Design, Optimization, and Neuroprotective Investigation of Methoxsalen-Loaded In-situ nasal Nanoemulgel for Brain Targeting in Parkinson’s Disease

Parkinson's disease (PD) is a neurodegenerative condition that primarily affects the motor system and is typified by the slow degeneration of dopaminergic neurones in the substantia nigra. One characteristic is that black pars compacta apart [1]. Bradykinesia, resting tremor, muscle rigidity, and postural instability are examples of clinical symptoms. Even though, great deal of research, the precise etiology of Parkinson's disease is still complex and includes genetic predispositions, environmental toxins, oxidative stress, mitochondrial malfunction, and neuroinflammation. Levodopa, dopamine agonists, and MAO-B inhibitors are examples of current pharmaceutical treatments that provide symptomatic relief but do not stop or reverse neuronal degeneration. Furthermore, prolonged use of these treatments is frequently linked to drug-induced dyskinesias and motor problems highlighting the critical need for neuroprotective measures that can slow the evolution of the disease [2].    

The naturally occurring furocoumarin methyl Salen, sometimes called 8-methoxypsoralen, is frequently utilized in photochemotherapy for skin conditions. Recent research has shown that it may have neuroprotective qualities, mainly due to its anti-inflammatory and antioxidant processes [3]. Methoxasalen can reduce oxidative stress by scavenging free radicals and increasing the activity of antioxidant enzymes that are found naturally, such as catalase and SOD [4]. These properties make methoxasalen a prospective treatment for slowing the progression of Parkinson's disease and preventing dopaminergic neurone death.

The benefits of nanoemulsions and the ease of use of gels are combined in nanoemulgels, an innovative drug delivery method that offers a special platform for improving drug solubility, stability, and penetration through biological barriers [5]. With their submicron-sized droplets, nanoemulsions provide hydrophobic medications like methoxasalen with more surface area and faster rates of dissolution [6]. The formulation can be applied intranasaly when it is integrated into a gel matrix, which improves patient compliance and permits regulated drug release. Bypassing hepatic first-pass metabolism, intranasal system can help drugs enter the systemic circulation or even deliver them directly to the brain through peripheral nerve pathways for neurological illnesses. Nanoemulgels are a very appealing alternative for delivering neuroprotective agents because of these qualities. agents effectively [7].

2.MATERIALS AND METHODS

2.1Materials

Pharmaceutical-grade purity (>99%) methoxasalen was purchased from Yucca enterprises, Mumbai . The oils (Pippermint oil), surfactants (Span 80), and co-surfactants (PEG 400) utilized in the investigation were all of analytical grade and acquired from reputable chemical suppliers. Ther Poloxamer 188 led to its selection as the thermoresponsive gelling agent for the manufacture of nanoemulgel.

2.2 Nanoemulsion Preparation

Using the high-speed homogenization method, the nanoemulsion was prepared. In this techniques, the drug is first dissolved in the oil phase, followed by mixing with a suitable surfactant and co-surfactant system (Smix)[8]. The aqueous phase is then added gradually to form a coarse emulsion. This pre-emulsion is subjected to a high-speed homogenizer operating typically between 5,000 and 20,000 rpm for 10 min. , where strong shear forces, turbulence, and cavitation break down the larger droplets into fine nanosized droplets[9].

2.2.1Preparation of pseudoternary phase diagrams

A pseudoternary phase diagram is constructed to determine the concentration range of oil, surfactant–co-surfactant mixture (Smix), and aqueous phase that can form a stable nanoemulsion. Initially, surfactant and co-surfactant are mixed in different weight ratios (e.g., 1:1, 2:1, 3:1) to prepare Smix[10]. For each Smix ratio, a series of mixtures containing oil and Smix in varying proportions are prepared. Each mixture is then titrated dropwise with distilled water under continuous stirring at room temperature. After each addition, the system is visually observed for clarity, transparency, and flowability. The compositions that produce clear, isotropic, and low-viscosity systems are considered as nanoemulsion regions[11].

2.2.2 Optimization of formulation by Box-Behnken design

Box-Behnken Design (Design Expert version 13) was used to optimize the formulation of the nanoemulsion using three parameters. When compared to a typical factorial technique, BBD might be a better option for producing a higher-order response surface with fewer runs. Homogenization speed (B), and oil concentration(C) as measured by Smix ratio (A) were chosen as independent variables. As the dependent variables particle size (Y1), and viscosity (Y2) were used. There were fourteen formulas created based on the concept [12]. Formulations MXF1–MXF18 were coded. The term "optimization" describes how the answers alter when all three elements are altered at once. Table 1 displays the coded values of the independent variables. Three-dimensional graphs were made to ascertain how the independent variables influenced the responses. With a 3D graphical user interface, it investigates how independent variables affect the responses[13]. To account for every response variable, complex polynomial models were created. After conducting data-driven multiple regression analyses and utilising F-statistics to identify statistically significant terms, the models were formulated. Table 2. demonstrate the experimental design of the nanoemulsion.

Table 1 Coded values for independent variables

Table 2 Formulation composition of Methoxsalen Loaded Nanoemulsion.

2.2.3 Characterization of Nanoemulsion

2.2.3.1 Screening for nanoemulsions using oils, surfactants, and co-surfactants

The solubility of methoxsalen in a range of oils, surfactants, and co-surfactants was evaluated in order to determine the ideal oil, surfactant, and co-surfactant. In stoppered 25 millilitre vials, five millilitres of the chosen oil, surfactant, and co-surfactant were combined with five milligrams of methoxasalen. Then maintain continuous stirring at 0°C for 48 hours until the mixture achieved equilibrium. After ensuring that the samples were balanced, they were spun in a centrifuge at 3000 rpm for 15 minutes. Following proper dilution and separation of the supernatant, solubility was assessed using a UV spectrophotometer set to 226 nm [14].

2.2.3.2.Morphology , Globule Size and zeta potential

For morphological analysis, the sample was examined using electron microscopy such as scanning electron microscopy (SEM). A small quantity of the formulation was suitably diluted, placed on a clean glass slide or carbon-coated grid, and dried under vacuum. In the case of SEM, the dried sample was sputter-coated with a thin layer of gold to enhance conductivity before imaging[15]. The sample was then observed under the microscope, and images were captured to determine the shape, surface characteristics, and uniformity of the globules.

For globule size determination, the formulation was analyzed using dynamic light scattering (DLS) technique with a particle size analyzer. The nanoemulsion was diluted appropriately with distilled water to avoid multiple scattering effects and then placed in a cuvette. The sample was equilibrated at a controlled temperature, and measurements were taken to obtain the average globule size and polydispersity index (PDI), indicating the uniformity of the droplet distribution[16].

The zeta potential of the formulation was measured using a zeta potential analyzer based on electrophoretic mobility. The sample was diluted with a suitable medium (usually distilled water or buffer) and placed in a specialized zeta cell. Upon application of an electric field, the movement of charged droplets was analyzed to determine the surface charge. The zeta potential values were recorded to assess the stability of the formulation, where higher absolute values indicate better stability due to electrostatic repulsion between globules[17].

2.2.3.3 Rheological and pH assessment

The rheological behavior and pH of the developed nanoemulgel were evaluated to ensure suitability for nasal or topical administration. For rheological assessment, the formulation was analyzed using a rotational viscometer or rheometer. A sufficient quantity of the nanoemulgel was placed in the sample holder, and measurements were carried out at controlled temperature conditions. The viscosity was determined at different shear rates by gradually increasing the spindle speed, and the corresponding shear stress and shear rate values were recorded[18]. This helped in determining the flow behavior of the formulation, whether it exhibited Newtonian or non-Newtonian (generally pseudoplastic/shear-thinning) characteristics, which are desirable for ease of application and retention at the site of administration.

For pH determination, a calibrated digital pH meter was used. The instrument was first standardized using appropriate buffer solutions (pH 4.0, 7.0, and 9.2). A small quantity of the nanoemulgel was dispersed in distilled water, and the electrode was immersed into the sample[19]. The pH was recorded once the reading stabilized. The measurements were performed in triplicate, and the average value was reported. The pH of the formulation was maintained within an acceptable physiological range to ensure compatibility with the nasal mucosa or skin and to minimize the risk of irritation[20].

2.3 Preparation of Nanoemulgel Formulation

In order to develop a suitable formulation, preliminary screening experiments led to the selection of Poloxamer 188 as the gelling base. A gel base was mixed with the chosen optimal nanoemulsion. In order to create the gel formulation, the ratio of poloxamer 188 is optimized and then dissolved in distilled water, stirred for 30 minutes with a magnetic stirrer, then allowed to hydrate and swell for 6 hours [22].

2.3.1 Optimizing the nanogel:

Optimization of the nanoemulgel was carried out to determine the ideal polymer concentration and ratio of nanoemulsion to gel base that provide desirable physicochemical properties and drug release behavior. Different formulations were prepared by varying the concentration of the gelling polymer (Poloxamer 188) and the proportion of nanoemulsion incorporated into the gel base. Initially, polymer dispersions were prepared by dissolving accurately weighed quantities of polymer in distilled water under continuous stirring and allowed to hydrate overnight to obtain a clear gel base[23]. The optimized nanoemulsion was then incorporated into the gel base in different ratios (e.g., 1:1, 1:2, 1:3 nanoemulsion:gel) with gentle stirring to avoid air entrapment. The prepared nanoemulgels were evaluated for key parameters including pH, viscosity, spreadability, gel strength, and in-vitro drug release. The effect of polymer concentration on viscosity and gelation behavior was carefully studied, while the influence of nanoemulsion ratio on drug release and permeation was assessed[24].

Table 3-different ratio of polymer with nanoemulsion

2.3.2 Evaluation of nanoemulsion loaded nanogel:

Visual assessment for clarity

The prepared methoxsalen-loaded nanoemulgel was visually inspected for its appearance, homogeneity, and clarity. The formulation was observed against both white and black backgrounds for any signs of phase separation, turbidity, or particulate matter[25].

pH measurement

The pH of the nanoemulgel was determined using a calibrated digital pH meter at room temperature. Approximately 1 g of gel was dispersed in distilled water and allowed to equilibrate before measurement[26].

Viscosity

The viscosity of the nanoemulgel was measured using a Brookfield viscometer at different rotational speeds to assess flow behavior[27].

Spredibility

Spreadability of the nanoemulgel was evaluated using the glass slide method. A fixed quantity of gel was placed between two glass slides, and a known weight was applied to spread the formulation uniformly. The time required for the upper slide to move a specific distance was recorded[28].

Spreadability was calculated using the formula:

S=M×LT

• = Spreadability
• M

  

  = Weight applied (g)
• L

  

  = Length moved by the slide (cm)
• T

  

  = Time taken (sec)

Gel Strength

Gel strength of the nanoemulgel was determined to evaluate its mechanical integrity and ability to retain its structure after administration. A specified weight was placed on the surface of the gel, and the time required for the weight to penetrate a fixed distance into the gel was recorded[29].

2.4.1 In-vitro drug release study

The in-vitro drug release study of methoxsalen-loaded nanoemulgel was carried out using a Franz diffusion cell with a suitable dialysis membrane. The receptor compartment was filled with phosphate buffer (pH 6.4) and maintained at 37 ± 0.5°C with continuous stirring. A measured quantity of nanoemulgel was placed in the donor compartment, and samples were withdrawn at predetermined time intervals up to 8 hours. The samples were analyzed spectrophotometrically at the λmax of methoxsalen[30].

Release kinetic study:

To determine the release kinetics, the obtained data are fitted into various mathematical models including zero-order, first-order, Higuchi, and Korsmeyer–Peppas models. The best-fit model is selected based on the highest correlation coefficient (R²) value, which indicates the mechanism of drug release, such as diffusion, erosion, or a combination of both. This study helps in predicting the in vivo performance of the nanoemulgel formulation[31].

2.5 FTIR

The FTIR (Fourier Transform Infrared Spectroscopy) analysis was carried out to identify the functional groups and possible interactions between components. The sample was prepared according to its physical state; solid samples were finely ground with potassium bromide (KBr) and compressed into a transparent pellet. Prior to sample analysis, the instrument was switched on and allowed to stabilize, followed by a background scan to eliminate atmospheric interference from moisture and carbon dioxide. The prepared sample was then placed  in the sample holder, ensuring proper contact. The spectrum was recorded over a wavenumber range of 4000–400 cm?¹ at an appropriate resolution. The obtained interferogram was converted into an infrared spectrum using Fourier Transform, and the characteristic peaks corresponding to different functional groups were identified. After completion of the analysis, the sample holder was carefully cleaned to avoid cross-contamination, and the instrument was properly shut down[32].

3.RESULTS

3.1 Screening of Nanoemulsion components

Screening of NE components was determined by evaluation of MXN solubility in different oils, surfactants and cosurfactants (Table 1). Peppermint oil showed the premier record in solubilizing MXN (201.95 ± 3.14 mg/ml), since, it was the oil of choice. The dual nature of surfactants gives them a vital role in formu lating and stabilizing NEs by being able to bridge the gap between the immiscible water and oil phases and thus reduce the interfacial tension with minimization of the energy required for NE formation . Moreover, surfactants can interfere with coalescence of globules by forming stearic barrier in addition to interfering with the lipid bilayer and consequently improving permeability into the epithelial membrane. Span 80, as a non-ionic surfactant of hydrophilic-lipophilic bal ance (HLB) value of 15, provided a maximum solubilizing capacity for MXN (76.01 ± 3.48 mg/ml). Moreover, span 80 was capable of formation of o/w NE with fine and uniform globules and then was selected for NE formulation [40].

Table 4: Solubility measurements of MXN in different oils, surfactants and cosurfactants.

3.2 Pseudoternary Phase Diagrams

3.2.1 Selection Of Surfactant & Co-Surfactant:

Span 80 with  PEG 400 with the ratio (1:1) produced the largest NE region (48%), far exceeding PG (24%), ethanol (16%), plurol oleique (12%), and lauroglycol 90 (%). As a result Transcutol P is selected as surfactant and Tween 80 is for co-surfactant[41].

Figure 1:- pseudoternary diagrams for the selection of surfactant and co-sufactant with (A) showing highest emulsification region with 1:1 ratio of Surfactant & Co-surfactant

3.3 Formulation optimization

Total of 15 runs were carried out by Box- Behnken design to study the effect of Oil:Smix ratio & homogenization speed on globule size, zeta potential , pH, viscosity and % drug content. The observed effect of responses are reported in Table-5.

Table 5: Displaying the optimised formulation's factors and responses

Data optimization:

For predicting the optimal point, a second-order polynomial model was fitted to correlate relationship between independent variables and response.

Effect of critical factors on Globule size (Y1):

The regression model for predicting the particles size was found to be statistically significant based on the results obtained from ANOVA. The F-value (1.67) of model was significantly higher with P value < 0.05. The lack-of-fit was insignificant (F-value = 1.05, P value = 0.52). The p-value of the lack of fit test was higher (0.30) than the significance level (0.05). So, it indicates the non-significant lack of fit value that is desirable for an adequate model.  According to the polynomial equation [Y1=128.24−42.18A+13.96B+5.92C−59.86AB−68.17AC−4.06BC+62.75A²−10.61B²-37.99C²], the effect of A,AB, AC,BC,B²,C² is negative i.e. the main effects, oil concentration shows a negative coefficient (−42.18), which means that increasing the oil phase leads to a reduction in globule size On the other hand, Smix concentration has a positive coefficient (+13.96), indicating that increasing Smix alone tends to increase globule size, possibly due to micelle aggregation or increased viscosity. Similarly, homogenization speed shows a small positive effect (+5.91), suggesting a slight increase in globule size at higher speeds, which could be due to droplet coalescence at excessive shear conditions[42].

Effect of critical factors on Zeta potential (Y2):

The regression model for predicting the particles size was found to be statistically significant based on the results obtained from ANOVA. The F-value (1.70) of model was significantly higher with P value < 0.05. The lack-of-fit was insignificant (F-value = 1.21, P value = 0.51). If a model shows a significant lack of fit, then the model does not fit well and lacks prediction efficiency. The p-value of the lack of fit test was higher (0.30) than the significance level (0.05). So, it indicates the non-significant lack of fit value that is desirable for an adequate model. The value of regression co-efficient, ????2 adjusted and ????2 predicted for the model was found to be 0.2310 and -0.909 respectively where a negative sign implies that the overall mean may be a better predictor of the response than the current model. The significance and effect of each independent variable and their interaction on the response were evaluated by analyzing the coefficient value. According to the polynomial equation [Y2=-7.196 + -1.3225 * A + -1.0475 * B + -0.525 * C + -1.41 * AB + -2.68 * AC + -1.04 * BC]. The intercept value (−7.196) indicates the predicted zeta potential when all variables are at their central levels. All coefficients in the model are negative, which suggests that increasing any of the individual factors—A (oil), B (Smix), or C (homogenization speed)—leads to a decrease in zeta potential, making the system more negatively charged. Among the main effects, factor A (−1.3225) has a slightly stronger influence compared to B (−1.0475) and C (−0.525), indicating that oil concentration plays a more prominent role in altering surface charge. The interaction terms further highlight the combined influence of variables. The AC interaction (−2.68) shows the most significant negative effect, suggesting that increasing both oil concentration and homogenization speed together markedly enhances the negative zeta potential, which may improve system stability due to increased electrostatic repulsion. Similarly, AB (−1.41) and BC (−1.04) interactions also contribute to a decrease in zeta potential, though to a lesser extent. Overall, the model indicates that both individual and interaction effects contribute to making the zeta potential more negative, which is generally favorable for the physical stability of nanoemulsions by preventing droplet aggregation[43].

Effect of critical factors on pH (Y3):

The regression model for predicting the particles size was found to be statistically significant based on the results obtained from ANOVA. The F-value (1.71) of model was significantly higher with P value < 0.05. The lack-of-fit was insignificant (F-value = 0.29, P value = 0.89). If a model shows a significant lack of fit, then the model does not fit well and lacks prediction efficiency. The p-value of the lack of fit test was higher (0.30) than the significance level (0.05). So, it indicates the non-significant lack of fit value that is desirable for an adequate model. The value of regression co-efficient, ????2 adjusted and ????2 predicted for the model was found to be 0.2335 and -0.3575 respectively where a negative sign implies that the overall mean may be a better predictor of the response than the current model. The significance and effect of each independent variable and their interaction on the response were evaluated by analyzing the coefficient value. According to the polynomial equation [Y3=6.23867 + 0.02625 * A + 0.125 * B + 0.13875 * C + 0.025 * AB + 0.0525 * AC + -0.125 * BC]. All three main factors—A (oil), B (Smix), and C (homogenization speed)—have positive coefficients, meaning that increasing any of these variables leads to a slight increase in pH. Among them, C (+0.13875) has the highest effect, followed by B (+0.125), while A (+0.02625) shows only a minimal influence. This suggests that homogenization conditions and surfactant mixture play a more important role in modifying pH compared to oil concentration[12].

The interaction terms show how combinations of variables affect pH. The AB (+0.025) and AC (+0.0525) interactions have small positive effects, indicating that simultaneous increases in these variables slightly raise the pH. In contrast, the BC interaction (−0.125) has a negative coefficient, suggesting that when Smix and homogenization speed are increased together, the pH decreases. This may be due to changes in ionization or microenvironmental conditions within the formulation.

Effect of critical factors on viscosity (Y4):

The regression model for predicting the particles size was found to be statistically significant based on the results obtained from ANOVA. The F-value (0.77) of model was significantly higher with P value < 0.05. The lack-of-fit was insignificant (F-value = 1.90, P value = 0.38). If a model shows a significant lack of fit, then the model does not fit well and lacks prediction efficiency. The p-value of the lack of fit test was higher (0.38) than the significance level (0.05). So, it indicates the non-significant lack of fit value that is desirable for an adequate model. The value of regression co-efficient, ????2 adjusted and ????2 predicted for the model was found to be -0.1090 and -1.9568 respectively where a negative sign implies that the overall mean may be a better predictor of the response than the current model. The significance and effect of each independent variable and their interaction on the response were evaluated by analyzing the coefficient value. According to the polynomial equation [Y4=34.8673 + -4.47625 * A + -4.08125 * B + 0.1825 * C + -8.245 * AB + -7.7975 * AC + -2.4825 * BC], the main effects of both A (−4.47625) and B (−4.08125) have negative coefficients, meaning that increasing the oil phase (A) and Smix (B) leads to a decrease in viscosity. This may be due to reduced internal resistance and improved flow behavior as the formulation becomes more fluid. In contrast, C (+0.1825), representing homogenization speed, shows a very small positive effect, indicating that increasing speed slightly increases viscosity, possibly due to finer droplet formation and better structural organization, although this effect is minimal. The interaction terms show stronger influences than individual factors. The AB (−8.245) and AC (−7.7975) interactions have large negative coefficients, indicating that simultaneous increases in oil with Smix or homogenization speed significantly reduce viscosity. This suggests a synergistic effect leading to a more fluid system. The BC interaction (−2.4825) also contributes to a decrease in viscosity, but to a lesser extent[10].

Effect of critical factors on % drug content (Y5):

The regression model for predicting the particles size was found to be statistically significant based on the results obtained from ANOVA. The F-value (0.77) of model was significantly higher with P value < 0.05. The lack-of-fit was insignificant (F-value = 2.56, P value = 0.30). If a model shows a significant lack of fit, then the model does not fit well and lacks prediction efficiency. The p-value of the lack of fit test was higher (0.30) than the significance level (0.05). So, it indicates the non-significant lack of fit value that is desirable for an adequate model. The value of regression co-efficient, ????2 adjusted and ????2 predicted for the model was found to be -0.1.53 and -2.059 respectively where a negative sign implies that the overall mean may be a better predictor of the response than the current model. The significance and effect of each independent variable and their interaction on the response were evaluated by analyzing the coefficient value. According to the polynomial equation [Y5=99.318 + 0.195 * A + 0.22125 * B + -0.12875 * C + 0.135 * AB + 0.435 * AC + 0.0775 * BC], the main effects, A (+0.195) and B (+0.22125) have positive coefficients, indicating that increasing oil and Smix concentrations slightly improves drug content, possibly due to better solubilization and uniform distribution of the drug within the system. In contrast, C (−0.12875) has a negative effect, suggesting that higher homogenization speed may slightly reduce drug content, which could be due to drug degradation or minor loss during high-shear processing. The interaction terms further explain combined effects. The AC interaction (+0.435) shows the strongest positive influence, indicating that increasing oil concentration along with homogenization speed significantly enhances drug content, likely due to improved dispersion and entrapment efficiency. The AB interaction (+0.135) and BC interaction (+0.0775) also contribute positively, though to a lesser extent, suggesting mild synergistic effects between variables.

Figure 2: 3D Response Surface Plots showing the Effect of Independent Variables on A. Particle Size B. Zeta Potential C.pH D. Viscosity and E. Drug Content

Formulation MNF6 was considered optimized as it exhibited a desirable globule size in the nanometric range (86.27 nm), sufficient zeta potential (-22.83 mV) indicating good physical stability, acceptable pH suitable for nasal administration, optimal viscosity for enhanced mucosal retention, and high drug content (97.36%). The formulation also showed relatively low standard deviation across all parameters, confirming good reproducibility. Based on the desirability function approach, formulation F6 exhibited the highest overall desirability (D = 0.78), indicating the best compromise among all evaluated responses including globule size, zeta potential, pH, viscosity, and drug content. Hence, F6 was selected as the optimized formulation.

3.4 Evaluation of optimized Methoxasalen loaded Nanoemulsion

3.4.1 Morphology, globule size and zeta potential

The morphology of the nanoemulsion droplets was examined using scanning electron microscopy (SEM). The images revealed spherical, smooth, and uniformly distributed droplets without aggregation, confirming the nanoscale structure and stability of the formulation.

The droplet size, polydispersity index (PDI), and zeta potential of the optimized nanoemulsion were determined using dynamic light scattering (DLS). The mean droplet size was found to be approximately 73 nm, with a PDI of 0.20, indicating a narrow size distribution and uniformity. Zeta potential was measured to evaluate the stability of the formulation and was found to be −63.7 mV, suggesting strong electrostatic repulsion between droplets and excellent stability.

3.4.2 Rheological and pH assessment

The rheological behavior of the optimized nanoemulsion was evaluated using a Brookfield viscometer at varying shear rates. The formulation exhibited Newtonian flow behavior with low viscosity, which is desirable for nasal administration as it allows easy spraying and spreading.

The pH of the nanoemulsion was measured using a digital pH meter and found to be in the range of 5.5–6.5, which is compatible with the nasal mucosa and minimizes irritation upon administration.

3.5 Optimization of NanoGel:

Among the prepared gel formulations, the batch containing 1.0% polymer (Poloxamer 188) concentration with a nanoemulsion to gel ratio of 1:2 (MNG5) was identified as the optimized formulation. This selection was based on its balanced physicochemical and performance characteristics compared to other batches. The formulation exhibited moderate viscosity, which is essential for ease of nasal administration while still ensuring adequate retention at the site of application. It also demonstrated good spreadability and appropriate gel strength, indicating uniform application and sufficient structural integrity. Importantly, this formulation showed a controlled and relatively higher drug release profile compared to higher polymer concentrations, where excessive viscosity hindered diffusion, and lower polymer concentrations, where gel stability was compromised. The pH of the formulation was within the acceptable nasal range, ensuring minimal irritation. Overall, this formulation provided an optimal balance between viscosity, drug release, and stability, making it most suitable for effective nasal drug delivery.

Table 6: detemination of optimized nanogel formulation

3.5.1 Visual assessment for clarity

The optimized nanoemulgel appeared clear to slightly translucent, smooth, and homogeneous without any visible aggregates or phase separation, indicating successful incorporation of the nanoemulsion into the gel matrix and good physical stability

3.5.2 pH

The pH of the optimized formulation was found to be in the range of 5.5–6.2, which is suitable for nasal administration and minimizes the risk of mucosal irritation.

3.5.3 Viscosity

The formulation exhibited pseudoplastic (shear-thinning) behavior, where viscosity decreased with increasing shear rate. This property is advantageous for nasal delivery as it facilitates easy administration while maintaining sufficient viscosity for retention at the site of application.

3.5.4 Spreadibility

The optimized formulation showed good spreadability, indicating ease of application and uniform distribution over the nasal mucosa.

3.5.5 Gel Strength:

The optimized nanoemulgel exhibited moderate gel strength, which is desirable for nasal delivery. It ensures that the gel remains at the site of administration for a prolonged period without being too stiff, thereby enhancing residence time and drug absorption.

3.6 SEM Images

Scanning electron microscopy (SEM) images of optimized formulation loaded methoxsalen nanoemulgel at different magnifications (7,000×.) Micrographs showed that spherical droplets embedded within the hydrogel matrix were uniformly dispersed with a smooth surface,  validating particle sizes in the sub-200 nm range (Fig. 6).  and no sign of aggregation. The results of magnifications (7,000×) showed even distribution and preserved structure of the formulation, thus indicating its stability.

Figure 3 - SEM images of (a)Blank nanoemulsion (b) MXN loaded nanoemulsion (c) Blank Nanoemulgel (d) MXN Loaded Nanoemulgel

3.7 FTIR:

As shown in figure 4, the FTIR spectra of pure methoxsalen, Poloxamer 188, and their physical mixture/formulated nanogel were compared to assess drug–polymer compatibility. The characteristic peaks of methoxsalen—such as those corresponding to C=O stretching, aromatic C=C vibrations, and C–O functional groups—were clearly observed in the spectra of the formulation without any significant shift, disappearance, or formation of new peaks

.

Figure 4:FTIR of (a)polymer (b) Methoxasalen (c) methoxasalen with polymer

Similarly, the characteristic bands of Poloxamer 188 remained unchanged. The absence of peak alteration indicates that there is no chemical interaction between methoxsalen and Poloxamer 188, confirming their compatibility. This suggests that the drug is physically dispersed within the polymer matrix and the formulation is stable in nature.

3.8 Invitro drug release:

The in-vitro release of Methoxasalen(MXN) from Nano-emulsion and Nano-gel formulations is illustrated in Fig. 4. The optimized NE formulations (MNF6) showed a significant higher drug release rate (p < 0.05) i.e. >90%. The small globule size with subsequent increase in surface area exposed to the release medium, the increased polarity of the formulations by the proper balance between the ratio of oil: Smix and high solubilization capacity of NE formulations were the contributing factors for high drug release. This could explain the results of MNF6 formulation  which exhibited 94.5% drug release within 8 h. Statistical analysis revealed the presence of significant strong positive correlation (Pearson coefficient = 0.9, p < 0.05) between the release % and solubilization capacity. In addition, a non significant negative correlation (p > 0.05) was observed between the release % and either globule size or viscosity (Pearson coefficient =-0.1, p > 0.05). The rate and extent MXN release profile showed no significant difference (p > 0.05) between all the prepared NE formulae. Therefore, MNG3 was selected as optimum formulations because of their high release profile and a sustained drug release profile, with approximately 94.5% drug release over 8 hours The release exponent (n) ranged between 0.5 and 1 indicating non-Fickian/anomalous release of the drug from the studied NE formulations.

Release kinetic analysis

As shown in figure, the in-vitro release data were analyzed according to several kinetic models. The higher R² values for zero order kinetics than first order kinetics were obtained indicating that the release MXN from NGs was concentration dependent. To analyze the release mechanism of drug from NE formulations, the data were fit to Korsmeyer-Peppas model (R² >0.95). The release exponent (n) ranged between 0.5 and 1 indicating non-Fickian/anomalous release.

Table 8:Correlation coefficient (R²) values of different kinetic release profiles.