3.1 Optimization of FIN Nanosuspensions
The selection of formulation parameters and the corresponding design space (Table S2) in the factorial design for fabricating FIN nanosuspensions were guided by preliminary data and technical considerations for nose-to-brain drug delivery. Without the aid of stabilizers, the resulting nanosuspensions exhibited visible precipitation within 5 minutes right after production, while the incorporation of PVP and CLT into the formulations significantly enhanced the physical stability of nanosuspensions (Table S4). Regarding the response specifications, as the diameter of olfactory axon in humans falls within a typical range of 100–700 nm, and previous research has demonstrated that nanoparticles with a size of less than 200 nm can be transported effectively to the brain via transcellular neuronal absorption, the target size for FIN nanoparticles was set in the range of 0-200 nm [50, 51]. In addition, considering that the nanosuspensions would be spray-dried into dry powders, they should remain colloidally stable before the drying process. Therefore, the physical stability was set as over 24 hours, allowing sufficient transit time in real manufacturing practice. To unleash the therapeutic potential of the formulations, targets for the EE of FIN and PDI of the nanoparticles were set in line with the literature standards at 90% and < 0.3, respectively [31, 52].
Although single-factor experiments are frequently used for optimizing drug formulations and manufacturing processes, it is relatively time-consuming and incapable of identifying potential interaction effects of factors on responses [53, 54]. Consequently, a full factorial DoE was employed in this study. Table 1 presents the physicochemical properties of FIN nanosuspensions obtained from the DoE. The particle size of the nanosuspensions ranged from 107.4 ± 5.4 nm to 187.2 ± 2.4 nm, which were all within the target range. The PDI of these formulations, except for NF2, satisfied the pre-set requirement of < 0.3. Notably, there was a discrepancy in the stability among these formulations. For example, NF2 and NF5 showed precipitation within 30 minutes, whereas NF3 remained stable for at least 72 hours. The EE of FIN among these formulations varied from 60.51 ± 1.84% to 95.73 ± 0.04%, while the EE of CUR consistently surpassed 99.99%. The coded regression equations for each response model of the FIN nanosuspensions are shown in Table S5. These models were tested by two-way nested ANOVA and displayed statistical significance (p < 0.0001) for all responses, suggesting that the factors and their interactions were significantly correlated with the responses. They were also considered reliable with predicted R² values close to or exceeding 0.85, which agreed with the adjusted R² values. All regression models were therefore used to probe the relative effects of factors and their interactions on the responses.
The Pareto charts in Fig. 3 show the standardized effects of factors and their interactions on the responses. All studied factors and their interactions exerted a significant effect on the particle size of FIN nanosuspensions (Y1). Specifically, increasing the initial FIN concentration (A), CLT: FIN ratio (B), the concentration of PVP solution (C), and their interaction terms AC and BC tended to produce larger particles. The observations in the main effects could be partly explained by the nucleation and growth theories. Upon bulk mixing of the aqueous and organic streams in the MIVM, an upsurge in the supersaturation of hydrophobic solutes (FIN, CUR, and CLT) would concurrently increase their nucleation and particle growth rates, resulting in a contrasting effect on the particle size. Quenching further particle growth requires adequate deposition of the water-soluble stabilizer (PVP) on nanoparticles (i.e., nuclei with dimensions beyond the critical size) via molecular diffusion, which is a time-dependent process [55]. This explains why a sole rise in factor A or B led to an increase in particle size, as the resulting nanoparticles were not instantly stabilized by PVP, and their effects on particle growth dominated that of nucleation. It is worth noting that factor C could influence particle size in both negative and positive ways. While PVP could inhibit nanoparticle growth and aggregation by surface capping, it also acted as a solubilizing agent to alter the supersaturation levels of FIN and CLT in the systems and, consequently, their nucleation kinetics. The latter effect was more apparent in the current design space. The dual roles of PVP also caused factor C to have a lower t-value in two-way nested ANOVA and coefficient in the regression equation compared to the other main factors. Surprisingly, the interaction terms AB and ABC, albeit marginally statistically significant, were negatively correlated with particle size. We speculate that when both factors A and B increased, the precipitated hydrophobic CLT was able to shorten the diffusion path and time of PVP from the bulk solution to the nuclei, thereby yielding smaller nanoparticles. However, the molecular interplays in nanoparticle formation and stabilization for a multi-component system are complex, and further research (e.g. using molecular dynamics simulation) is warranted to delineate their exact mechanisms.
Pertaining to the PDI (Y2), physical stability (Y3), and EE of FIN (Y4) responses, the factor-response relationships elucidated from the models generally aligned with the findings derived from the response model of particle size (Y1) and our expectations. As mentioned earlier, augmenting factor A or B would elevate the supersaturation level of the hydrophobic solutes, thereby facilitating the entrapment of more FIN molecules within the nanoparticle core during the FNP process. The resulting larger nanoparticles also possessed lower surface energy, which served to minimize particle aggregation and produce a more monodisperse nanosuspension. It is not surprising that factor C and its related 2-factor interactions (i.e., AC and BC) displayed erratic effects on the PDI, EE, and stability since PVP serves multiple functions as a crystallization inhibitor, solubilizer, and stabilizer in the nanosuspension formulation as aforementioned. Briefly, factor C had a positive, negative, and insignificant effect on PDI, EE, and stability, respectively, but its interaction factors AC and BC showed divergent trends (AC: negative for PDI, positive for EE and stability; BC: insignificant for PDI and stability, positive for EE). It should be noted that these regression models are of good predictive ability within our design space but their applicability to other systems may be limited, particularly if the design spaces of those systems differ significantly from the present study.
The regression models were simplified by eliminating non-statistically significant terms (Table 2), and the desirability function was applied to optimize the FIN nanosuspension, and the highest desirability value (0.951) was obtained under the condition of A = 5 (mg/mL), B = 1.5 (w/w) and C = 0.25 (% w/v). Additional experimental runs (n = 3) using such parameters were performed for model validation. The resulting nanosuspensions had a particle size of 134.0 ± 0.6 nm (Fig. S1a), PDI of 0.179 ± 0.021, physical stability of 72 ± 0 hours, and EE of FIN of 90.67 ± 0.08%, which were all in line with the predictions (particle size of 133.83 nm, PDI of 0.161, physical stability of 72 hours, and EE of FIN of 91.07%). The DLs of FIN (8.00 ± 0.25%) and CUR (4.43 ± 0.15%) were well aligned with the theoretical values, i.e., 8% for FIN and 4% for CUR. The zeta potential of the optimized nanosuspension was − 0.24 ± 0.24 mV, suggesting its near-neutral state. As shown in Fig. S1b, FIN nanoparticles displayed roughly spherical morphology with similar size to that of DLS. As a result, the optimized FIN nanosuspension was used for the subsequent development of nano-embedded microparticle dry powders.
Table 1
Physicochemical properties of FIN nanosuspensions prepared based on the 3-factor 2-level factorial design (n = 3).
Nanosuspension Formulation (NF) | Factors with levels | Responses and results |
A | B | C | Particle Size (nm) (Y1) | PDI (Y2) | Physical Stability (hours) (Y3) | EE of FIN (%) (Y4) |
NF1 | 5 | 0.5 | 0.25 | 136.6 ± 1.3 | 0.238 ± 0.008 | 0.7 ± 1.2 | 88.87 ± 1.25 |
NF2 | 5 | 0.5 | 1 | 107.4 ± 5.4 | 0.381 ± 0.072 | < 0.5 ± 0 | 60.51 ± 1.84 |
NF3 | 5 | 1.5 | 0.25 | 134.0 ± 0.6 | 0.179 ± 0.021 | 72.0 ± 0 | 90.67 ± 0.08 |
NF4 | 5 | 1.5 | 1 | 142.8 ± 2.2 | 0.251 ± 0.044 | 40.0 ± 13.9 | 68.44 ± 0.56 |
NF5 | 10 | 0.5 | 0.25 | 155.2 ± 1.0 | 0.171 ± 0.019 | < 0.5 ± 0 | 95.03 ± 0.27 |
NF6 | 10 | 0.5 | 1 | 166.4 ± 5.5 | 0.208 ± 0.025 | 1.3 ± 1.2 | 81.44 ± 0.41 |
NF7 | 10 | 1.5 | 0.25 | 151.8 ± 2.4 | 0.181 ± 0.016 | 6.0 ± 0 | 95.73 ± 0.04 |
NF8 | 10 | 1.5 | 1 | 187.2 ± 2.4 | 0.186 ± 0.006 | 24.0 ± 0 | 85.04 ± 0.45 |
NF9 | 7.5 | 1 | 0.625 | 154.8 ± 4.7 | 0.205 ± 0.013 | 6.0 ± 0 | 84.93 ± 1.73 |
A: Initial FIN concentration (mg/ml); B: CLT: FIN (w/w); C: Concentration of PVP solution (% w/v) |
Table 2
Coded regression equations of response models for optimization of FIN nanosuspension with ANOVA and multiple reliability test (R2) results.
Response | Regression equation | F value | p value | R2 | Adjusted R2 |
Particle Size (nm) (Y1) | = 147.68 + 17.48A + 6.28B + 3.26C + 8.4AC + 7.78BC | 128.04 | < 0.0001 | 0.970 | 0.962 |
PDI (Y2) | = 0.2244-0.0377A-0.0252B + 0.0321C + 0.0222AB-0.0218AC | 17.28 | < 0.0001 | 0.812 | 0.765 |
Physical Stability (hours) (Y3) | = 18.00-10.17A + 17.50B-1.67C-10.33AB + 6.50AC-1.83BC + 6.00ABC | 95.23 | < 0.0001 | 0.974 | 0.964 |
EE of FIN (%) (Y4) | = 83.22 + 6.10A + 1.75B-9.36C-0.6796AB + 3.29AC + 1.13BC | 500.23 | < 0.0001 | 0.994 | 0.992 |
Note: Factors BC and C in the equation for Y3 were retained to maintain the hierarchy despite being statistically non-significant. |
3.2 Optimization of FIN Dry Powder Formulations
As with the optimization of FIN nanosuspension, a 2-level, 3-factor full factorial DoE was used to study the influence and interactions of factors [A: mannitol-nanoparticle ratio (w/w); B: atomization flow rate (L/h); and C: feed rate (ml/min)] on the selected properties of the resulting spray-dried nano-embedded powders [Z1: redispersibility (RdI); Z2: PDI of the reconstituted nanosuspension; and Z3: volumetric size of the spray-dried powder] (Table S3). Mannitol was employed as a carrier in spray drying studies due to its promising safety for pulmonary delivery [56, 57]. To preserve the merits of nanoparticles for intranasal delivery, the spray-dried powder should be redispersible to its nanosuspension counterpart, ideally with the same particle size distribution as that freshly prepared from FNP, once in contact with the nasal fluid. Hence, the RdI was set in the range of 0.8–1.2, and the PDI of reconstituted nanosuspension was set as < 0.3. In addition, the particle size of dry powder is a pivotal factor in controlling their nasal deposition profile. Particles less than 5 µm tend to enter the respiratory tract, while particles larger than 20 µm remain in the anterior part of the nose [58, 59]. Only powder with a particle size around 10 µm can effectively deposit in the olfactory region [60, 61]. The target median particle size of the spray-dry powder was therefore set as 10 µm. The levels of each factor were determined based on previous studies [33, 62] and our preliminary data.
The physicochemical properties and volumetric particle size distribution of different FIN dry powder formulations obtained from the DoE were presented in Table 3 and Table S6, respectively. Their RdI varied from 1.02 ± 0.01 to 1.82 ± 0.08, and the PDI of the reconstituted nanosuspensions were close to 0.3. Among different formulations, PF1, PF6, PF7, and PF9 satisfied the pre-set RdI and PDI requirements. The volumetric particle sizes of these dry powder formulations ranged from 4.58 ± 0.57 µm to 10.84 ± 0.47 µm with span < 2 (except for PF3). The volumetric median particle size of these formulations, except for PF3 and PF4, was close to the pre-set 10 µm. The EEs of FIN and CUR for all dry powder formulations were all > 90%. The coded regression equations for each response model of FIN dry powder formulations are listed in Table S7. The regression equations for redispersibility and particle size had p values < 0.0001, demonstrating a significant relationship between factors and their interactions with the responses. These models are considered reliable as their R² values were > 0.90 and agreed with the adjusted R². However, the p-value and R² values for the PDI regression are 0.715 and 0.200, respectively, indicating that there was no significant factor-response correlation. Hence, the PDI was not included in the numerical optimization process.
The Pareto charts of standardized effects of factors and their interactions on the responses are shown in Fig. 4. All factors and their interactions, except for AC and ABC, showed a positive influence on the RdI of the dry powder. When factor A (mannitol-nanoparticle ratio) increased, the total solute concentration and drying time of nanosuspension droplets would also increase, leading to adverse outcomes on the nanoparticle stability and RdI. The most significant factor affecting RdI was factor B (atomization flow rate) in this study. This was not surprising and could be ascribed to greater shear stresses exerted on the nanoparticle surface during feed atomization. Moreover, a rise in factor C (feed rate) would generate larger droplets, resulting in a longer drying time and stronger thermal stress on the nanoparticles, hence the corresponding increase in RdI. The interaction terms AB and BC also shared the same trend and effect on RdI with their individual factors. Interestingly, all factors and their interactions did not have a significant effect on the PDI of the reconstituted nanosuspension. This implied that these factors had a minimal impact on the uniformity of nanoparticles embedded in the resulting spray-dried powders. Regarding the particle size of the powder, both factors A and B and their interactions AB and BC displayed a negative effect. An increase in factor B would yield smaller droplets and, thus, smaller particles. However, the trend for factor A deviated from literature reports, where particle size increases as solute concentration in feed solution increases [63]. We speculate that the denser dry powders (PF3 and PF4) produced by high solute concentrations with low atomization flow rates were fractured during the dispersion from the nasal device, resulting in a smaller volumetric particle size.
The regression models for redispersibility and particle size were simplified by removing non-significant terms (Table 4), and the desirability function was applied to optimize the FIN nano-embedded powder formulation. A moderate level of drug loading can reduce medication dosage. Since an increase in factor A (mannitol-nanoparticle ratio) is correlated with increasing RdI, and it is preferable to minimize the excipients used, the level of A is set as the lowest in the design space. The highest desirability value was achieved when A = 4:1 (w/w), B = 407 (L/h), and C = 4.2 (mL/min), and the corresponding predicted RdI and volumetric median particle size of the dry powder were 1.1 and 10 µm, respectively. Model validation was performed for fabricating the FIN dry powder (n = 3) using the optimized parameters. The obtained dry powder had a RdI of 1.09 ± 0.04 and a median particle size of 11.31 ± 1.7 µm, congruent with the predicted properties. The PDI (0.272 ± 0.019) of the nanosuspension reconstituted from the dry powders also fulfilled the target requirement. As shown in Fig. 5a, the optimized dry powder could be reconstituted back to nanosuspension without significant changes in particle size distribution compared to the freshly prepared nanosuspension. The volumetric size of the dry powder was 11.31 ± 1.7 µm, which was expected to be effectively deposited in the olfactory region of the nasal cavity. The drug contents of FIN (1.55 ± 0.09%) and CUR (0.68 ± 0.11%) of the dry powder were consistent with the theoretical values (i.e., 1.6% for FIN and 0.8% for CUR).
Table 3
Physicochemical properties of FIN dry powders prepared based on the 3-factor 2-level factorial design (n = 3).
Powder Formulation (PF) | Factors with levels | Responses and results |
A | B | C | RdI(Z1) | PDI (Z2) | Particle Size D50 (µm) (Z3) |
PF1 | 10 | 601 | 1.5 | 1.05 ± 0.03 | 0.299 ± 0.044 | 8.69 ± 0.21 |
PF2 | 10 | 601 | 4.5 | 1.02 ± 0.01 | 0.347 ± 0.092 | 9.91 ± 0.28 |
PF3 | 10 | 357 | 1.5 | 1.60 ± 0.08 | 0.338 ± 0.061 | 5.16 ± 0.62 |
PF4 | 10 | 357 | 4.5 | 1.82 ± 0.08 | 0.310 ± 0.020 | 4.58 ± 0.57 |
PF5 | 4 | 357 | 1.5 | 1.14 ± 0.02 | 0.303 ± 0.038 | 8.95 ± 1.14 |
PF6 | 4 | 357 | 4.5 | 1.08 ± 0.05 | 0.293 ± 0.038 | 10.84 ± 0.47 |
PF7 | 4 | 601 | 1.5 | 1.04 ± 0.06 | 0.285 ± 0.030 | 8.34 ± 0.28 |
PF8 | 4 | 601 | 4.5 | 1.30 ± 0.04 | 0.305 ± 0.029 | 7.44 ± 0.49 |
PF9 | 7 | 473 | 3 | 1.13 ± 0.03 | 0.298 ± 0.020 | 8.38 ± 0.53 |
A: Mannitol: nano (w/w); B: Atomization gas flow rate (L/h); C: Feed rate (ml/min) |
Table 4
Coded regression equations of response models for optimization of FIN dry powders with ANOVA and multiple reliability test (R2) results.
Response | Regression equation | F value | p value | R2 | Adjusted R2 |
Redispersibility (Z1) | = 1.26 + 0.1150A + 0.1850B + 0.0475C + 0.1558AB + 0.0717BC | 170.76 | < 0.0001 | 0.977 | 0.971 |
Volumetric Particle Size of dry powder (µm) (Z3) | = 7.99-0.9046A-1.61B + 0.2029C-0.6063AB-0.5738BC | 62.77 | < 0.0001 | 0.940 | 0.925 |
Note: Factor C in the equation for Z3 was retained to maintain the hierarchy despite being statistically non-significant. |
3.3 Characterization of the FIN Nano-Embedded Dry Powder Formulation
Presented in Fig. 5b and Fig. 5c are the SEM images of the optimized FIN dry powders. The particles exhibited a spherical morphology with embedded nanoparticles (as indicated by the black frame), contributing to their good flowability for ease of powder loading in nasal spraying devices. Considering that a higher aspect ratio of particles could augment powder deposition in the alveolar region, the spherical morphology minimizes the fraction of particles inadvertently deposited in deep lung areas during intranasal application [64]. As seen in the DSC and PXRD profiles (Figs. S2a and S2b), except PVP, all other raw materials, their physical mixture counterpart, and the optimized FIN dry powder were crystalline in nature. The TGA analysis revealed that approximately 3.01% of residual moisture was present in the optimized dry powders, and its moisture content gave a temperature-dependent reduction to 0.91–1.40% upon 1 month of storage (Fig. S2c). This was not surprising as unbound water in the powder evaporated with a moisture balance. The optimized FIN dry powder possessed satisfactory stability as no significant phase transformation and reduction in drug assays were detected under various storage conditions. Presented in Fig. S2d are the FTIR spectra of different samples. A very broad peak can be seen from around 3,600 cm− 1 to 3,100 cm− 1 in the optimized FIN dry powder. This is likely due to the formation of intermolecular hydrogen bonding between CUR and PVP [65], which improved the stability of the nanoparticles. Regarding the in vitro drug release, while the optimized FIN dry powder showed rapid and good redispersion (RdI < 1.2) in simulated nasal fluid, neither FIN nor CUR were detectable by HPLC throughout the 3-hour experiment. The FIN or CUR in the physical mixture in the simulated nasal fluid also could not be detected. However, the release of FIN and CUR from the optimized FIN dry powder surpassed those of its physical mixture counterpart in an 80:20 (v/v) solution of simulated nasal fluid and ethanol (Fig. S3). It is believed that no drug release occurred in the physical mixture due to its limited solubility, but for the nanosuspension reconstituted from the optimized FIN dry powder, most of the drugs were well encapsulated and protected within the nanoparticles. The effective encapsulation of drugs within the nanoparticles also renders the optimized dry powder less susceptible to nasal mucociliary clearance (typically occurring every 10–20 minutes) [66], making it suitable for nose-to-brain drug delivery.
The nasal deposition studies of the optimized FIN dry powder were conducted using two different nasal anatomical models at varying flow rates of 0, 7.5, and 15 L/min. The flow rates were selected based on the typical breathing patterns pertinent to clinical applications: a flow rate of 15 L/min mimics normal steady breathing [67], 7.5 L/min corresponds to slow inhalation [39], and 0 L/min represents an absence of breathing or a breath-holding scenario [14, 68]. All nasal models were connected to an NGI to simultaneously study powder deposition in the nose and the respiratory tract.
The powders demonstrated excellent dispersibility upon actuation of the nasal device, with the nasal device fraction consistently below 5% (Fig. 6), except for the 3D-printed nasal cast at 0 L/min flow rate. These findings suggest that the nasal device is generally effective for administering powder formulations to the nasal cavity. The high deposition in the nasal device (~ 20%) observed for the 3D-printed nasal cast at 0 L/min flow rate may be attributed to the powder backflow from the respiratory region of the cast to the exterior of the nasal device due to insufficient energy from the flow.
The olfactory pathway is an important route for nose-to-brain delivery [69, 70]. In the 3D-printed nasal cast (Fig. 6a), the deposition fractions of optimized FIN dry powders in the olfactory region were 45.4% for 15 L/min, 45.2% for 7.5 L/min, and 48.5% for 0 L/min. Hence, as high as 45% of the optimized FIN dry powder can deposit in the olfactory region. In the AINI model (Fig. 6b), the deposition fractions of optimized FIN dry powder in the olfactory region were 8.6% for 15 L/min, 4.7% for 7.5 L/min, and 7.3% for 0 L/min. Aside from the olfactory region being the primary focus for nose-to-brain delivery, the highly vascularized respiratory region also warrants attention, as drugs can be absorbed in this area for systemic delivery. In addition, parts of the turbinate region innervated by the trigeminal nerve can transport the drug from the nose to the brain via the trigeminal pathway [5, 68]. In the 3D-printed nasal cast, the deposition fractions of optimized FIN dry powder in the respiratory region were 48.3% for 15 L/min, 48.5% for 7.5 L/min, and 29.2% for 0 L/min. In the AINI model, the deposition fractions of optimized FIN dry powder in the respiratory region (sum of nasopharynx, turbinates, and vestibule) were 62.3% for 15 L/min, 62.3% for 7.5 L/min, and 85.0% for 0 L/min. In detail, the deposition fractions of optimized FIN dry powder in the turbinate region were 25.6% for 15 L/min, 29.2% for 7.5 L/min, and 62.5% for 0 L/min.
The discrepancy in regional deposition profiles between the 3D-printed nasal cast and the AINI was not surprising and can be ascribed to the differences in the design and construction materials of these models. Since the olfactory region of the 3D-printed nasal cast has a larger surface area, the deposition fraction was higher in this model. Apart from the design, the 3D-printed nasal cast was made of polylactic acid, while the AINI model was fabricated from stainless steel. The adhesion capabilities, texture, and potential impact of surface charges on dry powders may exhibit variations. In the present study, the FIN nano-embedded powders displayed a greater propensity to adhere to plastic surfaces. Powders in the olfactory region of the AINI model may have unintentionally fallen into the respiratory region (especially during the disassembly of the AINI), resulting in a lower deposition fraction. The in vitro-in vivo correlation of drug deposition using these nasal models should be further investigated.
It is worth noting that the inspiratory flow rate may vary among patients of different ages and disease states. Therefore, a holistic evaluation of how the inspiratory flow rate may influence the nasal deposition is desired. Perkušić et al. found that an increased inspiratory flow rate from 0 L/min to 60 L/min was associated with reduced drug deposition in the olfactory region [14], while Rigaut et al. found that flow rate (0, 15, and 60 L/min) was not a significant influencing factor for olfactory deposition [67]. In this study, the deposition fraction of the olfactory region was independent of the flow rate for the optimized FIN powder formulation under different flow rates in two different nasal models (Fig. S4). The reason for this observation could be due to the restricted range of inspiratory flow rate (0–15 L/min) studied. Besides, the manufacturer of the Unidose Nasal Spray Device (Aptar Pharma) has suggested that simultaneous actuation of the nasal device and inspiration is not required because the nasal device can generate sufficient air pressure for powder dispersion [67]. Hence, the optimized powder formulation is suitable for patients with different inspiratory flow rates.
3.4 In Vitro Cytotoxicity Studies of the Optimized FIN Nano-embedded Dry Powder Formulation
The cytotoxicity of FIN nanosuspension reconstituted from the optimized dry powder was evaluated in various cell lines. SH-SY5Y and PC 12 cells were used as neuronal cell models, while RPMI 2650 and Calu-3 were used as nasal epithelial cell models [71]. In addition, the cytotoxicity of raw FIN and a physical mixture of the optimized powder formulation composition were also investigated for comparison purposes. The concentration range studied (2.5–1,000 nM) was chosen based on literature reports and clinical pharmacokinetics of FIN [72, 73]. As presented in Fig. 7, the cell viability for all groups was around 100%, with no significant difference observed. This result indicates that the formulation has an acceptable in vitro safety profile.
3.5 In Vivo Neuroprotective Effects of the Optimized FIN Nano-embedded Dry Powder Formulation
A schematic diagram of the study design of in vivo neuroprotective effects is depicted in Fig. 8a. FIN nanosuspension was reconstituted from the optimized dry powder and separately delivered to the mice with MCAO surgery via intravenous (IV) and intranasal (IN) administration. To assess the neuroprotective effect with greater clinical relevance, the tested drugs were administrated 30 minutes after stroke surgery. As shown in Fig. 8b, the brain slices were stained with Nissl solutions on day 2 and the infarct regions were marked by the red line. The infarct size was significantly reduced to 1.01 ± 0.12 mm2 in the IV-treated group compared with the untreated MCAO group (1.54 ± 0.10 mm3). Remarkably, the infarct size was further reduced to 0.71 ± 0.07 mm2 in the IN-treated group (Fig. 8c). To precisely measure the cerebral infarct size, we also employed the 9.4T animal magnetic resonance imaging system (MRI) 24 hours post-treatment. Figure 9a shows the mouse brain MRI images with clear cerebral infarction after MCAO surgery in the left hemispheres, with an average infarct volume of 2.93 ± 0.71 mm3. The IV-treated mice had a lower infarct volume of 2.13 ± 0.85 mm3, while the IN-treated group gave a significant reduction to 1.31 ± 0.57 mm3 (Figs. 9b and 9c).
The neuroprotective effects were also evaluated using the adhesive removal test, balance beam test, rotarod test, and the Longa score. As expected, the time required for the mice to remove the adhesive tapes from their bilateral paws after MCAO surgery was significantly increased due to the neurological deficit after acute ischemia imposed by the MCAO surgery (Fig. 10a, b). However, the time required for MCAO mice with IV and IN treatment to remove the adhesive tapes decreased significantly compared to untreated MCAO mice on day 2 and day 3 (Figs. 10a and 10b) after stroke surgery. Notably, the effect on the IN-treated group on day 3 was more significant than that of the IV-treated group, as evidenced by the smaller p-value. Likewise, MCAO mice treated with reconstituted FIN nanosuspension intranasally required less average time to cross the beam compared to untreated MCAO mice and IV-treated MCAO mice (Figs. 10c and 10d). For the rotarod test, however, the time spent on the rod for treated groups was not statistically significant from that in the untreated group on day 2 (Fig. 10e), possibly due to the sensitivity of the rotarod test for the stroke model [74]. Nevertheless, a statistically significant difference (p < 0.01) was observed between the MCAO + IN FIN and MCAO groups on day 3 (Fig. 10f). The Longa scores of mice in both treatment groups significantly decreased compared to the untreated MCAO group (Fig. 10g). Taken together, these results suggest that the IN administration outperformed IV administration in terms of alleviating neurological deficits after a stroke.
Furthermore, a western blot analysis was performed on post-stroke mouse brains to understand the neuroprotective mechanism. Expression of pro-apoptotic proteins BAX and CC3, and the anti-apoptotic protein BCL-2 in the peri-infarct tissue of our MCAO stroke model were determined [49]. This was due to reports that FIN could increase the expression of BCL-2 and decrease the expressions of CC3 and BAX [75, 76]. As shown in Fig. 11, administration of the reconstituted FIN dry powder for nanosuspension by IV and IN routes showed significant neuroprotective effects by reducing the expression of apoptosis proteins CC3 and BAX and increasing the expression of anti-apoptotic protein BCL-2. Of note, expression of BAX in the peri-infarct tissue was statistically significantly lower in IN-treated mice compared to that in IV-treated mice. BCL-2 expression was also numerically higher in IN-treated mice relative to IV-treated mice despite the lack of statistically significant differences. Nevertheless, these results highlight that IN delivery of FIN nanoparticles was more effective in reducing infarct size in the peri-infarct tissue than IV delivery.
It is noteworthy that the neuroprotective effects could be achieved after single-dose IN administration. The non-invasive and convenient IN administration method also presents a unique treatment modality where FIN nanoparticles (in form of dry powder or reconstituted nanosuspension) can be timely administered by ambulance staff or caregivers to patients experiencing acute ischemic stroke symptoms before hospital admission. This strategy can attenuate neuronal injury in the hyperacute phase of ischemic stroke between stroke onset and in-hospital treatment procedures and promote post-stroke functional recovery. Whilst cardiovascular adverse effects such as bradycardia and atrioventricular blockade have been observed in patients on chronic oral FIN treatment for multiple sclerosis [77], we do not anticipate significant safety issues arising from single-dose IN nanoparticle administration due to its excellent cytocompatibility after single-dose treatment. A constraint of the current study is that mice were administered IN with reconstituted FIN nanosuspensions instead of FIN nano-embedded powder as precise powder administration to the small nares of rodents is challenging. Nonetheless, this study provides proof-of-concept evidence of the neuroprotective effects by FIN nanoparticles. It is anticipated that the administration of nano-embedded powders would offer prolonged nasal retention and thus further enhance nose-to-brain FIN transport and neuroprotective efficacy. Studies are ongoing to devise an appropriate technique for administering powder into the nares of rodents and investigate the dose-response relationship and pharmacokinetics of intranasally administered FIN nano-embedded powder in rodent MCAO models. The ultimate goal is to identify an optimal powder dose that can serve as an effective and rapid neuroprotective therapy after an acute ischemic stroke.