Development and Characterization of Diosgenin-incorporated Nanoemulsion Gel System for Transdermal Drug Delivery

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B. AKINSIPO, E. O. DARE, D. P. KATARE, F.O Oladoyinbo, L. O. SANNI, and 2 more This is a preprint; it has not been peer reviewed by a journal. https://doi.org/ 10.21203/rs.3.rs-7900020/v1 This work is licensed under a CC BY 4.0 License Status: Under Review Version 1 posted 22 You are reading this latest preprint version Abstract Nanoemulsion systems containing phytodrugs are efficient nanocarriers that can enhance the protective and bioavailability properties of poorly aqueous-soluble phyto-entities, thus, enhancing their use as transdermal drug delivery. This paper seeks to integrate distinctly the use of diosgenin (DG) into an oil-in-water (O/W) sub-micron-sized nanoemulsion (NE)/Nanogel (NeG) system that can increase its solubility in the transdermal therapeutic applications. Diosgenin-incorporated nanoemulsion (DGNe) was developed by the low-energy phase inversion composition (LE-PIC) technique. Excipients for NE preformulations, including sesame oil (SS) and bottle gourd (BG) seed oil, were screened for solubility and emulsifying ability. Pseudo-three-phase diagrams were plotted with BG seed oil and SS (BGSS) oil mix, including Tween 80 and glycerol as surfactant/cosurfactant mix. Using the Box Behnken design, optimum responses from 13 randomized NE preformulations were determined from particle size (nm), drug release (%), viscosity (cps), and pH. A Scanning Electron Microscope (SEM) and a Field Emission Scanning Electron Microscope were used to determine the characteristic surface morphology of DGNe and DGNeG. Differential Light Scanning Calorimeter (DLS) was used to determine the particle size, zeta potential, and polydispersity index (PDI) of DGNe. Fourier Transform Infrared (FT-IR) and DLS were used to determine the functional stability of the formulated DGNe and DGNeG. The outcome of the SEM indicated a near-spherical nanoemulsion matrix of diosgenin that was dispersed. DLS analysis revealed that the particles were between 82 -265 nm with a PDI of 0.01-0.40, and in the FT-IR technique, DGNe and DGNeG formulation were stable at day 0 and 90. It was also established that DGNe remained thermodynamically stable at 25 °C and 4 °C after 4 weeks. The viscosity result of DGNe showed that viscosity decreases with the increase in its water content. According to the in-vitro release profile, a slow release of the drug happened within 0.5 to 15 h. These results indicate a novel diosgenin-loaded nanoemulsion/nanogel system with interesting physicochemical and stability characteristics, prolonged release behavior needed for an effective transdermal delivery systems and therapeutic bioavailability of diosgenin. Diosgenin Transdermal drug delivery Nanoemulsion Nanogel Figures Figure 1 Figure 2 Figure 3 Figure 4 Figure 5 Figure 6 Figure 7 Figure 8 Figure 9 Figure 10 1 INTRODUCTION Medicinal plants are promising and exceptional therapeutic alternatives owing to their multiple bioactive compounds with uniquely tremendous therapeutic potentials(Malpotra et al., 2025 ); however, their diverse benefits are underexplored due to their reduced bioavailability and poor aqueous solubility (Ahmad Dar et al., 2023 ). Amongst these bioactive compounds is Diosgenin (3β-hydroxy-5-spirostene). Diosgenin (DG) is a phytosteroidal sapagenin, primarily present in edible pulses and roots such as roots of wild yam, also called the Dioscoroceae. (Oyelaja-akinsipo et al., 2020 ). DG exhibits a vast range of pharmacological potentials, including anti-aging, antidiabetic, anti-inflammatory, hypolipidemic effects, hepatoprotective, and neuroprotection. Its therapeutic efficacy in combating other metabolic pathological conditions, such as oxidative stress, lipid metabolism, cognitive function, etc, has also been extensively reported by researchers (Oyelaja-akinsipo et al., 2020 ). Unfortunately, the obstruction of DG lies in its high hydrophobicity, poor aqueous solubility, and limited oral bioavailability (Okawara et al., 2014 ). Okawara et al. ( 2014 ) also revealed that poor solubility of diosgenin in water renders it less bioavailable, and an absolute bioavailability as low as 7 % was reported. In addition, oral delivery of drugs, though common, displays an interactive possibility with food and drugs, includin the metabolic first-pass effect and gastric degradation (Liu et al., 2021 ). Since the success rate of a drug can be largely attributed to its bioavailability, which is often time dependent on the route of drug administration, the physiological features of the organ and the route of drug delivery are largely important (Nikolić et al., 2019 ). Cayen et al. ( 1979 ) reported a poor oral absorption of diosgenin with a recovery of 1 µg/mL (Cayen et al., 1979 ). The transdermal delivery route of drug administration is a non-invasive route that advantageously overcomes the drawbacks of oral and other delivery routes owing to the versatile nature of the skin. This system of drug delivery has no link with the gastrointestinal tract; hence, the limitation of first-pass metabolism in the conventional oral delivery route can be overcome (Akhtar et al., 2020 ). Interestingly, long study of the transdermal delivery systems has revealed that the route is an alternative to the oral route of drug delivery since the dermis is rich in blood supply that facilitates the unswerving delivery of the drug into the blood to cause the systemic effects and circulation to the body (Alkilani et al., 2015 ). Meanwhile, the success of a transdermal system of different drug preparations, especially the phytocompounds, solid and semi-solid counterparts, faces severe setbacks due to their large globule size, increased volatility, and stability effect. Importantly, the biophysical properties of the skin limit the supposed enhanced permeation of the drug because of the barrier imposed by the stratum corneum (Oyelaja-Akinsipo et al., 2021 ; Yamada et al., 2018 ). Nanosized drug delivery systems have proven worthy of providing solutions to both physicochemical and physiological challenges. The advent of nanotechnology in the field of drug delivery has brought into the limelight, improved delivery of hydrophobic drugs having mediocre aqueous solubility. Meanwhile, nano-sized drug delivery systems have been reported to increase the therapeutic efficacy of phytomolecules as they potentiate their protection against thermal and photodegradation (Sajid et al., 2019 ; Wicki et al., 2015 ). Nanoemulsions (NE) are lipid-based, low-viscosity, and isotropic systems that comprise oil, co-surfactant, and water as their main entities (Mohite et al., n.d.). NE represents an important technique that greatly influences the delivery of poorly water-soluble entities in various fields of drug delivery. Their submicron size, large surface area, high solubilizing potential, enhanced bioavailability effect, including their wettability, enhance their excellent penetrating ability through the skin to the systemic circulation for the required therapeutic outcome. For the first time, the present study reports the incorporation of DG, a poorly aqueous soluble phytomolecule with vast therapeutic potential, into a uniquely formulated oil-in-water (O/W) NE for improved solubility and bioavailability. Its transdermal application is not reported herein. 2. MATERIALS AND METHOD 2.1 Materials Plant Materials and Oil Source Pure Diosgenin was procured from Chromadex Irvine, USA. Bottle gourd fruits were obtained from Bode local market in Ibadan, Oyo State, Nigeria. Sesame oil (BNB Virgin cold-pressed sesame oil) was received as a gift from Professor Deepshikha Pande Katare, Proteomics and Translational Research Laboratory, Amity Institute of Biotechnology, Amity University, India. Other Materials Polyoxyethylene sorbitan monolaurate (Tween 20), sorbitan monostearate (Span 20), glycerol Hi LR sorbitan monooleate (Span 80), Polyoxyethylene sorbitan monooleate (Tween 80), and potassium dihydrogen phosphate were purchased from HiMedia Laboratories Pvt limited, India. Propylene glycol and dipotassium hydrogen orthophosphate were supplied by Thermofischer Scientific, India Pvt. Ltd, Mumbai, India. Ethanol was procured from Merck, Germany. Purified water system (Milli® Q 18.2 mΩ.cm-1 at 250) was easily assessed at the milliQ center, Amity Institute of Biotechnology, India. Dialysis membrane (MWCO = 12,000 Da) was purchased from Sigma Aldrich (Batch no. 3110 (D9652), Sigma-Aldrich Corp., St. Louis, MO, USA. All the material used in this study is of analytical purity, hence, used without further purification. 2.2 Bottle Gourd Seed Oil Collection and Extraction Fresh fruits of Lagenaria siceraria (bottle gourd) were procured locally from Bode market, Ibadan, Oyo State, Nigeria. The pods were manually broken, and the seeds were removed. The seeds were air-dried for 72 hours, and the good seeds were manually separated from the bad ones. Manual dehulling was done to remove the seeds from the shell before milling with a laboratory electric milling machine at the Postgraduate Laboratory, FST Department, COLFHEC, Federal University of Agriculture, Abeokuta, Nigeria. The oil from the gourd seeds was obtained using the Microwave-assisted oil extraction method. A Soxhlet apparatus was coupled with the microwave. 50g of the milled seed and the extracting solvent (n-hexane) were measured into the round-bottom flask, and the extraction process was carried out for 10 minutes at a microwave power of 233 watts. The resulting mixture was sieved and further centrifuged at 7000 rpm for 20 minutes to separate the oil-rich phase from the emulsion-like pigments. The supernatant was pipetted and placed into a water bath for complete evaporation of n-hexane. 2.3 Nanoformulation Studies 2.3.1 Calibration curve of Diosgenin 100 µg/mL diosgenin (DG) was prepared by weighing accurately 1 mg DG into a 15 mL Falcon tube. 10 mL of a pH 7.4 phosphate-buffered saline (PBS) was measured into the tube and left under constant and continuous shaking with a shaker for 72 hours. The resulting solution was filtered with Whatman filter paper (125 mm Whatman no. 1) and subsequently re-filtered with a 0.45 µm cellulose membrane filter. Mass of undissolved DG was weighed and recorded. From the stock solution obtained, serial dilutions of 1, 2, 3, 4, 5, 6, 7, 8, 9, and 10 µg/mL DG were prepared with PBS dilution. The maximum wavelength of absorption was determined with a 10 µg/mL DG solution using a UV-Visible spectrometer (Malvern), and the optical density (OD) of different DG concentrations was spectrometrically estimated at 225 nm. 2.3.2 Solubility of DG The solubility of DG in oils (Sesame oil (SS), Bottle gourd oil (BG), and BG in combination with SS), surfactants (Tweens 20, 80 and Spans 20, 80) and Co-surfactants (glycerol, polyethylene glycol, and ethanol) were determined by dissolving a large amount of DG (5mg) in 2.0 mL of each oil, oil mix, surfactant, and co-surfactant respectively. Each tube was labeled according to its dissolution media. The mixture was carried out in a 5 mL stoppered vial, and continuous agitation was enhanced with a vortex mixer for 72 hours at room temperature. After 72 hours, equilibrated samples were exposed to centrifugation at a rotating speed of 4000 rpm for 10 minutes to allow the settlement of undissolved DG particles. The amount of undissolved DG was physically examined with the naked eye and recorded based on the occurrence of undissolved particles. Spectrometrically, estimation of the amount of dissolved DG was estimated with a UV-Visible spectrometer at 225 nm by briefly taking 100 µL of each supernatant and diluting it 10 times with methanol. Afterward, different concentration was prepared and absorbance was recorded at 225 nm. The method described by Fernandes et al. ( 2014 ) was followed with slight modifications. (Fernandes et al., 2014 ). 2.3.3 Further screening of surfactants and Cosurfactants: Solubilization of Oil The surfactant that best solubilizes DG was chosen from the previous screening and incorporated for evaluation herein based on its suitability to further solubilize the selected oil. Briefly, 250 mg of each selected surfactant was introduced into an equal amount of oil, and the isotropic mixture was carefully stirred at 45°C/10 min, and then 100 mg of the mixture was poured into a 250 mL conical flask and diluted to 100 mL with distilled water. The flask was carefully swirled every time, and the number of times a uniform emulsion were obtained was noted. Every ensuing emulsion was then allowed to remain untouched after three (3) hours to observe any potential turbidity or phase transition. Observations were recorded. Spectrometrically, absorbance was studied for each resulting emulsion at different wavelengths with distilled water as a blank. The same process was repeated for selected co-surfactants from the DG solubilization above. 2.3.4 Pseudoternary phase diagram of Placebo nanoemulsion preparation The pseudo ternary phase diagram (PTD) is required to select the emulsification zone while working with varying ratios of surfactant mixture, i.e, a different mixture of surfactant and co-surfactant (S mix ) and oil. PTD is used to design mixtures of three components with proportions ranging from zero to a typical maximum percentage of 100% (Jhawat, Gulia, & Sharma, 2021). After successful screening of excipients, Tween 80 and Glycerol were chosen to be used as surfactant and co-surfactant in this study, while a combination of equal volumes of BG oil and SS oil (BGSS) showed more suitability as the oil phase. Distilled water was used as the aqueous phase, and the spontaneous emulsification/aqueous titration method was employed in this study. Varying S mix weight ratios (surfactant: co-surfactant) of 1:1, 1:2, and 2:1 were prepared with Tween 80 and glycerol, respectively. Each ternary diagram will represent each S mix with BGSS at different ratios. In this study, nine different combinations of each S mix and oil (1:9, 1:8, 1:7, 1:6, 1:5, 1:4, 1:3, 1:2, 1:1) represent each S mix ratio and were prepared for each ternary diagram to cover a maximum possible ratio and minimize the possibility of missing possible phase boundaries in the phase diagrams. For the three-phase diagrams, a total of 27 placebo formulations were slowly prepared by accurately weighing each S mix and oil combination in their respective ratio. Briefly, BGSS was introduced into a 100 mL glass, followed by the addition of the required S mix, and the mixture was properly mixed to attain homogeneity. The aqueous phase was slowly titrated with the homogenized mixture, and the formation of nanoemulsion was visually recorded when flowability and transparency were observed. Physical changes were reported in a pseudo-ternary plot (Prosim ternary plot) where the three components of each diagram represent oil, water, and S mix . 2.3.5 Selection of formulation From the PTD, the area showing optimal nanoemulsion area with a high level of oil, a low level of surfactant, and a maximum level of water was selected amongst the entire range of areas that display nanoemulsion occurrence. 0.02% DG was kept constant at all levels of PTD formulations, and its maximum incorporation into the oil phase was considered. Based on these conditions, a different formula was selected from the nanoemulsion region of each PTD, and selected formulations were enabled for further studies. 2.3.6 Experimental Design for DG-incorporated Nanoemulsion In this study, Design Expert ® (version 11, Stat-Ease, Minneapolis, USA) was employed as the software for creating our data, using a randomized response surface study type. Box Behnken Design (BBD) was chosen as the appropriate design toolbox for statistical optimization settings. BBD is a process or factorial design that explores the quadratic response surface and constructs second-order polynomial models while employing a smaller number of experimental runs in the optimization process. The number of required experiments is provided as indicated in the equation $$\:Number\:of\:experiments\:={Level}^{factor}$$ 2.3.7 Pre-optimization studies: Screening of influential factors From the PTD, high and low level of surfactant were selected and based on the inputted value of independent variables, including percentage of oil (BGSS %) - X 1 , S mix (Tween 80 / Glycerol %) - X 2 and water (aq. %) - X 3 , 3 - factor box behnken design (BBD) at three levels (-1, 0 and + 1) with 1 central point was chosen and 13 randomized formulations were suggested by BBD. For the 13 batches containing 2 mg DG each, nanoemulsions were prepared using the low-energy phase inversion composition (PIC) method and evaluated for four responses, including particle size, in vitro drug release, pH, and viscosity. The excipient concentration for optimization is shown in Table 1 . Table 1 Excepients concentration for optimization Run No Formulation Code Oil (%w/w) Smix (%w/w) Water (%) Shear time F1 AOSW1 45 25 30 350 F2 AOSW2 50 20 30 350 F3 AOSW3 40 30 30 350 F4 AOSW4 50 15 35 350 F5 AOSW5 50 25 25 350 F6 AOSW6 60 15 25 350 F7 AOSW7 55 15 30 350 F8 AOSW8 50 30 20 350 F9 AOSW9 60 10 30 350 F10 AOSW10 40 20 40 350 F11 AOSW11 60 20 20 350 F12 AOSW12 50 10 30 350 F13 AOSW13 40 25 35 350 2.3.8 Optimization Optimization aims to identify the best formulation that contains the maximum oil concentration and the least surfactant concentration while providing a nanoemulsion with an average size of less than 250 nm. Using the BBD, the responses of the 13 formulations, including pH, viscosity, particle size, and drug release, were studied for identification of the most suitable formulation, and one newly proposed formula was chosen based on the optimized set of parameters. 2.3.9 Studies on dependent variables for optimization 2.3.9.1 Droplet size analysis and distribution The measurement of size distribution, mean particle size (z-average diameter), and polydispersity index (PDI) of formulated emulsions was investigated with Malvern Zetasizer Nano ZS90 (Malvern Instruments, UK). This instrument performs size measurement on samples using the Dynamic Light Scattering technique (DLS) or Photon Correlation Spectroscopy (PCS). 100 µL of each formulated nanoemulsion (NE) was introduced into a 5mL stoppered vial and diluted 20 times with distilled water to produce proper scattering intensity. The NE was made up to 2000 µL with double-distilled water and then subjected to gentle but continuous swirling for a homogenized solution. The resulting 2 mL emulsion was gently pipetted into a clean and dry disposable cuvette, and measurement was taken at 25 ± 0.5°C at a scattering angle of 90°C. Average measurement was taken in triplicate, and the mean particle size, z-average in diameter, including PDI, was obtained. The PDI represents a dimensionless measure of the width of the distributed particle size analysis, ranging from a calculated cumulant analysis of 0 to 1. A small PDI value is usually preferred as it signifies a monodispersed particle population, while a larger PDI value could imply a broader particle size distribution. 2.3.9.2 pH The pH of each formulated emulsion was determined using the digital pH meter (TANCO digital pH meter, model number EE-011). Briefly, the probe of the calibrated pH meter was gently rinsed with distilled water and wiped with a laboratory paper tissue. pH of 5 mL samples was read in triplicate at a standby temperature of 25°C, and pH values were recorded after readings were stable. 2.3.9.3 Viscosity measurement Prepared nanoemulsion samples were exposed to viscosity measurements using the Brookfield–type rotary viscometer, USA. Briefly, the viscometer was turned on, and the sample container was decoupled from the bottom of the viscometer. About 15 mL of the test sample was introduced into the sample container in order to make the sample container 25% full. The container was then fitted back into its original position, and the spindle was inserted and centered into the test sample until it was fully submerged in the sample container. The spindle was then exposed to rotation at 5, 10, 20, and 50 rpm, respectively, at room temperature. Readings were recorded in triplicate, and the mean values were calculated. 2.3.9.4 In-vitro drug release study In-vitro drug release studies of the developed nanoformulations were carried out using a basket-type dissolution apparatus (Electrolab Dissolution tester USP TDT06L). Also, the pretreated dialysis membrane (dialysis membrane 50, LA 387-10MT), having a pore size of 2.4 nm, an average flat width of 24.26 mm, and an average diameter of 14.3 mm, with a weight cut-off of ~ 12–14 kD, was used. 500 mL of freshly prepared phosphate buffer of pH 7.5 was introduced as the release medium into a 3L capacity receiver compartment, and the temperature was maintained until a constant temperature of 37°C was attained. Afterward, one end of a 5 cm long dialysis bag was carefully tied, and 1 mL of the required formulation containing 2.0 mg of DG was carefully introduced into the dialysis bag. The other end of the dialysis bag was carefully tied, and the absence of leakage was ensured. The bag was gently rinsed with distilled water to ensure an impurity-free exterior. Afterward, the dialysis bag was gently placed inside the donor compartment (basket), and a stirring speed of 50 rpm at 37 ± 2°C was maintained for 6 h. At selected times of 0, 0.5, 1, 2, 4, 6, 8, 10, 12, 24, 36, and 48 h, 10 mL of the release medium was withdrawn, and the same volume of fresh medium was replaced at every withdrawal to ensure the maintenance of sink condition. Using a 0.45 µm cellulose membrane filter, collected samples were filtered before drug quantification was spectrophotometrically determined using a UV-Visible spectrophotometer. 2.3.10 Stability Assessment The stability of nanoemulsions is an important factor that needs to be considered for their viability and avoidance of metastable formulation. The stability studies were determined in many ways. Firstly, nanoemulsions were kept undisturbed at room temperature for 12 weeks, and signs of turbidity and color change were visually inspected. Secondly, the physical thermodynamic stability test as described by Shafiq et al (2007) was employed with slight modifications. At 4000 rpm, formulations were subjected to centrifugation for 30 minutes at 25°C. Formulation that shows no sign of phase disparity was taken further for the heating-cooling cycle. Briefly, each selected formulation was stored for 48 hours at 4°C; afterward, they were transferred to a water bath until a constant temperature of 45°C was attained. After 48 hours, the heated sample was returned to a 4°C cooling condition, and this cycle was repeated 6 times. Lastly, stable formulations selected from the heating-cooling cycle were subjected to three freeze-thaw cycles. Samples were exposed to a freezing temperature of -20°C and a simultaneous thawing temperature of + 25°C repeatedly for three times. 2.3.11 Characterization of Nanoemulsions 2.3.11.1 Scanning Electron Microscopy The morphology, shape, and size distribution of nanoemulsion and Nanogel containing DG were studied with a scanning electron microscope, SEM (GEISS EV018, Smartsem Software, Germany). Before the analysis, nanoemulsion and nanogel were lightly stained on an 18 mm microscopic cover glass and allowed to dry in a vacuum oven before microscopic examination. The accelerating voltage was set at 20.00 kV at a working distance of 7.5 mm. The microscopic examination of test samples was conducted at a magnification of 50.00X. 2.3.11.2 Field Emission Scanning Electron Microscopy To further image the sample surface with a high-energy beam of electrons, FESEM (IE 250X Max 80, FEI Quanta 200F SEM model, FEI Company, Netherlands) was used. Samples were pre-coated on an 18 mm microscopic cover glass slide and dried in a vacuum oven at 45°C overnight. Pre-coated plates were further subjected to gold coating and vacuum before being subjected to FESEM imaging at different resolutions. 2.3.11.3 Fourier Transform Infrared Spectroscopy (FT-IR) The stability, as well as functional properties of DG, DGNe, and DGNeG, was investigated with FT-IR (Perkin Elmer Frontier ATR/FTIR). Briefly, a 10 µL sample was micro pipetted and dropped on the slide for analysis. FT-IR spectra were generated at a range of 4000 − 400 cm − 1 and studied for each sample. The analysis was repeated after three months of sample storage to rule out functional changes as well as instability. 3 RESULT AND DISCUSSION 3.1 Solubility of DG and selection of suitable NE excipients Spectrometric evaluation of DG solubility in excipients, including oil, oil combination, surfactant, and co-surfactants, is presented in Table 2 . The result indicates DG solubility at 0.3877 ± 0.070 mg/dL, 0.3353 ± 0.003 mg/dL, and 0.714 ± 0.02 mg/dL in BG oil, Sesame oil (SS), and a combination of BG and SS (BGSS), respectively. Solubility of DG in surfactants includes 0.2320 ± 0.003 mg/dL, 0.238 ± 0.007 mg/dL, 0.223 ± 0.005 mg/dL, and 0.132 ± 0.008 mg/dL in Tween 20 (T20), Tween 80 (T80), Span 20 (S20), and Span 80 (S80), respectively. Cosurfactants, including PEG 400 and Glycerol, show DG solubility at 0.200 ± 0.002 mg/dL and 0.249 ± 0.002 mg/dL, respectively. The concentration of soluble DG out of the 2.5 mg/mL stock preparation of each experiment is presented as mean ± SD. From the result, the combination of BG and SS (BGSS) oil displayed the maximum solubilizing capacity for DG (0.714 ± 0.00145 mg/mL) when compared to BG and SS alone. In nanoemulsion, oil is an important and vital component required for the sufficient solubilisation of the active pharmaceutical ingredient (API), especially the hydrophobic API (Tayeb & Sainsbury, 2018). Amongst the surfactants selected for this study, T80 (HLB 15.0), followed by T20 (HLB 16.7) showed a higher solubility (0.2383 ± 0.00441 mg/mL and 0.2320 ± 0.00153 mg/mL) when compared to Span 20 and Span 80. The selection of surfactant is also an important criterion for nanoemulsion formation, as its safety and biocompatibility must be considered. In this study, non-ionic surfactants are considered as our preferred option for therapeutic application as their compatibility and reduced toxicity including its less irritant ability compared to the ionic counterparts has been reported (Gullapalli & Sheth, 1999 ). Research has also shown that the sole usage of single-chain emulsifier may not be sufficient in the capacity to reduce the interfacial tension to a significant extent and therefore, its application in combination with a co-surfactant with amphiphilic nature is also promptly offered. The co-emulsifier may also penetrate the interfacial layer and consequently decrease the fluidity and increase the entropy of the emulsion system (Rodrigues et al., 2015 ). The co-surfactants selected in this study include PEG and Glycerol and the result displayed a maximum solubilizing capacity in glycerol (0.2493 ± 0.00208 mg/mL) and was thereby selected for further use. Table 2 Solubility of DG in excipients S/N Name of Excipient Code Chemical name HLB Solubility of DG (mg/mL) mean ± SD 1. Bottle Gourd seed oil (O) BG Triglyceride - 0.3877 ± 0.070 2. Sesame oil (O) SS Triglyceride - 0.3353 ± 0.003 3. Bottle gourd and Sesame oil combination (O) BGSS Triglyceride - 0.7137 ± 0.002 4. Tween 20 (S) T20 Polyoxyethylene sorbitan monolaurate 16.7 0.2320 ± 0.003 5. Tween 80 (S) T80 Polyoxyethylene sorbitan monooleate 15.7 0.2383 ± 0.007 6. Span 20 (S) S20 Sorbitan monostearate 8.6 0.2233 ± 0.005 7. Span 80 (S) S80 Sorbitan monooleate 4.3 0.2317 ± 0.008 8. Polyethylene Glycol (Cs) PEG Polyethylene glycol 400 Monooleate 11.4 0.2000 ± 0.002 9. Glycerol (Cs) GC Glyceryl monostearate 3.8 0.2493 ± 0.002 O-oil; S-Surfactant; Cs-Cosurfactant 3.2 Further screening of surfactants : spectrometric evaluation and physical miscibility studies Physical miscibility studies of BGSS in T20 and T80 is shown in Fig. 1 . From the image, a clearer and isotropic mixture was found with T80 even after it is left to stand for 3 hours. Since T20 and T80 showed the maximum solubilizing effect on DG amongst other surfactants, further screening studies were carried out to select the most suitable surfactant based on its ability to solubilize BGSS oil. Table 3 shows the result of different parameters reported during observation of miscibility effect in both surfactants as shown in Fig. 1 . Although, tween 20 gave an almost instant emulsifying effect within the first few flask swirls, but T80 showed more stability and flowability as compared to T20. The solubilizing capacity of T20 and T80 on BGSS is shown in Fig. 2 . Spectrometric analysis was recorded at an absorbance of 0.348, 0.261 and 0.284 for T 80 and 0.47, 0.486 and 0.536 for T20 at an absorbing wavelength of 350 nm, 450 nm and 550 nm (Fig. 2 ). Results revealed a higher absorbance in T20 indicating an increased BGSS concentration, hence, less emulsifying effect when compared to T80. Polyoxyethylene sorbitan monooleate (Tween 80) is a non-ionic and hydrophilic surfactant that has wide prospect as an emulsifying agent in the food industry with excellent potential in reducing globule size of lipid emulsions and enhancing permeation of drugs in medicinal industry(de Freitas Araújo Reis et al., 2021 ) Table 3 Miscibility performance of BGSS oil mix in Tween 20 and Tween 80 S/N Tween 20 Tween 80 1. Only 3 swirls resulted in an homogenous emulsion 21 swirls gave an homogenous emulsion. Oil was gradually diffusing into the surfactant and water mix 2. Resulting emulsion showed less clarity Clearer emulsion was formed 3. More turbid Less turbid 4. Slight phase separation after 3 hours of an undisturbed position No separation after 3 hours of an undisturbed position 5. After 24 hours, the slight turbidity remains After 24 hours, clarity and transparency was maintained 3.3 Pseudoternary Phase Diagram (PTPD) In order to identify the O/W nanoemulsion region, the PTPDs of three different formulations A, B, and C ( f A, f B, and f C) at three different ratios 1:1, 2:1, and 1:2 of S mix are constructed as illustrated in Fig. 3 . From the phase diagram of f A, where T80 and glycerol were used at a ratio of 1:1, the nanoemulsion region was found to be higher than f B (2:1) and fC (1:2). After preformulation with different S mix ratios, the percentage of oil, S mix, and water obtained for each formulation ( f A, f B, and f C) was represented in a PTPD. PTPD was constructed using the aqueous titration method. Three PTPD diagrams for different surfactant mix (S mix ) ratios were plotted. From our plot, f A with S mix ratio 1:1 (T 80 and Glycerol) evidently displayed the maximum nanoemulsion region, while S mix ratio 1:2 (fC-T80 and Glycerol) showed the smallest nanoemulsion region. From the phase diagram in Fig. 3 , the maximum amount of oil (56.25%, 69.23% and 66.67%) in f A, f B, and f C, respectively, was emulsified by 6.25%, 7.69% and 7.41% S mix, respectively. This indicates that the surfactant in f B (S mix 2:1) does not significantly enlarge the nanoemulsion area as compared to f A (S mix 1:1) despite being doubled. This was also seen in f C (S mix 1:2), where the co-surfactant was double the surfactant, but the expected difference in the widening region of nanoemulsion was significantly reduced. In this study, the region that maximally incorporates high oil content with less surfactant while completely solubilizing the drug and aiding optimal Smix concentration and water was taken into consideration (Baboota et al., 2007 ). The expansion in the nanoemulsion region of f A can be attributed to the equal concentration of T80 and glycerol. Based on the observations marked during titration regarding the point of flowability of each formulation, PTPD was constructed separately for each S mix ratio (1:1, 2:1, and 1:2) so that a suitable region for the O/W emulsion phase can be identified. Formulations that showed less surfactant concentration with extensive accommodation of oil were selected from different points of the selected PTD. When tween 80 was used with less cosurfactant, the nanoemulsion region was lessened as seen in formulation C. 3.4 Experimental Design, Screening of Independent Variables and Optimization Studies Table 4 represents the study carried out to evaluate the various independent variables or factors at different surfactant, oil, and water concentrations. Based on this, the obtained responses are presented. Experimental designs are known to help in understanding the influence of several variables involved in a study, and a smaller number of experiments is advantageous. Further Statistical analysis of results would help in revealing the variables that show significant influence. Correlation of desired response can then be done with variables using polynomial equations (Sharma et al., 2013 ). Table 4 demonstrates the summary of the actual design of the experiment based on the highlighted influential factors presented for screening and obtained responses from 13 randomized and suggested runs to obtain optimum responses, including particle size (nm), drug release (%), pH, and viscosity (cps). From the table, a particle size with a minimum of 92.72 nm and a maximum of 280.9 nm was found for formulations 5 and 6, respectively. In addition, diosgenin release at a minimum and maximum of 0.95 to 36% was recorded for optimization. pH of all 13 formulations ranges from 3.88 to 5.89, with a recorded viscosity of 0.882–0.89 cps. A p -value of 0.0120, 0.0500, and 0.0001 indicates significance with a suggested linear, 2FI, and mean model. Further optimization and post-analysis report revealed 99% population, which gave approximately 95% confidence for response data (Table 4 ). A cubic model graph analysis report is represented in Fig. 4 to finally explore the report of each selected response based on the compositional relationship that exists amongst the influential factors. Formulation 6 was identified as the most suitable run since the variables of formulation 6 gave a mean droplet size response of 92.72 nm at 60%, 25% and 15% oil surfactant and water, respectively. Other responses, including drug release, viscosity, and pH, gave an appreciable value of 14.11%, 5.06, and 0.8822 cps, respectively. Table 4: Responses of influential factors and confirmatory selection criteria for preformulation based on selected responses Response Name Unit Observation Analysis Min Max Mean Std. Dev. Ratio Transform Model R1 Particle Size nm 13 Polynomial 92.72 280.9 200.66 61.21 3.03 None Linear R2 Drug Release % 13 Polynomial 0.95 36 16.49 12.80 37.89 None Reduced 2FI R3 pH 13 Polynomial 3.88 5.89 4.94 0.4880 1.52 None Mean R4 Viscosity cps 13 Polynomial 0.882 0.890 0.8879 0.0020 1.01 None Linear Response Pred Mean Pred Median Observed Std Dev n SE Prediction 95% PI low 95% PI high Particle Size 175.704 175.704 92.72 39.5523 1 44.0076 76.1516 275.256 Drug Release 21.7879 21.7879 14.11 8.35552 1 9.41943 0.479699 43.0962 pH 4.93846 4.93846 5.06 0.488038 1 0.506461 3.83498 6.04194 Viscosity 0.886283 0.886283 0.882 0.00174681 1 0.00194358 0.881886 0.890679 Two-sided Confidence = 95% (SE)- Standard error; Pred-Prediction 3.5 In-vitro Drug Release Studies The in-vitro release profile of DG from DGNe is presented in Figs. 5 A and 5 B. From the release profile, formulations F2, F3, F6 and F12 gave the most abundant release between 92.95% − 97.38%. Release profile of F6 within a 36 h period is shown in Fig. 5 B, where an initial burst release at 0.1 h was recorded with a continuous and sustained release till 8 hrs at a steady concentration of 33.20% − 51.60%. A maximum release of DG from DGNe in F6 gave a 92.95% released DG after 36 h. Interestingly, formulation 6 (60% v/v BGSS oil and 25% v/v tween 80/glycerol) showed more adequate release profile as indicated in Fig. 5 B because of the steady, continuous and sustained release of DG within a 36h period. The release profile displayed by F6 may be attributed to its small droplet size of 92.72 nm as previously described. 3.6. Surface Morphology and Particle Size Table 5 shows the particle size distribution and polydisperisty index (PDI) of all 13 preformulations using Differential Light Scanning Calorimetric (DLS) technique. Values are recorded in mean ± standard error of mean (mean ± SEM). From the table, particle size of all 13 formulations was found to be between a minimum of 82.30 ± 6.65 nm and a maximum of 299.36 ± 9.27 nm at F6 and F5 respectively. The PDI of all 13 formulations also presented a lowest and highest PDI of 0.072 ± 0.005 and 0.389 ± 0.12 in F7 and F2 respectively. Figure 6 shows the shape and size of DGNe. From the microscopic image presented in Fig. 6 , an unaggregated and spherical-shaped DG in DGNe was obtained with particle size distribution between 81.8 nm and 99.3 nm. Figures 6 and 7 displays the microscopic structural morphology of DG in DGNeG and the surface orientation of DGNeG respectively. From our result, a smallest particle size of 68.97 ± 19.67 nm with a minimum and maximum size of 29.93 nm and 92.72 nm was recorded at F6. The highest particle size obtained was 276.83 ± 10.89 nm with a minimum droplet size of 256.40 nm and a maximum of 293.60 nm. According to (Chiari-Andréo et al., 2019 ), nanoemulsions ability to penetrate the skin and mucous membrane can be actively associated to the size of the droplet including its large surface area with the dispersed phase acting as the reservoir for controlled release of active substance. Although an emulsion globule of approximately 20–500 nm is identified as a nanoemulsion (Mushtaq et al., 2023 ), 100–500 nm (Klang et al., 2012 ) or 10–1000 nm (McClements & Jafari, 2018 ), however, a droplet size less than 200 nm is assigned the characteristic of high stability and transparency (Chiari-Andréo et al., 2019 ). Furthermore, particles of a size range less than 100 nm have been reported to stay in the blood vessel as they can escape being engulfed by macrophages (Haroon et al., 2022 ). In order to ascertain the homogeneity of the nanoemulsion system, the PDI value of formulations was evaluated, with the lowest and highest PDI recorded to be 0.0717 ± 0.027 and 0.389 ± 0.12, respectively, with F6 having a PDI of 0.106 ± 0.023. According to Klang and Valenta (2011), the particle size distribution provides information on the homogeneity of the formulation, through the width of the distribution. The small PDI where PDI is less than 0.2 implies a small droplet size distribution hence better stability to destabilisation processes like Ostwald ripening.. (Klang et al., 2012 ). Figure 6 visualizes the structural orientation of dispersed particles of DGNe to be almost spherical, with an oil droplet having a mean particle size of 92.3 nm. From our results, a slight difference was observed when compared with DLS analysis. According to Champion et al (2014), a particle having a spherical shape can travel from the site of application to its site of action without restriction, while one with an uneven shape tends to fall or get entangled in filtering organs (Ledford et al., 2023 ). A further microscopic FESEM visualization and confirmation of the presence of DG in DGNeG (A) and the surface orientation of the nanogel system (B) is represented in Fig. 6 . From the microscopic visualisation, DG in DGNeG was found to be present and largely dispersed across the gel matrix. Table 5 Table showing the particle size distribution (nm) and polydispersity index of 13 DGNe formulations using the differential light scanning calorimetry technique (DLS) Formulation Particle Size (nm) n = 3 Polydispersity Index PDI n = 3 F1 245.50 ± 17.46 0.219 ± 0.05 F2 236.17 ± 8.71 0.389 ± 0.12 F3 213.80 ± 1.33 0.180 ± 0.03 F4 237.37 ± 13.29 0.107 ± 0.06 F5 299.36 ± 9.27 0.161 ± 0.029 F6 82.30 ± 6.65 0.086 ± 0.005 F7 171.23 ± 13.52 0.072 ± 0.027 F8 239.10 ± 37.12 0.224 ± 0.034 F9 154.43 ± 12.32 0.147 ± 0.028 F10 212.17 ± 4.51 0.170 ± 0.066 F11 276.83 ± 10.89 0.303 ± 0.063 F12 109.82 ± 5.82 0.133 ± 0.023 F13 216.53 ± 9.50 0.078 ± 0.016 Values are recorded as mean ± SEM Figure 7: Visualization of the nanoemulsion structure and orientation of (A) DGNe in its nanogel form at 15,000X magnification and (B) Nanogel surface using electron microscopic technique (FESEM) at 10,000X ma 4.9. Stability Studies 4.9.1 FT-IR Figure 8 illustrates the FT-IR spectra of bulk diosgenin. Figure 9 (a and b) and Fig. 10 (a and b) indicates the FT-IR stability spectra of DGNe and DGNeG at day 0 and Day 90 respectively. From the spectra in Fig. 8 , absorption peaks were recorded at 3454 cm − 1 , 2900 cm − 1 , 1644.0 cm − 1 , 1357.0 cm − 1 , 1440 cm − 1 , 1382.9 cm − 1 , 1057. 0 cm − 1 and 900 cm − 1 . Figure 4.14 showed absorption peaks at 3410 cm − 1 , 1635 cm − 1 , 1440 cm − 1 , 1060 cm − 1 , and 600 cm − 1 while Fig. 4.15 illustatres peak of absorption at 3450 cm − 1 , 2100 cm − 1 , 1640 cm − 1 , 1450 cm − 1 , 1100 cm − 1 and 700 cm − 1 . FTIR is employed in this study to further access the functional characteristics of diosgenin including the functional stability of the developed nanoemulsion/nanogel. Also, the possible interaction between the drug (DG) and the emulsion molecules is revealed from the spectra. Figure 8 represents the FT-IR spectra of raw DG, while Fig. 9 (a and b) and 10 (c and d) shows DG in nanoemulsion (DGNe) and DG in nanogel (DGNeG) at day 0 and after 90 days of storage at 4ºC. From the spectra in Fig. 8 , DG, with molecular formula C 27 H 42 O 3 showed a broad stretching vibration of O-H peak (ν O–H; 3454 cm − 1 ), a symmetric and assymetric absorption due to C-H stretching in –CH 3 , –CH 2 including –CH stretching vibration (ν as and ν s CH 3 , CH 2 ; 3000 − 2800 cm − 1 ), -C = C (ν C = C; 1644.0 cm − 1 ); a –CH 3 and –CH 2 deformation due to carbon – hydrogen bending (δ CH 3 ,and δ CH 2 ; 1357.0 and 1440 cm − 1 1382.9 cm − 1 ). Due to the ether group in the structural feature of DG, a C–O–C asymmetrical stretching vibration (ν C–O–C; 1051.0 cm − 1 and 1042.0 cm − 1 ) was recorded. A strong = C–H out-of-plane bending vibration (γ = C–H; 900.0 cm − 1 ) was also revealed. The IR spectra of DGNe at Fig. 9 A implicated the same peaks as observed with DG. After 90 days of DGNe storage (Fig. 9 B), almost same peaks with day 0 of DGNe was observed. Further investigation of DGNeG at day 0 and day 90 of storage revealed absorption at the same frequency as the former. This indicates that there exist little or no chemical interaction between DG and the emulsifying/gelling agents present in DGNe and DGNeG. 4.9.2 Physical and Thermodynamic Stability Studies Table 6 shows the accelerated thermodynamic and physical stability studies the preformulated emulsions were exposed to. This includes heating-cooling, centrifugation, freeze-thaw and dispersibility studies. From the table, the stability profile of each formulation that are categorized into three different subgroups of varying S mix ratio (SOA 1:1, SOB 2:1, and SOC 1:2) having 9 preformulations each (27 total number of preformulations) are presented therein. The stability status of all developed preformulations were determined by subjecting it to thermodynamic, mechanical stress and dispersibility studies. Results were ascertained visually for any form of turbidity, aggregation or phase separation. Based on our visual observation, SOA at a S mix (tween 80 and glycerol) ratio of 1:1 was selected as the S mix formula for the further development of the 13 formulations suggested by Box Behnken. Table 6 Thermodynamic and physical stability studies on preformulations at different oil Formulation Heating-cooling Centrifugation Freeze-thaw Dispersibility SOA 1:1 SOB (2:1) SOC (1:2) SOA 1:1 SOB (2:1) SOC (1:2) SOA 1:1 SOB (2:1) SOC (1:2) SOA 1:1 SOB (2:1) SOC (1:2) 1 (9:1) + + + + - - + - - + + - 2(8:2) + + + - - - + + - + + - 3(7:3) + + + + + - + - + + + - 4(6:4) - + - + - + + + + + - + 5(5:5) + + + + - + + + - + - + 6(4:6) + + + + + - + + + + - + 7(3:7) + + - + + - + + + + + + 8(2:8) + - + + + - + + + + + + 9(1:9) + - - + + - + - + + + + Oil mix, where A, B, C indicates surfactant mix (S mix ) at 1:1, 2:1 and 1:2 tween 80 and glycerol respectively. + (passed); - (failed) 4.9.3. Homogeneity and Dispersibility studies Further stability measurement of the studies was finally performed on the 13 formulations using DLS technique in order to monitor the level of homogeneity including possibility of wide or slim changes in the PDI of the formulation after 90 days of storage. The various particle size and PDI obtained at day 0 and day 90 are presented in Table 7 . From the table, the particle size of all 13 formulations at day 0 were almost replicated at day 90 as only a slight difference was observed. Characteristics such as pH, color and clarity was also maintained after the storage duration was completed. Based on the achieved results, F6 maintained the least particle size even after 90 days of storage with an improved homogeneity when compared to other formulations. Table 7 Homogeneity and dispersibility studies at day 0 and day 90 Formulation Particle size (nm) n = 3 PDI (n = 3) Day 0 Day 90 Day 0 Day 90 F1 245.50 ± 17.46 222.63 ± 5.44 0.219 ± 0.05 0.236 ± 0.197 F2 236.17 ± 8.71 207.87 ± 1.74 0.389 ± 0.12 0.237 ± 0.008 F3 213.80 ± 1.33 216.00 ± 2.80 0.180 ± 0.03 0.158 ± 0.025 F4 237.37 ± 13.29 226.77 ± 3.79 0.107 ± 0.06 0.617 ± 0.005 F5 299.36 ± 9.27 248.63 ± 3.99 0.161 ± 0.029 0.231 ± 0.019 F6 82.30 ± 6.65 97.57 ± 4.76 0.086 ± 0.005 0.085 ± 0.009 F7 171.23 ± 13.52 276.50 ± 6.67 0.072 ± 0.027 0.326 ± 0.022 F8 239.10 ± 37.12 231.37 ± 8.25 0.224 ± 0.034 0.302 ± 0.019 F9 154.43 ± 12.32 155.79 ± 13.9 0.147 ± 0.028 0.183 ± 0.010 F10 212.17 ± 4.51 236.50 ± 8.52 0.170 ± 0.066 0.237 ± 0.020 F11 276.83 ± 10.89 271.67 ± 14.90 0.303 ± 0.063 0.243 ± 0.019 F12 109.82 ± 5.82 212.18 ± 51.44 0.133 ± 0.023 0.225 ± 0.011 F13 216.53 ± 9.50 216.49 ± 0.17 0.078 ± 0.016 0.172 ± 0.015 4.10 Physicochemical Properties of the Final Formulation The physicochemical properties of the final nanoemulsion formulation is presented in Table 8 . From the table, a droplet size of 82.30 nm with a PDI of 0.086 ± 0.005 is recorded. Also, the table presented the pH of the formulation to be 7.2 having refractive index of 12.70 ± 0.34 and a viscosity of 11.80 ± 1.51 cps. Table 8 Physicochemical properties of the selected formulation (n = 3) Formulation Droplet size Polydispersity index (PDI) pH Refractive index Viscosity (cps) F6 82.30 ± 6.65 0.086 ± 0.005 7.2 12.70 ± 0.34 11.80 ± 1.51 Conclusion Conventional drug delivery through oral and parenteral route poses huge challenges that necessitates the lookout for alternative route of drug administration. In this study, the therapeutic transdermal potential of diosgenin was enhanced with an emulsified nanoformulation. This study revealed nanoemulsion as a versatile carrier that has the unique ability of resolving the solubility and bioavailability of a poorly soluble diosgenin, thereby, promoting the effective penetrability required for transdermal delivery of diosgenin. In this study, a low energy phase inversion composition method was employed as the simple, low-cost and effective emulsification technique in preparing an optimized nanoemulsion that comprises a 60% and 25% oil and surfactant mix respectively. A stable DGNe emulsion without phase disparity was obtained. The pseudoternary phase diagram constructed for the three major preformulations showed the largest area of nanoemulsion region when tween 80 and glycerol was used at a ratio of 1:1. The result of the microscopic structural morphology of the formulated nanoemulsion and nanoemulsified gel using SEM and FESEM confirmed a stable, unaggregated, smooth-surfaced and spherical-shaped particles with large dispersion in the emulsion and gel matrix. The particle size analysis using DLS has shown a size distribution between 81.8 nm and 99.3 nm with a PDI of 0.106 ± 0.023. This width of the particle size distribution has confirmed DGNe to be homogenous, and thus better stability against destabilisation phenomena, while the shape and narrow droplet size indicates an unrestricted capability of diosgenin to penetrate the skin and travel from the site of application to site of action for effective therapeutic action. ` In vitro release profile of the formulated nanoemulsion containing phytotherapeutic diosgenin showed a 92.95% maximum release of DG from DGNe after 36 h. This indicates the proper release of hydrophobic and insoluble DG from the formulated nanoemulsion. The derived DGNe formulation exhibits potentially good physicochemical characteristics that could be used in transdermal delivery techniques. Declarations Declaration of Competing Interest The authors declare the following financial interests/personal relationships, which may be considered as potential competing interests: Akinsipo, Oyesolape Basirat reports that financial support was provided by The World Academy of Science in collaboration with the Department of Biotechnology, India (TWAS-DBT). Other authors declare that they have no known competing financial interests or personal relationships that could have appeared to influence the work reported in this paper. Acknowledgment The authors appreciate the Sandwich Doctoral award from the TWAS-DBT and supervisory support from Amity University, Noida, India, and Federal University of Agriculture, Abeokuta. Availability of data and materials The data that support this study are available within this article and can also be requested from the authors. Ethics approval and consent to participate "Not applicable. Only in vitro experiments were done in this study to develop and characterize nanoemulsion formulations. The study did not involve any live vertebrates, human participants or human or animal biological materials. Competing interests The authors declare the following financial interests/personal relationships, which may be considered as potential competing interests: Akinsipo, Oyesolape Basirat reports that financial support was provided by The World Academy of Science in collaboration with the Department of Biotechnology, India (TWAS-DBT). Other authors declare that they have no known competing financial interests or personal relationships that could have appeared to influence the work reported in this paper. Consent to Publish : Not applicable Funding Funding was provided by The World Academy of Science in collaboration with the Department of Biotechnology, India (TWAS-DBT). The funding body is not involved in the design of the study and collection, interpretation of data, and writing of the manuscript. Authors' contributions Akinsipo, O. B : Conceptualization, Methodology, Visualization, Investigation, Formal analysis, Validation, Writing – original draft, Writing – review and editing, data curation. Dare, E. O : Project administration; Supervision, Conceptualization, Review, Methodology; Katare, D. P : Resources, Supervision, review, methodology; Oladoyinbo, F.O : Supervision, Validation, review, Sanni Lateef : Supervision Alayande, S. O. Review and data curation; Msagati A.M. Titus: Supervision and Review, Acknowledgements The authors appreciate the Sandwich Doctoral award from the TWAS-DBT and support from Amity University, Noida, India, Tai Solarin University of Education, Nigeria and Federal University of Agriculture, Abeokuta. Authors' information 1. Akinsipo, O. B.: Senior Lecturer, Department of Chemical Sciences, College of Science and Information technology, Tai Solarin University of Education, Ijagun, PMB 2118, Ijebu-Ode, Ogun State, Nigeria, Founder, MoreGreen Plus and Green Chemistry Champion, Beyond Benign 2. Dare, E. O: Professor, Department of Chemistry, College of Physical Sciences, Federal University of Agriculture, P.M.B. 2240, Alabata Road, Abeokuta, Ogun State, Nigeria, 110111. First Professor of Nanotechnology in Nigeria. Recipient, Alexander von Humboldt (AvH) – Georg Forster fellowship and a fellow of SCIAS, Wurzburg University, Germany 3. Katare, D. P.: Professor, Deputy Director and Centre Head Administration, Research & Teaching Centre for Medical Biotechnology, Amity Institute of Biotechnology, Sector 125, Amity University Uttar Pradesh, Noida, 201303. 4. Oladoyinbo, F.O.: Associate Professor, Department of Chemistry, College of Physical Sciences, Federal University of Agriculture, P.M.B. 2240, Alabata Road, Abeokuta, Ogun State, Nigeria, 110111. 5. Sanni, L. O: Professor, Department of Food Science and Technology, College of Food Science and Human Ecology, Federal University of Agriculture, P.M.B. 2240, Alabata Road, Abeokuta, Ogun State, Nigeria, 110111. Executive Director of the Nigerian Stored Products Research Institute (NSPRI) 6. Alayande, S. O.: Associate Professor, Department of Industrial Chemistry, Abiola Ajimobi Technical University, Ibadan, Oyo, 200261, Nigeria. Founder, Green Nano LLC, USA 7. Msagati, T. A. M: Professor, Institute for Nanotechnology and Water Sustainability (iNanoWS), College of Science, Engineering, and Technology, University of South Africa, Florida Park 1710, South Africa References Ahmad Dar, R., Shahnawaz, M., Ahmad Ahanger, M., & ul Majid, I. (2023). 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Supplementary Files GA.png Cite Share Download PDF Status: Under Review Version 1 posted Editorial decision: Revision requested 30 Mar, 2026 Reviews received at journal 12 Mar, 2026 Reviews received at journal 04 Mar, 2026 Reviews received at journal 04 Mar, 2026 Reviews received at journal 28 Feb, 2026 Reviews received at journal 21 Feb, 2026 Reviewers agreed at journal 20 Feb, 2026 Reviewers agreed at journal 20 Feb, 2026 Reviewers agreed at journal 20 Feb, 2026 Reviewers agreed at journal 20 Feb, 2026 Reviews received at journal 18 Feb, 2026 Reviewers agreed at journal 17 Feb, 2026 Reviewers agreed at journal 07 Feb, 2026 Reviewers agreed at journal 07 Feb, 2026 Reviewers agreed at journal 07 Feb, 2026 Reviewers agreed at journal 06 Feb, 2026 Reviewers agreed at journal 06 Feb, 2026 Reviewers agreed at journal 05 Feb, 2026 Reviewers invited by journal 19 Dec, 2025 Editor assigned by journal 01 Dec, 2025 Submission checks completed at journal 01 Dec, 2025 First submitted to journal 30 Nov, 2025 You are reading this latest preprint version Research Square lets you share your work early, gain feedback from the community, and start making changes to your manuscript prior to peer review in a journal. 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1","display":"","copyAsset":false,"role":"figure","size":723573,"visible":true,"origin":"","legend":"\u003cp\u003e\u003cstrong\u003eMiscibility of BGSS in Tween 20 and Tween 80\u003c/strong\u003e\u003c/p\u003e","description":"","filename":"1.png","url":"https://assets-eu.researchsquare.com/files/rs-7900020/v1/c9a00889cf185791ef58e0f5.png"},{"id":99306878,"identity":"1646f36b-dcea-4292-9d10-9e02c2271ae4","added_by":"auto","created_at":"2025-12-31 16:03:17","extension":"png","order_by":2,"title":"Figure 2","display":"","copyAsset":false,"role":"figure","size":41577,"visible":true,"origin":"","legend":"\u003cp\u003eUV-Visible spectra showing the absorbance of BGSS in Tween 20 and Tween 80 between 300-750 nm\u003c/p\u003e","description":"","filename":"2.png","url":"https://assets-eu.researchsquare.com/files/rs-7900020/v1/d23c627916d67e81fcdb1620.png"},{"id":99307147,"identity":"d317676c-9f8b-4296-8bab-a841359963c5","added_by":"auto","created_at":"2025-12-31 16:05:42","extension":"png","order_by":3,"title":"Figure 3","display":"","copyAsset":false,"role":"figure","size":255572,"visible":true,"origin":"","legend":"\u003cp\u003ePseudoternary phase diagrams of different ratios of Smix, oil (Bottle gourd and Sesame oil), and water at different Smix (Tween 80 and glycerol) blends of 1:1, 2:1, and 1:2 were constructed using an aqueous titration method.\u003c/p\u003e","description":"","filename":"3.png","url":"https://assets-eu.researchsquare.com/files/rs-7900020/v1/7ef5aa46852e91806a6e7e88.png"},{"id":99306975,"identity":"7a942bb8-f3cc-4720-af7e-09860c5d9d8c","added_by":"auto","created_at":"2025-12-31 16:05:04","extension":"png","order_by":4,"title":"Figure 4","display":"","copyAsset":false,"role":"figure","size":160561,"visible":true,"origin":"","legend":"\u003cp\u003eModel cubic graph analysis of responses: particle size (nm), drug release (%), viscosity (cps) and pH\u003c/p\u003e","description":"","filename":"4.png","url":"https://assets-eu.researchsquare.com/files/rs-7900020/v1/a0e1a9dd0737af32dec7c39e.png"},{"id":99306915,"identity":"3fac6c61-c5ce-4c0c-8fda-2a8835c51549","added_by":"auto","created_at":"2025-12-31 16:04:07","extension":"png","order_by":5,"title":"Figure 5","display":"","copyAsset":false,"role":"figure","size":63761,"visible":true,"origin":"","legend":"\u003cp\u003eFigure 5A: In-vitro drug release profile of 13 preformulations of DGNe\u003c/p\u003e\n\u003cp\u003eFigure 5B: In-vitro drug release profile of 13 preformulations of DGNe (up) and selected optimized formulation (down)\u003c/p\u003e","description":"","filename":"5.png","url":"https://assets-eu.researchsquare.com/files/rs-7900020/v1/873aee1544d64157a4cdf555.png"},{"id":99306977,"identity":"d034c657-955f-406f-b8c7-be04370cf032","added_by":"auto","created_at":"2025-12-31 16:05:04","extension":"png","order_by":6,"title":"Figure 6","display":"","copyAsset":false,"role":"figure","size":452300,"visible":true,"origin":"","legend":"\u003cp\u003eVisualization of nanoemulsion structure and orientation of DGNe using Scanning Electron Microscopic technique at 50.00 kX magnification\u003c/p\u003e","description":"","filename":"6.png","url":"https://assets-eu.researchsquare.com/files/rs-7900020/v1/067f9bb16b163c91b59dad9d.png"},{"id":99307234,"identity":"916d484f-fb0f-4b74-b120-623da6f0b7bd","added_by":"auto","created_at":"2025-12-31 16:05:50","extension":"png","order_by":7,"title":"Figure 7","display":"","copyAsset":false,"role":"figure","size":857921,"visible":true,"origin":"","legend":"\u003cp\u003eVisualization of the nanoemulsion structure and orientation of (A) DGNe in its nanogel form at 15,000X magnification and (B) Nanogel surface using electron microscopic technique (FESEM) at 10,000X ma\u003c/p\u003e","description":"","filename":"7.png","url":"https://assets-eu.researchsquare.com/files/rs-7900020/v1/869678864312ba197e5ac7ad.png"},{"id":99306958,"identity":"2ca3e3a3-ad85-4444-98bc-44da344297a1","added_by":"auto","created_at":"2025-12-31 16:04:53","extension":"png","order_by":8,"title":"Figure 8","display":"","copyAsset":false,"role":"figure","size":43318,"visible":true,"origin":"","legend":"\u003cp\u003eFT-IR Spectra of Diosgenin\u003c/p\u003e","description":"","filename":"8.png","url":"https://assets-eu.researchsquare.com/files/rs-7900020/v1/b0f5a543bab37b95fc52720f.png"},{"id":99307014,"identity":"6af740d5-c483-4d71-ab3c-dd4a0b37c414","added_by":"auto","created_at":"2025-12-31 16:05:20","extension":"png","order_by":9,"title":"Figure 9","display":"","copyAsset":false,"role":"figure","size":53279,"visible":true,"origin":"","legend":"\u003cp\u003eFT-IR spectra of DGNe at day 0 (A) and day 90 (B) as a measure of stability after three (3) months of storage.\u003c/p\u003e","description":"","filename":"9.png","url":"https://assets-eu.researchsquare.com/files/rs-7900020/v1/b0368fac6d407cdc47d9c3a0.png"},{"id":98807591,"identity":"236ff9f1-381c-4922-a4c4-fdeaacd278c4","added_by":"auto","created_at":"2025-12-22 14:45:00","extension":"png","order_by":10,"title":"Figure 10","display":"","copyAsset":false,"role":"figure","size":57126,"visible":true,"origin":"","legend":"\u003cp\u003eFT-IR spectra of DGNeG at day 0 (A) and day 90 (B) as a measure of stability after three (3) months of storage\u003c/p\u003e","description":"","filename":"10.png","url":"https://assets-eu.researchsquare.com/files/rs-7900020/v1/4f882d4d09afd36b6159f7c0.png"},{"id":99322340,"identity":"27a72e54-88a3-4a02-bbad-04dbb17c884c","added_by":"auto","created_at":"2025-12-31 16:43:26","extension":"pdf","order_by":0,"title":"","display":"","copyAsset":false,"role":"manuscript-pdf","size":4324700,"visible":true,"origin":"","legend":"","description":"","filename":"manuscript.pdf","url":"https://assets-eu.researchsquare.com/files/rs-7900020/v1/a845c818-a43c-450a-bab5-0886c24a1070.pdf"},{"id":99307100,"identity":"bf495c4d-8cf0-4fe3-81ec-18c0a1f78065","added_by":"auto","created_at":"2025-12-31 16:05:38","extension":"png","order_by":1,"title":"","display":"","copyAsset":false,"role":"supplement","size":374672,"visible":true,"origin":"","legend":"","description":"","filename":"GA.png","url":"https://assets-eu.researchsquare.com/files/rs-7900020/v1/4589694bef38dab83d118df5.png"}],"financialInterests":"No competing interests reported.","formattedTitle":"\u003cp\u003eDevelopment and Characterization of Diosgenin-incorporated Nanoemulsion Gel System for Transdermal Drug Delivery\u003c/p\u003e","fulltext":[{"header":"1 INTRODUCTION","content":"\u003cp\u003eMedicinal plants are promising and exceptional therapeutic alternatives owing to their multiple bioactive compounds with uniquely tremendous therapeutic potentials(Malpotra et al., \u003cspan citationid=\"CR14\" class=\"CitationRef\"\u003e2025\u003c/span\u003e); however, their diverse benefits are underexplored due to their reduced bioavailability and poor aqueous solubility (Ahmad Dar et al., \u003cspan citationid=\"CR1\" class=\"CitationRef\"\u003e2023\u003c/span\u003e). Amongst these bioactive compounds is Diosgenin (3β-hydroxy-5-spirostene). Diosgenin (DG) is a phytosteroidal sapagenin, primarily present in edible pulses and roots such as roots of wild yam, also called the Dioscoroceae. (Oyelaja-akinsipo et al., \u003cspan citationid=\"CR20\" class=\"CitationRef\"\u003e2020\u003c/span\u003e). DG exhibits a vast range of pharmacological potentials, including anti-aging, antidiabetic, anti-inflammatory, hypolipidemic effects, hepatoprotective, and neuroprotection. Its therapeutic efficacy in combating other metabolic pathological conditions, such as oxidative stress, lipid metabolism, cognitive function, etc, has also been extensively reported by researchers (Oyelaja-akinsipo et al., \u003cspan citationid=\"CR20\" class=\"CitationRef\"\u003e2020\u003c/span\u003e). Unfortunately, the obstruction of DG lies in its high hydrophobicity, poor aqueous solubility, and limited oral bioavailability (Okawara et al., \u003cspan citationid=\"CR19\" class=\"CitationRef\"\u003e2014\u003c/span\u003e). Okawara et al. (\u003cspan citationid=\"CR19\" class=\"CitationRef\"\u003e2014\u003c/span\u003e) also revealed that poor solubility of diosgenin in water renders it less bioavailable, and an absolute bioavailability as low as 7 % was reported. In addition, oral delivery of drugs, though common, displays an interactive possibility with food and drugs, includin the metabolic first-pass effect and gastric degradation (Liu et al., \u003cspan citationid=\"CR13\" class=\"CitationRef\"\u003e2021\u003c/span\u003e). Since the success rate of a drug can be largely attributed to its bioavailability, which is often time dependent on the route of drug administration, the physiological features of the organ and the route of drug delivery are largely important (Nikolić et al., \u003cspan citationid=\"CR18\" class=\"CitationRef\"\u003e2019\u003c/span\u003e). Cayen et al. (\u003cspan citationid=\"CR5\" class=\"CitationRef\"\u003e1979\u003c/span\u003e) reported a poor oral absorption of diosgenin with a recovery of 1 \u0026micro;g/mL (Cayen et al., \u003cspan citationid=\"CR5\" class=\"CitationRef\"\u003e1979\u003c/span\u003e). The transdermal delivery route of drug administration is a non-invasive route that advantageously overcomes the drawbacks of oral and other delivery routes owing to the versatile nature of the skin. This system of drug delivery has no link with the gastrointestinal tract; hence, the limitation of first-pass metabolism in the conventional oral delivery route can be overcome (Akhtar et al., \u003cspan citationid=\"CR2\" class=\"CitationRef\"\u003e2020\u003c/span\u003e). Interestingly, long study of the transdermal delivery systems has revealed that the route is an alternative to the oral route of drug delivery since the dermis is rich in blood supply that facilitates the unswerving delivery of the drug into the blood to cause the systemic effects and circulation to the body (Alkilani et al., \u003cspan citationid=\"CR3\" class=\"CitationRef\"\u003e2015\u003c/span\u003e). Meanwhile, the success of a transdermal system of different drug preparations, especially the phytocompounds, solid and semi-solid counterparts, faces severe setbacks due to their large globule size, increased volatility, and stability effect. Importantly, the biophysical properties of the skin limit the supposed enhanced permeation of the drug because of the barrier imposed by the stratum corneum (Oyelaja-Akinsipo et al., \u003cspan citationid=\"CR21\" class=\"CitationRef\"\u003e2021\u003c/span\u003e; Yamada et al., \u003cspan citationid=\"CR26\" class=\"CitationRef\"\u003e2018\u003c/span\u003e). Nanosized drug delivery systems have proven worthy of providing solutions to both physicochemical and physiological challenges. The advent of nanotechnology in the field of drug delivery has brought into the limelight, improved delivery of hydrophobic drugs having mediocre aqueous solubility. Meanwhile, nano-sized drug delivery systems have been reported to increase the therapeutic efficacy of phytomolecules as they potentiate their protection against thermal and photodegradation (Sajid et al., \u003cspan citationid=\"CR23\" class=\"CitationRef\"\u003e2019\u003c/span\u003e; Wicki et al., \u003cspan citationid=\"CR25\" class=\"CitationRef\"\u003e2015\u003c/span\u003e). Nanoemulsions (NE) are lipid-based, low-viscosity, and isotropic systems that comprise oil, co-surfactant, and water as their main entities (Mohite et al., n.d.). NE represents an important technique that greatly influences the delivery of poorly water-soluble entities in various fields of drug delivery. Their submicron size, large surface area, high solubilizing potential, enhanced bioavailability effect, including their wettability, enhance their excellent penetrating ability through the skin to the systemic circulation for the required therapeutic outcome. For the first time, the present study reports the incorporation of DG, a poorly aqueous soluble phytomolecule with vast therapeutic potential, into a uniquely formulated oil-in-water (O/W) NE for improved solubility and bioavailability. Its transdermal application is not reported herein.\u003c/p\u003e"},{"header":"2. MATERIALS AND METHOD","content":"\u003cdiv id=\"Sec3\" class=\"Section2\"\u003e \u003ch2\u003e2.1 Materials\u003c/h2\u003e \u003cp\u003e \u003cb\u003ePlant Materials and Oil Source\u003c/b\u003e \u003c/p\u003e \u003cp\u003ePure Diosgenin was procured from Chromadex Irvine, USA. Bottle gourd fruits were obtained from Bode local market in Ibadan, Oyo State, Nigeria. Sesame oil (BNB Virgin cold-pressed sesame oil) was received as a gift from Professor Deepshikha Pande Katare, Proteomics and Translational Research Laboratory, Amity Institute of Biotechnology, Amity University, India.\u003c/p\u003e \u003cp\u003e \u003cb\u003eOther Materials\u003c/b\u003e \u003c/p\u003e \u003cp\u003ePolyoxyethylene sorbitan monolaurate (Tween 20), sorbitan monostearate (Span 20), glycerol Hi LR sorbitan monooleate (Span 80), Polyoxyethylene sorbitan monooleate (Tween 80), and potassium dihydrogen phosphate were purchased from HiMedia Laboratories Pvt limited, India. Propylene glycol and dipotassium hydrogen orthophosphate were supplied by Thermofischer Scientific, India Pvt. Ltd, Mumbai, India. Ethanol was procured from Merck, Germany. Purified water system (Milli\u0026reg; Q 18.2 mΩ.cm-1 at 250) was easily assessed at the milliQ center, Amity Institute of Biotechnology, India. Dialysis membrane (MWCO\u0026thinsp;=\u0026thinsp;12,000 Da) was purchased from Sigma Aldrich (Batch no. 3110 (D9652), Sigma-Aldrich Corp., St. Louis, MO, USA. All the material used in this study is of analytical purity, hence, used without further purification.\u003c/p\u003e \u003c/div\u003e \u003cdiv id=\"Sec4\" class=\"Section2\"\u003e \u003ch2\u003e2.2 Bottle Gourd Seed Oil Collection and Extraction\u003c/h2\u003e \u003cp\u003eFresh fruits of \u003cem\u003eLagenaria siceraria\u003c/em\u003e (bottle gourd) were procured locally from Bode market, Ibadan, Oyo State, Nigeria. The pods were manually broken, and the seeds were removed. The seeds were air-dried for 72 hours, and the good seeds were manually separated from the bad ones. Manual dehulling was done to remove the seeds from the shell before milling with a laboratory electric milling machine at the Postgraduate Laboratory, FST Department, COLFHEC, Federal University of Agriculture, Abeokuta, Nigeria. The oil from the gourd seeds was obtained using the Microwave-assisted oil extraction method. A Soxhlet apparatus was coupled with the microwave. 50g of the milled seed and the extracting solvent (n-hexane) were measured into the round-bottom flask, and the extraction process was carried out for 10 minutes at a microwave power of 233 watts. The resulting mixture was sieved and further centrifuged at 7000 rpm for 20 minutes to separate the oil-rich phase from the emulsion-like pigments. The supernatant was pipetted and placed into a water bath for complete evaporation of n-hexane.\u003c/p\u003e \u003c/div\u003e \u003cdiv id=\"Sec5\" class=\"Section2\"\u003e \u003ch2\u003e2.3 Nanoformulation Studies\u003c/h2\u003e \u003cdiv id=\"Sec6\" class=\"Section3\"\u003e \u003ch2\u003e2.3.1 Calibration curve of Diosgenin\u003c/h2\u003e \u003cp\u003e100 \u0026micro;g/mL diosgenin (DG) was prepared by weighing accurately 1 mg DG into a 15 mL Falcon tube. 10 mL of a pH 7.4 phosphate-buffered saline (PBS) was measured into the tube and left under constant and continuous shaking with a shaker for 72 hours. The resulting solution was filtered with Whatman filter paper (125 mm Whatman no. 1) and subsequently re-filtered with a 0.45 \u0026micro;m cellulose membrane filter. Mass of undissolved DG was weighed and recorded. From the stock solution obtained, serial dilutions of 1, 2, 3, 4, 5, 6, 7, 8, 9, and 10 \u0026micro;g/mL DG were prepared with PBS dilution. The maximum wavelength of absorption was determined with a 10 \u0026micro;g/mL DG solution using a UV-Visible spectrometer (Malvern), and the optical density (OD) of different DG concentrations was spectrometrically estimated at 225 nm.\u003c/p\u003e \u003c/div\u003e \u003cdiv id=\"Sec7\" class=\"Section3\"\u003e \u003ch2\u003e2.3.2 Solubility of DG\u003c/h2\u003e \u003cp\u003eThe solubility of DG in oils (Sesame oil (SS), Bottle gourd oil (BG), and BG in combination with SS), surfactants (Tweens 20, 80 and Spans 20, 80) and Co-surfactants (glycerol, polyethylene glycol, and ethanol) were determined by dissolving a large amount of DG (5mg) in 2.0 mL of each oil, oil mix, surfactant, and co-surfactant respectively. Each tube was labeled according to its dissolution media. The mixture was carried out in a 5 mL stoppered vial, and continuous agitation was enhanced with a vortex mixer for 72 hours at room temperature. After 72 hours, equilibrated samples were exposed to centrifugation at a rotating speed of 4000 rpm for 10 minutes to allow the settlement of undissolved DG particles. The amount of undissolved DG was physically examined with the naked eye and recorded based on the occurrence of undissolved particles. Spectrometrically, estimation of the amount of dissolved DG was estimated with a UV-Visible spectrometer at 225 nm by briefly taking 100 \u0026micro;L of each supernatant and diluting it 10 times with methanol. Afterward, different concentration was prepared and absorbance was recorded at 225 nm. The method described by Fernandes et al. (\u003cspan citationid=\"CR8\" class=\"CitationRef\"\u003e2014\u003c/span\u003e) was followed with slight modifications. (Fernandes et al., \u003cspan citationid=\"CR8\" class=\"CitationRef\"\u003e2014\u003c/span\u003e).\u003c/p\u003e \u003c/div\u003e \u003cdiv id=\"Sec8\" class=\"Section3\"\u003e \u003ch2\u003e2.3.3 Further screening of surfactants and Cosurfactants: Solubilization of Oil\u003c/h2\u003e \u003cp\u003eThe surfactant that best solubilizes DG was chosen from the previous screening and incorporated for evaluation herein based on its suitability to further solubilize the selected oil. Briefly, 250 mg of each selected surfactant was introduced into an equal amount of oil, and the isotropic mixture was carefully stirred at 45\u0026deg;C/10 min, and then 100 mg of the mixture was poured into a 250 mL conical flask and diluted to 100 mL with distilled water. The flask was carefully swirled every time, and the number of times a uniform emulsion were obtained was noted. Every ensuing emulsion was then allowed to remain untouched after three (3) hours to observe any potential turbidity or phase transition. Observations were recorded. Spectrometrically, absorbance was studied for each resulting emulsion at different wavelengths with distilled water as a blank. The same process was repeated for selected co-surfactants from the DG solubilization above.\u003c/p\u003e \u003c/div\u003e \u003cdiv id=\"Sec9\" class=\"Section3\"\u003e \u003ch2\u003e2.3.4 Pseudoternary phase diagram of Placebo nanoemulsion preparation\u003c/h2\u003e \u003cp\u003eThe pseudo ternary phase diagram (PTD) is required to select the emulsification zone while working with varying ratios of surfactant mixture, i.e, a different mixture of surfactant and co-surfactant (S\u003csub\u003emix\u003c/sub\u003e) and oil. PTD is used to design mixtures of three components with proportions ranging from zero to a typical maximum percentage of 100% (Jhawat, Gulia, \u0026amp; Sharma, 2021). After successful screening of excipients, Tween 80 and Glycerol were chosen to be used as surfactant and co-surfactant in this study, while a combination of equal volumes of BG oil and SS oil (BGSS) showed more suitability as the oil phase. Distilled water was used as the aqueous phase, and the spontaneous emulsification/aqueous titration method was employed in this study. Varying S\u003csub\u003emix\u003c/sub\u003e weight ratios (surfactant: co-surfactant) of 1:1, 1:2, and 2:1 were prepared with Tween 80 and glycerol, respectively. Each ternary diagram will represent each S\u003csub\u003emix\u003c/sub\u003e with BGSS at different ratios. In this study, nine different combinations of each S\u003csub\u003emix\u003c/sub\u003e and oil (1:9, 1:8, 1:7, 1:6, 1:5, 1:4, 1:3, 1:2, 1:1) represent each S\u003csub\u003emix\u003c/sub\u003e ratio and were prepared for each ternary diagram to cover a maximum possible ratio and minimize the possibility of missing possible phase boundaries in the phase diagrams. For the three-phase diagrams, a total of 27 placebo formulations were slowly prepared by accurately weighing each S\u003csub\u003emix\u003c/sub\u003e and oil combination in their respective ratio. Briefly, BGSS was introduced into a 100 mL glass, followed by the addition of the required S\u003csub\u003emix,\u003c/sub\u003e and the mixture was properly mixed to attain homogeneity. The aqueous phase was slowly titrated with the homogenized mixture, and the formation of nanoemulsion was visually recorded when flowability and transparency were observed. Physical changes were reported in a pseudo-ternary plot (Prosim ternary plot) where the three components of each diagram represent oil, water, and S\u003csub\u003emix\u003c/sub\u003e.\u003c/p\u003e \u003c/div\u003e \u003cdiv id=\"Sec10\" class=\"Section3\"\u003e \u003ch2\u003e2.3.5 Selection of formulation\u003c/h2\u003e \u003cp\u003eFrom the PTD, the area showing optimal nanoemulsion area with a high level of oil, a low level of surfactant, and a maximum level of water was selected amongst the entire range of areas that display nanoemulsion occurrence. 0.02% DG was kept constant at all levels of PTD formulations, and its maximum incorporation into the oil phase was considered. Based on these conditions, a different formula was selected from the nanoemulsion region of each PTD, and selected formulations were enabled for further studies.\u003c/p\u003e \u003c/div\u003e \u003cdiv id=\"Sec11\" class=\"Section3\"\u003e \u003ch2\u003e2.3.6 Experimental Design for DG-incorporated Nanoemulsion\u003c/h2\u003e \u003cp\u003eIn this study, Design Expert \u0026reg; (version 11, Stat-Ease, Minneapolis, USA) was employed as the software for creating our data, using a randomized response surface study type. Box Behnken Design (BBD) was chosen as the appropriate design toolbox for statistical optimization settings. BBD is a process or factorial design that explores the quadratic response surface and constructs second-order polynomial models while employing a smaller number of experimental runs in the optimization process. The number of required experiments is provided as indicated in the equation\u003cdiv id=\"Equa\" class=\"Equation\"\u003e\u003cdiv format=\"TEX\" class=\"mathdisplay\" id=\"FileID_Equa\" name=\"EquationSource\"\u003e\n$$\\:Number\\:of\\:experiments\\:={Level}^{factor}$$\u003c/div\u003e\u003c/div\u003e\u003c/p\u003e \u003c/div\u003e \u003cdiv id=\"Sec12\" class=\"Section3\"\u003e \u003ch2\u003e2.3.7 Pre-optimization studies: Screening of influential factors\u003c/h2\u003e \u003cp\u003eFrom the PTD, high and low level of surfactant were selected and based on the inputted value of independent variables, including percentage of oil (BGSS %) - X\u003csub\u003e1\u003c/sub\u003e, S\u003csub\u003emix\u003c/sub\u003e (Tween 80 / Glycerol %) - X\u003csub\u003e2\u003c/sub\u003e and water (aq. %) - X\u003csub\u003e3\u003c/sub\u003e, 3 - factor box behnken design (BBD) at three levels (-1, 0 and +\u0026thinsp;1) with 1 central point was chosen and 13 randomized formulations were suggested by BBD. For the 13 batches containing 2 mg DG each, nanoemulsions were prepared using the low-energy phase inversion composition (PIC) method and evaluated for four responses, including particle size, \u003cem\u003ein vitro\u003c/em\u003e drug release, pH, and viscosity. The excipient concentration for optimization is shown in Table\u0026nbsp;\u003cspan refid=\"Tab1\" class=\"InternalRef\"\u003e1\u003c/span\u003e.\u003c/p\u003e \u003cp\u003e \u003cdiv class=\"gridtable\"\u003e\u003ctable float=\"Yes\" id=\"Tab1\" border=\"1\"\u003e \u003ccaption language=\"En\"\u003e \u003cdiv class=\"CaptionNumber\"\u003eTable 1\u003c/div\u003e \u003cdiv class=\"CaptionContent\"\u003e \u003cp\u003eExcepients concentration for optimization\u003c/p\u003e \u003c/div\u003e \u003c/caption\u003e \u003ccolgroup cols=\"6\"\u003e \u003cdiv align=\"left\" class=\"colspec\" colname=\"c1\" colnum=\"1\"\u003e\u003c/div\u003e \u003cdiv align=\"left\" class=\"colspec\" colname=\"c2\" colnum=\"2\"\u003e\u003c/div\u003e \u003cdiv align=\"char\" char=\".\" class=\"colspec\" colname=\"c3\" colnum=\"3\"\u003e\u003c/div\u003e \u003cdiv align=\"char\" char=\".\" class=\"colspec\" colname=\"c4\" colnum=\"4\"\u003e\u003c/div\u003e \u003cdiv align=\"char\" char=\".\" class=\"colspec\" colname=\"c5\" colnum=\"5\"\u003e\u003c/div\u003e \u003cdiv align=\"char\" char=\".\" class=\"colspec\" colname=\"c6\" colnum=\"6\"\u003e\u003c/div\u003e \u003cthead\u003e \u003ctr\u003e \u003cth align=\"left\" colname=\"c1\"\u003e \u003cp\u003eRun No\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c2\"\u003e \u003cp\u003eFormulation Code\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c3\"\u003e \u003cp\u003eOil (%w/w)\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c4\"\u003e \u003cp\u003eSmix (%w/w)\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c5\"\u003e \u003cp\u003eWater (%)\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c6\"\u003e \u003cp\u003eShear time\u003c/p\u003e \u003c/th\u003e \u003c/tr\u003e \u003c/thead\u003e \u003ctbody\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eF1\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003eAOSW1\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e \u003cp\u003e45\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e \u003cp\u003e25\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c5\"\u003e \u003cp\u003e30\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c6\"\u003e \u003cp\u003e350\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eF2\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003eAOSW2\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e \u003cp\u003e50\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e \u003cp\u003e20\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c5\"\u003e \u003cp\u003e30\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c6\"\u003e \u003cp\u003e350\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eF3\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003eAOSW3\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e \u003cp\u003e40\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e \u003cp\u003e30\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c5\"\u003e \u003cp\u003e30\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c6\"\u003e \u003cp\u003e350\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eF4\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003eAOSW4\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e \u003cp\u003e50\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e \u003cp\u003e15\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c5\"\u003e \u003cp\u003e35\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c6\"\u003e \u003cp\u003e350\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eF5\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003eAOSW5\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e \u003cp\u003e50\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e \u003cp\u003e25\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c5\"\u003e \u003cp\u003e25\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c6\"\u003e \u003cp\u003e350\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eF6\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003eAOSW6\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e \u003cp\u003e60\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e \u003cp\u003e15\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c5\"\u003e \u003cp\u003e25\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c6\"\u003e \u003cp\u003e350\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eF7\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003eAOSW7\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e \u003cp\u003e55\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e \u003cp\u003e15\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c5\"\u003e \u003cp\u003e30\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c6\"\u003e \u003cp\u003e350\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eF8\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003eAOSW8\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e \u003cp\u003e50\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e \u003cp\u003e30\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c5\"\u003e \u003cp\u003e20\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c6\"\u003e \u003cp\u003e350\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eF9\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003eAOSW9\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e \u003cp\u003e60\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e \u003cp\u003e10\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c5\"\u003e \u003cp\u003e30\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c6\"\u003e \u003cp\u003e350\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eF10\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003eAOSW10\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e \u003cp\u003e40\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e \u003cp\u003e20\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c5\"\u003e \u003cp\u003e40\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c6\"\u003e \u003cp\u003e350\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eF11\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003eAOSW11\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e \u003cp\u003e60\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e \u003cp\u003e20\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c5\"\u003e \u003cp\u003e20\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c6\"\u003e \u003cp\u003e350\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eF12\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003eAOSW12\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e \u003cp\u003e50\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e \u003cp\u003e10\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c5\"\u003e \u003cp\u003e30\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c6\"\u003e \u003cp\u003e350\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eF13\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003eAOSW13\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e \u003cp\u003e40\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e \u003cp\u003e25\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c5\"\u003e \u003cp\u003e35\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c6\"\u003e \u003cp\u003e350\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003c/tbody\u003e \u003c/colgroup\u003e \u003c/table\u003e\u003c/div\u003e \u003c/p\u003e \u003c/div\u003e \u003cdiv id=\"Sec13\" class=\"Section3\"\u003e \u003ch2\u003e2.3.8 Optimization\u003c/h2\u003e \u003cp\u003eOptimization aims to identify the best formulation that contains the maximum oil concentration and the least surfactant concentration while providing a nanoemulsion with an average size of less than 250 nm. Using the BBD, the responses of the 13 formulations, including pH, viscosity, particle size, and drug release, were studied for identification of the most suitable formulation, and one newly proposed formula was chosen based on the optimized set of parameters.\u003c/p\u003e \u003c/div\u003e \u003cdiv id=\"Sec14\" class=\"Section3\"\u003e \u003ch2\u003e2.3.9 Studies on dependent variables for optimization\u003c/h2\u003e \u003cdiv id=\"Sec15\" class=\"Section4\"\u003e \u003ch2\u003e2.3.9.1 Droplet size analysis and distribution\u003c/h2\u003e \u003cp\u003eThe measurement of size distribution, mean particle size (z-average diameter), and polydispersity index (PDI) of formulated emulsions was investigated with Malvern Zetasizer Nano ZS90 (Malvern Instruments, UK). This instrument performs size measurement on samples using the Dynamic Light Scattering technique (DLS) or Photon Correlation Spectroscopy (PCS). 100 \u0026micro;L of each formulated nanoemulsion (NE) was introduced into a 5mL stoppered vial and diluted 20 times with distilled water to produce proper scattering intensity. The NE was made up to 2000 \u0026micro;L with double-distilled water and then subjected to gentle but continuous swirling for a homogenized solution. The resulting 2 mL emulsion was gently pipetted into a clean and dry disposable cuvette, and measurement was taken at 25\u0026thinsp;\u0026plusmn;\u0026thinsp;0.5\u0026deg;C at a scattering angle of 90\u0026deg;C. Average measurement was taken in triplicate, and the mean particle size, z-average in diameter, including PDI, was obtained. The PDI represents a dimensionless measure of the width of the distributed particle size analysis, ranging from a calculated cumulant analysis of 0 to 1. A small PDI value is usually preferred as it signifies a monodispersed particle population, while a larger PDI value could imply a broader particle size distribution.\u003c/p\u003e \u003c/div\u003e \u003cdiv id=\"Sec16\" class=\"Section4\"\u003e \u003ch2\u003e2.3.9.2 pH\u003c/h2\u003e \u003cp\u003eThe pH of each formulated emulsion was determined using the digital pH meter (TANCO digital pH meter, model number EE-011). Briefly, the probe of the calibrated pH meter was gently rinsed with distilled water and wiped with a laboratory paper tissue. pH of 5 mL samples was read in triplicate at a standby temperature of 25\u0026deg;C, and pH values were recorded after readings were stable.\u003c/p\u003e \u003c/div\u003e \u003cdiv id=\"Sec17\" class=\"Section4\"\u003e \u003ch2\u003e2.3.9.3 Viscosity measurement\u003c/h2\u003e \u003cp\u003ePrepared nanoemulsion samples were exposed to viscosity measurements using the Brookfield\u0026ndash;type rotary viscometer, USA. Briefly, the viscometer was turned on, and the sample container was decoupled from the bottom of the viscometer. About 15 mL of the test sample was introduced into the sample container in order to make the sample container 25% full. The container was then fitted back into its original position, and the spindle was inserted and centered into the test sample until it was fully submerged in the sample container. The spindle was then exposed to rotation at 5, 10, 20, and 50 rpm, respectively, at room temperature. Readings were recorded in triplicate, and the mean values were calculated.\u003c/p\u003e \u003c/div\u003e \u003cdiv id=\"Sec18\" class=\"Section4\"\u003e \u003ch2\u003e2.3.9.4 \u003cem\u003eIn-vitro\u003c/em\u003e drug release study\u003c/h2\u003e \u003cp\u003e \u003cem\u003eIn-vitro\u003c/em\u003e drug release studies of the developed nanoformulations were carried out using a basket-type dissolution apparatus (Electrolab Dissolution tester USP TDT06L). Also, the pretreated dialysis membrane (dialysis membrane 50, LA 387-10MT), having a pore size of 2.4 nm, an average flat width of 24.26 mm, and an average diameter of 14.3 mm, with a weight cut-off of ~\u0026thinsp;12\u0026ndash;14 kD, was used. 500 mL of freshly prepared phosphate buffer of pH 7.5 was introduced as the release medium into a 3L capacity receiver compartment, and the temperature was maintained until a constant temperature of 37\u0026deg;C was attained. Afterward, one end of a 5 cm long dialysis bag was carefully tied, and 1 mL of the required formulation containing 2.0 mg of DG was carefully introduced into the dialysis bag. The other end of the dialysis bag was carefully tied, and the absence of leakage was ensured. The bag was gently rinsed with distilled water to ensure an impurity-free exterior. Afterward, the dialysis bag was gently placed inside the donor compartment (basket), and a stirring speed of 50 rpm at 37\u0026thinsp;\u0026plusmn;\u0026thinsp;2\u0026deg;C was maintained for 6 h. At selected times of 0, 0.5, 1, 2, 4, 6, 8, 10, 12, 24, 36, and 48 h, 10 mL of the release medium was withdrawn, and the same volume of fresh medium was replaced at every withdrawal to ensure the maintenance of sink condition. Using a 0.45 \u0026micro;m cellulose membrane filter, collected samples were filtered before drug quantification was spectrophotometrically determined using a UV-Visible spectrophotometer.\u003c/p\u003e \u003c/div\u003e \u003c/div\u003e \u003cdiv id=\"Sec19\" class=\"Section3\"\u003e \u003ch2\u003e2.3.10 Stability Assessment\u003c/h2\u003e \u003cp\u003eThe stability of nanoemulsions is an important factor that needs to be considered for their viability and avoidance of metastable formulation. The stability studies were determined in many ways. Firstly, nanoemulsions were kept undisturbed at room temperature for 12 weeks, and signs of turbidity and color change were visually inspected. Secondly, the physical thermodynamic stability test as described by Shafiq \u003cem\u003eet al\u003c/em\u003e (2007) was employed with slight modifications. At 4000 rpm, formulations were subjected to centrifugation for 30 minutes at 25\u0026deg;C. Formulation that shows no sign of phase disparity was taken further for the heating-cooling cycle. Briefly, each selected formulation was stored for 48 hours at 4\u0026deg;C; afterward, they were transferred to a water bath until a constant temperature of 45\u0026deg;C was attained. After 48 hours, the heated sample was returned to a 4\u0026deg;C cooling condition, and this cycle was repeated 6 times. Lastly, stable formulations selected from the heating-cooling cycle were subjected to three freeze-thaw cycles. Samples were exposed to a freezing temperature of -20\u0026deg;C and a simultaneous thawing temperature of +\u0026thinsp;25\u0026deg;C repeatedly for three times.\u003c/p\u003e \u003c/div\u003e \u003cdiv id=\"Sec20\" class=\"Section3\"\u003e \u003ch2\u003e2.3.11 Characterization of Nanoemulsions\u003c/h2\u003e \u003c/div\u003e \u003cdiv id=\"Sec21\" class=\"Section3\"\u003e \u003ch2\u003e2.3.11.1 Scanning Electron Microscopy\u003c/h2\u003e \u003cp\u003eThe morphology, shape, and size distribution of nanoemulsion and Nanogel containing DG were studied with a scanning electron microscope, SEM (GEISS EV018, Smartsem Software, Germany). Before the analysis, nanoemulsion and nanogel were lightly stained on an 18 mm microscopic cover glass and allowed to dry in a vacuum oven before microscopic examination. The accelerating voltage was set at 20.00 kV at a working distance of 7.5 mm. The microscopic examination of test samples was conducted at a magnification of 50.00X.\u003c/p\u003e \u003c/div\u003e \u003cdiv id=\"Sec22\" class=\"Section3\"\u003e \u003ch2\u003e2.3.11.2 Field Emission Scanning Electron Microscopy\u003c/h2\u003e \u003cp\u003eTo further image the sample surface with a high-energy beam of electrons, FESEM (IE 250X Max 80, FEI Quanta 200F SEM model, FEI Company, Netherlands) was used. Samples were pre-coated on an 18 mm microscopic cover glass slide and dried in a vacuum oven at 45\u0026deg;C overnight. Pre-coated plates were further subjected to gold coating and vacuum before being subjected to FESEM imaging at different resolutions.\u003c/p\u003e \u003c/div\u003e \u003cdiv id=\"Sec23\" class=\"Section3\"\u003e \u003ch2\u003e2.3.11.3 Fourier Transform Infrared Spectroscopy (FT-IR)\u003c/h2\u003e \u003cp\u003eThe stability, as well as functional properties of DG, DGNe, and DGNeG, was investigated with FT-IR (Perkin Elmer Frontier ATR/FTIR). Briefly, a 10 \u0026micro;L sample was micro pipetted and dropped on the slide for analysis. FT-IR spectra were generated at a range of 4000\u0026thinsp;\u0026minus;\u0026thinsp;400 cm\u003csup\u003e\u0026minus;\u0026thinsp;1\u003c/sup\u003eand studied for each sample. The analysis was repeated after three months of sample storage to rule out functional changes as well as instability.\u003c/p\u003e \u003c/div\u003e \u003c/div\u003e"},{"header":"3 RESULT AND DISCUSSION","content":"\u003cdiv id=\"Sec25\" class=\"Section2\"\u003e \u003ch2\u003e3.1 Solubility of DG and selection of suitable NE excipients\u003c/h2\u003e \u003cp\u003eSpectrometric evaluation of DG solubility in excipients, including oil, oil combination, surfactant, and co-surfactants, is presented in Table\u0026nbsp;\u003cspan refid=\"Tab2\" class=\"InternalRef\"\u003e2\u003c/span\u003e. The result indicates DG solubility at 0.3877\u0026thinsp;\u0026plusmn;\u0026thinsp;0.070 mg/dL, 0.3353\u0026thinsp;\u0026plusmn;\u0026thinsp;0.003 mg/dL, and 0.714\u0026thinsp;\u0026plusmn;\u0026thinsp;0.02 mg/dL in BG oil, Sesame oil (SS), and a combination of BG and SS (BGSS), respectively. Solubility of DG in surfactants includes 0.2320\u0026thinsp;\u0026plusmn;\u0026thinsp;0.003 mg/dL, 0.238\u0026thinsp;\u0026plusmn;\u0026thinsp;0.007 mg/dL, 0.223\u0026thinsp;\u0026plusmn;\u0026thinsp;0.005 mg/dL, and 0.132\u0026thinsp;\u0026plusmn;\u0026thinsp;0.008 mg/dL in Tween 20 (T20), Tween 80 (T80), Span 20 (S20), and Span 80 (S80), respectively. Cosurfactants, including PEG 400 and Glycerol, show DG solubility at 0.200\u0026thinsp;\u0026plusmn;\u0026thinsp;0.002 mg/dL and 0.249\u0026thinsp;\u0026plusmn;\u0026thinsp;0.002 mg/dL, respectively. The concentration of soluble DG out of the 2.5 mg/mL stock preparation of each experiment is presented as mean\u0026thinsp;\u0026plusmn;\u0026thinsp;SD. From the result, the combination of BG and SS (BGSS) oil displayed the maximum solubilizing capacity for DG (0.714\u0026thinsp;\u0026plusmn;\u0026thinsp;0.00145 mg/mL) when compared to BG and SS alone. In nanoemulsion, oil is an important and vital component required for the sufficient solubilisation of the active pharmaceutical ingredient (API), especially the hydrophobic API (Tayeb \u0026amp; Sainsbury, 2018). Amongst the surfactants selected for this study, T80 (HLB 15.0), followed by T20 (HLB 16.7) showed a higher solubility (0.2383\u0026thinsp;\u0026plusmn;\u0026thinsp;0.00441 mg/mL and 0.2320\u0026thinsp;\u0026plusmn;\u0026thinsp;0.00153 mg/mL) when compared to Span 20 and Span 80. The selection of surfactant is also an important criterion for nanoemulsion formation, as its safety and biocompatibility must be considered. In this study, non-ionic surfactants are considered as our preferred option for therapeutic application as their compatibility and reduced toxicity including its less irritant ability compared to the ionic counterparts has been reported (Gullapalli \u0026amp; Sheth, \u003cspan citationid=\"CR9\" class=\"CitationRef\"\u003e1999\u003c/span\u003e). Research has also shown that the sole usage of single-chain emulsifier may not be sufficient in the capacity to reduce the interfacial tension to a significant extent and therefore, its application in combination with a co-surfactant with amphiphilic nature is also promptly offered. The co-emulsifier may also penetrate the interfacial layer and consequently decrease the fluidity and increase the entropy of the emulsion system (Rodrigues et al., \u003cspan citationid=\"CR22\" class=\"CitationRef\"\u003e2015\u003c/span\u003e). The co-surfactants selected in this study include PEG and Glycerol and the result displayed a maximum solubilizing capacity in glycerol (0.2493\u0026thinsp;\u0026plusmn;\u0026thinsp;0.00208 mg/mL) and was thereby selected for further use.\u003c/p\u003e \u003cp\u003e \u003cdiv class=\"gridtable\"\u003e\u003ctable float=\"Yes\" id=\"Tab2\" border=\"1\"\u003e \u003ccaption language=\"En\"\u003e \u003cdiv class=\"CaptionNumber\"\u003eTable 2\u003c/div\u003e \u003cdiv class=\"CaptionContent\"\u003e \u003cp\u003eSolubility of DG in excipients\u003c/p\u003e \u003c/div\u003e \u003c/caption\u003e \u003ccolgroup cols=\"6\"\u003e \u003cdiv align=\"left\" class=\"colspec\" colname=\"c1\" colnum=\"1\"\u003e\u003c/div\u003e \u003cdiv align=\"left\" class=\"colspec\" colname=\"c2\" colnum=\"2\"\u003e\u003c/div\u003e \u003cdiv align=\"left\" class=\"colspec\" colname=\"c3\" colnum=\"3\"\u003e\u003c/div\u003e \u003cdiv align=\"left\" class=\"colspec\" colname=\"c4\" colnum=\"4\"\u003e\u003c/div\u003e \u003cdiv align=\"left\" class=\"colspec\" colname=\"c5\" colnum=\"5\"\u003e\u003c/div\u003e \u003cdiv align=\"char\" char=\"\u0026plusmn;\" class=\"colspec\" colname=\"c6\" colnum=\"6\"\u003e\u003c/div\u003e \u003cthead\u003e \u003ctr\u003e \u003cth align=\"left\" colname=\"c1\"\u003e \u003cp\u003eS/N\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c2\"\u003e \u003cp\u003eName of Excipient\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c3\"\u003e \u003cp\u003eCode\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c4\"\u003e \u003cp\u003eChemical name\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c5\"\u003e \u003cp\u003eHLB\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c6\"\u003e \u003cp\u003eSolubility of DG (mg/mL) mean\u0026thinsp;\u0026plusmn;\u0026thinsp;SD\u003c/p\u003e \u003c/th\u003e \u003c/tr\u003e \u003c/thead\u003e \u003ctbody\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003e1.\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003eBottle Gourd seed oil (O)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003eBG\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003eTriglyceride\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e-\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\"\u0026plusmn;\" colname=\"c6\"\u003e \u003cp\u003e0.3877\u0026thinsp;\u0026plusmn;\u0026thinsp;0.070\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003e2.\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003eSesame oil (O)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003eSS\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003eTriglyceride\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e-\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\"\u0026plusmn;\" colname=\"c6\"\u003e \u003cp\u003e0.3353\u0026thinsp;\u0026plusmn;\u0026thinsp;0.003\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003e3.\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003eBottle gourd and Sesame oil combination (O)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003eBGSS\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003eTriglyceride\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e-\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\"\u0026plusmn;\" colname=\"c6\"\u003e \u003cp\u003e0.7137\u0026thinsp;\u0026plusmn;\u0026thinsp;0.002\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003e4.\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003eTween 20 (S)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003eT20\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003ePolyoxyethylene sorbitan monolaurate\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e16.7\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\"\u0026plusmn;\" colname=\"c6\"\u003e \u003cp\u003e0.2320\u0026thinsp;\u0026plusmn;\u0026thinsp;0.003\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003e5.\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003eTween 80 (S)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003eT80\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003ePolyoxyethylene sorbitan monooleate\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e15.7\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\"\u0026plusmn;\" colname=\"c6\"\u003e \u003cp\u003e0.2383\u0026thinsp;\u0026plusmn;\u0026thinsp;0.007\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003e6.\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003eSpan 20 (S)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003eS20\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003eSorbitan monostearate\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e8.6\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\"\u0026plusmn;\" colname=\"c6\"\u003e \u003cp\u003e0.2233\u0026thinsp;\u0026plusmn;\u0026thinsp;0.005\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003e7.\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003eSpan 80 (S)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003eS80\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003eSorbitan monooleate\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e4.3\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\"\u0026plusmn;\" colname=\"c6\"\u003e \u003cp\u003e0.2317\u0026thinsp;\u0026plusmn;\u0026thinsp;0.008\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003e8.\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003ePolyethylene Glycol (Cs)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003ePEG\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003ePolyethylene glycol 400 Monooleate\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e11.4\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\"\u0026plusmn;\" colname=\"c6\"\u003e \u003cp\u003e0.2000\u0026thinsp;\u0026plusmn;\u0026thinsp;0.002\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003e9.\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003eGlycerol (Cs)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003eGC\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003eGlyceryl monostearate\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e3.8\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\"\u0026plusmn;\" colname=\"c6\"\u003e \u003cp\u003e0.2493\u0026thinsp;\u0026plusmn;\u0026thinsp;0.002\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003c/tbody\u003e \u003c/colgroup\u003e \u003ctfoot\u003e \u003ctr\u003e\u003ctd colspan=\"6\"\u003e\u003cem\u003eO-oil; S-Surfactant; Cs-Cosurfactant\u003c/em\u003e\u003c/td\u003e\u003c/tr\u003e \u003c/tfoot\u003e \u003c/table\u003e\u003c/div\u003e \u003c/p\u003e \u003c/div\u003e \u003cdiv id=\"Sec26\" class=\"Section2\"\u003e \u003ch2\u003e\u003cb\u003e3.2 Further screening of surfactants\u003c/b\u003e: \u003cb\u003espectrometric evaluation and physical miscibility studies\u003c/b\u003e\u003c/h2\u003e \u003cp\u003ePhysical miscibility studies of BGSS in T20 and T80 is shown in Fig.\u0026nbsp;\u003cspan refid=\"Fig1\" class=\"InternalRef\"\u003e1\u003c/span\u003e. From the image, a clearer and isotropic mixture was found with T80 even after it is left to stand for 3 hours. Since T20 and T80 showed the maximum solubilizing effect on DG amongst other surfactants, further screening studies were carried out to select the most suitable surfactant based on its ability to solubilize BGSS oil. Table\u0026nbsp;\u003cspan refid=\"Tab3\" class=\"InternalRef\"\u003e3\u003c/span\u003e shows the result of different parameters reported during observation of miscibility effect in both surfactants as shown in Fig.\u0026nbsp;\u003cspan refid=\"Fig1\" class=\"InternalRef\"\u003e1\u003c/span\u003e. Although, tween 20 gave an almost instant emulsifying effect within the first few flask swirls, but T80 showed more stability and flowability as compared to T20. The solubilizing capacity of T20 and T80 on BGSS is shown in Fig.\u0026nbsp;\u003cspan refid=\"Fig2\" class=\"InternalRef\"\u003e2\u003c/span\u003e. Spectrometric analysis was recorded at an absorbance of 0.348, 0.261 and 0.284 for T 80 and 0.47, 0.486 and 0.536 for T20 at an absorbing wavelength of 350 nm, 450 nm and 550 nm (Fig.\u0026nbsp;\u003cspan refid=\"Fig2\" class=\"InternalRef\"\u003e2\u003c/span\u003e). Results revealed a higher absorbance in T20 indicating an increased BGSS concentration, hence, less emulsifying effect when compared to T80. Polyoxyethylene sorbitan monooleate (Tween 80) is a non-ionic and hydrophilic surfactant that has wide prospect as an emulsifying agent in the food industry with excellent potential in reducing globule size of lipid emulsions and enhancing permeation of drugs in medicinal industry(de Freitas Ara\u0026uacute;jo Reis et al., \u003cspan citationid=\"CR7\" class=\"CitationRef\"\u003e2021\u003c/span\u003e)\u003c/p\u003e \u003cp\u003e \u003c/p\u003e \u003cp\u003e \u003cdiv class=\"gridtable\"\u003e\u003ctable float=\"Yes\" id=\"Tab3\" border=\"1\"\u003e \u003ccaption language=\"En\"\u003e \u003cdiv class=\"CaptionNumber\"\u003eTable 3\u003c/div\u003e \u003cdiv class=\"CaptionContent\"\u003e \u003cp\u003eMiscibility performance of BGSS oil mix in Tween 20 and Tween 80\u003c/p\u003e \u003c/div\u003e \u003c/caption\u003e \u003ccolgroup cols=\"3\"\u003e \u003cdiv align=\"left\" class=\"colspec\" colname=\"c1\" colnum=\"1\"\u003e\u003c/div\u003e \u003cdiv align=\"left\" class=\"colspec\" colname=\"c2\" colnum=\"2\"\u003e\u003c/div\u003e \u003cdiv align=\"left\" class=\"colspec\" colname=\"c3\" colnum=\"3\"\u003e\u003c/div\u003e \u003cthead\u003e \u003ctr\u003e \u003cth align=\"left\" colname=\"c1\"\u003e \u003cp\u003eS/N\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c2\"\u003e \u003cp\u003eTween 20\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c3\"\u003e \u003cp\u003eTween 80\u003c/p\u003e \u003c/th\u003e \u003c/tr\u003e \u003c/thead\u003e \u003ctbody\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003e1.\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003eOnly 3 swirls resulted in an homogenous emulsion\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e21 swirls gave an homogenous emulsion. Oil was gradually diffusing into the surfactant and water mix\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003e2.\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003eResulting emulsion showed less clarity\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003eClearer emulsion was formed\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003e3.\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003eMore turbid\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003eLess turbid\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003e4.\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003eSlight phase separation after 3 hours of an undisturbed position\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003eNo separation after 3 hours of an undisturbed position\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003e5.\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003eAfter 24 hours, the slight turbidity remains\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003eAfter 24 hours, clarity and transparency was maintained\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003c/tbody\u003e \u003c/colgroup\u003e \u003c/table\u003e\u003c/div\u003e \u003c/p\u003e \u003cp\u003e \u003c/p\u003e \u003c/div\u003e \u003cdiv id=\"Sec27\" class=\"Section2\"\u003e \u003ch2\u003e3.3 Pseudoternary Phase Diagram (PTPD)\u003c/h2\u003e \u003cp\u003eIn order to identify the O/W nanoemulsion region, the PTPDs of three different formulations A, B, and C (\u003csub\u003ef\u003c/sub\u003eA, \u003csub\u003ef\u003c/sub\u003eB, and \u003csub\u003ef\u003c/sub\u003eC) at three different ratios 1:1, 2:1, and 1:2 of S\u003csub\u003emix\u003c/sub\u003e are constructed as illustrated in Fig.\u0026nbsp;\u003cspan refid=\"Fig3\" class=\"InternalRef\"\u003e3\u003c/span\u003e. From the phase diagram of \u003csub\u003ef\u003c/sub\u003eA, where T80 and glycerol were used at a ratio of 1:1, the nanoemulsion region was found to be higher than \u003csub\u003ef\u003c/sub\u003eB (2:1) and fC (1:2). After preformulation with different S\u003csub\u003emix\u003c/sub\u003e ratios, the percentage of oil, S\u003csub\u003emix,\u003c/sub\u003e and water obtained for each formulation (\u003csub\u003ef\u003c/sub\u003eA, \u003csub\u003ef\u003c/sub\u003eB, and \u003csub\u003ef\u003c/sub\u003eC) was represented in a PTPD. PTPD was constructed using the aqueous titration method. Three PTPD diagrams for different surfactant mix (S\u003csub\u003emix\u003c/sub\u003e) ratios were plotted. From our plot, \u003csub\u003ef\u003c/sub\u003eA with S\u003csub\u003emix\u003c/sub\u003e ratio 1:1 (T 80 and Glycerol) evidently displayed the maximum nanoemulsion region, while S\u003csub\u003emix\u003c/sub\u003e ratio 1:2 (fC-T80 and Glycerol) showed the smallest nanoemulsion region. From the phase diagram in Fig.\u0026nbsp;\u003cspan refid=\"Fig3\" class=\"InternalRef\"\u003e3\u003c/span\u003e, the maximum amount of oil (56.25%, 69.23% and 66.67%) in \u003csub\u003ef\u003c/sub\u003eA, \u003csub\u003ef\u003c/sub\u003eB, and \u003csub\u003ef\u003c/sub\u003eC, respectively, was emulsified by 6.25%, 7.69% and 7.41% S\u003csub\u003emix,\u003c/sub\u003e respectively. This indicates that the surfactant in \u003csub\u003ef\u003c/sub\u003eB (S\u003csub\u003emix\u003c/sub\u003e 2:1) does not significantly enlarge the nanoemulsion area as compared to \u003csub\u003ef\u003c/sub\u003eA (S\u003csub\u003emix\u003c/sub\u003e 1:1) despite being doubled. This was also seen in \u003csub\u003ef\u003c/sub\u003eC (S\u003csub\u003emix\u003c/sub\u003e1:2), where the co-surfactant was double the surfactant, but the expected difference in the widening region of nanoemulsion was significantly reduced.\u003c/p\u003e \u003cp\u003eIn this study, the region that maximally incorporates high oil content with less surfactant while completely solubilizing the drug and aiding optimal Smix concentration and water was taken into consideration (Baboota et al., \u003cspan citationid=\"CR4\" class=\"CitationRef\"\u003e2007\u003c/span\u003e). The expansion in the nanoemulsion region of \u003csub\u003ef\u003c/sub\u003eA can be attributed to the equal concentration of T80 and glycerol. Based on the observations marked during titration regarding the point of flowability of each formulation, PTPD was constructed separately for each S\u003csub\u003emix\u003c/sub\u003e ratio (1:1, 2:1, and 1:2) so that a suitable region for the O/W emulsion phase can be identified. Formulations that showed less surfactant concentration with extensive accommodation of oil were selected from different points of the selected PTD. When tween 80 was used with less cosurfactant, the nanoemulsion region was lessened as seen in formulation C.\u003c/p\u003e \u003cp\u003e \u003c/p\u003e \u003c/div\u003e \u003cdiv id=\"Sec28\" class=\"Section2\"\u003e \u003ch2\u003e3.4 Experimental Design, Screening of Independent Variables and Optimization Studies\u003c/h2\u003e \u003cp\u003eTable\u0026nbsp;\u003cspan refid=\"Tab4\" class=\"InternalRef\"\u003e4\u003c/span\u003e represents the study carried out to evaluate the various independent variables or factors at different surfactant, oil, and water concentrations. Based on this, the obtained responses are presented. Experimental designs are known to help in understanding the influence of several variables involved in a study, and a smaller number of experiments is advantageous. Further Statistical analysis of results would help in revealing the variables that show significant influence. Correlation of desired response can then be done with variables using polynomial equations (Sharma et al., \u003cspan citationid=\"CR24\" class=\"CitationRef\"\u003e2013\u003c/span\u003e).\u003c/p\u003e \u003cp\u003eTable\u0026nbsp;\u003cspan refid=\"Tab4\" class=\"InternalRef\"\u003e4\u003c/span\u003e demonstrates the summary of the actual design of the experiment based on the highlighted influential factors presented for screening and obtained responses from 13 randomized and suggested runs to obtain optimum responses, including particle size (nm), drug release (%), pH, and viscosity (cps). From the table, a particle size with a minimum of 92.72 nm and a maximum of 280.9 nm was found for formulations 5 and 6, respectively. In addition, diosgenin release at a minimum and maximum of 0.95 to 36% was recorded for optimization. pH of all 13 formulations ranges from 3.88 to 5.89, with a recorded viscosity of 0.882\u0026ndash;0.89 cps. A \u003cem\u003ep\u003c/em\u003e-value of 0.0120, 0.0500, and 0.0001 indicates significance with a suggested linear, 2FI, and mean model.\u003c/p\u003e \u003cp\u003eFurther optimization and post-analysis report revealed 99% population, which gave approximately 95% confidence for response data (Table\u0026nbsp;\u003cspan refid=\"Tab4\" class=\"InternalRef\"\u003e4\u003c/span\u003e). A cubic model graph analysis report is represented in Fig.\u0026nbsp;\u003cspan refid=\"Fig4\" class=\"InternalRef\"\u003e4\u003c/span\u003e to finally explore the report of each selected response based on the compositional relationship that exists amongst the influential factors. Formulation 6 was identified as the most suitable run since the variables of formulation 6 gave a mean droplet size response of 92.72 nm at 60%, 25% and 15% oil surfactant and water, respectively. Other responses, including drug release, viscosity, and pH, gave an appreciable value of 14.11%, 5.06, and 0.8822 cps, respectively.\u003c/p\u003e\n\u003cp\u003eTable 4: Responses of influential factors and confirmatory selection criteria for preformulation based on selected responses\u003c/p\u003e\n\u003ctable border=\"0\" cellspacing=\"0\" cellpadding=\"0\" width=\"888\"\u003e\n \u003ctbody\u003e\n \u003ctr\u003e\n \u003ctd valign=\"top\" style=\"width: 66px;\"\u003e\n \u003cp\u003e\u003cstrong\u003eResponse\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd colspan=\"2\" valign=\"top\" style=\"width: 104px;\"\u003e\n \u003cp\u003e\u003cstrong\u003eName\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd colspan=\"2\" valign=\"top\" style=\"width: 66px;\"\u003e\n \u003cp\u003e\u003cstrong\u003eUnit\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 95px;\"\u003e\n \u003cp\u003e\u003cstrong\u003eObservation\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd colspan=\"3\" valign=\"top\" style=\"width: 94px;\"\u003e\n \u003cp\u003e\u003cstrong\u003eAnalysis\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 57px;\"\u003e\n \u003cp\u003e\u003cstrong\u003eMin\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd colspan=\"3\" valign=\"top\" style=\"width: 57px;\"\u003e\n \u003cp\u003e\u003cstrong\u003eMax\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 66px;\"\u003e\n \u003cp\u003e\u003cstrong\u003eMean\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd colspan=\"2\" valign=\"top\" style=\"width: 85px;\"\u003e\n \u003cp\u003e\u003cstrong\u003eStd. Dev.\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd colspan=\"2\" valign=\"top\" style=\"width: 57px;\"\u003e\n \u003cp\u003e\u003cstrong\u003eRatio\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 66px;\"\u003e\n \u003cp\u003e\u003cstrong\u003eTransform\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 76px;\"\u003e\n \u003cp\u003e\u003cstrong\u003eModel\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd valign=\"top\" style=\"width: 66px;\"\u003e\n \u003cp\u003eR1\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd colspan=\"2\" valign=\"top\" style=\"width: 104px;\"\u003e\n \u003cp\u003eParticle Size\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd colspan=\"2\" valign=\"top\" style=\"width: 66px;\"\u003e\n \u003cp\u003enm\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 95px;\"\u003e\n \u003cp\u003e13\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd colspan=\"3\" valign=\"top\" style=\"width: 94px;\"\u003e\n \u003cp\u003ePolynomial\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 57px;\"\u003e\n \u003cp\u003e92.72\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd colspan=\"3\" valign=\"top\" style=\"width: 57px;\"\u003e\n \u003cp\u003e280.9\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 66px;\"\u003e\n \u003cp\u003e200.66\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd colspan=\"2\" valign=\"top\" style=\"width: 85px;\"\u003e\n \u003cp\u003e61.21\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd colspan=\"2\" valign=\"top\" style=\"width: 57px;\"\u003e\n \u003cp\u003e3.03\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 66px;\"\u003e\n \u003cp\u003eNone\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 76px;\"\u003e\n \u003cp\u003eLinear\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd valign=\"top\" style=\"width: 66px;\"\u003e\n \u003cp\u003eR2\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd colspan=\"2\" valign=\"top\" style=\"width: 104px;\"\u003e\n \u003cp\u003eDrug Release\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd colspan=\"2\" valign=\"top\" style=\"width: 66px;\"\u003e\n \u003cp\u003e%\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 95px;\"\u003e\n \u003cp\u003e13\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd colspan=\"3\" valign=\"top\" style=\"width: 94px;\"\u003e\n \u003cp\u003ePolynomial\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 57px;\"\u003e\n \u003cp\u003e0.95\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd colspan=\"3\" valign=\"top\" style=\"width: 57px;\"\u003e\n \u003cp\u003e36\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 66px;\"\u003e\n \u003cp\u003e16.49\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd colspan=\"2\" valign=\"top\" style=\"width: 85px;\"\u003e\n \u003cp\u003e12.80\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd colspan=\"2\" valign=\"top\" style=\"width: 57px;\"\u003e\n \u003cp\u003e37.89\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 66px;\"\u003e\n \u003cp\u003eNone\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 76px;\"\u003e\n \u003cp\u003eReduced 2FI\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd valign=\"top\" style=\"width: 66px;\"\u003e\n \u003cp\u003eR3\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd colspan=\"2\" valign=\"top\" style=\"width: 104px;\"\u003e\n \u003cp\u003epH\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd colspan=\"2\" valign=\"top\" style=\"width: 66px;\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 95px;\"\u003e\n \u003cp\u003e13\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd colspan=\"3\" valign=\"top\" style=\"width: 94px;\"\u003e\n \u003cp\u003ePolynomial\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 57px;\"\u003e\n \u003cp\u003e3.88\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd colspan=\"3\" valign=\"top\" style=\"width: 57px;\"\u003e\n \u003cp\u003e5.89\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 66px;\"\u003e\n \u003cp\u003e4.94\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd colspan=\"2\" valign=\"top\" style=\"width: 85px;\"\u003e\n \u003cp\u003e0.4880\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd colspan=\"2\" valign=\"top\" style=\"width: 57px;\"\u003e\n \u003cp\u003e1.52\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 66px;\"\u003e\n \u003cp\u003eNone\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 76px;\"\u003e\n \u003cp\u003eMean\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd valign=\"top\" style=\"width: 66px;\"\u003e\n \u003cp\u003eR4\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd colspan=\"2\" valign=\"top\" style=\"width: 104px;\"\u003e\n \u003cp\u003eViscosity\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd colspan=\"2\" valign=\"top\" style=\"width: 66px;\"\u003e\n \u003cp\u003ecps\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 95px;\"\u003e\n \u003cp\u003e13\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd colspan=\"3\" valign=\"top\" style=\"width: 94px;\"\u003e\n \u003cp\u003ePolynomial\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 57px;\"\u003e\n \u003cp\u003e0.882\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd colspan=\"3\" valign=\"top\" style=\"width: 57px;\"\u003e\n \u003cp\u003e0.890\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 66px;\"\u003e\n \u003cp\u003e0.8879\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd colspan=\"2\" valign=\"top\" style=\"width: 85px;\"\u003e\n \u003cp\u003e0.0020\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd colspan=\"2\" valign=\"top\" style=\"width: 57px;\"\u003e\n \u003cp\u003e1.01\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 66px;\"\u003e\n \u003cp\u003eNone\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 76px;\"\u003e\n \u003cp\u003eLinear\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd colspan=\"2\" valign=\"top\" style=\"width: 105px;\"\u003e\n \u003cp\u003e\u003cstrong\u003eResponse\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd colspan=\"2\" valign=\"top\" style=\"width: 115px;\"\u003e\n \u003cp\u003ePred Mean\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd colspan=\"3\" valign=\"top\" style=\"width: 127px;\"\u003e\n \u003cp\u003ePred Median\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 76px;\"\u003e\n \u003cp\u003eObserved\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd colspan=\"3\" valign=\"top\" style=\"width: 90px;\"\u003e\n \u003cp\u003eStd Dev\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 22px;\"\u003e\n \u003cp\u003en\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd colspan=\"3\" valign=\"top\" style=\"width: 103px;\"\u003e\n \u003cp\u003eSE Prediction\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd colspan=\"2\" valign=\"top\" style=\"width: 90px;\"\u003e\n \u003cp\u003e95% PI low\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd colspan=\"3\" valign=\"top\" style=\"width: 159px;\"\u003e\n \u003cp\u003e95% PI high\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd colspan=\"2\" valign=\"top\" style=\"width: 105px;\"\u003e\n \u003cp\u003eParticle Size\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd colspan=\"2\" valign=\"top\" style=\"width: 115px;\"\u003e\n \u003cp\u003e175.704\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd colspan=\"3\" valign=\"top\" style=\"width: 127px;\"\u003e\n \u003cp\u003e175.704\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 76px;\"\u003e\n \u003cp\u003e92.72\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd colspan=\"3\" valign=\"top\" style=\"width: 90px;\"\u003e\n \u003cp\u003e39.5523\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 22px;\"\u003e\n \u003cp\u003e1\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd colspan=\"3\" valign=\"top\" style=\"width: 103px;\"\u003e\n \u003cp\u003e44.0076\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd colspan=\"2\" valign=\"top\" style=\"width: 90px;\"\u003e\n \u003cp\u003e76.1516\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd colspan=\"3\" valign=\"top\" style=\"width: 159px;\"\u003e\n \u003cp\u003e275.256\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd colspan=\"2\" valign=\"top\" style=\"width: 105px;\"\u003e\n \u003cp\u003eDrug Release\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd colspan=\"2\" valign=\"top\" style=\"width: 115px;\"\u003e\n \u003cp\u003e21.7879\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd colspan=\"3\" valign=\"top\" style=\"width: 127px;\"\u003e\n \u003cp\u003e21.7879\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 76px;\"\u003e\n \u003cp\u003e14.11\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd colspan=\"3\" valign=\"top\" style=\"width: 90px;\"\u003e\n \u003cp\u003e8.35552\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 22px;\"\u003e\n \u003cp\u003e1\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd colspan=\"3\" valign=\"top\" style=\"width: 103px;\"\u003e\n \u003cp\u003e9.41943\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd colspan=\"2\" valign=\"top\" style=\"width: 90px;\"\u003e\n \u003cp\u003e0.479699\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd colspan=\"3\" valign=\"top\" style=\"width: 159px;\"\u003e\n \u003cp\u003e43.0962\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd colspan=\"2\" valign=\"top\" style=\"width: 105px;\"\u003e\n \u003cp\u003epH\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd colspan=\"2\" valign=\"top\" style=\"width: 115px;\"\u003e\n \u003cp\u003e4.93846\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd colspan=\"3\" valign=\"top\" style=\"width: 127px;\"\u003e\n \u003cp\u003e4.93846\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 76px;\"\u003e\n \u003cp\u003e5.06\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd colspan=\"3\" valign=\"top\" style=\"width: 90px;\"\u003e\n \u003cp\u003e0.488038\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 22px;\"\u003e\n \u003cp\u003e1\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd colspan=\"3\" valign=\"top\" style=\"width: 103px;\"\u003e\n \u003cp\u003e0.506461\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd colspan=\"2\" valign=\"top\" style=\"width: 90px;\"\u003e\n \u003cp\u003e3.83498\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd colspan=\"3\" valign=\"top\" style=\"width: 159px;\"\u003e\n \u003cp\u003e6.04194\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd colspan=\"2\" valign=\"top\" style=\"width: 105px;\"\u003e\n \u003cp\u003eViscosity\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd colspan=\"2\" valign=\"top\" style=\"width: 115px;\"\u003e\n \u003cp\u003e0.886283\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd colspan=\"3\" valign=\"top\" style=\"width: 127px;\"\u003e\n \u003cp\u003e0.886283\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 76px;\"\u003e\n \u003cp\u003e0.882\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd colspan=\"3\" valign=\"top\" style=\"width: 90px;\"\u003e\n \u003cp\u003e0.00174681\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 22px;\"\u003e\n \u003cp\u003e1\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd colspan=\"3\" valign=\"top\" style=\"width: 103px;\"\u003e\n \u003cp\u003e0.00194358\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd colspan=\"2\" valign=\"top\" style=\"width: 90px;\"\u003e\n \u003cp\u003e0.881886\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd colspan=\"3\" valign=\"top\" style=\"width: 159px;\"\u003e\n \u003cp\u003e0.890679\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003c/tbody\u003e\n\u003c/table\u003e\n\u003cp\u003eTwo-sided \u0026nbsp; \u0026nbsp;Confidence = 95% \u0026nbsp;(SE)- Standard error; Pred-Prediction\u003c/p\u003e\n\u003cdiv id=\"Sec29\" class=\"Section2\"\u003e \u003ch2\u003e3.5 \u003cem\u003eIn-vitro\u003c/em\u003e Drug Release Studies\u003c/h2\u003e \u003cp\u003eThe \u003cem\u003ein-vitro\u003c/em\u003e release profile of DG from DGNe is presented in Figs.\u0026nbsp;\u003cspan refid=\"Fig6\" class=\"InternalRef\"\u003e5\u003c/span\u003eA and \u003cspan refid=\"Fig6\" class=\"InternalRef\"\u003e5\u003c/span\u003eB. From the release profile, formulations F2, F3, F6 and F12 gave the most abundant release between 92.95% \u0026minus;\u0026thinsp;97.38%. Release profile of F6 within a 36 h period is shown in Fig.\u0026nbsp;\u003cspan refid=\"Fig6\" class=\"InternalRef\"\u003e5\u003c/span\u003eB, where an initial burst release at 0.1 h was recorded with a continuous and sustained release till 8 hrs at a steady concentration of 33.20% \u0026minus;\u0026thinsp;51.60%. A maximum release of DG from DGNe in F6 gave a 92.95% released DG after 36 h. Interestingly, formulation 6 (60% v/v BGSS oil and 25% v/v tween 80/glycerol) showed more adequate release profile as indicated in Fig.\u0026nbsp;\u003cspan refid=\"Fig6\" class=\"InternalRef\"\u003e5\u003c/span\u003eB because of the steady, continuous and sustained release of DG within a 36h period. The release profile displayed by F6 may be attributed to its small droplet size of 92.72 nm as previously described.\u003c/p\u003e \u003cp\u003e \u003c/p\u003e \u003cp\u003e \u003c/p\u003e \u003c/div\u003e \u003cdiv id=\"Sec30\" class=\"Section2\"\u003e \u003ch2\u003e3.6. Surface Morphology and Particle Size\u003c/h2\u003e \u003cp\u003eTable\u0026nbsp;\u003cspan refid=\"Tab5\" class=\"InternalRef\"\u003e5\u003c/span\u003e shows the particle size distribution and polydisperisty index (PDI) of all 13 preformulations using Differential Light Scanning Calorimetric (DLS) technique. Values are recorded in mean\u0026thinsp;\u0026plusmn;\u0026thinsp;standard error of mean (mean\u0026thinsp;\u0026plusmn;\u0026thinsp;SEM). From the table, particle size of all 13 formulations was found to be between a minimum of 82.30\u0026thinsp;\u0026plusmn;\u0026thinsp;6.65 nm and a maximum of 299.36\u0026thinsp;\u0026plusmn;\u0026thinsp;9.27 nm at F6 and F5 respectively. The PDI of all 13 formulations also presented a lowest and highest PDI of 0.072\u0026thinsp;\u0026plusmn;\u0026thinsp;0.005 and 0.389\u0026thinsp;\u0026plusmn;\u0026thinsp;0.12 in F7 and F2 respectively.\u003c/p\u003e \u003cp\u003eFigure \u003cspan refid=\"Fig7\" class=\"InternalRef\"\u003e6\u003c/span\u003e shows the shape and size of DGNe. From the microscopic image presented in Fig.\u0026nbsp;\u003cspan refid=\"Fig7\" class=\"InternalRef\"\u003e6\u003c/span\u003e, an unaggregated and spherical-shaped DG in DGNe was obtained with particle size distribution between 81.8 nm and 99.3 nm. Figures\u0026nbsp;\u003cspan refid=\"Fig7\" class=\"InternalRef\"\u003e6\u003c/span\u003e and 7 displays the microscopic structural morphology of DG in DGNeG and the surface orientation of DGNeG respectively. From our result, a smallest particle size of 68.97\u0026thinsp;\u0026plusmn;\u0026thinsp;19.67 nm with a minimum and maximum size of 29.93 nm and 92.72 nm was recorded at F6. The highest particle size obtained was 276.83\u0026thinsp;\u0026plusmn;\u0026thinsp;10.89 nm with a minimum droplet size of 256.40 nm and a maximum of 293.60 nm.\u003c/p\u003e \u003cp\u003eAccording to (Chiari-Andr\u0026eacute;o et al., \u003cspan citationid=\"CR6\" class=\"CitationRef\"\u003e2019\u003c/span\u003e), nanoemulsions ability to penetrate the skin and mucous membrane can be actively associated to the size of the droplet including its large surface area with the dispersed phase acting as the reservoir for controlled release of active substance. Although an emulsion globule of approximately 20\u0026ndash;500 nm is identified as a nanoemulsion (Mushtaq et al., \u003cspan citationid=\"CR17\" class=\"CitationRef\"\u003e2023\u003c/span\u003e), 100\u0026ndash;500 nm (Klang et al., \u003cspan citationid=\"CR11\" class=\"CitationRef\"\u003e2012\u003c/span\u003e) or 10\u0026ndash;1000 nm (McClements \u0026amp; Jafari, \u003cspan citationid=\"CR15\" class=\"CitationRef\"\u003e2018\u003c/span\u003e), however, a droplet size less than 200 nm is assigned the characteristic of high stability and transparency (Chiari-Andr\u0026eacute;o et al., \u003cspan citationid=\"CR6\" class=\"CitationRef\"\u003e2019\u003c/span\u003e). Furthermore, particles of a size range less than 100 nm have been reported to stay in the blood vessel as they can escape being engulfed by macrophages (Haroon et al., \u003cspan citationid=\"CR10\" class=\"CitationRef\"\u003e2022\u003c/span\u003e). In order to ascertain the homogeneity of the nanoemulsion system, the PDI value of formulations was evaluated, with the lowest and highest PDI recorded to be 0.0717\u0026thinsp;\u0026plusmn;\u0026thinsp;0.027 and 0.389\u0026thinsp;\u0026plusmn;\u0026thinsp;0.12, respectively, with F6 having a PDI of 0.106\u0026thinsp;\u0026plusmn;\u0026thinsp;0.023.\u003c/p\u003e \u003cp\u003eAccording to Klang and Valenta (2011), the particle size distribution provides information on the homogeneity of the formulation, through the width of the distribution. The small PDI where PDI is less than 0.2 implies a small droplet size distribution hence better stability to destabilisation processes like Ostwald ripening.. (Klang et al., \u003cspan citationid=\"CR11\" class=\"CitationRef\"\u003e2012\u003c/span\u003e).\u003c/p\u003e \u003cp\u003eFigure \u003cspan refid=\"Fig7\" class=\"InternalRef\"\u003e6\u003c/span\u003e visualizes the structural orientation of dispersed particles of DGNe to be almost spherical, with an oil droplet having a mean particle size of 92.3 nm. From our results, a slight difference was observed when compared with DLS analysis. According to Champion \u003cem\u003eet al\u003c/em\u003e (2014), a particle having a spherical shape can travel from the site of application to its site of action without restriction, while one with an uneven shape tends to fall or get entangled in filtering organs (Ledford et al., \u003cspan citationid=\"CR12\" class=\"CitationRef\"\u003e2023\u003c/span\u003e). A further microscopic FESEM visualization and confirmation of the presence of DG in DGNeG (A) and the surface orientation of the nanogel system (B) is represented in Fig.\u0026nbsp;\u003cspan refid=\"Fig7\" class=\"InternalRef\"\u003e6\u003c/span\u003e. From the microscopic visualisation, DG in DGNeG was found to be present and largely dispersed across the gel matrix.\u003c/p\u003e \u003cp\u003e \u003cdiv class=\"gridtable\"\u003e\u003ctable float=\"Yes\" id=\"Tab5\" border=\"1\"\u003e \u003ccaption language=\"En\"\u003e \u003cdiv class=\"CaptionNumber\"\u003eTable 5\u003c/div\u003e \u003cdiv class=\"CaptionContent\"\u003e \u003cp\u003eTable showing the particle size distribution (nm) and polydispersity index of 13 DGNe formulations using the differential light scanning calorimetry technique (DLS)\u003c/p\u003e \u003c/div\u003e \u003c/caption\u003e \u003ccolgroup cols=\"3\"\u003e \u003cdiv align=\"left\" class=\"colspec\" colname=\"c1\" colnum=\"1\"\u003e\u003c/div\u003e \u003cdiv align=\"left\" class=\"colspec\" colname=\"c2\" colnum=\"2\"\u003e\u003c/div\u003e \u003cdiv align=\"left\" class=\"colspec\" colname=\"c3\" colnum=\"3\"\u003e\u003c/div\u003e \u003cthead\u003e \u003ctr\u003e \u003cth align=\"left\" colname=\"c1\"\u003e \u003cp\u003eFormulation\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c2\"\u003e \u003cp\u003eParticle Size (nm) n\u0026thinsp;=\u0026thinsp;3\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c3\"\u003e \u003cp\u003ePolydispersity Index PDI n\u0026thinsp;=\u0026thinsp;3\u003c/p\u003e \u003c/th\u003e \u003c/tr\u003e \u003c/thead\u003e \u003ctbody\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eF1\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e245.50\u0026thinsp;\u0026plusmn;\u0026thinsp;17.46\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e0.219\u0026thinsp;\u0026plusmn;\u0026thinsp;0.05\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eF2\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e236.17\u0026thinsp;\u0026plusmn;\u0026thinsp;8.71\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e0.389\u0026thinsp;\u0026plusmn;\u0026thinsp;0.12\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eF3\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e213.80\u0026thinsp;\u0026plusmn;\u0026thinsp;1.33\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e0.180\u0026thinsp;\u0026plusmn;\u0026thinsp;0.03\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eF4\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e237.37\u0026thinsp;\u0026plusmn;\u0026thinsp;13.29\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e0.107\u0026thinsp;\u0026plusmn;\u0026thinsp;0.06\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eF5\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e299.36\u0026thinsp;\u0026plusmn;\u0026thinsp;9.27\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e0.161\u0026thinsp;\u0026plusmn;\u0026thinsp;0.029\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eF6\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e82.30\u0026thinsp;\u0026plusmn;\u0026thinsp;6.65\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e0.086\u0026thinsp;\u0026plusmn;\u0026thinsp;0.005\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eF7\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e171.23\u0026thinsp;\u0026plusmn;\u0026thinsp;13.52\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e0.072\u0026thinsp;\u0026plusmn;\u0026thinsp;0.027\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eF8\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e239.10\u0026thinsp;\u0026plusmn;\u0026thinsp;37.12\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e0.224\u0026thinsp;\u0026plusmn;\u0026thinsp;0.034\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eF9\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e154.43\u0026thinsp;\u0026plusmn;\u0026thinsp;12.32\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e0.147\u0026thinsp;\u0026plusmn;\u0026thinsp;0.028\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eF10\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e212.17\u0026thinsp;\u0026plusmn;\u0026thinsp;4.51\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e0.170\u0026thinsp;\u0026plusmn;\u0026thinsp;0.066\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eF11\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e276.83\u0026thinsp;\u0026plusmn;\u0026thinsp;10.89\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e0.303\u0026thinsp;\u0026plusmn;\u0026thinsp;0.063\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eF12\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e109.82\u0026thinsp;\u0026plusmn;\u0026thinsp;5.82\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e0.133\u0026thinsp;\u0026plusmn;\u0026thinsp;0.023\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eF13\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e216.53\u0026thinsp;\u0026plusmn;\u0026thinsp;9.50\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e0.078\u0026thinsp;\u0026plusmn;\u0026thinsp;0.016\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003c/tbody\u003e \u003c/colgroup\u003e \u003c/table\u003e\u003c/div\u003e \u003c/p\u003e \u003cp\u003eValues are recorded as mean\u0026thinsp;\u0026plusmn;\u0026thinsp;SEM\u003c/p\u003e \u003cp\u003e \u003c/p\u003e \u003cp\u003eFigure 7: Visualization of the nanoemulsion structure and orientation of (A) DGNe in its nanogel form at 15,000X magnification and (B) Nanogel surface using electron microscopic technique (FESEM) at 10,000X ma\u003c/p\u003e \u003c/div\u003e \u003cdiv id=\"Sec31\" class=\"Section2\"\u003e \u003ch2\u003e4.9. Stability Studies\u003c/h2\u003e \u003cdiv id=\"Sec32\" class=\"Section3\"\u003e \u003ch2\u003e4.9.1 FT-IR\u003c/h2\u003e \u003cp\u003eFigure \u003cspan refid=\"Fig8\" class=\"InternalRef\"\u003e8\u003c/span\u003e illustrates the FT-IR spectra of bulk diosgenin. Figure\u0026nbsp;\u003cspan refid=\"Fig9\" class=\"InternalRef\"\u003e9\u003c/span\u003e (a and b) and Fig.\u0026nbsp;\u003cspan refid=\"Fig10\" class=\"InternalRef\"\u003e10\u003c/span\u003e (a and b) indicates the FT-IR stability spectra of DGNe and DGNeG at day 0 and Day 90 respectively. From the spectra in Fig.\u0026nbsp;\u003cspan refid=\"Fig8\" class=\"InternalRef\"\u003e8\u003c/span\u003e, absorption peaks were recorded at 3454 cm\u003csup\u003e\u0026minus;\u0026thinsp;1\u003c/sup\u003e, 2900 cm\u003csup\u003e\u0026minus;\u0026thinsp;1\u003c/sup\u003e, 1644.0 cm\u003csup\u003e\u0026minus;\u0026thinsp;1\u003c/sup\u003e, 1357.0 cm\u003csup\u003e\u0026minus;\u0026thinsp;1\u003c/sup\u003e, 1440 cm\u003csup\u003e\u0026minus;\u0026thinsp;1\u003c/sup\u003e, 1382.9 cm\u003csup\u003e\u0026minus;\u0026thinsp;1\u003c/sup\u003e, 1057. 0 cm\u003csup\u003e\u0026minus;\u0026thinsp;1\u003c/sup\u003e and 900 cm\u003csup\u003e\u0026minus;\u0026thinsp;1\u003c/sup\u003e. Figure\u0026nbsp;4.14 showed absorption peaks at 3410 cm\u003csup\u003e\u0026minus;\u0026thinsp;1\u003c/sup\u003e, 1635 cm\u003csup\u003e\u0026minus;\u0026thinsp;1\u003c/sup\u003e, 1440 cm\u003csup\u003e\u0026minus;\u0026thinsp;1\u003c/sup\u003e, 1060 cm\u003csup\u003e\u0026minus;\u0026thinsp;1\u003c/sup\u003e, and 600 cm\u003csup\u003e\u0026minus;\u0026thinsp;1\u003c/sup\u003e while Fig.\u0026nbsp;4.15 illustatres peak of absorption at 3450 cm\u003csup\u003e\u0026minus;\u0026thinsp;1\u003c/sup\u003e, 2100 cm\u003csup\u003e\u0026minus;\u0026thinsp;1\u003c/sup\u003e, 1640 cm\u003csup\u003e\u0026minus;\u0026thinsp;1\u003c/sup\u003e, 1450 cm\u003csup\u003e\u0026minus;\u0026thinsp;1\u003c/sup\u003e, 1100 cm\u003csup\u003e\u0026minus;\u0026thinsp;1\u003c/sup\u003e and 700 cm\u003csup\u003e\u0026minus;\u0026thinsp;1\u003c/sup\u003e.\u003c/p\u003e \u003cp\u003eFTIR is employed in this study to further access the functional characteristics of diosgenin including the functional stability of the developed nanoemulsion/nanogel. Also, the possible interaction between the drug (DG) and the emulsion molecules is revealed from the spectra. Figure\u0026nbsp;\u003cspan refid=\"Fig8\" class=\"InternalRef\"\u003e8\u003c/span\u003e represents the FT-IR spectra of raw DG, while Fig.\u0026nbsp;\u003cspan refid=\"Fig9\" class=\"InternalRef\"\u003e9\u003c/span\u003e (a and b) and 10 (c and d) shows DG in nanoemulsion (DGNe) and DG in nanogel (DGNeG) at day 0 and after 90 days of storage at 4\u0026ordm;C.\u003c/p\u003e \u003cp\u003eFrom the spectra in Fig.\u0026nbsp;\u003cspan refid=\"Fig8\" class=\"InternalRef\"\u003e8\u003c/span\u003e, DG, with molecular formula C\u003csub\u003e27\u003c/sub\u003eH\u003csub\u003e42\u003c/sub\u003eO\u003csub\u003e3\u003c/sub\u003e showed a broad stretching vibration of O-H peak (ν O\u0026ndash;H; 3454 cm\u003csup\u003e\u0026minus;\u0026thinsp;1\u003c/sup\u003e), a symmetric and assymetric absorption due to C-H stretching in \u0026ndash;CH\u003csub\u003e3\u003c/sub\u003e, \u0026ndash;CH\u003csub\u003e2\u003c/sub\u003e including \u0026ndash;CH stretching vibration (ν\u003csup\u003eas\u003c/sup\u003e and ν\u003csup\u003es\u003c/sup\u003e CH\u003csub\u003e3\u003c/sub\u003e, CH\u003csub\u003e2\u003c/sub\u003e; 3000\u0026thinsp;\u0026minus;\u0026thinsp;2800 cm\u003csup\u003e\u0026minus;\u0026thinsp;1\u003c/sup\u003e), -C\u0026thinsp;=\u0026thinsp;C (ν C\u0026thinsp;=\u0026thinsp;C; 1644.0 cm\u003csup\u003e\u0026minus;\u0026thinsp;1\u003c/sup\u003e); a \u0026ndash;CH\u003csub\u003e3\u003c/sub\u003e and \u0026ndash;CH\u003csub\u003e2\u003c/sub\u003e deformation due to carbon \u0026ndash; hydrogen bending (δ CH\u003csub\u003e3\u003c/sub\u003e,and δ CH\u003csub\u003e2\u003c/sub\u003e; 1357.0 and 1440 cm\u003csup\u003e\u0026minus;\u0026thinsp;1\u003c/sup\u003e 1382.9 cm\u003csup\u003e\u0026minus;\u0026thinsp;1\u003c/sup\u003e). Due to the ether group in the structural feature of DG, a C\u0026ndash;O\u0026ndash;C asymmetrical stretching vibration (ν C\u0026ndash;O\u0026ndash;C; 1051.0 cm\u003csup\u003e\u0026minus;\u0026thinsp;1\u003c/sup\u003e and 1042.0 cm\u003csup\u003e\u0026minus;\u0026thinsp;1\u003c/sup\u003e) was recorded. A strong\u0026thinsp;=\u0026thinsp;C\u0026ndash;H out-of-plane bending vibration (γ\u0026thinsp;=\u0026thinsp;C\u0026ndash;H; 900.0 cm\u003csup\u003e\u0026minus;\u0026thinsp;1\u003c/sup\u003e) was also revealed. The IR spectra of DGNe at Fig.\u0026nbsp;\u003cspan refid=\"Fig9\" class=\"InternalRef\"\u003e9\u003c/span\u003eA implicated the same peaks as observed with DG. After 90 days of DGNe storage (Fig.\u0026nbsp;\u003cspan refid=\"Fig9\" class=\"InternalRef\"\u003e9\u003c/span\u003eB), almost same peaks with day 0 of DGNe was observed. Further investigation of DGNeG at day 0 and day 90 of storage revealed absorption at the same frequency as the former. This indicates that there exist little or no chemical interaction between DG and the emulsifying/gelling agents present in DGNe and DGNeG.\u003c/p\u003e \u003cp\u003e \u003c/p\u003e \u003cp\u003e \u003c/p\u003e \u003cp\u003e \u003c/p\u003e \u003c/div\u003e \u003cdiv id=\"Sec33\" class=\"Section3\"\u003e \u003ch2\u003e4.9.2 Physical and Thermodynamic Stability Studies\u003c/h2\u003e \u003cp\u003eTable\u0026nbsp;\u003cspan refid=\"Tab6\" class=\"InternalRef\"\u003e6\u003c/span\u003e shows the accelerated thermodynamic and physical stability studies the preformulated emulsions were exposed to. This includes heating-cooling, centrifugation, freeze-thaw and dispersibility studies. From the table, the stability profile of each formulation that are categorized into three different subgroups of varying S\u003csub\u003emix\u003c/sub\u003e ratio (SOA 1:1, SOB 2:1, and SOC 1:2) having 9 preformulations each (27 total number of preformulations) are presented therein.\u003c/p\u003e \u003cp\u003eThe stability status of all developed preformulations were determined by subjecting it to thermodynamic, mechanical stress and dispersibility studies. Results were ascertained visually for any form of turbidity, aggregation or phase separation. Based on our visual observation, SOA at a S\u003csub\u003emix\u003c/sub\u003e (tween 80 and glycerol) ratio of 1:1 was selected as the S\u003csub\u003emix\u003c/sub\u003e formula for the further development of the 13 formulations suggested by Box Behnken.\u003c/p\u003e \u003cp\u003e \u003cdiv class=\"gridtable\"\u003e\u003ctable float=\"Yes\" id=\"Tab6\" border=\"1\"\u003e \u003ccaption language=\"En\"\u003e \u003cdiv class=\"CaptionNumber\"\u003eTable 6\u003c/div\u003e \u003cdiv class=\"CaptionContent\"\u003e \u003cp\u003eThermodynamic and physical stability studies on preformulations at different oil\u003c/p\u003e \u003c/div\u003e \u003c/caption\u003e \u003ccolgroup cols=\"13\"\u003e \u003cdiv align=\"left\" class=\"colspec\" colname=\"c1\" colnum=\"1\"\u003e\u003c/div\u003e \u003cdiv align=\"left\" class=\"colspec\" colname=\"c2\" colnum=\"2\"\u003e\u003c/div\u003e \u003cdiv align=\"left\" class=\"colspec\" colname=\"c3\" colnum=\"3\"\u003e\u003c/div\u003e \u003cdiv align=\"left\" class=\"colspec\" colname=\"c4\" colnum=\"4\"\u003e\u003c/div\u003e \u003cdiv align=\"left\" class=\"colspec\" colname=\"c5\" colnum=\"5\"\u003e\u003c/div\u003e \u003cdiv align=\"left\" class=\"colspec\" colname=\"c6\" colnum=\"6\"\u003e\u003c/div\u003e \u003cdiv align=\"left\" class=\"colspec\" colname=\"c7\" colnum=\"7\"\u003e\u003c/div\u003e \u003cdiv align=\"left\" class=\"colspec\" colname=\"c8\" colnum=\"8\"\u003e\u003c/div\u003e \u003cdiv align=\"left\" class=\"colspec\" colname=\"c9\" colnum=\"9\"\u003e\u003c/div\u003e \u003cdiv align=\"left\" class=\"colspec\" colname=\"c10\" colnum=\"10\"\u003e\u003c/div\u003e \u003cdiv align=\"left\" class=\"colspec\" colname=\"c11\" colnum=\"11\"\u003e\u003c/div\u003e \u003cdiv align=\"left\" class=\"colspec\" colname=\"c12\" colnum=\"12\"\u003e\u003c/div\u003e \u003cdiv align=\"left\" class=\"colspec\" colname=\"c13\" colnum=\"13\"\u003e\u003c/div\u003e \u003cthead\u003e \u003ctr\u003e \u003cth align=\"left\" colname=\"c1\"\u003e \u003cp\u003eFormulation\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colspan=\"3\" nameend=\"c4\" namest=\"c2\"\u003e \u003cp\u003eHeating-cooling\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colspan=\"3\" nameend=\"c7\" namest=\"c5\"\u003e \u003cp\u003eCentrifugation\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colspan=\"3\" nameend=\"c10\" namest=\"c8\"\u003e \u003cp\u003eFreeze-thaw\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colspan=\"3\" nameend=\"c13\" namest=\"c11\"\u003e \u003cp\u003eDispersibility\u003c/p\u003e \u003c/th\u003e \u003c/tr\u003e \u003c/thead\u003e \u003ctbody\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003eSOA 1:1\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003eSOB (2:1)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003eSOC (1:2)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003eSOA 1:1\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e \u003cp\u003eSOB (2:1)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c7\"\u003e \u003cp\u003eSOC (1:2)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c8\"\u003e \u003cp\u003eSOA 1:1\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c9\"\u003e \u003cp\u003eSOB (2:1)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c10\"\u003e \u003cp\u003eSOC (1:2)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c11\"\u003e \u003cp\u003eSOA 1:1\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c12\"\u003e \u003cp\u003eSOB (2:1)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c13\"\u003e \u003cp\u003eSOC (1:2)\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003e1 (9:1)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e+\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e+\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e+\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e+\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e \u003cp\u003e-\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c7\"\u003e \u003cp\u003e-\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c8\"\u003e \u003cp\u003e+\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c9\"\u003e \u003cp\u003e-\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c10\"\u003e \u003cp\u003e-\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c11\"\u003e \u003cp\u003e+\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c12\"\u003e \u003cp\u003e+\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c13\"\u003e \u003cp\u003e-\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003e2(8:2)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e+\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e+\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e+\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e-\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e \u003cp\u003e-\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c7\"\u003e \u003cp\u003e-\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c8\"\u003e \u003cp\u003e+\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c9\"\u003e \u003cp\u003e+\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c10\"\u003e \u003cp\u003e-\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c11\"\u003e \u003cp\u003e+\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c12\"\u003e \u003cp\u003e+\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c13\"\u003e \u003cp\u003e-\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003e3(7:3)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e+\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e+\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e+\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e+\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e \u003cp\u003e+\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c7\"\u003e \u003cp\u003e-\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c8\"\u003e \u003cp\u003e+\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c9\"\u003e \u003cp\u003e-\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c10\"\u003e \u003cp\u003e+\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c11\"\u003e \u003cp\u003e+\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c12\"\u003e \u003cp\u003e+\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c13\"\u003e \u003cp\u003e-\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003e4(6:4)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e-\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e+\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e-\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e+\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e \u003cp\u003e-\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c7\"\u003e \u003cp\u003e+\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c8\"\u003e \u003cp\u003e+\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c9\"\u003e \u003cp\u003e+\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c10\"\u003e \u003cp\u003e+\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c11\"\u003e \u003cp\u003e+\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c12\"\u003e \u003cp\u003e-\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c13\"\u003e \u003cp\u003e+\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003e5(5:5)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e+\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e+\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e+\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e+\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e \u003cp\u003e-\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c7\"\u003e \u003cp\u003e+\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c8\"\u003e \u003cp\u003e+\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c9\"\u003e \u003cp\u003e+\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c10\"\u003e \u003cp\u003e-\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c11\"\u003e \u003cp\u003e+\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c12\"\u003e \u003cp\u003e-\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c13\"\u003e \u003cp\u003e+\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003e6(4:6)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e+\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e+\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e+\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e+\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e \u003cp\u003e+\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c7\"\u003e \u003cp\u003e-\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c8\"\u003e \u003cp\u003e+\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c9\"\u003e \u003cp\u003e+\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c10\"\u003e \u003cp\u003e+\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c11\"\u003e \u003cp\u003e+\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c12\"\u003e \u003cp\u003e-\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c13\"\u003e \u003cp\u003e+\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003e7(3:7)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e+\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e+\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e-\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e+\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e \u003cp\u003e+\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c7\"\u003e \u003cp\u003e-\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c8\"\u003e \u003cp\u003e+\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c9\"\u003e \u003cp\u003e+\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c10\"\u003e \u003cp\u003e+\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c11\"\u003e \u003cp\u003e+\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c12\"\u003e \u003cp\u003e+\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c13\"\u003e \u003cp\u003e+\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003e8(2:8)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e+\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e-\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e+\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e+\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e \u003cp\u003e+\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c7\"\u003e \u003cp\u003e-\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c8\"\u003e \u003cp\u003e+\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c9\"\u003e \u003cp\u003e+\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c10\"\u003e \u003cp\u003e+\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c11\"\u003e \u003cp\u003e+\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c12\"\u003e \u003cp\u003e+\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c13\"\u003e \u003cp\u003e+\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003e9(1:9)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e+\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e-\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e-\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e+\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e \u003cp\u003e+\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c7\"\u003e \u003cp\u003e-\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c8\"\u003e \u003cp\u003e+\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c9\"\u003e \u003cp\u003e-\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c10\"\u003e \u003cp\u003e+\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c11\"\u003e \u003cp\u003e+\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c12\"\u003e \u003cp\u003e+\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c13\"\u003e \u003cp\u003e+\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003c/tbody\u003e \u003c/colgroup\u003e \u003ctfoot\u003e \u003ctr\u003e\u003ctd colspan=\"13\"\u003eOil mix, where A, B, C indicates surfactant mix (S\u003csub\u003emix\u003c/sub\u003e) at 1:1, 2:1 and 1:2 tween 80 and glycerol respectively. + (passed); - (failed)\u003c/td\u003e\u003c/tr\u003e \u003c/tfoot\u003e \u003c/table\u003e\u003c/div\u003e \u003c/p\u003e \u003c/div\u003e \u003cdiv id=\"Sec34\" class=\"Section3\"\u003e \u003ch2\u003e4.9.3. Homogeneity and Dispersibility studies\u003c/h2\u003e \u003cp\u003eFurther stability measurement of the studies was finally performed on the 13 formulations using DLS technique in order to monitor the level of homogeneity including possibility of wide or slim changes in the PDI of the formulation after 90 days of storage.\u003c/p\u003e \u003cp\u003eThe various particle size and PDI obtained at day 0 and day 90 are presented in Table\u0026nbsp;\u003cspan refid=\"Tab7\" class=\"InternalRef\"\u003e7\u003c/span\u003e. From the table, the particle size of all 13 formulations at day 0 were almost replicated at day 90 as only a slight difference was observed. Characteristics such as pH, color and clarity was also maintained after the storage duration was completed. Based on the achieved results, F6 maintained the least particle size even after 90 days of storage with an improved homogeneity when compared to other formulations.\u003c/p\u003e \u003cp\u003e \u003cdiv class=\"gridtable\"\u003e\u003ctable float=\"Yes\" id=\"Tab7\" border=\"1\"\u003e \u003ccaption language=\"En\"\u003e \u003cdiv class=\"CaptionNumber\"\u003eTable 7\u003c/div\u003e \u003cdiv class=\"CaptionContent\"\u003e \u003cp\u003eHomogeneity and dispersibility studies at day 0 and day 90\u003c/p\u003e \u003c/div\u003e \u003c/caption\u003e \u003ccolgroup cols=\"5\"\u003e \u003cdiv align=\"left\" class=\"colspec\" colname=\"c1\" colnum=\"1\"\u003e\u003c/div\u003e \u003cdiv align=\"left\" class=\"colspec\" colname=\"c2\" colnum=\"2\"\u003e\u003c/div\u003e \u003cdiv align=\"left\" class=\"colspec\" colname=\"c3\" colnum=\"3\"\u003e\u003c/div\u003e \u003cdiv align=\"left\" class=\"colspec\" colname=\"c4\" colnum=\"4\"\u003e\u003c/div\u003e \u003cdiv align=\"left\" class=\"colspec\" colname=\"c5\" colnum=\"5\"\u003e\u003c/div\u003e \u003cthead\u003e \u003ctr\u003e \u003cth align=\"left\" colname=\"c1\"\u003e \u003cp\u003eFormulation\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colspan=\"2\" nameend=\"c3\" namest=\"c2\"\u003e \u003cp\u003eParticle size (nm) n\u0026thinsp;=\u0026thinsp;3\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colspan=\"2\" nameend=\"c5\" namest=\"c4\"\u003e \u003cp\u003ePDI (n\u0026thinsp;=\u0026thinsp;3)\u003c/p\u003e \u003c/th\u003e \u003c/tr\u003e \u003c/thead\u003e \u003ctbody\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003eDay 0\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003eDay 90\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003eDay 0\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003eDay 90\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eF1\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e245.50\u0026thinsp;\u0026plusmn;\u0026thinsp;17.46\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e222.63\u0026thinsp;\u0026plusmn;\u0026thinsp;5.44\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e0.219\u0026thinsp;\u0026plusmn;\u0026thinsp;0.05\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e0.236\u0026thinsp;\u0026plusmn;\u0026thinsp;0.197\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eF2\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e236.17 \u0026plusmn; 8.71\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e207.87\u0026thinsp;\u0026plusmn;\u0026thinsp;1.74\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e0.389\u0026thinsp;\u0026plusmn;\u0026thinsp;0.12\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e0.237\u0026thinsp;\u0026plusmn;\u0026thinsp;0.008\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eF3\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e213.80\u0026thinsp;\u0026plusmn;\u0026thinsp;1.33\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e216.00\u0026thinsp;\u0026plusmn;\u0026thinsp;2.80\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e0.180\u0026thinsp;\u0026plusmn;\u0026thinsp;0.03\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e0.158\u0026thinsp;\u0026plusmn;\u0026thinsp;0.025\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eF4\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e237.37\u0026thinsp;\u0026plusmn;\u0026thinsp;13.29\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e226.77\u0026thinsp;\u0026plusmn;\u0026thinsp;3.79\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e0.107\u0026thinsp;\u0026plusmn;\u0026thinsp;0.06\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e0.617\u0026thinsp;\u0026plusmn;\u0026thinsp;0.005\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eF5\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e299.36\u0026thinsp;\u0026plusmn;\u0026thinsp;9.27\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e248.63\u0026thinsp;\u0026plusmn;\u0026thinsp;3.99\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e0.161\u0026thinsp;\u0026plusmn;\u0026thinsp;0.029\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e0.231\u0026thinsp;\u0026plusmn;\u0026thinsp;0.019\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eF6\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e82.30\u0026thinsp;\u0026plusmn;\u0026thinsp;6.65\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e97.57\u0026thinsp;\u0026plusmn;\u0026thinsp;4.76\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e0.086\u0026thinsp;\u0026plusmn;\u0026thinsp;0.005\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e0.085\u0026thinsp;\u0026plusmn;\u0026thinsp;0.009\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eF7\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e171.23\u0026thinsp;\u0026plusmn;\u0026thinsp;13.52\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e276.50\u0026thinsp;\u0026plusmn;\u0026thinsp;6.67\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e0.072\u0026thinsp;\u0026plusmn;\u0026thinsp;0.027\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e0.326\u0026thinsp;\u0026plusmn;\u0026thinsp;0.022\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eF8\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e239.10\u0026thinsp;\u0026plusmn;\u0026thinsp;37.12\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e231.37\u0026thinsp;\u0026plusmn;\u0026thinsp;8.25\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e0.224\u0026thinsp;\u0026plusmn;\u0026thinsp;0.034\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e0.302\u0026thinsp;\u0026plusmn;\u0026thinsp;0.019\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eF9\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e154.43\u0026thinsp;\u0026plusmn;\u0026thinsp;12.32\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e155.79\u0026thinsp;\u0026plusmn;\u0026thinsp;13.9\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e0.147\u0026thinsp;\u0026plusmn;\u0026thinsp;0.028\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e0.183\u0026thinsp;\u0026plusmn;\u0026thinsp;0.010\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eF10\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e212.17 \u0026plusmn; 4.51\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e236.50\u0026thinsp;\u0026plusmn;\u0026thinsp;8.52\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e0.170\u0026thinsp;\u0026plusmn;\u0026thinsp;0.066\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e0.237\u0026thinsp;\u0026plusmn;\u0026thinsp;0.020\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eF11\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e276.83\u0026thinsp;\u0026plusmn;\u0026thinsp;10.89\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e271.67\u0026thinsp;\u0026plusmn;\u0026thinsp;14.90\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e0.303\u0026thinsp;\u0026plusmn;\u0026thinsp;0.063\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e0.243\u0026thinsp;\u0026plusmn;\u0026thinsp;0.019\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eF12\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e109.82\u0026thinsp;\u0026plusmn;\u0026thinsp;5.82\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e212.18\u0026thinsp;\u0026plusmn;\u0026thinsp;51.44\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e0.133\u0026thinsp;\u0026plusmn;\u0026thinsp;0.023\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e0.225\u0026thinsp;\u0026plusmn;\u0026thinsp;0.011\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eF13\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e216.53\u0026thinsp;\u0026plusmn;\u0026thinsp;9.50\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e216.49\u0026thinsp;\u0026plusmn;\u0026thinsp;0.17\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e0.078\u0026thinsp;\u0026plusmn;\u0026thinsp;0.016\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e0.172\u0026thinsp;\u0026plusmn;\u0026thinsp;0.015\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003c/tbody\u003e \u003c/colgroup\u003e \u003c/table\u003e\u003c/div\u003e \u003c/p\u003e \u003c/div\u003e \u003c/div\u003e \u003cdiv id=\"Sec35\" class=\"Section2\"\u003e \u003ch2\u003e4.10 Physicochemical Properties of the Final Formulation\u003c/h2\u003e \u003cp\u003eThe physicochemical properties of the final nanoemulsion formulation is presented in Table\u0026nbsp;\u003cspan refid=\"Tab8\" class=\"InternalRef\"\u003e8\u003c/span\u003e. From the table, a droplet size of 82.30 nm with a PDI of 0.086\u0026thinsp;\u0026plusmn;\u0026thinsp;0.005 is recorded. Also, the table presented the pH of the formulation to be 7.2 having refractive index of 12.70\u0026thinsp;\u0026plusmn;\u0026thinsp;0.34 and a viscosity of 11.80\u0026thinsp;\u0026plusmn;\u0026thinsp;1.51 cps.\u003c/p\u003e \u003cp\u003e \u003cdiv class=\"gridtable\"\u003e\u003ctable float=\"Yes\" id=\"Tab8\" border=\"1\"\u003e \u003ccaption language=\"En\"\u003e \u003cdiv class=\"CaptionNumber\"\u003eTable 8\u003c/div\u003e \u003cdiv class=\"CaptionContent\"\u003e \u003cp\u003ePhysicochemical properties of the selected formulation (n\u0026thinsp;=\u0026thinsp;3)\u003c/p\u003e \u003c/div\u003e \u003c/caption\u003e \u003ccolgroup cols=\"6\"\u003e \u003cdiv align=\"left\" class=\"colspec\" colname=\"c1\" colnum=\"1\"\u003e\u003c/div\u003e \u003cdiv align=\"left\" class=\"colspec\" colname=\"c2\" colnum=\"2\"\u003e\u003c/div\u003e \u003cdiv align=\"left\" class=\"colspec\" colname=\"c3\" colnum=\"3\"\u003e\u003c/div\u003e \u003cdiv align=\"left\" class=\"colspec\" colname=\"c4\" colnum=\"4\"\u003e\u003c/div\u003e \u003cdiv align=\"left\" class=\"colspec\" colname=\"c5\" colnum=\"5\"\u003e\u003c/div\u003e \u003cdiv align=\"left\" class=\"colspec\" colname=\"c6\" colnum=\"6\"\u003e\u003c/div\u003e \u003cthead\u003e \u003ctr\u003e \u003cth align=\"left\" colname=\"c1\"\u003e \u003cp\u003eFormulation\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c2\"\u003e \u003cp\u003eDroplet size\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c3\"\u003e \u003cp\u003ePolydispersity\u003c/p\u003e \u003cp\u003eindex (PDI)\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c4\"\u003e \u003cp\u003epH\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c5\"\u003e \u003cp\u003eRefractive index\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c6\"\u003e \u003cp\u003eViscosity (cps)\u003c/p\u003e \u003c/th\u003e \u003c/tr\u003e \u003c/thead\u003e \u003ctbody\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eF6\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e82.30\u0026thinsp;\u0026plusmn;\u0026thinsp;6.65\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e0.086\u0026thinsp;\u0026plusmn;\u0026thinsp;0.005\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e7.2\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e12.70\u0026thinsp;\u0026plusmn;\u0026thinsp;0.34\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e \u003cp\u003e11.80\u0026thinsp;\u0026plusmn;\u0026thinsp;1.51\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003c/tbody\u003e \u003c/colgroup\u003e \u003c/table\u003e\u003c/div\u003e \u003c/p\u003e \u003c/div\u003e"},{"header":"Conclusion","content":"\u003cp\u003eConventional drug delivery through oral and parenteral route poses huge challenges that necessitates the lookout for alternative route of drug administration. In this study, the therapeutic transdermal potential of diosgenin was enhanced with an emulsified nanoformulation. This study revealed nanoemulsion as a versatile carrier that has the unique ability of resolving the solubility and bioavailability of a poorly soluble diosgenin, thereby, promoting the effective penetrability required for transdermal delivery of diosgenin. In this study, a low energy phase inversion composition method was employed as the simple, low-cost and effective emulsification technique in preparing an optimized nanoemulsion that comprises a 60% and 25% oil and surfactant mix respectively. A stable DGNe emulsion without phase disparity was obtained. The pseudoternary phase diagram constructed for the three major preformulations showed the largest area of nanoemulsion region when tween 80 and glycerol was used at a ratio of 1:1. The result of the microscopic structural morphology of the formulated nanoemulsion and nanoemulsified gel using SEM and FESEM confirmed a stable, unaggregated, smooth-surfaced and spherical-shaped particles with large dispersion in the emulsion and gel matrix. The particle size analysis using DLS has shown a size distribution between 81.8 nm and 99.3 nm with a PDI of 0.106\u0026thinsp;\u0026plusmn;\u0026thinsp;0.023. This width of the particle size distribution has confirmed DGNe to be homogenous, and thus better stability against destabilisation phenomena, while the shape and narrow droplet size indicates an unrestricted capability of diosgenin to penetrate the skin and travel from the site of application to site of action for effective therapeutic action. `\u003cem\u003eIn vitro\u003c/em\u003e release profile of the formulated nanoemulsion containing phytotherapeutic diosgenin showed a 92.95% maximum release of DG from DGNe after 36 h. This indicates the proper release of hydrophobic and insoluble DG from the formulated nanoemulsion. The derived DGNe formulation exhibits potentially good physicochemical characteristics that could be used in transdermal delivery techniques.\u003c/p\u003e"},{"header":"Declarations","content":"\u003cp\u003e\u003cstrong\u003eDeclaration of Competing Interest\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003eThe authors declare the following financial interests/personal relationships, which may be considered as potential competing interests: Akinsipo, Oyesolape Basirat reports that financial support was provided by The World Academy of Science in collaboration with the Department of Biotechnology, India (TWAS-DBT). Other authors declare that they have no known competing financial interests or personal relationships that could have appeared to influence the work reported in this paper.\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eAcknowledgment\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003eThe authors appreciate the Sandwich Doctoral award from the TWAS-DBT and supervisory support from Amity University, Noida, India, and Federal University of Agriculture, Abeokuta.\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eAvailability of data and materials\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003eThe data that support this study are available within this article and can also be requested from the authors.\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eEthics approval and consent to participate\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003e\u0026quot;Not applicable. Only \u003cem\u003ein vitro\u003c/em\u003e experiments were done in this study to develop and characterize nanoemulsion formulations. The study did not involve any live vertebrates, human participants or human or animal biological materials.\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eCompeting interests\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003eThe authors declare the following financial interests/personal relationships, which may be considered as potential competing interests: Akinsipo, Oyesolape Basirat reports that financial support was provided by The World Academy of Science in collaboration with the Department of Biotechnology, India (TWAS-DBT). Other authors declare that they have no known competing financial interests or personal relationships that could have appeared to influence the work reported in this paper.\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eConsent to Publish\u003c/strong\u003e: Not applicable\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eFunding\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003eFunding was provided by The World Academy of Science in collaboration with the Department of Biotechnology, India (TWAS-DBT). The funding body is not involved in the design of the study and collection, interpretation of data, and writing of the manuscript.\u0026nbsp;\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eAuthors\u0026apos; contributions\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eAkinsipo, O. B\u003c/strong\u003e: Conceptualization, Methodology, Visualization, Investigation, Formal analysis, Validation, Writing \u0026ndash; original draft, Writing \u0026ndash; review and editing, data curation. \u003cstrong\u003eDare, E. O\u003c/strong\u003e: Project administration; Supervision, Conceptualization, Review, Methodology; \u003cstrong\u003eKatare, D. P\u003c/strong\u003e: Resources, Supervision, review, methodology; \u003cstrong\u003eOladoyinbo, F.O\u003c/strong\u003e: Supervision, Validation, review, \u003cstrong\u003eSanni Lateef\u003c/strong\u003e: Supervision \u003cstrong\u003eAlayande, S. O.\u003c/strong\u003e Review and data curation; Msagati A.M. Titus: Supervision and Review,\u0026nbsp;\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eAcknowledgements\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003eThe authors appreciate the Sandwich Doctoral award from the TWAS-DBT and support from Amity University, Noida, India, Tai Solarin University of Education, Nigeria and Federal University of Agriculture, Abeokuta.\u0026nbsp;\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eAuthors\u0026apos; information\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003e1. Akinsipo, O. B.: Senior Lecturer, Department of Chemical Sciences, College of Science and Information technology, Tai Solarin University of Education, Ijagun, PMB 2118, Ijebu-Ode, Ogun State, Nigeria, Founder, MoreGreen Plus and Green Chemistry Champion, Beyond Benign\u003c/p\u003e\n\u003cp\u003e2. \u0026nbsp; Dare, E. O: Professor, Department of Chemistry, College of Physical Sciences, Federal University of Agriculture, P.M.B. 2240, Alabata Road, Abeokuta, Ogun State, Nigeria, 110111. \u0026nbsp;First Professor of Nanotechnology in Nigeria. \u0026nbsp;Recipient, Alexander von Humboldt (AvH) \u0026ndash; Georg Forster fellowship and a fellow of SCIAS, Wurzburg University, Germany\u003c/p\u003e\n\u003cp\u003e3. \u0026nbsp; Katare, D. P.: Professor, Deputy Director and Centre Head Administration, Research \u0026amp; Teaching Centre for Medical Biotechnology, Amity Institute of Biotechnology, Sector 125, Amity University Uttar Pradesh, Noida, 201303.\u0026nbsp;\u003c/p\u003e\n\u003cp\u003e4. \u0026nbsp; Oladoyinbo, F.O.: Associate Professor, Department of Chemistry, College of Physical Sciences, Federal University of Agriculture, P.M.B. 2240, Alabata Road, Abeokuta, Ogun State, Nigeria, 110111.\u0026nbsp;\u003c/p\u003e\n\u003cp\u003e5. Sanni, L. O: Professor, Department of Food Science and Technology, College of Food Science and Human Ecology, Federal University of Agriculture, P.M.B.\u0026nbsp;2240, Alabata Road, Abeokuta, Ogun State, Nigeria, 110111.\u0026nbsp;Executive Director of the Nigerian Stored Products Research Institute (NSPRI)\u003c/p\u003e\n\u003cp\u003e6. Alayande, S. O.: Associate Professor, Department of Industrial Chemistry, Abiola Ajimobi Technical University, Ibadan, Oyo, 200261, Nigeria. Founder, Green Nano LLC, USA\u003c/p\u003e\n\u003cp\u003e7. Msagati, T. A. M: Professor, Institute for Nanotechnology and Water Sustainability (iNanoWS), College of Science, Engineering, and Technology, University of South Africa, Florida Park 1710, South Africa\u003c/p\u003e"},{"header":"References","content":"\u003col\u003e\n\u003cli\u003eAhmad Dar, R., Shahnawaz, M., Ahmad Ahanger, M., \u0026amp; ul Majid, I. (2023). Exploring the Diverse Bioactive Compounds from Medicinal Plants: A Review. \u003cem\u003eThe Journal of Phytopharmacology\u003c/em\u003e, \u003cem\u003e12\u003c/em\u003e(3), 189\u0026ndash;195. https://doi.org/10.31254/phyto.2023.12307\u003c/li\u003e\n\u003cli\u003eAkhtar, N., Singh, V., Yusuf, M., \u0026amp; Khan, R. A. (2020). Non-invasive drug delivery technology: Development and current status of transdermal drug delivery devices, techniques and biomedical applications. \u003cem\u003eBiomedizinische Technik\u003c/em\u003e, \u003cem\u003e65\u003c/em\u003e(3), 243\u0026ndash;272. https://doi.org/10.1515/BMT-2019-0019/PDF\u003c/li\u003e\n\u003cli\u003eAlkilani, A. Z., McCrudden, M. T. C., \u0026amp; Donnelly, R. F. (2015). Transdermal drug delivery: Innovative pharmaceutical developments based on disruption of the barrier properties of the stratum corneum. In \u003cem\u003ePharmaceutics\u003c/em\u003e. https://doi.org/10.3390/pharmaceutics7040438\u003c/li\u003e\n\u003cli\u003eBaboota, S., Shakeel, F., Ahuja, A., Ali, J., \u0026amp; Shafiq, S. (2007). Design, development and evaluation of novel nanoemulsion formulations for transdermal potential of celecoxib. \u003cem\u003eActa Pharmaceutica\u003c/em\u003e. https://doi.org/10.2478/v10007-007-0025-5\u003c/li\u003e\n\u003cli\u003eCayen, M. N., Ferdinandi, E. S., Greselin, E., \u0026amp; Dvornik, D. (1979). Studies on the disposition of diosgenin in rats, dogs, monkeys and man. \u003cem\u003eAtherosclerosis\u003c/em\u003e. https://doi.org/10.1016/0021-9150(79)90199-0\u003c/li\u003e\n\u003cli\u003eChiari-Andr\u0026eacute;o, B. G., Almeida-Cincotto, M. G. J. de, Oshiro, J. A., Taniguchi, C. Y. Y., Chiavacci, L. A., \u0026amp; Isaac, V. L. B. (2019). Nanoparticles for cosmetic use and its application. \u003cem\u003eNanoparticles in Pharmacotherapy\u003c/em\u003e, 113\u0026ndash;146. https://doi.org/10.1016/B978-0-12-816504-1.00013-2\u003c/li\u003e\n\u003cli\u003ede Freitas Ara\u0026uacute;jo Reis, M. Y., de Ara\u0026uacute;jo R\u0026ecirc;go, R. I., Rocha, B. P., Guedes, G. G., de Medeiros Ramalho, \u0026Iacute;. M., de Medeiros Cavalcanti, A. L., Guimar\u0026atilde;es, G. P., \u0026amp; de Lima Damasceno, B. P. G. (2021). A General Approach on Surfactants Use and Properties in Drug Delivery Systems. \u003cem\u003eCurrent Pharmaceutical Design\u003c/em\u003e, \u003cem\u003e27\u003c/em\u003e(42), 4300\u0026ndash;4314. https://doi.org/10.2174/1381612827666210526091825/CITE/REFWORKS\u003c/li\u003e\n\u003cli\u003eFernandes, C. P., de Almeida, F. B., Silveira, A. N., Gonzalez, M. S., Mello, C. B., Feder, D., Apolin\u0026aacute;rio, R., Santos, M. G., Carvalho, J. C. T., Tietbohl, L. A. C., Rocha, L., \u0026amp; Falc\u0026atilde;o, D. Q. (2014). Development of an insecticidal nanoemulsion with Manilkara subsericea (Sapotaceae) extract. \u003cem\u003eJournal of Nanobiotechnology\u003c/em\u003e, \u003cem\u003e12\u003c/em\u003e(1), 1\u0026ndash;9. https://doi.org/10.1186/1477-3155-12-22/TABLES/2\u003c/li\u003e\n\u003cli\u003eGullapalli, R. P., \u0026amp; Sheth, B. B. (1999). Influence of an optimized non-ionic emulsifier blend on properties of oil-in-water emulsions. \u003cem\u003eEuropean Journal of Pharmaceutics and Biopharmaceutics\u003c/em\u003e, \u003cem\u003e48\u003c/em\u003e(3), 233\u0026ndash;238. https://doi.org/10.1016/S0939-6411(99)00048-X\u003c/li\u003e\n\u003cli\u003eHaroon, H. B., Hunter, A. C., Farhangrazi, Z. S., \u0026amp; Moghimi, S. M. (2022). A brief history of long circulating nanoparticles. \u003cem\u003eAdvanced Drug Delivery Reviews\u003c/em\u003e, \u003cem\u003e188\u003c/em\u003e, 114396. https://doi.org/10.1016/J.ADDR.2022.114396\u003c/li\u003e\n\u003cli\u003eKlang, V., Matsko, N. B., Valenta, C., \u0026amp; Hofer, F. (2012). Electron microscopy of nanoemulsions: An essential tool for characterisation and stability assessment. \u003cem\u003eMicron\u003c/em\u003e, \u003cem\u003e43\u003c/em\u003e(2\u0026ndash;3), 85\u0026ndash;103. https://doi.org/10.1016/J.MICRON.2011.07.014\u003c/li\u003e\n\u003cli\u003eLedford, B. T., Wyatt, T. G., Vang, J., Weiss, J., Tsihlis, N. D., \u0026amp; Kibbe, M. R. (2023). Effects of Particle Size, Charge, Shape, Animal Disease State, and Sex on the Biodistribution of Intravenously Administered Nanoparticles. \u003cem\u003eParticle \u0026amp; Particle Systems Characterization\u003c/em\u003e, \u003cem\u003e40\u003c/em\u003e(7), 2300001. https://doi.org/10.1002/PPSC.202300001\u003c/li\u003e\n\u003cli\u003eLiu, J., Leng, P., \u0026amp; Liu, Y. (2021). Oral drug delivery with nanoparticles into the gastrointestinal mucosa. \u003cem\u003eFundamental \u0026amp; Clinical Pharmacology\u003c/em\u003e, \u003cem\u003e35\u003c/em\u003e(1), 86\u0026ndash;96. https://doi.org/10.1111/FCP.12594\u003c/li\u003e\n\u003cli\u003eMalpotra, M., Garg, M., Singh, N., Sadhu, S. D., Chopra, R., \u0026amp; Sharma, B. (2025). An overview of bioactive components and phytopharmaceutical potentials of Hygrophila auriculata\u0026ndash;A herbaceous medicinal plant. \u003cem\u003ePhytomedicine Plus\u003c/em\u003e, \u003cem\u003e5\u003c/em\u003e(1), 100737. https://doi.org/10.1016/J.PHYPLU.2025.100737\u003c/li\u003e\n\u003cli\u003eMcClements, D. J., \u0026amp; Jafari, S. M. (2018). General Aspects of Nanoemulsions and Their Formulation. In \u003cem\u003eNanoemulsions: Formulation, Applications, and Characterization\u003c/em\u003e. https://doi.org/10.1016/B978-0-12-811838-2.00001-1\u003c/li\u003e\n\u003cli\u003eMohite, P., Sule, S., Pawar, A., Alharbi, H. M., Maitra, S., Subramaniyan, V., Kumarasamy, V., Uti, D. E., Ogbu, C. O., Oodo, S. I., Kumer, A., Oluwafemi Idowu, A., \u0026amp; Okoye, O. N. N. (n.d.). \u003cem\u003eDevelopment and characterization of a self-nano emulsifying drug delivery system (SNEDDS) for Ornidazole to improve solubility and oral bioavailability of BCS class II drugs\u003c/em\u003e. https://doi.org/10.1038/s41598-024-73760-7\u003c/li\u003e\n\u003cli\u003eMushtaq, A., Mohd Wani, S., Malik, A. R., Gull, A., Ramniwas, S., Ahmad Nayik, G., Ercisli, S., Alina Marc, R., Ullah, R., \u0026amp; Bari, A. (2023). Recent insights into Nanoemulsions: Their preparation, properties and applications. \u003cem\u003eFood Chemistry: X\u003c/em\u003e, \u003cem\u003e18\u003c/em\u003e, 100684. https://doi.org/10.1016/J.FOCHX.2023.100684\u003c/li\u003e\n\u003cli\u003eNikolic, V., Ilic-Stojanovic, S., Petrovic, S., Tacic, A., \u0026amp; Nikolic, L. (2019). Administration Routes for Nano Drugs and Characterization of Nano Drug Loading. \u003cem\u003eCharacterization and Biology of Nanomaterials for Drug Delivery: Nanoscience and Nanotechnology in Drug Delivery\u003c/em\u003e, 587\u0026ndash;625. https://doi.org/10.1016/B978-0-12-814031-4.00021-0\u003c/li\u003e\n\u003cli\u003eOkawara, M., Hashimoto, F., Todo, H., Sugibayashi, K., \u0026amp; Tokudome, Y. (2014). Effect of liquid crystals with cyclodextrin on the bioavailability of a poorly water-soluble compound, diosgenin, after its oral administration to rats. \u003cem\u003eInternational Journal of Pharmaceutics\u003c/em\u003e. https://doi.org/10.1016/j.ijpharm.2014.06.032\u003c/li\u003e\n\u003cli\u003eOyelaja-akinsipo, O. B., Dare, E. O., \u0026amp; Katare, D. P. (2020). Heliyon Protective role of diosgenin against hyperglycaemia-mediated cerebral ischemic brain injury in zebra fi sh model of type II diabetes mellitus ?. \u003cem\u003eHeliyon\u003c/em\u003e, \u003cem\u003e6\u003c/em\u003e(3240300002), e03296. https://doi.org/10.1016/j.heliyon.2020.e03296\u003c/li\u003e\n\u003cli\u003eOyelaja-Akinsipo, O. B., Dare, E. O., Oladoyinbo, F. O., Katare, D. P., Sanni, L. O., \u0026amp; Alayande, S. O. (2021). NANOEMULSION: A PROMISING AND NOVEL NANOTHERAPEUTIC VEHICLE FOR TRANSDERMAL DRUG DELIVERY APPLICATION. \u003cem\u003eJournal of Chemical Society of Nigeria\u003c/em\u003e, \u003cem\u003e46\u003c/em\u003e, 0618\u0026ndash;0632. https://doi.org/https://doi.org/10.46602/jcsn.v46i4.639\u003c/li\u003e\n\u003cli\u003eRodrigues, R. F., Costa, I. C., Almeida, F. B., Cruz, R. A. S., Ferreira, A. M., Vilhena, J. C. E., Florentino, A. C., Carvalho, J. C. T., \u0026amp; Fernandes, C. P. (2015). Development and characterization of evening primrose (Oenothera biennis) oil nanoemulsions. \u003cem\u003eRevista Brasileira de Farmacognosia\u003c/em\u003e, \u003cem\u003e25\u003c/em\u003e(4), 422\u0026ndash;425. https://doi.org/10.1016/J.BJP.2015.07.014\u003c/li\u003e\n\u003cli\u003eSajid, M., Cameotra, S. S., Khan, A. M. S., \u0026amp; Ahmad, I. (2019). Nanoparticle-Based Delivery of Phytomedicines: Challenges and Opportunities. In \u003cem\u003eNew Look to Phytomedicine: Advancements in Herbal Products as Novel Drug Leads\u003c/em\u003e. https://doi.org/10.1016/b978-0-12-814619-4.00024-0\u003c/li\u003e\n\u003cli\u003eSharma, N., Mishra, S., Sharma, S., Deshpande, R. D., \u0026amp; Kumar Sharma, R. (2013). Preparation and Optimization of Nanoemulsions for targeting Drug Delivery. In \u003cem\u003eInternational Journal of Drug Development and Research\u003c/em\u003e.\u003c/li\u003e\n\u003cli\u003eWicki, A., Witzigmann, D., Balasubramanian, V., \u0026amp; Huwyler, J. (2015). Nanomedicine in cancer therapy: Challenges, opportunities, and clinical applications. In \u003cem\u003eJournal of Controlled Release\u003c/em\u003e. https://doi.org/10.1016/j.jconrel.2014.12.030\u003c/li\u003e\n\u003cli\u003eYamada, M., Tayeb, H., Wang, H., Dang, N., Mohammed, Y. H., Osseiran, S., Belt, P. J., Roberts, M. S., Evans, C. L., Sainsbury, F., \u0026amp; Prow, T. W. (2018). Using elongated microparticles to enhance tailorable nanoemulsion delivery in excised human skin and volunteers. \u003cem\u003eJournal of Controlled Release\u003c/em\u003e. https://doi.org/10.1016/j.jconrel.2018.09.012\u003c/li\u003e\n\u003c/ol\u003e"}],"fulltextSource":"","fullText":"","funders":[],"hasAdminPriorityOnWorkflow":false,"hasManuscriptDocX":true,"hasOptedInToPreprint":true,"hasPassedJournalQc":"","hasAnyPriority":false,"hideJournal":false,"highlight":"","institution":"","isAcceptedByJournal":false,"isAuthorSuppliedPdf":false,"isDeskRejected":"","isHiddenFromSearch":false,"isInQc":false,"isInWorkflow":false,"isPdf":false,"isPdfUpToDate":true,"isWithdrawnOrRetracted":false,"journal":{"display":true,"email":"[email protected]","identity":"discover-nano","isNatureJournal":false,"hasQc":true,"allowDirectSubmit":false,"externalIdentity":"narl","sideBox":"Learn more about [Discover Nano](https://www.springer.com/journal/11671)","snPcode":"11671","submissionUrl":"https://submission.nature.com/new-submission/11671/3","title":"Discover Nano","twitterHandle":"@SpringerOpen","acdcEnabled":true,"dfaEnabled":true,"editorialSystem":"stoa","reportingPortfolio":"Discover Series","inReviewEnabled":true,"inReviewRevisionsEnabled":true},"keywords":"Diosgenin, Transdermal drug delivery, Nanoemulsion, Nanogel","lastPublishedDoi":"10.21203/rs.3.rs-7900020/v1","lastPublishedDoiUrl":"https://doi.org/10.21203/rs.3.rs-7900020/v1","license":{"name":"CC BY 4.0","url":"https://creativecommons.org/licenses/by/4.0/"},"manuscriptAbstract":"\u003cp\u003eNanoemulsion systems containing phytodrugs are efficient nanocarriers that can enhance the protective and bioavailability properties of poorly aqueous-soluble phyto-entities, thus, enhancing their use as transdermal drug delivery. This paper seeks to integrate distinctly the use of diosgenin (DG) into an oil-in-water (O/W) sub-micron-sized nanoemulsion (NE)/Nanogel (NeG) system that can increase its solubility in the transdermal therapeutic applications. Diosgenin-incorporated nanoemulsion (DGNe) was developed by the low-energy phase inversion composition (LE-PIC) technique. Excipients for NE preformulations, including sesame oil (SS) and bottle gourd (BG) seed oil, were screened for solubility and emulsifying ability. Pseudo-three-phase diagrams were plotted with BG seed oil and SS (BGSS) oil mix, including Tween 80 and glycerol as surfactant/cosurfactant mix. Using the Box Behnken design, optimum responses from 13 randomized NE preformulations were determined from particle size (nm), drug release (%), viscosity (cps), and pH. A Scanning Electron Microscope (SEM) and a Field Emission Scanning Electron Microscope were used to determine the characteristic surface morphology of DGNe and DGNeG. Differential Light Scanning Calorimeter (DLS) was used to determine the particle size, zeta potential, and polydispersity index (PDI) of DGNe. Fourier Transform Infrared (FT-IR) and DLS were used to determine the functional stability of the formulated DGNe and DGNeG. The outcome of the SEM indicated a near-spherical nanoemulsion matrix of diosgenin that was dispersed. DLS analysis revealed that the particles were between 82 -265 nm with a PDI of 0.01-0.40, and in the FT-IR technique, DGNe and DGNeG formulation were stable at day 0 and 90. It was also established that DGNe remained thermodynamically stable at 25 °C and 4 °C after 4 weeks. The viscosity result of DGNe showed that viscosity decreases with the increase in its water content. According to the \u003cem\u003ein-vitro\u003c/em\u003erelease profile, a slow release of the drug happened within 0.5 to 15 h. These results indicate a novel diosgenin-loaded nanoemulsion/nanogel system with interesting physicochemical and stability characteristics, prolonged release behavior needed for an effective transdermal delivery systems and therapeutic bioavailability of diosgenin.\u003c/p\u003e","manuscriptTitle":"Development and Characterization of Diosgenin-incorporated Nanoemulsion Gel System for Transdermal Drug Delivery","msid":"","msnumber":"","nonDraftVersions":[{"code":1,"date":"2025-12-22 14:44:53","doi":"10.21203/rs.3.rs-7900020/v1","editorialEvents":[{"type":"communityComments","content":0},{"type":"decision","content":"Revision requested","date":"2026-03-30T10:20:20+00:00","index":"","fulltext":""},{"type":"editorInvitedReview","content":"","date":"2026-03-12T18:13:48+00:00","index":"hide","fulltext":""},{"type":"editorInvitedReview","content":"","date":"2026-03-04T20:17:27+00:00","index":"hide","fulltext":""},{"type":"editorInvitedReview","content":"","date":"2026-03-04T18:20:00+00:00","index":"hide","fulltext":""},{"type":"editorInvitedReview","content":"","date":"2026-02-28T08:12:21+00:00","index":"hide","fulltext":""},{"type":"editorInvitedReview","content":"","date":"2026-02-21T07:45:44+00:00","index":"hide","fulltext":""},{"type":"reviewerAgreed","content":"78787805202419977713463162863534132543","date":"2026-02-21T02:04:25+00:00","index":"hide","fulltext":""},{"type":"reviewerAgreed","content":"176317733021774673085483413206779430128","date":"2026-02-20T16:02:41+00:00","index":"hide","fulltext":""},{"type":"reviewerAgreed","content":"891353658699106141456750039285291887","date":"2026-02-20T14:32:41+00:00","index":"hide","fulltext":""},{"type":"reviewerAgreed","content":"168634460551201272501731638656929108710","date":"2026-02-20T13:22:34+00:00","index":"hide","fulltext":""},{"type":"editorInvitedReview","content":"","date":"2026-02-18T05:07:34+00:00","index":"hide","fulltext":""},{"type":"reviewerAgreed","content":"204808595710134978366802005899769899599","date":"2026-02-18T04:20:49+00:00","index":"hide","fulltext":""},{"type":"reviewerAgreed","content":"37238485278753113920242202123230497660","date":"2026-02-07T12:45:29+00:00","index":"hide","fulltext":""},{"type":"reviewerAgreed","content":"207527908466376799275612718612131396151","date":"2026-02-07T10:12:27+00:00","index":"hide","fulltext":""},{"type":"reviewerAgreed","content":"200046729551849914337693146358490231545","date":"2026-02-07T06:19:49+00:00","index":"hide","fulltext":""},{"type":"reviewerAgreed","content":"203539064812921233033204455849215971307","date":"2026-02-06T06:45:04+00:00","index":"hide","fulltext":""},{"type":"reviewerAgreed","content":"103036627547007310628959158666263564532","date":"2026-02-06T05:50:18+00:00","index":"hide","fulltext":""},{"type":"reviewerAgreed","content":"237203235873382036089830591967255482028","date":"2026-02-06T03:55:11+00:00","index":"hide","fulltext":""},{"type":"reviewersInvited","content":"","date":"2025-12-19T14:15:08+00:00","index":"","fulltext":""},{"type":"editorAssigned","content":"","date":"2025-12-01T12:48:41+00:00","index":"","fulltext":""},{"type":"checksComplete","content":"","date":"2025-12-01T09:34:23+00:00","index":"","fulltext":""},{"type":"submitted","content":"Discover Nano","date":"2025-11-30T21:21:54+00:00","index":"","fulltext":""}],"status":"published","journal":{"display":true,"email":"[email protected]","identity":"discover-nano","isNatureJournal":false,"hasQc":true,"allowDirectSubmit":false,"externalIdentity":"narl","sideBox":"Learn more about [Discover Nano](https://www.springer.com/journal/11671)","snPcode":"11671","submissionUrl":"https://submission.nature.com/new-submission/11671/3","title":"Discover Nano","twitterHandle":"@SpringerOpen","acdcEnabled":true,"dfaEnabled":true,"editorialSystem":"stoa","reportingPortfolio":"Discover Series","inReviewEnabled":true,"inReviewRevisionsEnabled":true}}],"origin":"","ownerIdentity":"a47e1148-58a7-40e4-b157-c8d52dc9f0dc","owner":[],"postedDate":"December 22nd, 2025","published":true,"recentEditorialEvents":[],"rejectedJournal":[],"revision":"","amendment":"","status":"under-review","subjectAreas":[],"tags":[],"updatedAt":"2026-04-17T11:08:48+00:00","versionOfRecord":[],"versionCreatedAt":"2025-12-22 14:44:53","video":"","vorDoi":"","vorDoiUrl":"","workflowStages":[]},"version":"v1","identity":"rs-7900020","journalConfig":"researchsquare"},"__N_SSP":true},"page":"/article/[identity]/[[...version]]","query":{"redirect":"/article/rs-7900020","identity":"rs-7900020","version":["v1"]},"buildId":"8U1c8b4HqxoKbykW_rLl7","isFallback":false,"isExperimentalCompile":false,"dynamicIds":[84888],"gssp":true,"scriptLoader":[]}