1. Introduction
Wound healing is a dynamic and complex process involving the replacement of devitalized cellular structures and tissue layers. A successful wound dressing should keep the site moist, allow for gas exchange, act as a microbial barrier, and eliminate excess exudates [
1]. Oral wounds can be caused by trauma, recurrent ulcers, inflammation, irradiation, and surgery for the extirpation of congenital or pathological lesions. If not properly treated, intraoral wounds can lead to pain, infection, and subsequent undesirable scarring and adhesion, resulting in functional deficits, such as dysphagia, dysarthria, and a poor quality of life. Advanced dressings, such as hydrocolloid foam and hydrogel, provide a moist environment for wounds today [
2].
Hydrogel is a three-dimensional (3D) network made up of hydrophilic polymer chains that can hold abundant water in their structures, allowing it to be used as a drug carrier system [
3] and wound dressing [
4,
5]. Various wound dressings have recently been explored, although certain defects have been discovered, such as poor mechanical qualities and low fluid absorption capability [
6,
7]. Furthermore, because antibiotics might speed up wound healing [
8,
9], neomycin sulfate (NES) was included as a medication in this hydrogel dressing. The aminoglycoside antibiotic NES has a broad range and has been used topically on the skin and mucous membranes [
10,
11].
Solid lipid nanoparticles (SLNs) are colloidal lipid nanocarriers with a solid core. They are one of the ideal medication delivery devices since they are made of biocompatible materials [
12]. SLNs can improve the loading of both water- and fat-soluble medicines. SLNs have several advantages over other drug delivery systems, including being biocompatible and biodegradable with low toxicity, enabling a regulated or sustained drug release profile, and having high protective and loading capacities with minimum adverse effects [
13,
14]. SLNs have previously been shown to improve valsartan transdermal penetration for systemic antihypertensive action [
15].
Furthermore, SLNs are an effective dermal drug delivery method, with improved dermal penetration, occlusive properties, and a longer residence duration inside the skin layers [
16]. The integration of SLNs in gels has previously been shown to result in higher drug permeability than standard gel formulations, which has been attributed to the close interaction of SLNs with the stratum corneum, resulting in a longer residence time in skin layers [
17]. As a result, there has been a lot of focus in recent years on developing SLN-based formulations for topical medication administration.
Pharmaceutical development is a lengthy and complex process that begins with the formulation and ends with the finished product. In any case, the pharmaceutical product should provide the patient with a certain level of quality. Despite advances in pharmaceutical technology and medicine manufacturing, the pharmaceutical industry still relies heavily on traditional process development and drug manufacturing methods. The classic formulation development technique employs raw material controls, product manufacturing controls, in-process controls, and final product controls to ensure product quality.
As a result, detecting the link between product features and product quality using an empirical technique is challenging, and quality is not always guaranteed [
18]. International initiatives established the idea of Quality by Design (QbD), and regulatory organizations such as the FDA and EMA have forcefully encouraged the pharmaceutical industry to utilize it. The International Conference on Harmonization (ICH) published significant recommendations in Q8 Pharmaceutical Development, Q9 Quality Risk Management, and Q10 Pharmaceutical Quality System, which defined the concepts and use of the QbD approach. In summary, QbD is a systematic, scientific, risk-based method for formulating and refining manufacturing processes to achieve a set quality for the final product. The idea’s primary motivation, according to QbD, is that “quality cannot be tested into items; it should be built-in or by design” [
19,
20]. This study’s primary goal was to combine cutting-edge technologies for formulation development, such as nanoparticle technology and QbD. The study was also performed using a modified solvent injection approach to manufacture lipid nanoparticles, followed by the construction of a suitable lipid nanoparticle-containing gel formulation.
2. Results and Discussion
2.1. Compatibility Studies
The characteristic band of NES and the physical mixture of optimized formulation ingredients are shown in
Figure 1 and
Figure 2, respectively. The bands at 3423.89, 3782.24, and 2348.74 cm
−1 were due to NH stretching, OH stretching, and C-H stretching (aromatic), respectively. C=C stretching and C-H stretching were observed at 1521.57 and 3898.75 cm
−1, and 1628.52 and 1119.43 cm
−1 correspond to C-O and C=O stretching. In the end, these bands were identified in the formula. The spectrum revealed that pure NES and selected ingredients were mutually compatible.
2.2. Optimization of Preparation of NES-SLN
The optimal degree of the parameters chosen and their interconnection in achieving desirable EE and mucoadhesion qualities was evaluated using the Box–Behnken design of response surface methodology (RSM).
Table 1 summarizes the results of 17 different experimental runs. EE was between 72 and 89 percent for all trial preparations, with particle sizes ranging from 201 to 398. Individual responses were examined, and statistical modeling, such as ANOVA and fx, was used to determine the effects of parameters.
The quadratic model was chosen for all responses based on the sum of squares (Type I) and fit summary (adjusted and projected R2) (
Table 2). A quadratic model (high-order polynomial) was chosen since the auxiliary terms are visible and the model does not alias. The predicted R2 values of 0.9130 and 0.9628 agree with the adjusted R2 of 0.9730 and 0.9639, respectively, because the difference is smaller than 0.2. Adequate precision measures the S/N ratio. A fraction greater than four is usually preferred. The particle size ratio to EE was 27.5567 to 26.0962, showing a relevant signal. As a result, the model’s efficiency in operating the design space is confirmed. The model F-values of both responses were 65.10 and 48.51, respectively, indicating that the model is valid. Only a 0.01 percent possibility exists that the high F-value was caused by noise.
The model’s repeatability is indicated by the coefficient of variation (CV) value. The current model’s repeatability was determined to be CV 10%. The investigation found relatively low CV values, indicating that the model is accurate and reliable. Insufficient fit can result in a model that fails to depict all of the data (
Table 3). As a result, determining that the model-generated equations predict the results rationally requires a lack of fit. The chosen model was appropriate for the investigation because the
p-values for both responses were insignificant [
21].
The inference of the quantitative impacts of the factor components was tested using ANOVA. Multiple regressions were used to extract polynomial equations from the data. [
22]. The ANOVA results outperformed the quadratic equation’s statistical significance; the
p-value was 0.0500, indicating the importance of model terms. The test design stipulated that particle size was primarily influenced by (a) antagonistic effects of A, AB, AC, and BC and the polynomial term of A, with
p-values of 0.0031, 0.0001, 0.0441, and 0.0175; (b) synergistic effects of B and C and polynomial terms of B and C, with
p-values of 0.0169, 0.0017, 0.0086, and 0.0001, respectively, with (i) an antagonist effect of polynomial terms of B and C with
p-values of 0.0001 and 0.0003; (ii) a synergistic effect of B, C, AB, and AC and the polynomial term of A, with
p-values of 0.0072, 0.0003, 0.0001, 0.0001, and 0.0018, respectively, and the term AC affected EE with a high magnitude among the essential variables.
Table 4 shows the ANOVA coefficients and their
p-values for both solutions. The following are the equations obtained from the responses for the best possible model:
RSM also looked into and presented the effect of individual modifiers on responses [
23].
Figure 3 depicts the link between the response and the variables and a contour plot depicting the variable influences. RSM was used to determine and explain the effect of non-dependent variables on the resulting individual responses. Three-dimensional response surface graphs are required to demonstrate the interaction and primary effect. The acquired solutions are displayed using contour plots [
24]. The global desirability (D) function optimized the model order. Every response was limited to the maximum to obtain an inlay graph to augment the non-dependent variables. All three possible independent variables were encompassed in the design for optimization [
25]. The independent variables (ideal level) in the desirability function plot in
Figure 4 reflected a maximum desirability value of 1.000 for both responses. As a consequence, the particle size is 196.253 nm, and the EE is 89.275 when utilizing this setting. These altered concentrations were used to construct the O-NES-SLN formulation and subsequently used to make gels. The optimization result was confirmed by creating a formulation based on the optimization results and comparing the experimental results. The relative error was less than 2%, indicating that the experiment was successful [
26].
2.3. Formulation and Characterization of NES-SLN-G
In support of the optimization findings, a gel formulation prepared using NES-SLN revealed a particle size of 196.5 ± 1.5 nm. Because of the decreased interfacial tension, particle size decreases with increasing surfactant content, facilitating miscibility between the layers of SLN dispersions. Due to the production of micelles, any subsequent rise in concentration results in a rising trend in particle size. The surface becomes fully loaded with surfactant molecules at the critical micelle concentration. Thus, the interfacial tension change is virtually minimal beyond the critical micelle concentration, resulting in larger particle sizes. The type of surfactants and the lipid components of SLNs are the two key components that can affect drug encapsulation efficiency. With increasing surfactant concentrations at constant lipid concentrations, drug content and encapsulation efficiency improved [
27]. The partition phenomenon, characterized by increased drug partitioning from the inner to outer phase due to the presence of a high surfactant concentration in the exterior phase, which supports increased drug leakage from the internal to external phase, could also explain the decreased entrapment efficiency. The NES-SLN-G polydispersity index was found to be 0.15. The particle size of pure NES in a gel formulation was 542.5 ± 4.2 nm, with a polydispersity index (PDI) of 0.58
Table 5. In general, the numerical value of PDI ranges from 0.0 (for a perfectly uniform sample concerning particle size) to 1.0 (for a perfectly uniform sample with respect to particle size) (for a highly polydisperse sample with multiple particle size populations). In reality, polymer-based nanoparticle materials with values of 0.2 and below are usually deemed acceptable. The average zeta potential of NES-SLN-G was determined to be −32.5 mV, indicating that the formulation is stable. This negative potential has been attributed to glyceryl monostearate, a fatty acid ester.
SEM is a typical technique for evaluating surface morphology and nanoformulation properties. A scanning electron microphotograph of both the gel formulations revealed that NES-SLN-G was uniformly formed and had a well-defined perimeter [
28]. In addition, there was no visible agglomeration of the lipid nanoparticles in the SEM images, indicating consistent dispersion of the formulation (
Figure 5). NES-G showed variable sizes and forms and nonuniformity in the size distribution, in contrast to the basic gel formulation. For all of the samples, the size of the lipid nanoparticles was found to be consistent with the dynamic light scattering data. Particle size and shape are critical in nanotechnology-based formulations because they affect physical features such as texture and flowability and medicinal efficacy.
The NES-SLN-based gel had a smooth, uniform composition with a pH of 5.85 ± 0.15, making it appropriate for topical use. The final gel formulation’s viscosity was determined to be 92,105 mPa∙s. Similarly, the value of spreadability was determined to be 5.92 g.cm/sec, indicating that the gel could be spread easily with minimal shear. Even with a blank gel, the same findings were obtained. Surface tension governs the interactions between two immiscible substances. With increasing surfactant concentration, the surface tension of the SLNs usually decreases. The higher surfactant content reduces the surface area of each particle in the formulation by reducing the superficial tension. The presence of optimized surfactant concentration with additional surfactant properties of glyceryl monostearate was found to have a minimum surface tension of 20.52 ± 1.52 dynes/cm2, which can be attributed to the additional surfactant properties of glyceryl monostearate, which likely increases the retention and permeation of the NES. The specific gravity [0.997 ± 0.04 and 0.995 ± 0.06] and density [0.996 ± 0.05 and 0.998 ± 0.03] of the NES-SLN-G and NES-G gel formulations were similar to those of deionized water.
2.4. Ex Vivo Drug Release Studies
NES release, a valuable indicator of in vivo drug performance, was examined using a modified Franz diffusion cell at a skin temperature of 32+− 2 °C. The Franz diffusion cell had a cellulose acetate membrane with a pore size of 0.45 m clamped between the donor and recipient compartments. The drug release from the SLNs was biphasic, with a burst of drug release followed by regulated drug release (
Figure 6). The drug’s first abrupt release could be owing to the drug’s availability in the interphase and hydrophilic phases of SLNs. By the end of 24 h, almost 97.25 percent of NES had been released. The NES-SLN-G formulation demonstrated substantially faster NES release until the end of 4 h, followed by controlled release. Due to increased viscosity and density after forming into gels, medication release was significantly delayed after 4 h. Only 84.78 percent of the NES had been released by the end of the study. By the end of just 2 h, a blank gel formulation containing simple NES had shown very rapid and complete release.
2.5. In Vitro Antimicrobial Activity Assessment
Different Kolliphor-407 P gel formulations containing NES-SLN and NES were tested for antibacterial efficacy against
S. aureus and
E. coli. After 24 h of treatment, the Kolliphor-407 P gel containing NES-SLN had more significant antibacterial activity against
S. aureus than the pure NES gel (
p < 0.05) (
Figure 7). Recent investigations [
1,
15] have demonstrated similar antibacterial activity against
S. aureus and
E. coli based on determining the zone of inhibition. The findings mentioned above revealed that after 24 h of treatment, the NES-SLN-encapsulated gel had improved antibacterial activity against
S. aureus.
2.6. Confocal Laser Scanning Microscope Study
This investigation aimed to see how far the prepared NES-SLN-G could penetrate the skin.
Figure 8 shows the CLSM after applying transdermal films containing either pure rhodamine (control) or fluorescence-labeled NES-SLN-G. Compared to the control, the NES-SLN-G carrier was widely dispersed with vigorous fluorescence intensity throughout the mucosal membrane layers. The degree of fluorescence at the end of 9 h reflects the excellent distribution of NES into deeper skin layers.
These findings imply that compounded SLN-G has the potential to improve medication bioavailability by improving NES mucosal membrane penetration deep into the dermal region. Furthermore, because no discrete pores were detected, the permeation was thought to be transcellular. Because the produced particles are lipid-based, their penetration favors direct passage through the phospholipid-based cell membrane.
2.7. Stability Studies
The stability of the gel formulation, including MTZ SLNs, was assessed by measuring particle size and in vitro drug release from HEC gels maintained at room temperature (25 °C, 60% RH) for one month. After 1 month of storage, the particle size of NES-SLN in the gel formulation remained stable [197.3 nm] compared to the initial content [196.5 nm]. As evidenced by the f2 value of 82.54, the release profiles of the gel formulations followed the same trends over time (which is more than 50). The inclusion of NES-SLN in the gel formulation helped its stability over a one-month storage period, according to the stability study results.
4. Conclusions
This research looked at the adaptability of QbD in the lab and provided high-level suggestions on the scope and applicability of QbD. The research was carried out to formulate solid lipid nanoparticles, and the integration of the QbD concept with modern methodologies was confirmed to be a significant advancement for the pharmaceutical industry since this time- and cost-saving process ensures a high-quality result. The Box–Behnken design was used to successfully optimize NES-SLNs. According to the desirability approach, a formulation having 0.467 percent stearic acid, 0.275 percent glyceryl monostearate, and 1.23 percent P-F 68 can achieve the optimal formulation parameters for NES-SLN preparation. The relative inaccuracy was found to be within acceptable bounds, validating the design’s correctness. Particle size, PDI, and zeta potential were assessed after the improved formulation was successfully manufactured in gel systems. SEM analysis indicated that the lipid nanoparticles did not aggregate and that the formulation was dispersed uniformly. In comparison to NES-G and NES-SLN, drug release experiments showed that the NES-SLN-G formulation had a more regulated release. Owing to the enhanced antibacterial activity, the designed gel formulation was proven to be efficacious in treating wounds.