becoming an important tool to overcome challenges of short half-life, poor bioavailability, side effects, and instability in biological milieu [7-10]. The objective of this work is the production and evaluation of surface-functionalized nanoparticles (NP) of rhMBP using an FDA-approved biodegradable poly (D,L-lactic: glycolic acid) polymer (PLGA) and surface modified antibodies that help in remyelination and recovery of damaged neurons. The optimum formulation will be evaluated in MS animal model. Preliminary trials were carried out using human serum albumin (HSA) as a model protein used to load the PLGA NP to lower the cost of formulation. The loading technique was single o/w emulsification using a homogenizer (WiseMixTM HG15D, Daihan Scientific Co., Ltd, Korea) at 12,500 rpm to emulsify 2 mL dichloromethane containing different polymer concentrations in 10 mL aqueous solution of different protein and PVA concentrations for 3 min. Further stirring of the emulsion on magnetic stirrer (WiseStirTM, Wisd Lab. Instruments, USA) at 1200 rpm for 2 h at ambient conditions was carried out and a centrifuge was used to separate NP formed from the free protein drug (18,000 rpm) for 20 min. HSA NP formulations were freeze dried before characterization. D-optimal response surface experimental design was applied to investigate the effect of three independent variables including the polymer PLGA concentration (A), the surface activator PVA concentration (B) and protein HSA concentration (C) on the properties of the NP. The dependent variables are the entrapment efficiency of the protein in NP, the size and charge of the PLGA NP (Table 1). Design Expert software (version 10.0.3, Stat-Ease, USA) was used for analysis and modeling of the responses. Based on the software calculations, a set of points consisted of 20 runs were selected as shown in table 1. The software was also used for data statistical analysis and plotting of the response graphs. ANOVA test was used to evaluate the significance effect of the variables on the responses (P-value < 0.05). Second order polynomial function was fitted to correlate the design variables and the responses. Results showed that the percent of HSA entrapment efficiency varied from 6.25 (F4) to 63.984% (F12). The PLGA NP acquired size ranging from 224 (F14) to 441.1 nm (F5) and a negative charge ranging from -5.53 (F10) to -33 mV (F1). The following quadratic equation describes the quantitative effects of the independent variables on entrapment efficiency Entrapment efficiency = +47.95 +0.91*A +5.44*B +3.25*C -0.69*AB -5.62*AC -3.72*BC +5.44A2 -15.36*B2 -13 .4*C2 (Equation 1) ANOVA analysis of the data indicated that B, B2 and C2 were significant model terms (P < 0.05). Entrapment efficiency increased by increasing PVA concentration and decreased by increasing the square of polymer concentration. The opposite signs indicated that PVA behaved differently in the low concentration and in the high concentration states. As shown in figure 1, Initial increase in entrapment efficiency can be observed by increasing PVA concentration till a certain point, followed by a significant decrease in entrapment efficiency by any further increase. The negative signs of the regression coefficients of C2 indicated that high protein concentration significantly decreased entrapment efficiency. This effect can be attributed to the high viscosity of solutions with high PVA concentration. Linear 2FI model best fitted the relation between independent variables and particle size of the formulated nanoparticles as shown in equation 2: Z-average = +317.59 -4.99*A -47.59*B -22.43*C +48.42*AB -30.65*AC +18.57*BC (Equation 2) ANOVA analysis indicated that B, C, AB and AC were significant model terms (P < 0.05). Figure 2 shows that increasing both PVA concentration and protein concentration significantly decreased particle size while increasing PLGA concentration increased particle size. Linear 2FI model best fitted the relation between independent variables and zeta potential of the formulated nanoparticles as shown in equation 3: Zeta potential = -20.4 -4.55*A -1.20*B -5.20*C +7.04*AB – 4.50*AC +2.87*BC (Equation 3) ANOVA analysis indicated that A, C, AB, AC and BC were significant model terms (P < 0.05). Figure 3 shows that increasing both PLGA and protein concentration significantly increased negative charge of the formulated nanoparticles hence increasing its stability. It was concluded that the optimum NP was (run 3) and further work will be carried out to load it with rhMBP and carrying on further in vitro and in vivo evaluations.
Keywords: .
Aliaa ElMeshad, Mohamed Abdulla, Medhat Al-Ghobashy, Ahmed Attia, Muhammad Al-Shorbagy, Wael Mamdouh