Nanocarrier Aided Nasal Vaccination: An Experimental and

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Ind. Eng. Chem. Res. 2011, 50, 590–601

Nanocarrier Aided Nasal Vaccination: An Experimental and Computational Approach O. Kammona, A. H. Alexopoulos, P. Karakosta, K. Kotti, V. Karageorgiou, and C. Kiparissides* Department of Chemical Engineering, Aristotle UniVersity of Thessaloniki and Centre for Research and Technology Hellas, P.O. Box 472, 54124 Thessaloniki, Greece

In the present study, an experimental and theoretical investigation on the nasal vaccine delivery is reported. Poly(lactide-co-glycolide) (PLGA) nanoparticles (NPs) containing a model antigen, i.e., ovalbumin (OVA), and an adjuvant, i.e., monophosphoryl lipid A (MPLA), were prepared for nasal vaccine delivery. The nanoparticles, prepared by the double emulsion method, had an average particle size in the range of 296-401 nm and a zeta potential value of -14.3 to -23.2 mV. Depending on the initial OVA and MPLA concentrations in the recipe, the corresponding loadings varied from 1.07 to 10.16 wt % and from 0.177 to 1.081 wt %, respectively. The PLGA nanoparticles were found to be stable during their storage in a 9 wt % sucrose solution at 4 °C for 4 weeks. Examination of the OVA release profile from the PLGA nanoparticles in a physiological buffer solution (PBS) at 37 °C revealed an initial fast release of OVA located in the nanoparticles external surface area, followed by a lag phase of minimum release and a subsequent continuous release profile. The delivery and deposition of carrier droplets, containing PLGA nanoparticles, to the nasal cavity was determined using computational fluid dynamics (CFD) for steady-state inspiration and inlet velocities (Vin) in the range of 1-20 m/s. Deposition efficiencies and spatial deposition distributions were found to be strongly dependent on the droplet size and volumetric flow rate. A nanoparticle release model was developed to determine the amount of nanoparticles delivered from the deposited carrier droplets to the nasal cavity surface by taking into account the droplet formation size and the residence time of deposited droplets in the nasal cavity. It was found that the average droplet size increased when the viscosity of the liquid droplet increased or the surface tension decreased, leading to longer residence times and, thus, to higher nanoparticle release rates from the deposited droplets. Introduction Vaccination is considered to be the most effective way of fighting infectious diseases like HIV, malaria, influenza, etc. Among the potential needle-free administration routes, delivery of vaccines via the nasal cavity is a particularly attractive method. Nasal vaccines can easily be self-administered, thus eliminating the need of trained personnel for vaccine administration.1 The nose is easily accessible, and the nasal cavity is equipped with a high density of dendritic cells (DCs) that can mediate strong systemic and local immune responses, particularly if the vaccine is adjuvanted with an immunostimulator. In contrast to the oral vaccination route, vaccination via the intranasal administration route requires much lower doses of antigen, because of the limited dilution of the vaccine formulations by the nasal fluids and minimal exposure of vaccine to low pH and/or secreted degradative enzymes.2,3 Additionally, due to the relatively low enzymatic activity in the nasal cavity, the antigen’s stability at the administration site can be maintained.2 Moreover, nasally administered vaccines induce the secretion of mucosal immunoglobulin A (sIgA),2 an important element of the mucosal defense system that provides a local defensive mechanism against pathogens entering the human organism through other mucosal surfaces.2-4 Finally, vaccination via the nasal administration route can offer simplified and cost-effective vaccination protocols with improved patient compliance as compared to parenteral administration.1 It should be noted that intranasal administration of free antigens cannot readily elicit immune responses and, thus, a nanocarrier-based vaccine delivery system is required in order * To whom correspondence should be addressed. Tel.: +30 2310 996211. Fax: +30 2310 996198. E-mail: [email protected].

to improve the antigen’s efficacy.1 In general, a nanoparticulate vaccine delivery system can increase the antigen protection, allow the incorporation of an adjuvant (i.e., immunostimulant), increase the nanocarrier retention time at the nasal mucosa via bioadhesion, and facilitate the transport of the encapsulated antigen through the microfold (M) epithelial cells to the nasal associated lymphoid tissue (NALT). Furthermore, nanocarrierbased vaccine delivery systems can allow the sustained release of antigen, thus increasing the antigen contact time with antigen presenting cells (APCs) (i.e., dendritic cells (DCs), macrophages, and B-cells).2-4 The use of nanocarriers as delivery vehicles for nasal vaccination has been the subject of a great number of experimental studies. However, only recently nanocarrier-based nasal vaccination formulations have reached clinical trials.1 In general, a nasal vaccination formulation consists of a stable dispersion of nanocarriers, containing the antigen and the adjuvant, in an isotonic buffer that is commonly delivered in the form of droplets into the nasal cavity. The physicochemical properties of the nanocarriers are considered to be critical for the effectiveness of a vaccine formulation. It is generally accepted that M-cells can rapidly uptake nanoparticles but no definite boundaries for particle size have been established. For example, it has been reported that particles in the size range of 0.2-2 µm can be taken up by the NALT, and that the particle uptake rate increases as the particle size decreases.2 Furthermore, particles of different sizes might be internalized by different cell uptake mechanisms in vivo.3 Regarding the effect of particle size on the immune responses, the experimental findings are somehow conflicting, largely linked with the specific polymer system examined.3 Concerning the effect of a nanocarrier’s surface charge on its cellular uptake

10.1021/ie100307t  2011 American Chemical Society Published on Web 06/18/2010

Ind. Eng. Chem. Res., Vol. 50, No. 2, 2011

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a

Table 1. Recent Micro- and Nanocarrier Developments for Nasal Vaccination Applications carrier

size (µm)

loading method

PLA5

1.56

encapsulation

PLA5 PLA6 PLA6 γ-PGA7 PLGA8 PLGA9

2.12 1.27 1.15 0.25-0.30 3.37 1.8

encapsulation encapsulation encapsulation encapsulation encapsulation encapsulation

PLGA10 PCL10 PCL11 PCL11 PMMA12 dextran13 PLA-PEG14 PLGA: pluronic15

0.249 0.267 0.45 0.423 0.22 0.128 0.2-10 0.161-0.187

encapsulation encapsulation incubation encapsulation incubation incubation encapsulation encapsulation

PLGA-PCL copolymer10 0.267 PLGA-PCL blend10 0.252

encapsulation encapsulation

chitosan16 chitosan17

5.78 4.30

incubation incubation

chitosan18 chitosan19 chitosan20 chitosan21 chitosan22

0.643 0.4-3 1.324 0.350 0.337

incubation incubation incubation encapsulation encapsulation

chitosan23

5.23

17

TMC

2.40

incubation

TMC24 TMC25

0.306 0.85


TMC26 MCC24 mannosylated chitosan27

0.350 0.04-0.09 5.73

encapsulation encapsulation incubation

chitosan: pluronic28

5.21

incubation

alginate

up to 4

encapsulation

liposomes30 liposomes31 liposomes32 liposomes33

0.373 0.774 2.3 0.05-10

encapsulation encapsulation encapsulation encapsulation

lipid microparticle34 virosomes35

1.6 0.2


29

antigen

loading (%)

hepatitis B surface antigen (HBsAg) HbsAg Caf1 LcrV ovalbumin HBsAg S. mutans glucan-binding protein D diphtheria toxoid diphtheria toxoid S. equi extract proteins S. equi extract proteins HIV-1 Tat tetanus toxoid tetanus toxoid beta-galactosidase encoding gene diphtheria toxoid diphtheria toxoid tuberculosis subunit vaccine group C meningococcal conjugated vaccine HBsAg ovalbumin bovine serum albumin tetanus toxoid plasmid DNA encoding surface protein of hepatitis B virus B. bronchiseptica dermonecrotoxin group C meningococcal conjugated vaccine tetanus toxoid monovalent influenza subunit vaccine ovalbumin tetanus toxoid B. bronchiseptica dermonecrotoxin B. bronchiseptica dermonecrotoxin tetanus toxoid diphtheria toxoid plasmid pRc/CMV-HBs(S) tetanus toxoid monovalent subunit antigen from influenza HBsAg ovalbumin

entr eff (%)

adjuvant

1.462

98.24

alum

1.255 1.4 2.8 up to 12

86.36

loading entr eff (%) (%)

up to 50 80

1.84 1.43 1.61 11.38 8.42 Up to 13

71.38 80.26 91 84 >90 20.3 0.248 - 0.357 19.9 - 32.2 up to 1 33.6 - 48.7

1.62 1.55

81.00 77.40 >99 99

8.39 6

84.1 >80 60 50-60 96.2

LTK63 CpG

43 9.7

97.0

65-75 85 13 up to 50

LTK63

50

93 78 up to 95 95-97 74.66 62-66 47.7 65 53 54.0 up to 71

CpG

CpG

0.1

34.2

60.1

58.17-67.90 4

a PLA poly(lactic acid), γ-PGA poly(γ-glutamic acid), PLGA poly(lactic-co-glycolic acid), PCL poly-ε-caprolactone, PMMA poly(methylmethacrylate), PEG polyethylenglycol, TMC N-trimethyl chitosan chloride, MCC mono-N-carboxymethyl chitosan, alum aluminum hydroxide.

rate, it has been postulated that a positive zeta potential is beneficial for M-cell transport, since the M-cell membrane is negatively charged. However, mucus and epithelial cells carry a negative charge as well, making electrostatic interactions with the nanocarriers very unspecific.2 With respect to release characteristics, there appears to be no direct correlation between in vitro antigen release profile and in vivo immunogenic responses. It should be noted that different nanocarrier-based vaccine delivery systems, characterized by either rapid or prolonged in vitro antigen release profiles, can induce similar immunogenic responses in animal models.3 Thus, more systematic studies are required to analyze the effects of different nanocarrier parameters (e.g., nanocarrier degradation kinetics, release profiles of antigen and adjuvant, etc.) on the induction of immune responses. Moreover, the identification of the

mechanisms by which different vaccine delivery systems work immunologically is a subject of present research efforts. In Table 1, recent studies on the synthesis, entrapment efficiency, and loading of antigen and adjuvant in different types of micro- and nanocarrier systems are summarized. In order to determine the nanoparticle deposition efficiency, the vaccine release dynamics, and the cell uptake rate, in terms of system parameters (e.g., particle size, airflow velocity, formulation viscosity, etc.), an integrated modeling approach is required. This means that a computational fluid dynamics (CFD) model, a droplet deposition model, and a dynamic nanocarrier release model need to be combined and solved simultaneously. In what follows, the synthesis of poly(lactic-co-glycolic acid) (PLGA) nanoparticles, prepared by the double emulsion method,

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Figure 1. Schematic representation of the nasal cavity.

containing ovalbumin (OVA), as a model antigen, and monophosphoryl lipid A (MPLA), as an immunostimulant is presented. The effect of the initial OVA and MPLA concentrations on the antigen and adjuvant loadings is examined as well as the effect of the initial OVA concentration on the encapsulation efficiency of MPLA. The storage stability of the synthesized PLGA nanoparticles in a 9 wt % sucrose solution is assessed with respect to particle stability, at 4 °C. The release profile of OVA from the PLGA nanoparticles is also experimentally analyzed. In addition, a CFD droplet deposition model combined with a nanoparticle release model is developed to predict the airflow and droplet deposition rate in the nasal cavity as well as the nanoparticle release kinetics. The flow field in the nasal cavity and the droplet deposition rate are determined by CFD and Eulerian/Lagrangian particle deposition simulations, respectively. The effects of the inlet air velocity and droplet size on the flow field and droplet deposition rate are also examined. Finally, the mean residence time of deposited carrier droplets in the nasal cavity is determined and employed as input into a simplified nanoparticle release model to analyze the effect of the droplet deposition profile in the nasal cavity on the amount of nanoparticles released from deposited carrier droplets. Nasal Vaccination Mechanism The nasal cavity is a 10-12 cm long by 5 cm tall intricate folded structure36,37 consisting of two main airway channels separated by the nasal septum that converge posteriorly to the nasopharynx, Figure 1. Air first flows through the nostrils into the nasal vestibule region and then changes direction by about 90° and enters the nasal valve region, which is the narrowest part of the nasal cavity. Air exits the nasal valve region in the form of a narrow jet into a wider region leading to the main airway of the nasal cavity. Secondary flows created by the nasal valve jet assist air circulation as well as heat and mass transfer into the lateral parts of the nasal cavity. The main airways are characterized by three bony folds: the inferior, middle, and superior turbinates or conchae (see Figure 1). Beneath the turbinates there are narrow airway passages called the meatuses. Airflow from the nasal region is directed mostly toward the middle meatus and large particle deposition and local gas uptake is observed at the

anterior region of the middle turbinate. The narrow width of the meatuses (i.e., 1-2.5 mm) helps maintain contact between the airstream and the epithelium lining of the nasal passage. Only small amounts of airflow are directed to the lateral meatus regions. The cells that line the nasal cavity change from dermal and squamous epithelial at the vestibule to ciliated secretory epithelial in the main nasal airway. The olfactory region is lined with nonciliated neuro-epithelium and is situated at the top of the nasal cavity. Consequently, the nasal mucosa offers direct access to the central nervous system via the olfactory route.38 Local immunity in the upper airways as well as systemic immunity are mainly mediated by the NALT, which is situated underneath the nasal epithelium and is comprised of agglomerates of cells (e.g., DCs, T-cells, and B-cells) involved in the initiation and execution of immune responses.2 In humans, the NALT is associated with the Waldeyer’s ring (an anatomical term describing the lymphoid tissue ring located in the pharynx including the nasopharyngeal, tubal, palatine, and lingual tonsils), which plays an important role in the respiratory immune defense.1,2 The NALT together with the gut associated lymphoid tissue (GALT) comprise a mucosa associated lymphoid tissue (MALT) that continuously supplies antigen-specific memory B and T cells to diffuse mucosal effector sites. The migration of lymphocytes from inductive to mucosal effector tissues is the basis for the concept of the mucosal immune system, where either nasal or oral vaccination induces mucosal immunity in multiple distal effector sites.39 It should be mentioned that the NALT exhibits a slower rate of immunosenescence as compared to the GALT.39 The nasal epithelium is composed of a thin layer of pseudostratified epithelial cells, connected by tight junctions of diameters of 3.9-8.4 Å.40 Thus, transcellular transport is the most likely route for vaccine-loaded nanocarriers to reach the NALT mostly via uptake by the M-cells. Following nanoparticles uptake, the M-cells transport the antigen-loaded particulates to the NALT by transcytosis and, thus, deliver them to a lymphatic environment where they are presented by APCs. APCs activate naive T-cells and initiate antigen-specific immune responses. Among the APCs, DCs are efficient stimulators of primary immune responses and a subsequent establishment of immunologic memory. At this point, it should be noted that


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Figure 2. Schematic presentation of the nasal vaccination mechanism.

the ultimate goal of therapeutic vaccination is the simulation of a specific immune response and the induction of a long lasting immunologic memory to protect against subsequent disease.2,3,41 Following nasal administration of particulate antigens, both cellular and humoral immune responses against pathogens can be induced. The cellular immune response of the adaptive immune system is comprised of both T-helper (Th) lymphocytes (CD4+) and cytolytic T-lymphocytes (CTL), also known as killer T-lymphocytes (CD8+). CD4+ T-cells are responsible for orchestrating and directing an immune response, whereas CD8+ T-cells traffic to the sites of infection and lyse infected cells.41 Humoral immunity at the mucosal surface is principally mediated by the production of immunoglobulin A (IgA) (i.e., large Y-shaped proteins) which follows the activation of B-cells. IgA is found in mucosal secretions. Additionally, mucosal immunization can result in the production of serum IgA and serum IgG antibodies.1,2 The nasal vaccination mechanism is schematically presented in Figure 2. In order to enhance the potency of nasal vaccines, adjuvants (i.e., immunostimulatory molecules) need to be included in the formulation. Adjuvants can be classified according to their mechanisms of action into (i) toll like receptor (TLR) ligands (e.g., CpG oligodeoxynucleotides, Monophosphoryl lipid A), (ii) toxin-based adjuvants (e.g., enterotoxins), and (iii) cytokines and costimulatory molecules.2 Co-administration of antigen and adjuvant, especially TLR ligands, results in concurrent antigen processing and presentation as well as signaling of TLR pathways leading to the generation of mature and activated DCs capable of inducing primary T-cell responses.41 Synthesis of PLGA Nanocarriers for Nasal Vaccination PLGA nanoparticles containing the model antigen OVA and the adjuvant MPLA were prepared by the double emulsion

method. Initially, 2.9 mL of a PLGA chloroform solution (31 mg/mL) were mixed with 0.1 mL of an MPLA solution in methanol:chloroform (1:4 v/v), at various MPLA concentrations (2, 5, and 15 mg/mL). A water-in-oil (w/o) emulsion was then formed by adding 0.3 mL of an OVA solution in physiological buffer solution (PBS), at various OVA concentrations (3.33, 5, 8.33, and 33.33 mg/mL), into the PLGA-MPLA solution.41 The emulsification was performed in an ice bath with the aid of a microtip sonicator (Sonicator Sonics Vibra Cell VC-505) at 40% amplitude for 45 s. Subsequently, the primary emulsion (w/o) was added into an aqueous polyvinyl alcohol (PVA) solution (12 mL, 1% w/v). The mixture was then emulsified via the action of a sonicator operating at a 40% amplitude for 2 min. The resulting double (w/o/w) emulsion was left overnight under magnetic stirring to allow the evaporation of the solvent(s). The PLGA nanoparticles were first purified by means of four successive centrifugation (at 20 000 rpm and 4 °C for 10 min) and redispersion (in sterilized water) cycles and subsequently lyophilized (Thermo Electron Corp. Micro Modulyo). Blank, OVA loaded, and MPLA loaded PLGA nanoparticles were also prepared and used as controls. For the preparation of the OVA loaded PLGA nanoparticles, 0.3 mL of an OVA solution in PBS, at a concentration of 33.33 mg/ mL, was added into 3.0 mL of a PLGA chloroform solution (30 mg/mL). For the synthesis of the MPLA loaded PLGA nanoparticles (NPs), 0.3 mL of PBS were added to 2.9 mL of a PLGA chloroform solution (31 mg/mL) previously mixed with 0.1 mL of an MPLA solution in methanol:chloroform (1:4 v/v), at an MPLA concentration of 2 mg/mL. Finally, for the synthesis of the blank nanoparticles, 0.3 mL of PBS were added into 3.0 mL of a PLGA chloroform solution (30 mg/mL). The surface morphology of the PLGA nanoparticles was assessed by scanning electron microscopy (JEOL JSM 6300). Accordingly, the lyophilized nanoparticles were first double

594


Table 2. Main Characteristics of PLGA Nanoparticles experiment

OVA in recipe (mg)

MPLA in recipe (mg)

av diam (nm)

zeta potential (mV)

PLGA PLGA-OVA PLGA-MPLA PLGA-OVA-MPLA-1 PLGA-OVA-MPLA-2 PLGA-OVA-MPLA-3 PLGA-OVA-MPLA-4 PLGA-OVA-MPLA-5 PLGA-OVA-MPLA-6

0 10 0 1 2.5 10 10 10 1.5

0 0 0.2 0.2 0.2 0.2 0.5 0.5 1.5

300 414 351 325 344 338 401 345 296

-21.0 -14.1 -16.8 -22.3 -14.3 -15.7 -22.2 -21.4 -23.2

coated with a gold layer under vacuum and then examined by scanning electron microscopy (SEM). The average particle diameter and the particle size distribution (PSD) of the PLGA nanoparticles were determined by photon correlation spectroscopy, and their zeta potential was calculated using aqueous electrophoresis measurements (Malvern Nano ZS90). The measurements were performed with aqueous dispersions of NPs prior to their lyophilization. A microbicinchoninic acid (micro-BCA) protein assay kit (Pierce Biotechnology, Rockford, IL) was employed to determine the OVA loading (wt %) in the PLGA nanoparticles. Accordingly, 2.4 mg of lyophilized nanoparticles were dissolved in 0.4 mL of 0.1 M NaOH aqueous solution. Following the overnight incubation of NPs at 4 °C, the antigen concentration was determined using the micro-BCA protein assay kit according to the manufacturer’s instructions for 96-microwell plates (Corning Inc., Corning, NY). The absorbance of the samples was measured at 562 nm using a microplate reader (EL808 IU-PC, BioTek Instruments, Inc., Winooski VT). Blank PLGA nanoparticles were used as controls. The OVA encapsulation efficiency was calculated by the ratio of the OVA mass in the NPs over the total mass of OVA in the recipe. Similarly, the OVA loading was calculated by the ratio of the encapsulated mass of OVA over the total mass of PLGA/OVA/MPLA nanoparticles. A Limulus Amebocyte Lysate (LAL) kit (Kinetic-QCL 192 test kit, 50-650U, LONZA) was used for the determination of the MPLA loading (wt %) in the nanoparticles. Accordingly, aqueous MPLA solutions (0.01-10 ng/mL) were initially assayed with LAL using a microplate reader (BioTek EL808 IU-PC) for establishing a calibration curve. The calibration curve was found to be linear over the MPLA concentration range of 0.01-10 ng/mL with a correlation coefficient of R2 ) 0.9994. The mass of MPLA in the NPs was determined by subtracting the measured mass of MPLA in the four supernatants (collected after each washing cycle of the PLGA nanoparticles and diluted ×10 000 in LAL reagent water) from the initial mass of MPLA in the recipe. The encapsulation efficiency of MPLA was calculated by the ratio of the measured MPLA mass in the NPs over the total mass of MPLA in the recipe. Similarly, the MPLA loading was calculated by the ratio of encapsulated MPLA mass over the total mass of the PLGA/OVA/MPLA NPs. The stability of the PLGA nanoparticles, containing the antigen and the immunostimulant, was examined in a 9 wt % sucrose solution at 4 °C. Nine samples of PLGA nanoparticles (i.e., 1 mg each) were dispersed in respective sucrose solutions (1 mL each and 9 wt % sucrose concentration), and the particle size distribution was measured at different times (i.e., 0, 1, 2, 3, and 4 weeks). The in vitro release of OVA from PLGA NPs was carried out in PBS (DPBS, Gibco, Invitrogen) at 37 °C. Several vials containing 1 mg of NPs dispersed in 1 mL of PBS were incubated at 37 °C in a thermomixer (Thermomixer Compact, Eppendorf) at 1400 rpm. At predetermined times (i.e., 1, 2, 4,

OVA enc effic (%)

OVA loading (wt %)

57.55

6.39

96.30 97.56 82.53 91.48 89.64 85.20

1.07 2.71 9.17 10.16 9.96 1.42

MPLA loading (wt %)

0.127 0.177 0.187 0.190 0.477 0.490 1.081

6, 8, 12, 24, 48 h and 1, 2, 3, and 4 weeks), 1 mL of the dispersion was collected following centrifugation at 12 500 rpm for 10 min. The amount of OVA released from the nanoparticles in the supernatant was estimated by the micro-BCA kit. Experimental Results and Discussion In all cases (blank and OVA/MPLA loaded NPs), perfectly spherical PLGA nanoparticles were obtained with an average diameter in the range of 296-414 nm while their zeta potential values varied from -14.1 to -23.2 mV (Table 2, Figure 3). The negative zeta potential value of the blank PLGA NPs (see in Table 2) was attributed to the presence of carboxyl groups residing on the surface of NPs that are negatively charged at physiological pH.42 The observed slight decrease in the zeta potential value of the OVA loaded PLGA NPs (see in Table 2) was attributed to the presence of antigen that partially neutralizes the free anionic surface carboxyl groups. On the other hand, the negative zeta potential values observed for the OVA/MPLA loaded NPs are due to the combined negative charges of the PLGA carboxyl groups and MPLA molecules that have a zeta potential value of approximately -50 mV in water. The respective values for OVA encapsulation efficiency as well as for OVA and MPLA loading are also reported in Table 2. As can be seen, a high encapsulation efficiency for OVA is obtained independently of the initial antigen and adjuvant concentrations in the recipe. The observed high encapsulation efficiencies for OVA can be attributed to the ionic interactions between the positively charged OVA amino groups and the negatively charged uncapped carboxylic end groups of PLGA.43 Furthermore, it is apparent that PLGA nanoparticles containing various amounts of OVA (1.07-10.16 wt %) and MPLA (0.177 - 1.081 wt %) can be successfully prepared by varying the initial antigen and adjuvant concentrations.

Figure 3. SEM photomicrograph of PLGA nanoparticles containing OVA (9.17 wt %) and MPLA (0.19 wt %) (the scale bar corresponds to 1 µm).


Figure 4. Effect of the initial amount of OVA in the recipe on the encapsulation efficiency of MPLA.

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Figure 6. Release profile of OVA from PLGA nanoparticles in PBS at 37 °C. The PLGA NPs were loaded with 1.42 wt % OVA and 1.081 wt % MPLA.

value. In fact, Figure 6 shows that only 70% of OVA was released after 4 weeks since the polymer matrix was not completely hydrolyzed during this period. It is important to point out that there is not a direct correlation between the antigen release profile (in vitro) and the induction of immune responses (in vivo). Moreover, it has been reported that different nanocarrier-based vaccine delivery systems, characterized by either rapid or prolonged in vitro antigen release profiles, induce similar immunogenic responses in animal models.3 Figure 5. Stability of PLGA nanoparticles, containing OVA and MPLA, in a 9 wt % sucrose solution at 4 °C over a period of 4 weeks.

The reproducibility of the double emulsion method for preparing different batches of NPs was tested in experiments PLGA-OVA-MPLA-4 and 5 (Table 2). As can be seen, there is an excellent reproducibility with respect to OVA and MPLA loadings. In Figure 4, the effect of the initial amount of OVA in the recipe (e.g., 0-10 mg) on the encapsulation efficiency of 0.2 mg MPLA is depicted. As can be seen, the encapsulation efficiency of MPLA increases significantly as the initial amount of OVA in the recipe increases. This could be attributed to the ionic interactions between the negatively charged MPLA and the positively charged amino groups of OVA that are enhanced by increasing the initial mass of OVA in the recipe. In Figure 5, the evolution of the particle size distribution of the PLGA nanoparticles during their stability testing in a 9 wt % aqueous sucrose solution (i.e., incubation of NPs for 4 weeks at 4 °C) is shown. It is apparent that the particle size distribution does not significantly change with time (see Figure 5). In Figure 6, the release profile of OVA from PLGA NPs in PBS at 37 °C is shown. As can be observed, the antigen release profile is characterized by a fast initial release followed by a lag phase of minimum release and a phase of continuous release. More specifically, about 16% of the total mass of OVA is released from the NPs during the first hour. This initial fast release of OVA is directly related with the amount of antigen located near the NPs external surface. An increased amount of OVA has also been reported44 to reside on the external surface of the PLGA particles and/or in the NPs pores connected to the surface. The initial burst release was followed by a lag phase of minimum release (see inserted graph in Figure 6). The latter kinetic behavior depends on the polymer degradation rate that, in turn, decreases as the glycolide content in the PLGA decreases and/or the polymer molecular weight increases.45 The lag phase was followed by a continuous release profile controlled by the polymer degradation kinetics.44,45 As can be seen, after approximately 3 weeks, the OVA release rate reached a plateau

CFD Model Developments For many years, researchers have been involved with the investigation of airflow and particle deposition in the nasal cavity for pharmacological studies (e.g., drug delivery), health-related issues (e.g., noxious fume uptake and toxic particle deposition), and medical applications (e.g., prediction of surgical intervention on nasal cavity airflow and function).46-48 CFD simulations of airflow and particle deposition in the nasal cavity have been the subject of a great number of publications over the last 15 years.49-53 CFD simulations can reveal not only the flow patterns and particle paths in the nasal cavity but can also provide detailed information on the distribution of deposited particles, wall stresses, and surface mass flow rates that are all crucial to the proper function of the nasal cavity. Computational fluid dynamics (CFD) deals with the determination of the velocity field in systems involving fluids and dispersions of particles, droplets, and bubbles. Commonly, the velocity field and pressure drop are determined by solving the fluid conservation equations for mass and momentum. For nonisothermal systems, the variation of temperature can also be determined by considering the energy conservation equation. The continuity or mass conservation equation for a fluid can be written as follows: ∂F + ∇ · (Fu) ) Sm ∂t

(1)

where F is the fluid density, u is the fluid velocity, and Sm is a source term. Accordingly, the momentum conservation equation is written as ∂Fu + ∇ · (Fuu) ) -∇P + ∇ · τ + Fg + F ∂t

(2)

where P is the static pressure, τ is the stress tensor, and Fg and F represent the gravitational and external body forces, respectively.

596


Both mass and momentum conservation equations can be expressed in a scalar form as ∂Fφ + ∇ · (Fφu) ) ∇ · Γ∇φ + Sφ ∂t

(3)

where φ is a scalar quantity (e.g., velocity component, temperature, concentration), Γ is the diffusivity, and Sφ is the source term. Particle pathlines and deposition behavior can be determined by the Eulerian/Lagrangian approach, that is, by solving the force balance equation for each particle assuming an unperturbed airflow solution. This is a valid approach for dilute systems, that is, when the dispersed phase volume fraction is less than 10%. The force balance on a dispersed particle can be written as dup ) FD(u - up) + g(Fp - F)/Fp + FB + F dt

(4)

where the terms on the right-hand side represent the drag force per unit particle mass, the gravitational acceleration per unit mass, the Brownian forces, and an additional acceleration term (e.g., Saffman lift force, etc.). The above equations can be solved for either laminar or turbulent flow conditions. Depending on the value of the Reynolds number and the type of flow (e.g., transitional, fully developed, extensional, swirling, etc.), various two-equation models (e.g., k-ε, RNG k-ε, k-ω) can be employed for turbulent flow conditions. Here, k, ε, and ω denote the turbulent kinetic energy, the dissipation rate of turbulent kinetic energy, and the specific dissipation, respectively. In the present study, the determination of the velocity field and particle deposition in the nasal cavity was achieved by a sequence of computational steps. First, the geometry of the nasal cavity was specified (e.g., from a series of CT or MRI scans). Next, the nasal cavity volume was discretized into a sufficiently large number (i.e., 104-107) of computational cells. The mass and momentum conservation equations were then solved for each computational cell of the domain. Finally, the motion and deposition of particles were determined from the solution of eq 4. CFD Simulation Results and Discussion Numerical simulations of the steady-state airflow as well as of particle deposition in the nasal cavity were performed using the commercial CFD software FLUENT (v6.3). A number of different computational grids were constructed using the software tool GAMBIT. Grid construction was difficult due to the highly curved shape of the nasal cavity (see Figure 1) but also due to the narrowness of the air passages. The computational grids were constructed from 0.27-2.21 × 106 tetrahedral or polyhedral cells and with a maximum skewness of 0.7. Simulation results presented in this work were obtained using a computational grid consisting of 1.24 × 106 tetrahedral cells for which computational convergence could be achieved. The numerical simulations were performed with double precision, implicit time stepping, and second-order gradients. The convergence criteria for all the residuals were set to 10-4. The flow field calculations were conducted on a PC type Dell Workstation (8 core Xeon 5300) with 20 Gb of memory. The CPU time for simulating the flow field was 1-50 h per processor depending on the computational mesh, type of flow, and the Reynolds number. The particle simulations were conducted by assuming droplet injections uniformly distributed over the nasal inlet

surface and air flow fields obtained via CFD simulations. The numerical simulations required a CPU time of about 1-20 min depending on the number of droplets simulated. Droplet simulation data were expressed in terms of an overall deposition efficiency and an axial droplet deposition distribution. A number of computational simulations were performed with different inlet velocity magnitudes (i.e., Vin ) 1-20 m/s), corresponding to flow rates of 9-180 L/min. As convergence could often not be achieved directly, successive simulations were performed starting from a solution corresponding to a low inlet velocity (e.g., Vin ) 1 m/s) and laminar flow conditions. The air inflow was assumed to be normal to the inlet surface and the inlet turbulence intensity was determined by the following equation: I ) 0.16ReD-1/8. The calculation of the Reynolds number, ReD, was based on the inlet hydraulic radius, that was equal to DH ) 0.868 cm. At the nasal cavity outlet, the gauge pressure was set equal to zero, the turbulent intensity equal to 1%, and the value of DH was set equal to 2 cm. In Figure 7, contour plots of the relative velocity magnitude (V/Vin) are shown at four different coronal planes (i.e., cross-sectional planes normal to the z-direction, see Figure 1) for an inlet velocity of Vin ) 3.37 m/s. As can be seen in Figure 7a and b, an acceleration by more than 50% of the airflow in the nasal valve region is observed. Figure 7c and d show that the airflow is directed toward the meatus intersection regions where the crosssectional area for flow is large and the resistance to flow is small. Figure 8 displays the y and x components of the velocity on a sagittal plane section of the nasal cavity for an inlet velocity of Vin ) 10 m/s. The secondary flow structures can be identified by the variations in the x-velocity. The y-velocity is strongest in the inlet region and then decreases as the flow becomes axially dominated. At the end of the nasal cavity, a recirculation region is observed where the flow is forced downward toward the nasopharyx. The present CFD simulations were compared to literature computational and experimental results on pressure and areaaveraged velocity magnitudes.52 The CFD simulations were found to be in good agreement with previously reported experimental and simulation results, considering the different approximations of nasal geometry and experimental set-ups. Eulerian/Lagrangian particle tracking was employed to describe the motion and deposition of particles/droplets of different sizes in the nasal cavity for different air inlet velocities. Singlesize as well as distributed (e.g., Rosin-Rammler) particle injections were considered to be released uniformly from the entire inlet surface. For particles larger than 500 nm, Brownian forces in eq 4 were ignored. In Figure 9, calculated particle streamlines are illustrated for two particle sizes (i.e., 5 and 20 µm). It is clear that for an air inlet velocity Vin ) 10 m/s larger particles deposit more efficiently than smaller ones due to higher inertial forces. It should be noted that the particle deposition efficiency in inertia-dominated processes depends on the “impact factor” or “inertial parameter” QD2 where Q is the volumetric flow rate and D is the particle diameter. This means that particle deposition is very sensitive to D. Figure 10 displays the particle deposition efficiency in terms of the impact factor QD2 (µm2 cm3/s) calculated for different values of airflow rate, Q, and particle sizes (D > 1 µm). The particle deposition efficiencies are compared to the experimental data of Kelly et al.54 and Guillmette et al.55 as well as to CFD simulations of Shi et al.52 As can be seen, the calculated deposition efficiencies all fall on the same curve and generally underpredict the experimentally measured particle depositions. However, when surface roughness (e.g., 0.02-0.2 mm) and


597

Figure 7. Contour plots of the relative velocity magnitude (V/Vin) at different coronal planes z ) (a) 1.2, (b) 3, (c) 4.5, and (d) 7 cm of the nasal cavity for an inlet velocity of 3.37 m/s.

Figure 8. Contour plots of the x- and y-velocity components on a sagittal plane of the nasal cavity for an inlet velocity Vin ) 10 m/s: (a) x-component, (b) y-component.

turbulent dispersion (via the discrete random walk model with a constant time of 0.15 s) are taken into account and a more refined resolution of the nasal cavity inner surface is employed (by increasing the grid cell number from 1.61 × 106 to 2.21 × 106), the calculated particle deposition efficiencies are closer to the experimental results. In Figure 11, the axial distribution profile of inhaled particles is shown. It is clear that the larger particles deposit strongly in the anterior region of the nasal cavity due to the nearly 90° change in the direction of airflow after their entry into the nasal cavity. It should also be noted that smaller particles (i.e., 1 µm)

deposit less than larger ones but much more evenly with respect to the axial position of the nasal cavity. Prediction of Drug Release Kinetics In order to deliver a sufficient amount of vaccine to the nasal epithelium, both large deposition efficiencies and homogeneous deposition profiles are desired. According to the particle deposition results in Figures 10 and 11, particle deposition efficiencies increase with particle size (i.e., for D > 1 µm). However, from Figure 11, it can be seen that 1 µm particles

598


Figure 9. Particle streamlines in the nasal cavity for an inlet velocity of 10 m/s. Uniform injection of particles: (a) 5 and (b) 20 µm.

Figure 10. Overall particle deposition efficiency with respect to the inertial parameter QD2 (uniform particle size).

droplet properties (e.g., surface tension and viscosity) on the droplet mean size and the total amount of NPs released was calculated. Nanoparticle transfer and drug release from droplets deposited on the mucosal layer is a dynamic and complicated process and depends on the NP and droplet properties (e.g., size, composition, etc.), type, and concentration of the active ingredient, as well as on the physiological state of the nasal cavity, the mucosal layer, and the underlying nasal epithelial cells. For slow-release, drug delivery systems, the droplet residence time in the nasal cavity can be an important factor as it limits the available time for NP transfer from the liquid droplet to mucosal layer and the subsequent release of the active ingredient from the released NPs to cells. In general, the droplet residence time, τ, depends on the mean droplet deposition distance, zd, according to τ)

∫

L

Zd

1/Vm(z) dz

(5)

where Vm is the mucosal velocity which is a function of z:36 Vm ) v0 ; z > 2 cm

Figure 11. Axial distribution of droplet deposition. A single and uniform injection of droplets at the inlet surface (uniform and Rosin-Rammler (2.5 spread parameter) particle size distributions, Vin ) 10 m/s).

deposit much more evenly in the nasal cavity than 10 µm particles. On the other hand, according to the results of Figure 10, for lower inspiration flow rates, larger particles are required to achieve the same deposition efficiency (i.e., the same value of the impact factor, QD2). The antigen loaded PLGA particles produced in this work are too small (i.e., 300-400 nm) to be delivered directly (individually) to the nasal cavity in significant quantities. Thus, dispersions of PLGA nanoparticles in liquid droplets can be employed to achieve significant deposition in the nasal cavity. In this work, the PLGA NPs were assumed to be dispersed in liquid droplets, generated by a jet nebulizer. Accordingly, the rate of NPs release from the deposited liquid droplets to the nasal mucosal surface was determined and the effects of liquid

and

Vm ) zV0 /2; z < 2 cm (6)

where V0 ) 0.5 cm/min is the translational velocity of mucosal layer in the well-ciliated interior of the nasal cavity (i.e., z > 2 cm). On the basis of the axial distribution of droplets deposition shown in Figure 11, it was found that the mean deposition axial distance, zd, (cm) exhibits the following dependence on the impact factor: zd ) 9.3 - 1.5 log(QD2)

(7)

Accordingly, a simple transfer model was developed, describing a limiting case of the full dynamic conjugate transfer model involving the mucosal layer, the deposited droplets, and the dispersed nanoparticles. In the case of an aqueous droplet dispersion of PLGA NPs, the deformation of the deposited droplets and their wetting behavior must also be taken into account. Assuming an instantaneous relaxation of the deposited droplets toward a hemispherical shape (for a contact angle of 90°) and a fast transport of NPs through the mucosal layer, the NPs uptake rate will depend on the NPs diffusion coefficient from the droplets into the mucosal layer. For liquid droplets, the droplet diameter will depend on the formulation properties and the nasal delivery device. For


Figure 12. Effect of droplet viscosity and surface tension on the droplet size and NP release. Vin ) 2 m/s, D0 ) 0.1 cm.

example, assuming a jet spray delivery device, the mean droplet diameter is given by the following correlation:56 D ) D0NG(FL/FG)0.25[1 + 331We1/2 /Re]

(8)

where D0 is the nozzle diameter, We is the nozzle Weber number, Re is the nozzle Reynolds number, and NG is given by NG ) γg/FL

(9)

Assuming that the NP release kinetics follow a quasi steadystate unidirectional diffusion process, the amount of NPs released, Cr, from the liquid droplets will be given by Cr/C0 ) 1 - exp(-ΓAdτ/VLd)

599

prepared by the double emulsion method. The nanoparticles were found to be stable, with respect to particle aggregation, during their storage in a 9 wt % sucrose solution at 4 °C for 4 weeks. The in vitro release studies showed that OVA release kinetics were characterized by a fast initial release rate, followed by a lag phase of minimum release and a final continuous release phase. CFD simulations of airflow in the nasal cavity can provide a wealth of information on velocity magnitudes, wall stresses, turbulence, and the pressure drop. Additionally, CFD simulations reveal velocity flow patterns and secondary flows in the nasal cavity. The computed airflow can be used to determine particle motion and deposition by Eulerian/Lagrangian simulations. More specifically, overall deposition efficiency, particle deposition distributions in terms of axial distance, and local deposition rates can be determined. In the case of slow release dynamics, the limited droplet residence time can be connected to the amount of NPs released according to a simple diffusion model and the effects of droplet surface tension and viscosity on the NPs release rate can be determined. Future work should involve coupling of nanoparticle cellular penetration and clearance mechanisms and drug release dynamics with the current carrier droplet deposition/nanoparticle release dynamics model. This fully multiscale approach will provide a comprehensive calculation of the total amount of drug release to target tissues per inhalation dose. Acknowledgment We gratefully acknowledge EC for supporting this research under the FP6-2004-NMP-NI4 026723-2 Project.

(10)

where C0 is the initial amount of NPs in the carrier droplet. Γ is the diffusivity of NPs, Ad is the contact area of the deposited droplets with the mucosal layer, V is the droplets volume, and Ld is an effective transfer length. For an inlet velocity of Vin ) 2 m/s, the mean droplet residence time and the mean droplet diameter will be given by eqs 5 and 8, respectively. For a hemispherical deposited droplet, the parameters Ld and Ad will be Ld ) Rd/3, Ad ) πRd2, where Rd is the radius of the contact circle given by Rd ) (3V/2π)1/3. Assuming an effective diffusivity value for NPs of the order of Γ ) 10-11 cm2/s and a surface tension of γ ) 60 mN/m, the amount of NPs released from the deposited liquid droplets can be determined from eq 10. In Figure 12, the mean droplet size generated by the jet nebulizer and the NPs released ratio, r ) Cr/C0, are plotted with respect to the kinematic viscosity of the liquid phase for two different values of the surface tension (i.e., γ ) 20 and 60 mN/m). It can be seen that when the kinematic viscosity increases both the droplet size and the value of the impact factor are increased, resulting in a decrease of the mean axial deposition distance, zd, and an increase of the residence time, τ. As a result, the amount of released NPs from the deposited liquid droplets increases. This is in qualitative agreement with experimental results for drug release from droplet carriers,57,58 showing an increase in the drug release rate with an increase of the formulation viscosity. Conclusions PLGA nanoparticles with an average size in the range of 296 to 401 nm and different OVA (1.07-10.16 wt %) and MPLA (0.177-1.081 wt %) loadings were successfully

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ReceiVed for reView February 8, 2010 ReVised manuscript receiVed May 26, 2010 Accepted June 1, 2010 IE100307T

Nanocarrier Aided Nasal Vaccination: An Experimental and

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