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Microfluidic Directed Synthesis of Alginate Nanogels with Tunable Pore Size for Efficient Protein Delivery Salime Bazban-Shotorbani, Erfan Dashtimoghadam, Akbar Karkhaneh, Mohammad Mahdi Hasani-Sadrabadi, and Karl I. Jacob Langmuir, Just Accepted Manuscript • DOI: 10.1021/acs.langmuir.5b04645 • Publication Date (Web): 03 Mar 2016 Downloaded from http://pubs.acs.org on March 3, 2016

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Microfluidic Directed Synthesis of Alginate Nanogels with Tunable Pore Size for Efficient Protein Delivery Salime Bazban-Shotorbani†,+, Erfan Dashtimoghadam‡,+ , Akbar Karkhaneh†, Mohammad Mahdi Hasani-Sadrabadi†,§,*, Karl I. Jacob§,∆,*

†

Department of Biomedical Engineering, Amirkabir University of Technology, Tehran, Iran.

‡

Department of Developmental Sciences, Marquette University School of Dentistry, Milwaukee,

53201 WI, United States. § Parker H. Petit Institute for Bioengineering and Bioscience, G.W. Woodruff School of Mechanical Engineering, Georgia Institute of Technology, Atlanta 30332, GA, USA. ∆ School of Materials Science and Engineering, Georgia Institute of Technology, Atlanta 30332, GA, USA.

KEYWORDS: Alginate nanogel; Microfluidics platform; structure-property relationship; Controlled release; Oral protein delivery.

ACS Paragon Plus Environment

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ABSTRACT: Alginate is a biopolymer with favorable pH-sensitive properties for oral delivery of peptides and proteins. However, conventional alginate nanogels have limitations such as low encapsulation efficiency because of drug leaching during bead preparation and burst release in high pH values. These shortcomings originate from large pore size of the nanogels. In this work, we proposed an on-chip hydrodynamic flow focusing approach for synthesis of alginate nanogels with adjustable pore size to achieve fine-tunable release profile of the encapsulated bioactive agents. It is demonstrated that the microstructure of nanogels can be controlled through adjusting flow ratio and mixing time directed on microfluidic platforms consisting of cross-junction microchannels. In this study, the average pore size of alginate nanogels (i.e. average molecular weight between crosslinks, Mc) was related to synthesis parameters. Mc was calculated from equations based on equilibrium swelling theory and proposed methods to modify the theory for pH-sensitive nanogels. In the equations we derived, size and compactness of nanogels are key factors, which can be adjusted by controlling the flow ratio. It was found that increase in flow ratio increases the size of nanogels and decreases their compactness. The size of on-chip generated nanogels for flow ratio of 0.02-0.2 was measured to be in the range of 68-138 nm. Moreover, a method based on the Mie theory was implemented to estimate the aggregation number (Nagg) of polymer chains inside the nanogels as an indicator of compactness. According to the size and compactness results along with equations of modified swelling theory Mc obtained to be in the range of 0.5-0.8 kDa. The proposed method could be considered as a promising approach for efficient polypeptides encapsulation and their sustained release.

Introduction


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During last decade, medical application of nanotechnology, termed nanomedicine has received tremendous attention to develop novel nanoscale therapeutic and diagnostic systems. One of the most interesting area in this field is drug delivery systems, which offer approaches and formulations to transport pharmaceutical compounds with the main purpose of targeted and/or rate controlled delivery [1, 2]. Among various nanostructured systems, polymeric nanoparticles are promising carriers [3, 4]. One of the most important parameters in design of ideal nanoparticulate drug delivery systems is understanding and prediction of their structure-property relationship (SPR). In this regard, the central challenge is to achieve a platform for synthesize of homogenous nanoparticles in a reproducible manner. Recently, we have implemented microfluidic approach to synthesis monodisperse polymeric nanoparticles based on hydrodynamic flow focusing technique [5-8]. In this method, time of mixing which profoundly affects size and microstructure of nanoparticles could be finely adjusted, which in turn determine release rate of bioactive agents from the polymeric nanocarriers. The objective of the present study is to demonstrate a correlation between on-chip time of mixing and the average pore size of the synthesized alginate nanogels. To this end, calculating of the average molecular weight between crosslinks, Mc, is needed which can be obtained from our proposed equations based on the equilibrium swelling theory [9]. Alginate is a linear polysaccharide, composed of alternating blocks of 1–4 linked α-L-guluronic and β-D-mannuronic acid residues. The gelation and crosslinking of this biopolymer is achieved by the exchange of sodium ions from the guluronic acid with divalent cations such as Ca2+. pHsensitivity, biocompatibility, bio-adhesiveness and mild gelation conditions have made alginate suitable for designing of controlled delivery systems [10-12]. One of the main applications of alginate is oral delivery of peptides and proteins [13]. The central challenge in oral delivery of


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peptides and proteins is their instabilities in the harsh acidic environment of stomach. The pH environment of gastrointestinal tract varies from acidic in the stomach to alkaline in the intestine and colon; therefore, using a pH-sensitive polymer such as alginate is an effective way for successful oral delivery of proteins [14]. Calculation of Mc for pH-responsive networks is more complicated than the others. In this study, we will explain the procedure for calculating Mc for pH-sensitive alginate nanogels at intestinal fluid condition after passing simulated gastric fluid. This procedure can be implemented for other pH-sensitive systems, which designed for delivery in gastrointestinal tract or other environments with different pH values. Finally, the loading efficiency and release profiles of a model protein, Bovine serum albumin (BSA), from synthesized nanogels in correlation with their pore size will be studied. Experimental Section A cross-junction poly (dimethylsiloxane) (PDMS) microfluidic device with three inlets and one outlet was microfabricated for synthesizing nanogels. To make the master mold, a silicon wafer was covered with SU-8 50 photocurable epoxy by spin-coating. This process resulted in a uniform layer of the photoresist with a thickness of 60 µm on the wafer surface. The prebaking process was then used to evaporate the coating solvent and to densify the photoresist after spincoating. Subsequently, the photolithography procedure was performed to obtain microchannels on the wafer. The wafers were then annealed at 150°C to eliminate cracks from the surface of SU-8. For preventing the PDMS from sticking to the mold, the surface of the annealed molds was covered with self-assembled monolayer (SAM) of trimethylethoxy silane by vapor exposure for 40 min. A mixture of PDMS (Sylgard 184) and curing agent (10:1 weight ratio) was poured over the fabricated molds, degassed and cured at 80°C for 1 h in an oven. After removal of the cured PDMS from the mold, access holes for channels with 150 µm diameter were punched.


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Finally, the PDMS layer was bonded to a glass slide by exposure of both surfaces to oxygen plasma (100 mW) for 1 min. The designed microchips were consisting of microchannels with 60 µm height, 150 µm width, and 1 cm length. The microchip dimensions are selected as optimized values based on our recent results. Alginate nanogels were synthesized using the newly fabricated microchip based on the hydrodynamic flow focusing procedure. For this purpose, alginate (Mn=208,000 g.mol-1 , PDI=3.1) aqueous solution at initial concentration of 1 mg.ml-1 as the core flow (flow rate: 0.5 µl min−1) was hydrodynamically focused by the lateral CaCl2 (1 mM; flow rate: 24.0-2.8µl min−1) streams into a narrowly focused stream; so, polymer chains were crosslinked by Ca2+ ions to form alginate nanogels (Scheme 1). Other polymer concentration of 0.5, 2 and 5 mg.ml-1, were used as well but the constant salt concentration was used which was selected in excess to grantee the complete gelation of alginate during the flow focusing process.

Mixing time,

τ mix , is an critical parameter in this procedure and can be estimated based on

equation 1 [15],

τ mix ≈

wf 2 4D

≈

w2 1 2 9D  1  1 +   FR 

(1)

where, D is diffusivity of the calcium ions (1.3×10-9 m2 s-1 at 25 ˚C [16]), wf is width of the focused stream ,w is channel width (150 µm) and FR is the ratio of flow rate of the polymeric stream to the total flow rate of water. In this study, the diffusive mixing time for FR= 0.02-0.2 (based on core flow of 0.5 µl.min-1 and sheath flow of 24-2.8 µl.min-1 ) is in the range of 2-65 ms based on equation 1.


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The size and size distribution of the obtained nanogels in pH 7.4 were studied by dynamic light scattering method (DLS; Zetasizer 3000HS, Malvern Instruments Ltd., Worcestershire, UK). The nanogels were washed and then suspended in the simulated gastric fluid (SGF). SGF was prepared by dissolving 2 g sodium chloride and 3.2 g purified pepsin in 7ml of hydrochloric acid and diluted with water to 1000 ml. The pH of the test solution was 1.2 [17]. In the equilibrium state, the size of nanogels was measured. The size of suspended nanogels in the simulated intestinal fluid (SIF) was also measured. SIF was prepared by dissolving 68.05 g potassium dihydrogen phosphate (KH2PO4) and 8.96g sodium hydroxide in 10 L water. The pH of this solution was 6.8 [17]. The size of nanogels was determined in their equilibrium swelling state by DLS method. The spectrophotometeric method (Shimadzu UVmini-1240 UV-Vis spectrophotometer) was employed to measure the transmittance of the alginate nanogel suspensions at room temperature. The turbidity, τ , was calculated from the transmittance based on the Beer- Lambert law [18],

1 L

 It    I0 

τ = − ln 

(2)

where L is the thickness of the cell (1 cm quartz cuvette), It and I0 are the intensity of the light transmitted through the sample and solvent (deionized water), respectively. Measurements were performed at 635 nm at least three times and the mean values are reported.

n Deionized water was used as solvent and the refractive index of this solution, 0 , was measured with a refractometer (PTR 46, Index Instruments, UK). The values of the refractive index increment (dn/dc) of the alginate solutions (1 mg.ml-1) were measured using the asymmetric flow


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field-flow fractionation (AFFFF) method (AF2000 FOCUS system, Postnova Analytics, Landsberg, Germany) at the wavelength of 635 nm. To study the in vitro release profile, Bovine serum albumin (BSA)-loaded alginate nanogels were dispersed in phosphate buffered saline (PBS, pH 7.4). The suspensions were then placed into dialysis Cassettes (3.5K MWCO, Thermo Scientific), which were immersed in 1L of similar solutions, gently shaken and incubated at 37°C. At predetermined time points, samples were collected and replaced with an equivalent volume of the fresh test solution. BSA-FITC was used for entrapment in alginate nanogels and encapsulation efficiency (EE) was determined by spectrofluorimetry method (Shimadzu UVmini-1240 UV-Vis spectrophotometer). The EE of nanogels was calculated as: EE (%) = (1 – C1/C0)*100, where C0 and C1 are the total and supernatant concentration of BSA.

Results and discussion Hydrodynamic size of nanogels A cross-junction microfluidic platform was used to fabricate alginate nanogels through the hydrodynamic flow focusing. As displayed in Scheme 1, the chip consists of one inlet channel for alginate solution as core flow, two inlets for CaCl2 as lateral focusing flows and one outlet for the particles. The key parameter in determining the size of formed nanogels is the flow ratio of inlet sheath and core flows, which affects the mixing time according to Equation 1. Accordingly, by controlling the flow ratio, we can adjust the mixing time of the ionic crosslinking and modulate the size of the formed nanogels. Considering alginate chains as anionic polyelectrolyte,


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nanogels are formed through diffusion-mediated mass transfer of Ca2+ ions into the focused polymer solution stream in a controlled manner.

Scheme 1. The schematic representation of the microfluidic assisted approach to generate alginate nanogels with tunable size and pore size.

Figure 1 show the effect of flow ratio and time of mixing on the diameter of the synthesized nanogels. As can be seen, the size of the nanogels increases with increasing flow ratio. Increase in flow ratio increases the mixing time and results in larger nanogels, which is attributed to more


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available alginate chains in the core flow to be crosslinked with diffusing ions. This observation will be discussed more quantitatively based on the compactness of formed nanoparticles and number of aggregated chains.

Figure 1. Effect of flow ratio and mixing time on hydrodynamic size of microfluidic synthesized alginate nanogels and their turbidity at pH=7.4.

It should be indicated that the microfluidic alginate nanogels, over all flow ratios, were found to be smaller and more monodisperse than the corresponding bulk synthesized nanogels. The polydispersity index (PDI) for on-chip generated particles was found to be lower (PDI< 0.2) than those prepared by the bulk mixing method (PDI >0.4). The broader PDI of Bulk synthesized particles is typically associated with the residence time distributions due to turbulent and


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convective mixing. The narrower size distribution of microfluidic particles could be ascribed to their rapid nucleation followed by growth of nanoparticles with locally limited number of accessible polymer chains provided by microfluidic flow focusing. In other words, aggregate formation is governed by the competition between contact time of colliding particles and the time frame required for formation of permanent interactions between particles. As the former time scale is suppressed in the hydrodynamically focused streams, the forming nanogels behave as elastic particles in collision. The other important parameter affects the size of microfluidic nanoparticles is polymer concentration. At a constant flow ratio, increasing the initial polymer concentration increases the number of polymeric chains available for nanoparticle formation. So, increases the size of formed nanogels as can be seen in Figure 2.


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Figure 2. Effect of polymer concentration and flow ratio on hydrodynamic diameter of resulted alginate nanogels.

pH sensitivity of nanogels Anionic pH-responsive nanoparticles such as alginate nanogels swell more at elevated pH values. In fact, ionization takes place as the pH of the medium rises above the pKa of the ionizable moieties (i.e. carboxyl groups). At high pH values, the COOH groups convert to COO-, which leads to electrostatic repulsion among charges present on the chains. This repulsion increases the swelling ratio, so the size of nanogels is increased. In lower pH values, the COOgroups convert to COOH and the present charges on the chain are screened; so, the nanogels start to shrink [19].


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Swelling ratio of pH-sensitive nanogels depends on the degree of ionization of the polymer. This parameter can be approximated using different methods. Equation 3 can be used to calculate the ionization degree of anionic networks comprising carboxyl ionizable groups with equilibrium constant of Ka [20].

i=

Ka 10 − pK a = − pK − pH K a +  H +  10 a + 10

(3)

The pKa values of the carboxyl groups have been reported to be 3.65 for guluronate and 3.38 for manuronate [21]; so, we can safely assume that the mean pKa value for alginate chains is around 3.5. By using Equation 3, the ionization degree of alginate nanogels in SGF (pH 1.2) and SIF (pH 6.8) are obtained as 0.004 and 0.99, respectively. In view of this, the equilibrium swelling ratio and size of nanogels in SGF is much lower than in SIF. The size of synthesized nanogels in SGF and SIF is displayed in Figure 3. As shown, there is no significant difference observed in size of nanogels at neutral and SIF; however, nanogels were found to shrink at SGF which is related to the screening of the electrostatic repulsion forces among carboxyl groups on alginate chains. Such a difference becomes more pronounced with increasing flow ratio, which will be discussed in terms of the compactness of particles.


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Figure 3. Size of microfluidic alginate nanogels formed at various flow ratios in simulated gastric (SGF) and intestinal fluids (SIF) in comparison with their size in neutral pH.

Compactness and aggregation number of polymer chains in the nanogels Generally the hydrodynamic size of the colloidal particles is determined by the polymer contents and their state of swelling in the suspension. The interplay of inter- and intraparticle associations is affected by various parameters such as temperature, electrostatic interactions, and pH, which makes interpretation of changes in particle size complicated. As the size of alginate nanogels depends on the number of crosslinked chains and their swelling ratio, it is important to investigate the local polymer concentration inside the nanogels, which directly affect their release behavior


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Here, we calculate compactness (i.e. degree of swelling) of the spherical nanogels based on their size and turbidity of the corresponding suspensions in order to provide insight to the effect of various parameters (Figure 1). The turbidity of nanoparticulate suspensions is dependent on size of particles, number density of particles, and the difference in refractive index between the particles and the surrounding media [18]. According to a recently developed method on the basis of Mie theory [18, 22], Equation 4 can be employed to estimate the local polymer concentration, cNP, inside the spherical nanoparticles:

τ=

3ct 2c NP Rh

  2  1 1 − cos ( wc NP )     1 − sin ( wc NP ) −  wc NP   wc NP 

(4)

where τ , ct , Rh are the turbidity of the nanogels suspension, the total polymer concentration in suspension, the hydrodynamic radius of the particles, respectively, and  dn  w = 4π Rh   / λ n0  dc 

(5)

where λ is the wavelength for the turbidity measurements, n0 is the refractive index of the solvent and dn/dc is the refractive index increment of the polymer. In this study, at the wavelength of 635 nm, the values of n0 and dn/dc were measured to be 1.3337 and 0.154ml/g, respectively. It should be noticed that as this approach is valid for spherical particles with a narrow size distribution, low PDI of the synthesized microfluidic nanogels fulfill this prerequisite. Figure 4a shows the values of calculated cNP at different flow ratios based on Equation 4. As shown, compactness of nanogels is decreased with increasing flow ratio. This trend could be explained with decreasing number of diffusing calcium ions per alginate chains in the focused stream, which results in less crosslinked forming nanogels. It is also worthy of note that this observation is well consistent with the increasing time of mixing with increasing flow


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ratio (Figure 1). Despite higher polydispersity of nanogels synthesized using bulk mixing method, their compactness was also calculated to provide a rough estimation. The obtained results showed lower compactness values for the bulk particles compared to the microfluidic nanogels even at the highest flow ratio. As indicated above, the nanogels synthesized at higher flow ratio were found to show higher change in size with decreasing pH (SGF condition), which could be interpreted that less compact microstructure of such nanogels make them more susceptible for diffusion of ions and shrinkage as a consequence. After determination of cNP , the molecular weight of the spherical nanogels, M NP can be obtained as, 4 M NP = π Rh 3cNP N A 3

(6)

where N A is Avogadro’s number; so, we can calculate the aggregation number of polymer chains in the nanogels, N agg from,

N agg =

M NP M0

(7)

where M 0 is the number average molecular weight of the polymer chains. The calculated M NP and N agg values at different flow ratios were shown in Figure 4a and 4b, respectively. As can be seen, MNP and Nagg are increased with increasing flow ratio. Taking into account such findings combined with increasing the size of nanoparticles with increasing flow ratio corroborates less compact nanogels comprising more aggregated chains are formed with increasing mixing time (i.e. increasing ratio of alginate chains to calcium ions at higher FR range).


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Another feature of a nanoparticulate suspension is the average number density of particles per volume (NNP), which is scarcely reported due to experimental difficulties. Having estimated the local polymer concentration inside the nanoparticles, NNP can be estimated by Equation 8:

=

(8)

As can be seen in Figure 4b, NNP is decreased with increasing flow ratio, which is directly related to the increasing average number of aggregated chains in particles. Overall, such findings imply that the microstructure of nanogels could be manipulated through tuning the flow properties using the microfluidic method.


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a)


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b)

Figure 4. Local polymer concentration (cNP) inside the alginate nanogels (a), the molecular weight (Mnp) of alginate nanogels (a), aggregation number (Nagg) of alginate chains in the nanogels synthesized at various flow ratios (b) and number density of nanogels (NNP). The lines are only guides for the eyes.

Calculation of average molecular weight between crosslinks, Mc, in alginate nanogels based on the equilibrium swelling theory The theory to explain the equilibrium swelling of polymeric networks was first proposed by Flory and Rehner [23, 24]. This theory was then modified for polyelectrolytes such as pHsensitive polymers by Peppas [25]. As discussed in supporting information, the modified swelling theory can lead to equation 9 which can be used for calculation of M c for nonGaussian polyions such as alginate [26] in electrolyte solutions such as GIT solutions.


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V  2M c ln (1 − υ2,s ) + 0.06υ2,s 3 + 0.44υ2,s 2 + υ2,s  + 1 1 −   M υ M0  c

 υ2,r 

  υ2,s  υ2,r 

1

 3 1  υ2,s −   2  υ 2,r 

  Mr   1 +    2M c

 υ2,s   υ 2,r

  

1

3

   

2

−3

2   Mr υ2,s 3  = 1 −  2M c 

V 1 C di − υ (C s∗ − C s ) 

(9) The parameters appearing in this equation are the specific volume of the polymer, υ , the molar volume of swelling agent, V1, the polymer volume fraction in swollen gel, υ2,s , the polymer volume fraction in relaxed gel (formed right after crosslinking and before swelling), υ2,r and the concentration of dissociated ions of the polymer, C di which can be obtained by C di =

φ i υ2,s υM r

(10)

Where M r is the molecular weight of the monomer and φ is the fraction of effective fixed charges of polyion, which does not participate in ion-pairing phenomenon (look at supporting information). The other parameters of equation 9 are Cs and Cs∗ which are the concentrations of electrolyte inside and outside of the gel. Our proposed method for prediction of these two parameters is discussed in supporting information, which leads to a simple graph as a function of υ2,s .

υ2,r and υ2,s not only are the main parameters of equation 9 but also have an important rule in calculation of C di , C s and C s∗ . So, these two parameters are the key parameters in calculation of

M c . υ2,r and υ2,s can be expressed from


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υ2,s =

Vp

υ2,r =

Vp

Vs

Vr

=

=

mpυ Vs mpυ Vr

=

=

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M 0 N agg υ

(11)

Vs M 0 N agg υ

(12)

Vr

where Vr and Vs are volumes of the network polymer in relaxed and swollen states respectively, and Vp is the dry polymer volume which is equal to m p υ , where m p is the dry polymer mass. As seen in two latter equations, υ2,r and υ2,s are dependent on compactness and size which both affected by flow ratio. So, M c and pore size of the nanogels can easily be adjusted by controlling the flow ratio. cm , V = 18 cm With M0 = 208 kg , M r = 198 g , , φ = 0.4 and average 1 mol mol υ = 0.623 mol g 3

3

pKa of 3.5 [21] and using the compactness and size data of previous sections, M c can be

calculated. Figure 5 shows M c of synthesized nanogels in various flow ratios.


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Figure 5. Average molecular weight of alginate chains between crosslinks, Mc, for different nanogels formed at different flow ratios

Calculation of average pore size

Mesh size or correlation length is the average linear distance between two adjacent crosslinks and can be calculated using Equation 13 [27], 1

 2C M  2 ζ =υ  n c  l  Mr  1 3 2, s

(13)

where Cn is the characteristic ratio, l is the bond length of the polymer backbone and M c is the average molecular weight between crosslinks that can be calculated using the equilibrium 0

swelling theory as discussed. As shown in Figure 6, with l = 5.15 A [28] and Cn = 22.31 for average molecular weight of 208 kg

mol

[29], average pore size of alginate nanogels in SIF after

passing SGF can be calculated.


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Figure 6. Calculated results for average pore size of alginate nanogels synthesized in various flow ratios.

Encapsulation efficiency and release profile of BSA-loaded alginate nanogels

Conventional Ca2+-alginate nanoparticles have limitations such as low encapsulation efficiency because of drug loss during preparation by leaching through the pores and burst release especially in high pH values. These shortcomings originate from large pore size of nanogels. So, many modifications of alginates such as chemical modification of polymer chains and mixing of polymer solution with an oppositely charge polyelectrolyte have been experimented to overcome this problem[10]. Chemically modified alginate hydrogels can be prepared by covalent binding


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of alkyl groups on to the polymer chains. Encapsulation efficiency of protein drugs such as BSA in these hydrophobically modified alginate nanogels were found to be acceptable and controlled release of proteins was obtained[30]. Furthermore, covalent attachment of cysteine to alginate backbone can reduce pore size of nanogels and improve the encapsulation yields[31, 32]. Mixing of a polyanion like alginate with polycations like chitosan and poly-L- lysine have been found to help solve the problem of large pore size[33, 34]. Chemical modification and polyelectrolyte complexes can improve the encapsulation efficiency and release pattern of alginate nanocarriers but these procedures are not only complicated but also can cause the loss of pH-sensitivity of alginate nanogels. Our simple procedure results high pH-sensitive nanogels (as discussed previously) and much better encapsulation efficiency and more sustained release without any modification of polymer. BSA encapsulation efficiency is defined as the fraction of available BSA incorporated into the alginate nanogels. The obtained results revealed the on-chip generated nanogels can reach high encapsulation efficiencies (>70% in 10 wt% initial BSA content). Encapsulation efficiency (%EE) is an important characteristic for delivery systems and higher EE could result in increased system efficiency. Figure 7 shows the in vitro release profiles of BSA-loaded alginate nanogels at pH 7.4. As discussed in the previous section, decreasing the flow ratio decreases the average pore size of nanogels. There is a direct relationship between pore size of nanogels and release rate. Therefore, as shown in Figure 7, decreasing the flow ratio provides nanogels with slower release profiles. It is worthy of note that microfluidic nanogels, over all flow ratios, exhibit more sustained patterns compared to bulk synthesized nanogels. Such an observation could be attributed to higher compactness and lower average pore size of on-chip synthesized particles. The other important


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parameter in determining the rate of release from nanogels is tortuosity. Tortuosity is the path that drug molecules must navigate to penetrate the gel [35]. High polymeric content in the matrix of our microfluidic nanogels, over all flow ratios, increases the tortuosity leading to decreasing the release rate, as seen in Figure 7.


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Figure 7. In vitro release patterns of BSA from microfluidic alginate nanogels at flow ratios of 0.02, 0.07, 0.1 and 0.18 in comparison with bulk synthesized nanogels.

Conclusion Pore size is one of the most important parameters in release of pharmaceutical compounds from diffusion-based drug delivery systems. Finding a relationship between pore size and syntheses parameters can be helpful in adjusting the syntheses procedure with desired release profile. Our chosen polymer, alginate is a pH-sensitive polysaccharide and can be used in designing of oral protein delivery nanogels. The main limitation of these nanogels is their large pore size. The developed microfluidic NPs in this study have relatively small pore size and their pore size can be controlled and adjusted by changing the synthesis parameters. Furthermore our proposed method provides a controlled and tunable platform and enables us to achieve a relationship between pore size and synthesis parameters. The number of polymer chains inside the NPs was estimated from a method based on the Mie theory, since this parameter is needed for calculation of Mc. Calculation of Mc for alginate chains in electrolyte solutions such as simulated gastric and intestinal fluid was achieved based on the equilibrium swelling theory and methods that proposed by Peppas to modify this theory for pH-sensitive hydrogels. For nanogels with diameter of 136-288 nm, Mc was estimated to be 0.5-0.8 kDa in SIF after passing SGF. Finally, the average pore size of these nanogels was calculated to be 11-24 nm. Entrapment of a model protein drug, BSA, in the alginate nanogels shows high encapsulation efficiency and high protein release with slow release profile. Although we tried to establish a correlation between pore size and protein release from alginate nanogels by preparing a set of nanoparticles using microfluidic


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platform, further experiments are needed to confirm the pore size of prepared nanogels. Overall, the results presented demonstrate that microfluidic alginate nanogels are favorable carriers for oral protein delivery. The proposed methods and relationships can be used to design efficient nanocarriers for labile bioactive agents (i.e. proteins and drugs) with customized and fine-tunable release patterns.

AUTHOR INFORMATION

Corresponding Author * [email protected], [email protected].

Author Contributions The manuscript was written through contributions of all authors. All authors have given approval to the final version of the manuscript. + These authors contributed equally. ACKNOWLEDGMENT Authors would like to thank Prof. Philippe Renaud and Dr. Arnaud Bertsch from LMIS4-EPFL for their helpful technical assistance and from Dr. Mahnaz Eskandari (BME-AUT) for her helpful discussions. This research is performed in the framework of Biologically Inspired Developing Advanced Research (BiDAR) group. REFERENCES 1. 2.

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