Assigning suitable lipids
The different liquid lipids' RES solubility orders includeMaisine CC > Labrafac PG > Capmul MCM > GMO > Captex 100 > Ethyl Oleate > Oleic Acid. In terms of solid lipids, Compritol888 ATO > GMS > Precirol ATO 5 > Stearic acid,cetyl alcohol > Palmitic acid > Geleol > Cetyl Palmitate. Maisine CC (liquid lipid, 3.76 +/- 1.07 mg/ml) and Compritol 888 ATO (solid lipid, 24 +/- 1.5 mg/g) were determined to have the highest solubility of RES (Fig. 1).
Selection of surfactant
The emulsification capability of the lipids was taken into consideration while choosing the kind of surfactant. The lipid surfactant solution's % transmission was used to calculate it. Various surfactants were employed in the screening procedure. Poloxamer 188 demonstrated the maximum transmittance in this instance, at almost 97%. Poloxamer 188 is a non-ionic surfactant that poses no physiological concern to human health [33]. This particular surfactant has a high capacity for emulsification and is amphiphilic (HLB = 29) [34]. Transmittance of various surfactant expressed in Fig. 1.
Binary Lipid phase selection
Solid lipid -liquid lipid ratio 70:30 was best fit for melting point of solid lipid which is 60°C -70°C.
Optimization
Design-Expert 13 software was used to optimize the optimized initial batch of prepared RES-NLCs (Box-Behnken design) [35, 36]. The all seventeen formulations with five centre points along with their corresponding results are listed in Table 1. All of the responded data were fitted into several models, including quadratic, second-order, and linear ones. Because the quadratic model had a greater regression coefficient than the other models, it was determined to be the best match for all answers. For each response (PS, PdI, and EE), an ANOVA of the best-fit model (quadratic) was computed and presented inTable 2. A model in quadratic form encompassing each response, yielded a P < 0.0001 value, suggesting the significance of the model (Table 3). 3-D graph of how factors contributed more than individual solutions are represented in Fig. 2.
Table 1
Formulation composition of NLCs
Formulation code | Factor | Response |
X1 | X2 | X3 | Y1 | Y2 | Y3 |
A: Lipid concentration (%) | B: Surfactant concentration (%) | C:Homogenization speed (rpm) | Particle size (nm) | PDI | Entrapment efficiency (%) |
F1 | 2.5 | 2.5 | 12500 | 365.4 | 0.523 | 43 |
F2 | 3.75 | 5 | 15000 | 450 | 0.621 | 72 |
F3 | 5 | 2.5 | 12500 | 550 | 0.576 | 72.5 |
F4 | 2.5 | 3.75 | 15000 | 421 | 0.621 | 42.4 |
F5 | 3.75 | 2.5 | 10000 | 487.1 | 0.157 | 69.6 |
F6 | 3.75 | 5 | 10000 | 630 | 0.372 | 73 |
F7 | 5 | 3.75 | 10000 | 599.8 | 0.04 | 75.4 |
F8 | 2.5 | 3.75 | 10000 | 502.5 | 0.294 | 51 |
F9 | 5 | 5 | 12500 | 430 | 0.377 | 78.24 |
F10 | 3.75 | 2.5 | 15000 | 470 | 0.421 | 70 |
F11 | 5 | 3.75 | 15000 | 445 | 0.321 | 76.4 |
F12 | 2.5 | 5 | 12500 | 332 | 0.271 | 49 |
*F13 | 3.75 | 3.75 | 12500 | 783 | 0.345 | 66 |
*F14 | 3.75 | 3.75 | 12500 | 785 | 0.287 | 66.72 |
*F15 | 3.75 | 3.75 | 12500 | 786.5 | 0.382 | 65.5 |
*F16 | 3.75 | 3.75 | 12500 | 787.3 | 0.342 | 65.2 |
*F17 | 3.75 | 3.75 | 12500 | 784.3 | 0.311 | 65 |
*The Center Point: consisting of the same parts
Table 2
Quadratic model's ANOVA findings for all answers
Response | Source | Sum of square | DF | Mean | F Value | P Value | Results |
Y1 (PS) | Model | 4.149E + 05 | 9 | 4.6096.91 | 24.82 | 0.0002 | Significant |
A2 | 1.545E + 05 | 1 | 1.545E + 05 | 83.18 | < 0.0001 | Significant |
B2 | 1.280E + 05 | 1 | 1.280E + 05 | 68.91 | < 0.0001 | Significant |
C2 | 43471.97 | 1 | 4347.97 | 23.41 | 0.0019 | Significant |
Y2 (PDI) | Model | 0.1767 | 3 | 0.089 | 3.79 | 0.0373 | Significant |
A2 | 1.437.52 | 1 | 1.437.52 | 4.62 | 0.0214 | Significant |
B2 | 0.542.9 | 1 | 0.542.9 | 6.73 | 0.032 | Significant |
C2 | 0.1571 | 1 | 0.1571 | 10.12 | 0.0072 | Significant |
Y3 (EE) | Model | 2065.80 | 9 | 229.53 | 122.90 | < 0.0001 | Significant |
A2 | 232.10 | 1 | 232.10 | 124.8 | < 0.0001 | Significant |
B2 | 24.77 | 1 | 24.77 | 13.26 | 0.0083 | Significant |
C2 | 38.92 | 1 | 38.92 | 20.84 | 0.0026 | Significant |
Table 3
Each model's regression coefficient that was used and recommended by design expert software
Model | R2 | Adjusted R2 | Predicted R2 | Standard Deviation | %CV | Comment |
Particle Size (Y1) |
Linear | 0.9412 | 0.9321 | 0.9114 | 9.97 | - | - |
2F1 | 0.9245 | 0.8953 | 0.8765 | 9.56 | - | - |
Quadratic | 0.9696 | 0.9306 | 0.7942 | 0.46 | 7.62 | Suggested |
PDI (Y2) |
Linear | 0.7741 | 0.7653 | 0.7213 | 5.43 | - | - |
2F1 | 0.7953 | 0.7863 | 0.7754 | 5.96 | - | - |
Quadratic | 0.9946 | 0.8418 | 0.6443 | 0.12 | 33.84 | Suggested |
Entrapment Efficiency (Y3) |
Linear | 0.7831 | 0.7712 | 0.5783 | 3.56 | - | - |
2F1 | 0.8653 | 0.7624 | 0.6542 | 2.74 | - | - |
Quadratic | 0.9937 | 0.9856 | 0.9126 | 1.37 | 2.11 | Suggested |
Independent factors' influence on particle size
The study examined the impact of three distinct parameters, namely A, B, and C regarding the particle size of NLC dispersions loaded with RES. The chosen model, Response Y1, has a 24.82 F-value, which demonstrates the model's importance, according to the ANOVA's table result.
Regression equation representing the coded value for the chosen variable (particle size) is shown.
Particle size (Y1) = 760.38 + 157.72A + 84.30 B − 42.37C − 31.18AB-21.99AC − 48.87BC -275.81 A2 − 251.04 B2 − 101.61 C2
A-Lipid concentration (%), B- Surfactant concentration (%), C- Homogenization speed (rpm). The rising and decreasing effects on the response value in relation to the input parameters are shown by the positive (+) and negative (-) symbols. A sufficient signal was indicated by an adequate precision of 13.13. It seems like the model suited the applied data well, as evidenced by the non-significant lack of fit (F = 1.54, P = 0.52, p > 0.05) [37].
All 17 RES-loaded NLC dispersions had particle sizes ranging from 332 nm to 787.3 nm. Increased Lipid content was shown to increase the size of NLC particles because it raises the solution’s viscosity, reduces the surfactant's capacity to emulsify, and increases the interfacial tension, which causes particle agglomeration. It was shown that an increase in the concentration of surfactant was associated with a reduction in the size of the particles. The reason for this might be that surfactants are adsorbed on the interface, lowering the surface tension that separates the lipid and aqueous phases. These outcomes align with earlier research that has been published [38, 39]. The NLCs dispersion's particle size rises when the homogenization speed is increased because the particles agglomerate.
Independent factors' influence on PdI
The study examined the impact of three distinct parameters, namely A, B, and C, on the PdI of NLC dispersions loaded with RES. The chosen model, Response Y2, has a 3.79 F-value, which demonstrates the model's importance, according to the ANOVA's table result.
Regression equation representing the coded value for the chosen variable (PdI) is shown.
PdI (Y2) = 0.3791–0.0593A − 0.0054B + 0.401C + 0.320AB − 0.235AC + 0.642BC -0.0072A2 + 0.572B2 + 0.072C2
A sufficient signal was indicated by an adequate precision of 6.26. It seems like the model suited the provided data well, as evidenced by the non-significant lack of fit (F = 0.74, P = 0.4). According to the study's findings, all 17 formulations' PdIs fell between 0.04 and 0.621. Increasing lipid concentration resulted in a decrease in PdI, indicating a narrower size distribution. Higher lipid content promotes tighter packing around the core, favouring uniform particle formation. Increasing surfactant concentration decreased PdI up to a critical point, beyond which excessive micelle formation could lead to re-coalescence and increased PdI. This finding aligned with prior published studies [40].
Independent factors' influence on Entrapment efficiency
The study examined the impact of three distinct parameters, namely A, B, and C, regarding the Entrapment Efficiency (EE%) of NLC dispersions loaded with RES. The chosen model, Response Y3, has a 122.90 F-value, which demonstrates the model's importance, according to the ANOVA's table result.Regression equation representing the coded value for the chosen variable (Entrapment Efficiency) is shown.
Entrapment efficiency (Y3) = 62.12 + 21.15A + 1.42B − 1.4C 0.0936AB + 2.88AC- 0.4200BC -10.69 A2 + 3.49B2 + 3.04 C2
A sufficient signal was indicated by an adequate precision of 32.6. It seems like the model suited the applied data well, as evidenced by the non-significant lack of fit (F = 0.92, P = 0.8). It was discovered that when lipid content got higher, the EE rose owing to the extra space available for lodging and the reduced escape of medicines to the exterior phase. Surfactant presence increased the viscosity of the aqueous phase, leading to lower medication dispersion in the external phase [41]. The homogenization speed increased, and EE rose as particle size decrease.
Point Prediction
The optimum formulation was chosen from Desirability Value 1 (Run 1) by Design Expert program. The variables lipid concentration (5%), surfactant concentration (2%), and homogenization speed (10000 rpm) were determined to be within our desired range and chosen as an optimum formulation. The improved formulation was developed and determined to be 274.3 nm in particle size, 0.246 PdI, and 75.42% entrapment efficiency, with a Zeta potential of -34.5 mV, respectively. The RES-loaded NLCs optimal formulation has a 95% confidence interval with a projected value for response (Y1, Y2, Y3) and a p-value of < 0.05. The refined formulation was employed for upcoming investigation.
Measurement of Particle size, PdI, Zeta Potential (ZP), and Entrapment Efficiency regarding the optimized RES-NLC dispersions
The PS ranged from 332 to 787.3 nm for all formulations, as measured by the Malvern Zeta sizer (Table 1).With a PdI value of 0.246, the PS of the improved RES-NLC formulation was determined to be 274.3 nm (Fig. 3). Homogeneity is indicated by PdI values that are closer to 0, whereas heterogeneity particles are indicated by values that are closer to 1 [42]. As shown in Fig. 3, the improved composition exhibits a high negative zeta potential value of -34.5 mV, indicating its stability and lack of agglomeration [43]. The improved formula's entrapment efficiency was 75.42%, which matched the software's expected value.
ScanningElectron Microscopy (SEM) morphologic analysis
The SEM image of RES loaded NLC (Fig. 3)and RES incorporated Alginate beads (Fig. 3) depicts a smooth surfaced and spherically shaped particle.
Differential Scanning Calorimetry (DSC)
Pure resveratrol's DSC thermos image revealed a distinct endothermic peak at 172.4°C. The physical mixture's thermogram revealed maxima at 53°C and 73.7°C respectively, for Poloxamer 188 and Compritol 888 ATO. Thermogram of NLC loaded with resveratrol revealed peaks at 71°C, corresponding to Compritol 888 ATO, without an endothermic resveratrol peak (Fig. 4). There was little to no contact between the NLCs' constituent parts, and this data shows that there was entrapped RES in the NLCs [44].
NLC loaded-Alginate Bead Formulation
Formulation of NLC-Alginate Sol
A gentle agitation was employed to create the NLC-alginate sol because shear might potentially cause disruptions while mixing the NLC dispersion with the alginate stock solution. The structure of NLC. Phase separation was not seen following the mixing process, suggesting that alginate and NLC dispersion were well suited to each other. Alginate couldn't harm NLC's oil-water interface as it was a hydrophilic hydrocolloid with little surface activity.
Formulation of NLC-Alginate beads
Catalysed by calcium ions, NLC-alginate beads are created by the extrusion-dropping technique. First, it was looked into how calcium ions affected the physical stability of NLC[45]. The NLC's average size of particles and PdI were 274 ± 2 nm and 0.246 ± 0.025, correspondingly, following an hour of incubation in the crosslinked fluid. The crosslinked fluid did not significantly affect NLC stability while the process of crosslinking is underway, as seen by the distribution of particle sizes being close to the initial NLC dispersion [21]. The typical radius of the NLC-alginate beads that were created was around one millimetre. With a yield efficiency of 95.6% for RES after the crosslinking process, there may have been some leakage.
Analysis of NLC-Alginate beads
Particle size
The produced NLC-alginate beads had an average radius of around 1 mm. After drying the particle size was reduced around 100 to 500 micrometres.
Surface Morphology
Spherical beads exhibited a rough surface that was scattered with different fractures, wrinkles, and pores was found according to the SEM image (Fig. 3). These traits most likely have anything to do with the beads' dehydration process[46].
Examining NLC-Alginate beads with FT-IR Analysis
The infrared spectra of NLC displayed Peaks at 2900.00 cm − 1 (the C-H stretching), 3311.89 cm − 1 (O-H stretching),1383.00 cm − 1 (CH2 stretching). All of the unique peaks of NLC were conserved in the NLC-alginate beads infrared spectra indicating that NLC and alginate did not interact with one another intermolecularly (Fig. 5). This outcome provided additional evidence that NLC and alginate were only physically mixed together without any intermolecular interaction.
Investigation of In vitro drug release *
The simultaneous discharge of resveratrol from NLC beads, Optimized NLC and NLC-Sol plotted against time over the course of a day, the drug release from the NLCs was examined in enzyme-free SGF and SIF (Fig. 6). The NLC dispersion showed a profile of two-stage release profile, with a comparatively quick release of approximately 19.7% RES within the first two hours and a gradual release of approximately 98% RES until the study’s end. These findings suggest that, significant number of resveratrol was securely bonded to the lipid framework and enclosed inside the solid NLC core [47]. Given that there was a very small distance between NLC and the medium, the first quick release may have been caused by the RES that was abundant in the surface of NLC formulation. NLC-Sol exhibited a two-phase release profile, akin to that of NLC dispersion, along with a comparable rate of release during the first stage and a limited simultaneous drug release during the protracted phase of release. This suggests that the release may be influenced by the interaction between polymers and NLC. Polymers around NLC have been observed to cause a delayed release [48]. Resveratrol released from NLC-alginate beads did not release as quickly as it did from NLC dispersion and NLC-alginate sol without burst release.
Drug release kinetics
Using the DD solver the kinetic model for the drug release pattern was chosen based on which model had the greatest correlation value (R2). The optimum NLC formulation, NLC Alginate sol, and NLC Alginate beads exhibit first-order release kinetics and zero-order release kinetics, respectively. The Weibull model provides the greatest match for all three formulations, it was shown in Table 4 [49].
Table 4
Release kinetics mode | Optimized NLC Formulation R2 | NLC-Sol R2 | NLC Alginate beads R2 |
Zero-order Release | 0.8555 | 0.8983 | 0.9717 |
First-order Release | 0.9401 | 0.9168 | 0.8622 |
Higuchi model | 0.8863 | 0.8368 | 0.6857 |
Korsmeyer-Peppas model | 0.9500 n = 0.708 | 0.9394 n = 0.790 | 0.9942 n = 1.226 |
Weibull Model | 0.9901 | 0.9845 | 0.9810 |
Hixson Crowell’s kinetics | 0.9011 | 0.9462 | 0.8983 |
In vitro Lipid Digestion Study
The average particles size ofamong samples varies at each digestion stage. The particle size in optimized NLC and NLC alginate sol remained almost unaltered after passing. Gastric digestion simulations did not destabilize or aggregate NLC in dispersion and Sol of NLC-alginate. The NLC particle present in NLC-alginate beads shrank considerably after passing through the stomach. The inclusion of NLC in Alginate beads did not degrade its structure, and simulated gastric digestion did not cause any issues [50]. The alginate beads remained same in Simulated Gastric Fluid (SGF) as the production of non-soluble alginic acid in the low-level pH solution removed calcium ions from the alginate. NLC aggregated after two hours of intestinal digestion, as evidenced by a rise in the mean size of NLC formulation over 780 nm (Fig. 6).
The increase of the surface area of exposed lipid, which was derived from the displacement of emulsifiers in the surface of NLC by bile salts in SIF and the lipolysis by pancreatic lipases, could cause the aggregation [51]. The NLC-alginate sol exhibited a stable pattern of size distribution despite an increase in particle size to 450 nm. Following 30 minutes of intestine processing, the particle size of NLC-alginate beads in SIF reached approximately 280 nm, and after two hours, it climbed to 320 nm. In a neutral pH SIF, alginate beads made from were unstable, resulting in swelling-dissolution-erosion and release of contained chemicals [52]. The extent of swelling varied according on the period of intestinal residence. Visual inspection revealed swelling of NLC-alginate beads, which changed in size during intestinal digestion. This suggests that the incorporated NLC diffused into SIF and was hydrolysed by lipases outside the beads.
Stability study
The study on stability for the RES-NLCs-opt, NLC-Sol and the NLC-Alginate bead formulations. For the time period of 90 days. For Optimized RES-NLC, the particle size was raised beyond 400 nm. Large particle size peaks typically suggest nanoparticle agglomeration. The reformation and polymorphism of the lipid matrix in NLC dispersion can increase the exposed lipid surface area, which improves nanoparticle interaction [53]. The particle size in NLC-alginate sol was raised to around 370 nm. The addition of NLC-to-NLC alginate beads resulted in particle size around 303 nm. Thus, the inclusion of NLC in alginate beads might significantly expand the physical durability, it was shown in Fig. 6.