Multivariate analysis for the optimization of polysaccharide-based nanoparticles prepared by self-assembly
Graphical abstract
Introduction
Alginate is a natural polysaccharide extracted from brown algae that is widely used in several different fields [1], [2], [3]. FDA classifies alginate among the generally regarded as safe (GRAS) food additives, and purified alginate is commonly used also for biomedical and pharmaceutical applications [4], [5], [6] due to its biocompatibility, biodegradability and low toxicity.
Alginate is a linear unbranched copolymer composed of β-d-mannuronic units (M) bound with units of the C-5 epimer α-l-guluronic acid (G) through (1 4) linkages [7] (Fig. 1). M and G residues, when deprotonated, are negatively charged and in this form they can bind divalent or multivalent cations, thus creating three-dimensional ionotropic gel networks [7], [8]. The gel-forming properties make alginate an interesting compound for drug delivery, in particular for formulations with sustained and controlled release [9], [10], [11]. In fact, the alginate gel network can create a viscous barrier that controls the diffusion of the entrapped molecules depending on the pH and ionic strength of the environment [12], [13]. Moreover, alginate has mucoadhesive properties that can also improve the bioavailability by facilitating prolonged delivery at the site of administration [14].
Depending on the desired application, alginate gels can be prepared in different forms, such as block gels, films, beads, fibers, microparticles, and nanoparticles. Alginate nanoparticles have mainly been prepared through two methods; the alginate-in-oil emulsification method and the self-assembly method [15]. The self-assembly method is particularly interesting since organic solvents are avoided and mild preparation conditions are used allowing for encapsulation of sensitive material [16], [17], [18], [19].
In the self-assembly method, solutions of one or two different cationic crosslinkers are added to an alginate solution under controlled conditions while mixing. This can be followed by further processing, such as sonication and centrifugation. Also other polysaccharide nanoparticles can be prepared through the self-assembly method, such as nanoparticles made with chitosan or pectin [20], [21]. This method of preparation of polysaccharide nanoparticles involves many factors that can affect important characteristics of the nanoparticles. For example, the concentration of the polysaccharide solution, the molecular weight of the polysaccharide and the preparation conditions can affect the size; the crosslinker concentration can modify the polydispersity and the zeta potential; the pH and the ionic strength of the solvent can change the compactness of the particles [17], [21], [22], [23], [24], [25], [26], [27], [28]. Therefore, by tuning different factors, it is possible to achieve the desired characteristics of the nanoparticles.
Nevertheless, empirically finding the optimal levels for all the factors is seldom an easy task since many factors are involved. Moreover, the variation of one factor at a time as in univariate approaches could be limiting since possible interactions between the factors can be hidden. Multivariate analysis can be a valuable tool for analyzing the data when more than one factor is involved in the process [29], [30]. In particular, the partial least squares regression analysis (PLS) is a statistical method that can be used to determine the interactions between the factors and their square effects, the significant factors that modify the response, and the trends of the effects on the response. In addition, PLS can also predict the values of the factors that, based on the model, can provide the optimal response value [31].
In a recent study [26], we have successfully prepared stable alginate nanoparticles by self-assembly using zinc as the crosslinker without the need of additional polycations, which are commonly used for stabilization. In the present study, a PLS was performed for the first time for the investigation of important factors influencing the characteristics of polysaccharide nanoparticles prepared by self-assembly. The formulation factors of the new nanoparticulate system made with alginate were optimized by the use of factorial design and multivariate evaluation for providing nanoparticles suitable for drug delivery purposes with a low polydispersity and monomodal size distributions. Two different designs were investigated. The first design was used to study the factors crosslinker (zinc) concentration and polysaccharide concentration. The second design was used to investigate the possibility of producing nanoparticles at the highest level of alginate concentration, since a high polysaccharide concentration can be preferred for drug delivery formulations. This was examined by varying the ionic strength of the solvent and the crosslinker concentration. The response variable investigated in the multivariate analysis was the polydispersity index (PDI) of the samples. In addition, the average size, the size distributions, the zeta potential and the pH were recorded.
Section snippets
Materials
Water-soluble alginate (sodium alginate, Protanal LF 10/60) was manufactured by FMC BioPolymer (Norway). The alginate was purified prior to utilization and characterized for a viscosity average molecular weight of 147 kDa [26]. The alginate content of G stated by the manufacturer was 6575%. Zinc chloride (purity 98.0%) was supplied from Merck (Germany), and sodium chloride (purity 99.9%) was supplied from VWR BDH Prolabo (USA). The water used throughout the study was purified by deionization
Results and discussion
The size is one of the most important characteristics of the nanoparticles, since it influences important pharmaceutical properties, such as the bioavailability, the drug release rate, the toxicity, the biodistribution and passive targeting [16], [33], [34], [35]. Consequently, it is important for the particles within a batch to be homogeneously sized in order to have the same properties. Accordingly, the width of the size distribution of the nanoparticles, measured as the PDI, should be as
Conclusions
In this study, multivariate evaluation was applied to scrutinize the preparation of nanoparticles by self-assembly. PLS allowed us not only to investigate the effect of the factors on the PDI of the nanoparticles as in univariate analyses, but it also revealed the relation and the trends of the factors in term of a combined effect on the PDI. Therefore, PLS can be a useful tool for obtaining a more complete understanding of the effects of the factors involved in the formulation of
Acknowledgments
The authors acknowledge FMC BioPolymer for kindly donating the alginate.
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