Development, Optimization Using Box–Behnken, In Vitro, Ex Vivo Characterization & Pharmacokinetic Evaluation of Elagolix Sodium Loaded Self-Nano Emulsifying Drug Delivery System

Development, Optimization Using Box–Behnken, In Vitro, Ex Vivo Characterization & Pharmacokinetic Evaluation of Elagolix Sodium Loaded Self-Nano Emulsifying Drug Delivery System | Research Square window.SnipcartSettings = { analytics: { enabled: false } }; (function() { var accessVector = localStorage.getItem('access_vector') || ''; window.dataLayer = window.dataLayer || []; if (accessVector) { window.dataLayer.push({ user: { profile: { profileInfo: { snid: accessVector } } } }); } })(); (function(w,d,s,l,i){w[l]=w[l]||[];w[l].push({'gtm.start':new Date().getTime(),event:'gtm.js'});var f=d.getElementsByTagName(s)[0],j=d.createElement(s),dl=l!='dataLayer'?'&l='+l:'';j.async=true;j.src='https://www.googletagmanager.com/gtm.js?id='+i+dl;f.parentNode.insertBefore(j,f);})(window,document,'script','dataLayer','GTM-K279D39R'); Browse Preprints In Review Journals COVID-19 Preprints AJE Video Bytes Research Tools Research Promotion AJE Professional Editing AJE Rubriq About Preprint Platform In Review Editorial Policies Our Team Advisory Board Help Center Sign In Submit a Preprint Cite Share Download PDF Research Article Development, Optimization Using Box–Behnken, In Vitro, Ex Vivo Characterization & Pharmacokinetic Evaluation of Elagolix Sodium Loaded Self-Nano Emulsifying Drug Delivery System Jonee Panwar, Garima Garg, Hasan Ali This is a preprint; it has not been peer reviewed by a journal. https://doi.org/ 10.21203/rs.3.rs-8424074/v1 This work is licensed under a CC BY 4.0 License Status: Posted Version 1 posted You are reading this latest preprint version Abstract Elagolix sodium is a novel, orally active non-peptide GnRH receptor antagonist used to treat endometriosis and uterine fibroids, as a BCS class III drug, it exhibits high solubility but low permeability, leading to poor and variable oral absorption. Limited permeability restricts bioavailability, but strategies such as lipid-based systems (nanoemulsions, SEDDS/SNEDDS), solid lipid or nanostructured lipid nanoparticles (SLN/NLC), permeation enhancers and cyclodextrin complexes can enhance oral absorption. The current research focused on the formulation and optimization of a self-nanoemulsifying drug delivery system (SNEDDS) for elagolix sodium aims to overcome low permeability and enhance its oral bioavailability. ELAG-SNEDDS were prepared using Labrafil M2125 CS, Tween 80, and Transcutol P at various ratios (1:1 to 4:1), and nanoemulsion region was determined using pseudo-ternary phase diagrams. The evidence from our research shows that optimized ELAG-SNEDDS was stable under thermodynamic conditions and possessed a droplet size of 216.8 ± 1.044 nm, zeta (ζ) potential − 38.04 ± 0.372 mV, exhibited a PDI of 0.439 ± 0.024 and time of emulsification < 1 minute. In pharmacokinetic study, ELAG-SNEDDS substantially increased drug absorption in the female Sprague Dawley (SD) rats, producing a higher C max and lower T max compared with the raw drug. These early outcomes suggest that ELAG-SNEDDS has the potential to serve as an effective delivery system of improving the permeability, absorption and oral bioavailability of elagolix sodium and may offer therapeutic potential in the treatment of endometriosis. Elagolix sodium SNEDDS Permeability Oral bioavailability Optimization Figures Figure 1 Figure 2 Figure 3 Figure 4 Figure 5 Figure 6 Figure 7 Figure 8 1 Introduction The purpose of drug development is to balance the activity of a drug at its target with absorption, distribution, metabolism and excretion (ADME) properties, ensuring it can successfully treat the disease [ 1 ]. The Biopharmaceutics Classification System (BCS) was established to provide a scientifically rigorous framework for classifying oral immediate-release drug formulations on the basis of their aqueous solubility and intestinal permeability, in conjunction with their dissolution behavior [ 2 ]. BCS Class III pharmaceutical compounds are largely hydrophilic and exhibit poor intestinal permeability. This classification may also be applied to new chemical entities (NCEs) possessing similar characteristics. Although such molecules typically show robust pharmacological activity in vitro, they pose considerable challenges in the development of oral dosage forms due to their limited permeability emerges as the key limitation in achieving adequate bioavailability [ 3 ]. Poor oral absorption due to low permeability prevents many drug candidates from being commercialized, despite the fact that this suggests possible pharmacodynamic effect. Furthermore, therapeutic agents with poor permeability are occasionally administered at much greater single doses than required to reach targeted plasma drug concentrations [ 4 ]. Pharmaceutical techniques to overcome poororal permeability and bioavailability of less permeable drugs include pH modification within the microenvironment, development of solute–solvent interactions, solid dispersion, micronization, lipid-based systems, permeation enhancerand cyclodextrin-based molecular encapsulation using solvent deposition [ 5 , 6 ]. Lipid-based systems offer great potential as a promising technology, including microemulsion, nanoemulsion, self-nanoemulsifying drug delivery systems (SNEDDS) and related approaches for improving the oral bioavailability of low-permeability drugs [ 7 ]. Self-nanoemulsifying drug delivery systems (SNEDDS) are isotropic, thermodynamically stable formulations that contain a drug, oils, surfactants, and co-surfactants. They are administered as oil-in-water (O/W) emulsions, with their characteristics influenced by the selected components and formulation method, they can produce coarse, micro-, or nano-size emulsions when they come into contact with stomach contents. The GI system naturally produces an emulsion as a result of mild agitation brought on by stomach motility, which is the main mechanism that helps SNEDDS increase the rate of permeability. The interfacial area expands as a result of the droplets' decreasing size, which facilitates drug uptake. As a result, SNEDDS makes less permeable drugs more permeate. It has been demonstrated that the usage of SNEDDS improves drug permeability, and lymphatic uptake, which increase drug absorption [ 8 ]. Elagolix sodium (ELAG) exhibits poor permeability and high solubility, making it a BCS class III pharmacological agent [ 9 ]. According to in vitro research, elagolix sodium has a log D 7.4 value of 4.6, indicating a high level of lipophilicity. An increased affinity for lipids (fats) and a decreased affinity for water are indicated by a higher log D value. This characteristic is important for the body's absorption and distribution of drugs [ 10 ]. ELAG showed quick absorption with a (T max ) of roughly one hour. ELAG exposure (maximum concentration [C max ] and area under the curve [AUC] is higher than dosage proportional with single doses of 600–1200 mg and dose proportional between 100 and 400 mg twice day. However, oral darunavir administration suffers from low oral bioavailability (37%) due to high low permeability, which is the primary reason of the low bioavailability [ 9 , 11 ] substantial first pass metabolism. An exhaustive analysis of the literature showed dearth of knowledge regarding the use of SNEDDS developed to enhance the bioavailability of poorly permeable drug of elagolix sodium. In order to improve oral bioavailability, the present study set out to create a SNEDDS. The solubility of elagolix sodium was examined in a range of oils, surfactants, and co-surfactants. The selected oils, surfactants, and co-surfactants showing the highest solubility for elagolix sodium were chosen to be the components of SNEDDS. Nanoemulsion (NEs) optimization, droplet size, in vitro drug release, self-emulsification time, thermodynamic stability, ex vivo studies and oral bioavailability of the proposed ELAG -SNEDDS formulations were tested in female SD rats. 2 Chemical Reagents, Animals and Methods 2.1 Chemical Reagents Elagolix sodium was a kind gift sample from Alkem Laboratory, Mumbai, India. Transcutol P (Diethylene glycol monoethyl ether), Labrafil M2125-CS (Linoleoyl Polyoxyl-6 glycerides), were received as gift samples from Gattefosse (France). Castor oil, Span-20 (sorbitan monolaurate), Span-80 (sorbitan monooleate), caprylic acid, Propylene glycol, Tryethylamain, Tetraglycol were purchased by Central Drug House (CDH), Gujarat, India. Tween 80 (Polyoxyethylene 20 sorbitan monooleate), Tween-20 (polyoxyethylene sorbitan monolaurate), sodium hydroxide, potassium dihydrogen phosphate, and sodium dihydrogen phosphate were purchased by Sisco Research Laboratories, Mumbai, India. Acetonitrile purchased from Thermofisher Scientific, Mumbai, India. Methanol was purchased from Sigma Aldrich (Merck) Germany. Ethanol was procured from MSB Chemical Limited, India. Coconut oil was purchased from local vender. Dialysis membrane (DM–50, LA387-1MT) was sourced from HI-MEDIA, Mumbai, India. All other chemicals and reagents employed in this research were of high-purity grade and HPLC grade. Throughout the experimental investigations, freshly prepared double-distilled water was utilized. 2.2 Animals About 8–12 weeks old, 24 Female Sprague Dawley (SD) rats, with a body weight of 250–300 g, were used in this research model. The animals were sourced from National Institute of Biologicals (NIB), Noida, Uttar Pradesh, India. The Sprague Dawley (SD) rats resided in cages with plastic bottoms and provided access to the food and water. The rats were kept at a temperature of 25 ± 2°C and a relative humidity of 45 ± 5%, respectively. All animals were given a ten-day adaptation period at the experimental facility prior to the actual testing. 2.3 Methods 2.3.1 Solubility Studies The solubility of elagolix sodium was studied in various excipients (oils, surfactants, and co-surfactants) and selection of the excipients was performed based on their capacity to dissolve the highest amount of drug. Various oils (Coconut oil, Labrafil M2125-CS, Castor oil and Caprylic acid), surfactants (Tween 80, Span-20, Span-80 and Tween-20) and co-surfactants (Tetraglycol, Propylene glycol, Transcutol P, and Ethanol) were studied by using shake flask method. A surplusquantity of drug was added to 2 ml of each oil, surfactant and co-surfactant possessing vortexing (V1000, Benchmark Scientific, Inc.) until homogeneous a mixture of drug was achieved. The capped vials were then shaken in a water bath at 37°C for 72 hours. The collected test samples were subsequently centrifuged at 1500 rpm for 15 minutes, and the supernatant layer was carefully collected for further analysis. Concentration of drug was calculated using a UV-visible spectrophotometer (UV-1900i, Shimadzu Corporation, Tokyo, Japan) at 275 nm following an appropriate dilution with methanol, in comparison to a blank (methanol). 2.3.2 Phase Diagram The solubility data were used to choose the appropriate components for the preparation of SNEDDS in order to construct the pseudo-ternary phase diagram and to find out a maximum self-emulsifying region. Labrafil M2125-CS as an oil phase, Tween 80 served as a surfactant and Transcutol P employed as a co-surfactant were selected for the construction of pseudo-ternary diagram. In order to identify self-emulsification area and choose the optimal concentration of Labrafil M2125-CS, Tween 80 and Transcutol P for the development of the most successful SNEDDS by using water titration method at room temperature. A selected surfactant and co-surfactant (S mix ) were mixed by using various weighed proportions (1:1, 2:1, 3:1, and 4:1) for each experimental group. Subsequently, the oil and certain S mix proportions were rigorously mixed in different ratios of (1:9, 2:8, 3:7, 4:6, 5:5, 6:4, 7:3, 8:2, and 9:1), sequentially. A certain amount of aqueous phase volume (100µL) was steadily added to each combination, while gently stirring continuously on a magnetic stirrer and maintaining the temperature at 37°C and its physical characteristics were recorded. If it remained transparent, increasing volumes of the aqueous phase were added until the system turned cloud and lost its clarity. At this stage, the weight fraction of oil, surfactant, co-surfactant, and aqueous phase of this combination was calculated on a weight-by-weight basis for all S mix ratios. The phase diagrams were developed using the grapher software version to depict the results and an area of the clear micro-emulsion was chosen as the suitable region. 2.3.3 Experimental Model Experimental models were developed using a three-factor, three-level Box–Behnken design (BBD) via Design Expert ® V.13.5 (Stat-Ease Inc., Minneapolis, MN). Among various several response surface methodologies, BBD is considered as an effective method for identifying effect of formulation variables (independent factors) with respect to the response variable. Essentially, The Box–Behnken design (BBD) is a response surface methodology involving three factors, each at three levels used in the current research to analyze both the primary and interaction impacts of the independent factors (amount of oil, surfactant, and co-surfactants) on the behavior of the prepared SNEDDS and the appropriateness of the desirability function for formulation optimization [ 12 ]. To estimate the experimental error and evaluate the accuracy of the design, Box–Behnken design needs fifteen experimental trials with three center points. A non-linear quadratic model equation as follows (Eq. 1): Y = α 0 + α 1 X 1 + α 2 X 2 + α 3 X 3 + α 4 X 1 X 2 + α 5 X 2 X 3 + α 6 X 1 X 3 + α 7 X 1 2 + α 8 X 2 2 + α 9 X 3 2 ……….[ 1 ] Where, Y is the response variable for every combination of factors and the factor level; α0 represents the intercept, while α1–α9 are the regression coefficients; X 1 , X 2 , and X 3 represent the independent variables employed in the research given in Table 2 . Table 2 Different Types of variables used in Box–Behnken design S. No. Dependent variables Independent variables Objectives for dependent variables 1 Globule size (Y 1 ) Quantity of oil added (X 1 ) Minimize 2 Polydispersity index (Y 2 ) Quantity of surfactant added (X 2 ) Minimize 3 Zeta Potential (Y 3 ) Quantity of co-surfactant added (X 3 ) Maximize 4 Percentage transmittance (Y 4 ) - Maximize The proportion of independent variables, primarily the percentage of oil phase (Labrafil M2125-CS; X 1 ; 5–20%), surfactant percentage (Tween 80; X 2 ; 64–76%), and percentage of co-surfactant (Transcutol P; X 3 ; 16–19%) were chosen in accordance with the findings of pseudo-ternary diagram. The preferred response variables included average droplet size ( Y 1 ), polydispersity index ( Y 2 ), zeta potential ( Y 3 ), % transmittance ( Y 4 ). The standardized coded variables chosen based on preliminary trials and pseudo-ternary phase diagrams presented in Table 3 and were run experimentally with the coded factor levels in Table 4 . ANOVA, multiple correlation coefficients (R 2 ), and lack of fit tests were applied to validate the models. Table 3 Transformation of coded variables into real measurements Standardized levels Low* (-1) Middle* (0) High* (1) Factor 1 (X 1 ) - Quantity of oil 5% 12.5% 20% Factor 2 (X 2 ) - Quantity of surfactant 64% 70% 76% Factor 3 (X 3 ) - Quantity of co-surfactant 16% 17.5% 19% *The independent variables were assigned low, medium, and high coded levels based on preliminary trials and pseudo-ternary phase diagrams developed to achieve optimal percentage transmittance. Table 4 Experimental runs with coded factor levels for three variables Run Number Factor 1 (X 1 ) Factor 2 (X 2 ) Factor 3 (X 3 ) 1 0 0 0 2 0 1 -1 3 1 -1 0 4 0 -1 -1 5 0 0 0 6 1 0 -1 7 1 0 1 8 -1 1 0 9 1 1 0 10 -1 0 -1 11 0 0 0 12 -1 0 1 13 0 1 1 14 0 -1 1 15 -1 -1 0 2.4 Preparation of Elagolix Sodium-Loaded SNEDDS Considering the findings on saturated solubility and pseudo-ternary diagram of the development of a SNEDDS of elagolix sodium, different amount of oil, surfactant, and co-surfactant were employed. A number of combinations of SNEDDS (Table 5 ) have been developed by varying the proportions of the oil phase, surfactant, and co-surfactant. Firstly the required amount of co-surfactant (Transcutol P) and surfactant (Tween 80) were mixed together separately to produce the surfactant to the co-surfactant combination (S mix ). The drug (150 mg) was added to oil (Labrafil M2125-CS) in a clean glass vials under the continuous stirring using vortex (V1000, Benchmark Scientific, Inc.). The S-mix was incorporated to the drug-containing oil phase and stirred continuously for ten to fifteen minutes by means of a vortex mixer at 37°C to obtain a homogenous and transparent formulation [ 12 ]. A total of fifteen formulations of elagolix sodium were developed as per BBD model. Table 5 Observed responses obtained from the randomized Box–Behnken experimental runs Experimental run Formulation code Independent Variables Dependent Variables X 1 Labrafil M2125 CS (mg) X 2 Tween 80 (mg) X 3 Transcutol P (mg) Y 1 Globule size (nm) Y 2 Polydispersity index (PDI) Y 3 Zeta potential (mV) Y 4 Transmittance (%) 1 F1 125 700 175 204 ± 1.61 0.382 ± 0.028 -38.04 ± 0.37 91.17 ± 0.62 2 F2 125 760 160 181.1 ± 2.05 0.51 ± 0.047 -35.91 ± 0.48 92.34 ± 0.3 3 F3 200 640 175 241.8 ± 1.96 0.498 ± 0.032 -5.82 ± 0.22 79.73 ± 0.44 4 F4 125 640 160 235.9 ± 2.69 0.531 ± 0.052 -35.25 ± 0.5 82.85 ± 0.74 5 F5 125 700 175 204 ± 1.61 0.382 ± 0.028 -38.04 ± 0.37 91.17 ± 0.62 6 F6 200 700 160 181 ± 1.61 0.361 ± 0.044 -2 ± 0.26 89.75 ± 0.45 7 F7 200 700 190 176.5 ± 3.72 0.373 ± 0.034 -2.04 ± 0.34 91.6 ± 0.56 8 F8 50 760 175 169.5 ± 1.4 0.366 ± 0.048 -21.9 ± 0.3 96.33 ± 0.55 9 F9 200 760 175 171.5 ± 0.64 0.389 ± 0.057 -1.88 ± 0.22 91.88 ± 0.69 10 F10 50 700 160 186.7 ± 1.99 0.424 ± 0.042 -24.07 ± 0.65 91.47 ± 0.37 11 F11 125 700 175 204 ± 1.61 0.382 ± 0.028 -38.04 ± 0.37 91.17 ± 0.62 12 F12 50 700 190 179.8 ± 1.26 0.407 ± 0.058 -24.72 ± 0.61 92.43 ± 0.75 13 F13 125 760 190 176.3 ± 2.76 0.579 ± 0.032 -37.97 ± 0.38 93.34 ± 0.61 14 F14 125 640 190 227.5 ± 0.96 0.515 ± 0.092 -36.88 ± 0.59 85.16 ± 0.84 15 F15 50 640 175 195.6 ± 1.6 0.372 ± 0.049 -25.91 ± 0.61 90.7 ± 0.56 2.5 Characterization of Elagolix Sodium SNEDDS 2.5.1 Droplet Size and PDI Globule size was measured at room temperature employing dynamic light scattering. Droplet size and distribution uniformity are represented by the polydispersity index. The low PDI results show that the droplet diameter of prepared formulation is relatively uniform [ 13 ]. It is preferred that the PDI value for self-nanoemulsifying systems should be less than 0.3 [ 14 ]. The Malvern Zetasizer, version-3.3.1.5 (MAL1278078, Malvern Panalytical Ltd.) was used to assess the PDI and droplet size of a SNEDDS after incorporating 250 mL of water to 1 mL of elagolix sodium-loaded SNEDDS formulation at 25 ± 0.5°C. All of the information obtained was the mean of triplicate. 2.5.2 Zeta Potential To assess the stability of SNEDDS pre-concentrate, the electrical charge of the tiny particles in liquid SNEDDS was confirmed by using Malvern Zetasizer, version-3.3.1.5 (MAL1278078, Malvern Panalytical Ltd.) employing a 50 mV laser at room temperature with a fixed 90 degree angle. Particles with the value of zeta potential more than 30 millivolt (mV) are often seen as stable irrespective of their charge [ 15 ]. 2.5.3 Percent Transmittance The percent transmittance of ELAG-SNEDDS formulations was determined spectrophotometrically (UV-1900i, Shimadzu Corporation, Tokyo, Japan) at a wavelength of 275 nm after diluting a 0.1 mL of nanoemulsion up to 10 mL of distilled water against water as a blank reference (configured at transmittance 100%)[ 16 – 18 ]. 2.5.4 Self-Nanoemulsification Efficiency Test In order to assess the self emulsification efficiency, the precipitation of drug or separation of phase after dilution with water was examined visually [ 19 ]. Dispersibility and self-emulsification times were determined in order to evaluate the efficiency of self-nanoemulsification (Table 1 ). Table 1 A grading system used to evaluate the emulsions formed after dilution. S. No. Grade Physical appearance of the formed emulsion Time required 1 A A transparent, clear and rapid forming nanoemulsion with bluish appearance < 1 minutes 2 B The emulsion forms rapidly and exhibits reduced clarity with a bluish-white appearance < 2 minutes 3 C Formation of a cloudy or finely dispersed milky emulsion Approximately 2 minutes 4 D Slow emulsification, producing a dull, grayish to faintly white appearance with a mild oily character. > 2 minutes 2.5.5 Dispersibility In order to determine the unpredictability of the nanoemulsion development process, the dispersibility of the developed SNEDDS is evaluated. The optimized SNEDDS (1 mL) was introduced into 500 mL of aqueous medium at 37 ± 0.5°C and stirred at 50 rpm using a magnetic stirrer (REMI 2MLH, India) that delivered mild agitation. The in vitro efficiency of optimized SNEDDS was typically determined based on the grading methodology [ 20 ]. 2.5.6 Time Required for Self-Emulsification In order to calculate required time for emulsification (period of time required toform a stable emulsion, after dilution, a single-phase and clear colloidal suspension was obtained), A 1 mL aliquot of the drug-loaded premix was admixed to 250 mL of aqueous medium at 37 ± 0.5°C and stirred at 50 rpm by using magnetic stirrer (REMI 2MLH, India). The period of time required to achieve self-emulsification was visually observed and assessed [ 21 ]. Based on the emulsification time SNEDDS were graded on a grading system of A, B, C. 2.5.7 Thermodynamic Stability Studies The phase integrity and thermal stability of the optimized SNEDDS was examined under different centrifugal force and temperature conditions [ 22 ]. The optimized formulation was run into the following test in order to explore the impact of temperature and centrifugal force [ 23 ]. 2.5.8 Centrifugal Test In this test, the optimized SNEDDS was centrifuged at 5000 rpm (Singhla scientific India) for 30 minutes. A visual assessment was done in order to check for instabilities such as cracking, creaming, or phase separation. Further tests were conducted on the stable formulations [ 24 ]. 2.5.9 Heating and Cooling Cycle The SNEDDS preconcentrate was surpassed through three cooling cycles and heating by maintaining the formulation was maintained at 4°C and 45°C, with a minimum duration of 48 hours at each temperature. SNEDDS preconcentrate was then diluted with distilled water after being initially evaluated to assess drug precipitation and phase separation. The stability of the resulting nanoemulsion was subsequent evaluated [ 25 ]. 2.5.10 Freeze − Thaw Stress Cycle Three cycles of freezing and thawing were carried out over the course of 48 hours at a temperature between (-21 and 25°C). After centrifuging at 5000 rpm by using centrifuge (Singhla scientific India) for 5–10 minutes, all SNEDDS were checked for instability, such as phase separation, cracking, and creaming [ 24 , 26 ]. The optimized SNEDDS was determined by these studies. 2.5.11 Impact of pH and Dilution Robustness The optimized SNEDDS formulation was diluted 10, 100, and 1000 times using various dissolution media, including distilled water, 0.1N HCl (pH 1.2), and phosphate buffer (pH 6.8). After being stored for 24 hours, After dilution, the samples were inspected for signs of instability, including the occurrence of precipitation or phase separation [ 12 ]. 2.5.12 Determination of Cloud Point When the clear formulation becomes cloudy it is considered to have attained its cloud point [ 27 ]. The purpose of the cloud point study was to evaluate the stability of the optimized SNEDDS at physiological temperature, similar as in vivo. Cloud point determination was performed for the optimized SNEDDS using water bath (UQ – 2021101940, Singhla scientific India). Upon dilution of optimized SNEDDS with 100 times with aqueous medium, the formulation was then placed on a water bath that steadily raised the temperature (between 25°C and 80°C, or about 2°C per minute). The temperature at which visible cloudiness first appeared was recorded as the cloud point [ 28 ]. 2.5.13 Morphological Study The optimized SNEDDS was analyzed by transmission electron microscope (TEM) to evaluate its morphological features. To accomplish this, a transmission electron microscope (Talos L120C, Thermo Scientific ™) set up at the Jamia Hamdard University, New Delhi, India, was employed. The optimized SNEDDS was diluted with aqueous medium in a 1:10 ratio [ 29 ]. One or two drops of diluted SNEDDS were applied to the carbon grid, and the TEM was utilized to examine particles at the ideal magnifications [ 30 ]. 2.6 In Vitro Release Study of the Elagolix Sodium The in vitro release studies of elagolix sodium from optimized SNEDDS as well as the pure drug suspension was examined using USP Type II (paddle) dissolution apparatus (ECO-2070, Singhla Scientific India) which rotated at 100 rpm and maintained at 37°C. Drug release was evaluated using the dialysis bag method to ensure that only unbound drugs reached the dissolving media, limiting the effect of the unreleased drug. The dialysis membranes were kept in the dissolution media (PBS 6.8) at room temperature for about 12 h. About 150 mg of drug loaded SNEDDS were diluted with 10 mL of aqueous medium, and then 5 mL of pure drug suspensions and diluted SNEDDS were poured into dialysis membranes (DM – 50, LA387-1MT, Hi-media, Mumbai, India) that were already presoaked. After securely tying the dialysis membranes at both ends to prevent leakage, they were attached to the rotating paddles and immersed in the dissolving media. The sample was withdrawn using a pipette from the dissolution vessel in aliquots of 5 mL at regular intervals of 0.25, 0.5, 1, 1.5, 2, 3, 4, 5, 6, and 12 hours. Five mL of fresh dissolving medium were introduced to keep the sink condition stable. The samples were examined for drug release at a wavelength of 275 nm using a UV spectrophotometer (UV-1900i, Shimadzu Corporation, Tokyo, Japan) [ 31 ]. 2.7 Evaluation of Drug Permeation Using Ex Vivo Model Permeation assessment of elagolix sodium from the suspension of pure drug and optimized SNEDDS was studied using a non-everted gut sac approach. Approval for the study protocol was obtained from the Ethical Review Committee. For this research, a female Sprague Dawley rat, weighing between 150 and 250 g, was euthanized, followed by careful removal of the small intestine. A 5–6 cm portion of the duodenum was carefully removed and washed sequentially using Krebs–Ringer phosphate buffer and normal saline at a controlled temperature of 37°C. The duodenal sac was filled with suspension (1 mL) of pure drug and the optimized SNEDDS using a 3 mL syringe, and both ends were tightly secured with cotton thread. The intestinal sacs were immersed in 250 mL of phosphate-buffered saline (PBS, pH 6.8) with 0.2% polysorbate, stirred at 50 rpm, and maintained at 37°C. At predetermined time points 0.25, 0.5, 1, 1.5, 2,3, 4, 5 and 6 h, 3 mL of the sample was withdrawn from the beaker and immediately replaced with an equal volume of PBS (pH 6.8). The absorbance of each collected sample was determined by UV–visible spectrophotometric analysis (UV-1900i, Shimadzu Corporation, Tokyo, Japan) set to 275 nm. The percentage of drug transport across the membrane into the receptor compartment, the apparent permeability (Papp), and the permeation flux (µg/min) were calculated. The equation below was used to determine the Papp, which is represented in cm/min [ 32 ]: P app value = d Q/ d t × 1 / AC o Where 𝑑𝑄/𝑑𝑡 represents the permeation rate of the drug, 𝐶 0 is the initial concentration of drug in the mucosal compartment at 𝑡=0 and 𝐴 denotes the cross-sectional surface area of the tissue, calculated as 2𝜋𝑟ℎ. The slope of the linear segment of the graph depicting cumulative drug permeation per unit area (µg/cm²) versus time (min) was used to calculate the permeability flux. 2.8 Pharmacokinetic Study In the current study, female Sprague Dawley (SD) rats, weighing 200 ± 50 g were utilized as the experimental animals. Prior to the study, there were no restrictions on their intake of food or drink. Elagolix sodium (15 mg/kg) was given orally to each rat as a single dosage and at 0, 0.33, 0.67, 1, 1.5, 2, 3, 4, 6, 9, 12, 24, and 36 h post delivery, we collected the blood samples (300 µL) from each rat into EP tubes containing the anticoagulant heparin. Immediately after collection, plasma samples were separated by centrifugation for 8 minutes at 4000 rpm and stored in refrigerator until required. Data on the primary pharmacokinetic parameters of elagolix sodium in rats were assessed using the Drug and Statistics (DAS) Version 3.0 program. 2.9 Statistical Evaluation In the current study, Statistical analyses were carried out using SPSS® software V 22 (SPSS Inc., Chicago, IL, USA). The statistical comparisons were conducted using ANOVA. The difference was deemed significant when the p-value was less than 0.05. 3 Results 3.1 Solubility The excipients used to make SNEDDS should have a wider self-nanoemulsification area within the pseudo-ternary phase diagram and be able to solubilize a large quantity of drug. The excipients were selected based on the compatibility with the added drug, safety and solubilizing capacity. The solubility of elagolix sodium in various excipients at equilibrium is showed in Fig. 1 . Four oils were investigated in our study for potential use as the oil phase in the preparation of SNEDDS. The study clearly showed that elagolix sodium was most soluble in Labrafil M2125-CS (92.12 ± 1.93 mg/mL), followed by Castor oil (46.39 ± 2.34 mg/mL), Coconut oil (22.28 ± 2.72 mg/mL), and Caprylic acid (5.50 mg/mL ± 0.86 mg/mL). Furthermore, Tween 80, a non-ionic surfactant, was employed in the current investigation as Tween 80 exhibited a good HLB value (15) and an adequate solubilizing capacity for elagolix sodium (129.59 ± 4.82 mg/mL), followed by Tween 20 (37.77 ± 2.77 mg/mL), Span 80 (23.21 ± 1.09 mg/mL) and Span 20 (15.94 ± 1.92 mg/mL). Tween 80 was used as a surfactant for the preparation of SNEDDS to define a stable nanoemulsion area. The material that exhibits the highest solubility of elagolix sodium was selected because co-surfactants aid the surfactants in drug solubilization. Transcutol P was chosen among the co-surfactants because it exhibited the highest elagolix sodium solubility (97.59 ± 1.97 mg/mL), followed by tetraglycol (55.91 ± 1.50 mg/mL), propylene glycol (36.37 ± 1.67 mg/mL) and ethanol (9.58 ± 0.86 mg/mL). The complex composition of linoleoyl polyoxyl-6 glycerides (PEG-6 linoleate) and different mono-, di-, and triglycerides of linoleic acid may have contributed to the high solubility of elagolix sodium in Labrafil M2125-CS [ 33 ]. It is commonly known that adding mixed glycerides to a lipid formulation increases its emulsifying and solubilizing capacity. Furthermore, they are beneficial to use in lipid formulations due to their resemblance to the lipid digestion product [ 33 ]. Since the therapeutic agent mustalways remain in dissolved form in SNEDDS, forming a concentration gradient that initiates the transport of drug molecules from areas of higher concentration to areas of lower concentration (in the blood) the penetration of therapeutic agent through the GI tract, the higher solubility of therapeutic agent in the oil phase is critical for self-nanoemulsification [ 34 ]. Hence because of its excellent solubilization capacity, Labrafil M2125-CS was chosen as the oil phase for further studies. Since surfactants are primarily play an important role in the stabilizing the emulsion, their choice is crucial in the lipid system. Compared to ionic surfactants, non-ionic surfactants are less irritable, safer, and show better emulsion stability across a wide range of ionic strength and pH. Additionally, they cause a reversible change in intestinal mucosal permeability, which speeds up drug adsorption [ 35 ]. Consequently, Tween 80, a non-ionic surfactant, was used for the SNEDDS preparation. Micellization of surfactant molecules in water, play an important role in SNEDDS. Furthermore, they decrease the chances of drug precipitation and improve drug solubility in micelles [ 36 ]. In this study, Transcutol P was selected because it dissolved the highest amount of the elagolix sodium. 3.2 Phase diagram To make out the self-emulsifying zone and choose the best lipid phase and S mix ratios (surfactant: co-surfactant) for SNEDDS were determined through the construction of pseudo-ternary phase diagrams. These pseudo-ternary phase diagrams are essential for optimizing the preconcentrate for SNEDDS and comprehending the phase behaviour of nanoemulsions. Three elements make up the ternary diagram: S-mix, water, and oil. One of the components with a 100% concentration is represented by each corner (Fig. 2 ). Here in this study, Tween 80 served as the surfactant, Transcutol P as the co-surfactant, and Labrafil M2125-CS as the oily phase in this study. This experiment employed the following surfactant-to-co-surfactant (S mix ) ratios: 1:1, 2:1, 3:1, and 4:1. Various S mix ratios were mixed with various ratios of Labrafil M2125-CS; thereafter resulting mixtures were titrated using a predetermined volume of water [ 25 ]. The inclusion of Tween 80 within the self-emulsion region was observed to enhance the spontaneous nature of the self-emulsification process. When the S mix in the SNEDDS exceeded 80%, the emulsification efficiency was noticeably favourable. It is evident from the experimental set-up that when the surfactant level in the SNEDDS was less than 50%, spontaneous emulsion formation was ineffective. These findings were included to the grapher software version to generating pseudo-ternary phase diagrams following the aqueous titration. The various areas of the triangles are depicted in Fig. 2 , where the transparent region stands in for the nanoemulsion area. This specific zone is distinguished by its clear and homogenous nature in Fig. 2 . A wide nanoemulsion region is indicated by the maximum self-nanoemulsifying activity and optimal intermolecular interaction between water and pre-concentrate (S mix and oil). Additionally, Fig. 2 shows that a larger nanoemulsion area results from an increase in S mix ratio. However, the water titration revealed oil streaks as the oil ratio increased [ 37 ]. Tween 80 and Transcutol P were utilized as the S mix in pseudo-ternary phase diagram assessments using Labrafil M2125-CS as oil. As a result, the S mix ratio of 4:1 was selected because it provides the biggest nanoemulsion area for SNEDDS and the highest water uptake capacity for Tween 80 and Transcutol P. In order to get stable formulations and smaller droplet sizes, a greater S mix concentration is necessary. This results in a bigger surface area and a smaller droplet size. As a result, more S mix is needed to stabilize the oil droplets [ 38 , 39 ]. 3.3 Experimental Model In order to examine the impact of three independent variables on response variables, BBD was chosen and used in the current study. Tables 2 and 3 show the limitations of the independent and dependent factors. In this research, fifteen formulations were developed in accordance with the BBD, and their response variables—globule size (Y 1 ), PDI (Y 2 ), zeta potential (Y 3 ), and percentage transmittance (Y 4 ) were evaluated (Table 5 ). Malvern Zetasizer, version-3.3.1.5 (MAL1278078, Malvern Panalytical Ltd.) was used to collect all of the data. ANOVA, the multiple correlation (R 2 ) tests, lack of fit test, were used to evaluate the model's significance after every response was fitted independently to a complete polynomial equation. A model p-value of less than 0.05 was considered statistically significant. The lack-of-fit test is employed to evaluate the fluctuation of data around the fitted value, and it must be negligible (p < 0.05). The multiple correlation coefficients (R 2 value) indicate the extent of variance around the mean. An R 2 value near 1 is ideal [ 12 ]. 3.3.1 Globule Size The BBD revealed that the selected independent factor arrangement of Labrafil M2125-CS (X 1 ), Tween 80 (X 2 ) and Transcutol P (X 3 ) produced distinct effects for the globule size (Y 1 ). The following equation [ 2 ] describes the polynomial equation that represents the mathematical relationship of independent factors with response (Y 1 ). Y 1 (Globule size) = 204 + 4.9 X 1 – 25.3 X 2 – 3.075 X 3 – 11.05 X 1 X 2 + 0.6 X 2 X 3 + 0.9 X 1 X 3 – 16.8 X 1 2 + 7.4 X 2 2 – 6.2 X 3 2 ………………… [ 2 ] The quadratic model provided a good fit for globule size (Y 1 ). The multiple correlation tests (R 2 ) and ANOVA were used to confirm the model's effectiveness. Along with larger coefficients (Eq. 2), the polynomial term (X 1 2 ) and surfactant concentration (X 2 ) have a substantial impact (p < 0.05). However, the concentration of lipids (X 1 ), co-surfactants (X 3 ), interactive terms (X 1 X 2 ), (X 2 X 3 ), (X 1 X 3 ), and X 2 2 , X 3 2 , had a low coefficient indicating no significant influence (p > 0.05) on the response Y 1 . Consequently, the globule size rose marginally as the lipid levels in the SNEDDS increased. The significant effect of independent factors in predicting the response variable (Y 1 ) was corroborated by the observed R 2 value (0.9456) and p value (< 0.05). Since there is an insufficient surfactant to emulsify and break the interfacial barrier between the lipid micro globular surface and the water phase, the higher amount of lipid depicted the increase in droplet size [ 12 , 40 ]. On the other hand, a significant reduction in particle size was shown as the level of surfactant increased, indicating the opposite impact (negative). This fact supported the single (X 2 ) negative effect of the X 2 factor; thus, an excessive quantity of surfactant significantly emulsified the oil and substantially decreased the interfacial energy between the oil phase and aqueous phase, which led to a notable reduction in the droplet size [ 41 ]. Co-surfactants in SNEDDS, generally, break the interfacial film and reduce the interfacial tension as a result help in the reduction of droplet size, enabling the production of nanoemulsion, however, in the current study co-surfactant (X 3 ) reduces emulsion droplet size insignificantly (p > 0.05). Additionally, the binary interaction term (X 1 X 2 ) and a negative coefficient indicated that in a mutual setting, the emulsion droplet size is mostly influenced by the quantity of surfactant (X 2 ) rather than the lipid content (X 1 ) (Eq. 2). In this experimental setup, a positive but an in-significant effect (p > 0.05) of binary interaction (X 2 X 3 ) was noted. A noteworthy decrease in the droplet size was suggested by the significant effect (p < 0.05) of the lipid's exponential additions (X 1 2 ). However, a smaller positive effect was seen when exponential effect (X 1 2 ) compared with the single effect (X 1 ) (Eq. 2). A certain degree of the lipid's self-emulsification property is predicted by the decrease in the coefficient [ 12 ]. However, the surfactant concentration's additive impact (X 2 2 ) has a positive effect and demonstrated a notable rise in droplet size. This could be because a long-chain surfactant molecule is overcrowded, or has several layers [ 21 ]. The current investigation found that the globule size of the nanoemulsion was not markedly affected by an exponential increase in the co-surfactant concentration (X 3 2 ). The response surface plot for droplet size is depicted in Fig. 3 . 3.3.2 PDI The BBD revealed that the combinations of Labrafil M2125-CS (X 1 ), Tween 80 (X 2 ), and Transcutol P (X 3 ) varied effect on dependent factor PDI (Y 2 ). The following equation [ 3 ] describes the mathematical relationship between the observed responses (PDI), Y 2 and various effects of independent variables: Y 2 (PDI) = 0.382 + 0.0065 X 1 – 0.009 X 2 + 0.006 X 3 – 0.0258 X 1 X 2 + 0.00725 X 2 X 3 + 0.02125 X 1 X 3 – 0.0591 X 1 2 + 0.08338 X 2 2 + 0.06838 X 3 2 ………………… [ 3 ] According to the above equation, the response Y 2 is quantitatively affected by the independent variables X 1 (amount of oil), X 2 (amount of surfactant), and amount of co-surfactant (X 3 ), as well as by their binary interactions and exponential effects. Their significant impact on Y 2 was demonstrated by the coefficient's p value (Table 6 ). The coefficient's negative value indicates that independent factors work together to lower PDI, whereas its positive sign indicates that independent variables have a positive impact on PDI. Their significant impact on the outcome is indicated by the factor's higher coefficient value. The quadratic model was well-fitted for PDI response. Multiple correlation and ANOVA were used to confirm the effectiveness of the model. The quadratic model's multiple correlation test (R 2 ) and ANOVA results are exhibited in Table 7 . The significant effect of independent factors in predicting the response variable (Y 2 ) was corroborated by the observed R 2 value (0.8524) and p value (< 0.05). Table 6 Regression coefficient values and corresponding p-values for each measured response. Intercept X 1 X 2 X 3 X 1 X 2 X 1 X 3 X 2 X 3 X 1 2 X 2 2 X 3 2 α0 α1 α2 α3 α4 α5 α6 α7 α8 α9 Globule size (Y 1 ) 204 4.9 -25.3 -3.075 -11.05 0.6 0.9 -16.8 7.4 -6.2 p-values 0.1913 0.0006 0.3867 0.061 0.901 0.8522 0.0169 0.1819 0.2508 Polydispersity index (Y 2 ) 0.382 0.0065 -0.009 0.006 -0.0258 0.00725 0.02125 -0.0591 0.08338 0.06838 p-values 0.7125 0.6123 0.7335 0.3243 0.7707 0.4085 0.0608 0.0193 0.0385 Zeta Potential (Y 3 ) -38.04 10.6075 0.775 -0.5475 -0.0175 0.1525 -0.1075 23.7288 0.43375 1.10375 p-values < 0.0001 0.2694 0.4205 0.9849 0.8696 0.9078 < 0.0001 0.6567 0.2833 Percentage transmittance (Y 4 ) 90.0727 -2.24625 4.43125 0.765 - - - - - - p-values 0.0148 0.0001 0.3469 - - - - - - *p < 0.05 indicate significant terms Table 7 Statistical analysis of the measured responses S. No. Variables Responses Model Predicted R 2 Adjusted R 2 Model p value Lack of fit P value 1 Y 1 (nm) Quadratic 0.9456 0.8477 0.0214 0.489 2 Y 2 Quadratic 0.8524 0.7868 0.0197 0.253 3 Y 3 (mV) Quadratic 0.9948 0.9855 < 0.0001 0.562 4 Y 4 (%) Linear 0.9713 0.8343 0.0005 0.324 P 0.05) with the X 1 factor (amount of oil) (Table 6 ). Furthermore, a non significant drop in PDI value was seen at the higher lipid content (X 1 2 ). This slight variation in PDI could be the result of adding oil at a constant surfactant concentration, which raises PDI. The PDI also exhibited a not statistically significant decline when the X 2 factor (surfactant concentration) was taken into account. The droplet size reduces and homogeneity improves as a result of the surfactant's known ability to break interfacial tension [ 42 ]. As a result, the PDI decreased negligibly as the amount of surfactant (X 2 ) increased. PDI increased non-significantly (p > 0.05) as a result of the single effect of co-surfactant (X 3 ). An increase in the surfactant's intrinsic emulsifying properties combined with the emulsifying properties of the mono- and diglycerides present in the oil phase may be the cause of the not demonstrating statistical significance (p > 0.05) reduction in PDI shown by the interactive impact of the quantity of oil and surfactant (X 1 X 2 ). A not statistically significant (p > 0.05) rise in PDI was seen as a result of the interrelationship between the amount of surfactant and co-surfactant (X 2 X 3 ). The amount of oil and co-surfactant (X 1 X 3 ) had an interactive impact that increased the PDI non-significantly (p > 0.05). However, the emulsifying properties of the mono- and diglycerides present in the oil phase may be the cause of the reduction in PDI shown by the exponential effect of the amount of oil (X 1 2 ). The relatively small size micelle of Tween 80 (CMC of Tween 80 is (13–15 mg/L) may be the cause of the considerable (p < 0.05) increase in PDI that was observed in the exponential effect of the amount of surfactant (X 2 2 ). PDI may therefore rise as a result of using too much surfactant (Tween 80). Because a higher concentration of Transcutol P can interfere with the emulsifier's ability to stabilize the emulsion, an exponential effect of the quantity of co-surfactant (X 3 2 ) also exhibited a substantial (p < 0.05) increase in PDI. The surfactant molecules may be dislodged or form micelles in place of stable films around droplets, increasing the size of the distribution [ 43 ]. The response surface plot for PDI is depicted in Fig. 3 . 3.3.3 Zeta Potential Zeta potential was examined to determine the stability of the resulting nanoemulsion. Zeta potential results vary from − 1.88 mV to -38.04 mV. Labrafil M2125-CS (X 1 ), Tween 80 (X 2 ), and Transcutol P (X 3 ), their interactive exponential effects showed variable on zeta potential (Y 3 ). The following equation [ 4 ] describes the mathematical relationship between observed response (zeta potential), Y 3 , and various independent factors. Y 3 (Zeta potential) = − 38.4 + 10.6075 X 1 + 0.775 X 2 – 0.5475 X 3 – 0.0175 X 1 X 2 + 0.1525 X 2 X 3 – 0.1075 X 1 X 3 + 23.7288 X 1 2 + 0.43375 X 2 2 + 1.10375 X 3 2 ………………… [ 4 ] The measurable effects of the independent variables X 1 (amount of oil), X 2 (amount of surfactant), and amount of co-surfactant (X 3 ) on the zeta potential (Y 3 ) are depicted in the equation above, along with their binary interactions and exponential effects. Collegial effect is shown by a positive coefficient sign, whereas the opposite effect of the independent variables on response is indicated by a negative coefficient sign. Their significant impact on the response is indicated by the higher coefficient value of the factor. The quadratic model showed a good fit in this setting. The multiple correlation tests (R 2 ) and ANOVA were used to confirm the effectiveness of the model. The results showed that the R 2 value was 0.9948 and the p value was p < 0.05. The R 2 value (0.9948) and observed p value (p < 0.05) verified that independent variables significantly influenced the response (Y 3 ) prediction. The zeta potential increased significantly (p < 0.05) with the X 1 factor (amount of oil) (Table 6 ). The zeta potential values significantly decreased at the greater lipid content (X 1 ). Oil droplets dilute the stabilizing surfactant at the interface, lowering the concentration of charge carriers and shifting the shear plane, consequently adding more oil to an oil-in-water emulsion generally lowers the zeta potential [ 44 ]. Additionally, there was also a not statistically significant drop in the zeta potential for the X 2 factor (surfactant concentration). Zeta potential increased insignificantly (p > 0.05) as a result of the co-surfactant (X 3 ) single effect. The amount of oil and surfactant (X 1 X 2 ) had an interactive influence that increased zeta potential did not reach statistical significance (p > 0.05). Zeta potential decreased in a not statistically significant (p > 0.05) way as a result of the interaction between the amount of surfactant and co-surfactant (X 2 X 3 ). The amount of oil and co-surfactant (X 1 X 3 ) had an interactive impact that increased zeta potential in a not significant at the statistical level (p > 0.05). Nevertheless, the zeta potential showed a significant reduction (p 0.05) when the amount of surfactant (X 2 2 ) increased exponentially. Therefore, a reduction in zeta potential may result from using too much surfactant (Tween 80). Similarly, an insignificant (p > 0.05) drop in zeta potential was seen as the quantity of co-surfactant (X 3 2 ) increased exponentially. The response surface plot for zeta potential is depicted in Fig. 3 . 3.3.4 Percentage Transmittance To make sure the resulting nanoemulsion was clear and transparent, % transmittance was examined. Higher transmittance is produced by clear solutions and dispersions, whereas lower transmittance is produced by cloudier or turbid solutions and dispersions because the latter scatter more incident radiation. The transmittance percentage ranges from 79.73% to 96.33%. It was found that when the amount of oil in the mixture decreased and the amount of surfactant and co-surfactant increased, correspondingly, the percentage transmittance increased. Below is the complete quadratic equation [ 5 ] for the observed response variable. Y 4 (% Transmittance) = 90.0727–2.24625 X 1 + 4.43125 X 2 + 0.765 X 3 ………………… [ 5 ] The measurable effects of the independent variables X 1 (amount of oil), X 2 (amount of surfactant), and amount of co-surfactant (X 3 ) on the % Transmittance (Y 4 ) are depicted in the equation above. Collegial effect is shown by a positive coefficient sign; whereas the opposite effect was observed for the independent variables on response is indicated by a negative coefficient sign. Their significant impact on the response is indicated by the higher coefficient value of the factor. The linear model showed a good fit in this setting. The multiple correlation tests (R 2 ) and ANOVA were used to confirm the effectiveness of the model. Given that the R 2 value (0.9713) and p value (p < 0.05) showed the model was well-fitted to the linear model, the aforementioned equation [ 5 ] demonstrated a strong fit to the response variable (Y 4 ). The percentage transmittance showed a significant (p < 0.05) decline with the X 1 factor (quantity of oil) (Table 6 ). The extent of the percentage transmittance significantly decreased at the increased amount of lipid (X 1 ). Since the oil droplets dilute the stabilizing surfactant at the interface, lowering the concentration of surfactant at the interface and producing a more turbid emulsion because of larger droplet size, adding more oil to an oil-in-water emulsion typically lowers the transmittance percentage [ 12 , 45 ]. However, the X 2 factor (surfactant amount) demonstrated a notable rise (p 0.05) increase in transmittance percentage. According to this, the percentage transmittance raises when the concentration of Tween 80 (surfactant) and Transcutol P (co-surfactant) rises and the amount of oil (Labrafil M2125-CS) decreases. The response surface plot for percentage transmittance is depicted in Fig. 3 . 3.4 Optimization The range of the percent bias is + 0.789 to -1.616%. The desirability function is based on transforming each response into a dimensionless desirability value. The desirability function has a value between 0 and 1. A number of 1 indicates the best response for the elements being studied, while a value of 0 is seen when the factors produce undesired consequences [ 12 ]. Using this method, the intended robust formulation that satisfies the highest requirements of all responses within the specified limitations is produced. In this study, Design Expert® V.13.5 (Stat-Ease Inc., Minneapolis, MN) was used to implement the desirability function process. All of the responses were subject to the constraints. Table 3 shows the range in which the independent variables (factors) were specified. Every response received the same weight (1) and importance (+++), which are determined by the software's constraints. The default setting for weight and importance is three pluses (+++), which denotes that every response is equally important. To combine all of the responses into a single measurement, the desirability function approach requires the calculation of an individual desirability function. This will assist in predicting the independent factors optimum amounts [ 12 ]. The desirability function was utilized to optimize the process after a polynomial equation was generated and the impacts of independent factors on responses were examined. The optimal formulation that satisfied the greatest number of response variable requirements and had the best desirability function was chosen. The overall desirability of the chosen optimized formulation, which has X 1 = 125.92 mg w/w, X 2 = 665.81 mg w/w, and X 3 = 185.86 mg w/w, was determined to be 0.794. For the responses Y 1 , Y 2 , Y 3 and Y 4 , the optimum formulation predicted values of 216.8 ± 1.044 nm, 0.439 ± 0.024, -38.04 ± 0.372 mV, and 88.12 ± 0.14%, respectively. The optimized formulation was created in triplicate to verify and validate the optimization. Every response was assessed using observed values for every formulation. (Table 8 ) displays a comparison between the observed and predicted values. All of the obtained values were in agreement with the expected values, as was evident from the findings, suggesting that BBD in conjunction with the desirability function is a potential method for SNEDDS optimization and assessment. Table 8 Quantitative analysis of the predicted and observed value S. No. Variables Responses Predicted Value Observed Value % Bias 1 Globule size (Y 1 ) 215.1 216.8 0.789 2 Polydispersity index (Y 2 ) 0.446 0.439 -1.616 3 Zeta potential (Y 3 ) -37.98 -38.04 0.167 4 Percentage transmittance (Y 4 ) 88.08 88.12 0.051 3.5 Characterization of Elagolix Sodium SNEDDS 3.5.1 Droplet Size and PDI The developed ELAG-SNEDDS formulation was characterized by measuring droplet size and polydispersity index (PDI) to assess the quality and uniformity of the nanoemulsion system. In SNEDDS studies, acceptable PDI values are typically considered to be below 0.5, indicating narrow size distribution and formulation uniformity. Droplet size is a critical parameter in SNEDDS characterization because nanoscale droplets provide a high interfacial area that can enhance drug dissolution and absorption in the gastrointestinal tract, contributing to improved oral bioavailability [ 46 ]. Smaller and uniform droplets also favour consistent drug release profiles during in vitro and in vivo performance evaluation. Polydispersity index reflects the breadth of the droplet size distribution, with lower values indicating a more homogeneous population and stable nanoemulsion dispersion [ 47 ]. Thus, the observed droplet size and PDI results, 216.8 nm and 0.439 (Fig. 4 ) confirmed that the optimized ELAG-SNEDDS possess desirable physicochemical properties that support efficient drug delivery and stability. 3.5.2 Zeta potential Zeta potential is an important physical quantity in the determination of stability of emulsion. The potential of a nanoparticle applied to the shear plane under an electric field is known as the zeta potential [ 48 ]. According to several publications, zeta potential values between ± 0 and 10 mV are considered extremely unstable, between ± 10 and 20 mV are quite stable, between ± 20 and 30 mV are considered reasonably stable, whereas those exceeding ± 30 mV are regarded as highly stable [ 49 ]. Nevertheless, according to DLVO theory, some colloids have lower Zeta ( ζ) -potential but remain stable, which could be attributed to the combined action of the electrical double layer's electrostatic repulsive and van der Waals attractive forces [ 50 ]. The colloidal stability is also caused by certain steric interactions [ 51 ]. Since the hydrocarbon tail of the Tween 80 (surfactant) and lipid phase surface put forth a lipophilic interaction that create higher energy barriers among the dispersed globules, hence colloidal formulations will not exhibit any instability (coalescence) due to the steric effects and negative zeta potential. Furthermore, certain non-DLVO factors, such as the high concentration of nonionic surfactant and the hydration of its polar group, contribute to the system's inherent stability [ 52 ]. In the current setting zeta potential was found to be -38.04 ± 0.37 mV (Fig. 4 ). The negative zeta potential in this study may be caused by the esters and free fatty acids’ negative charge over the oil droplets [ 53 ]. 3.5.3 Percent Transmittance The percent transmittance of the ELAG-SNEDDS was determined to assess the optical clarity and homogeneity of the nanoemulsion upon dilution. Percent transmittance represents the amount of light transmitted through the sample, with high values indicating a clear and transparent nanoemulsion with minimal light scattering due to small droplet size [ 54 ]. High transmittance values are often correlated with droplet sizes in the nanometric range and uniform dispersion of droplets throughout the continuous phase, reflecting efficient self‑emulsification and formulation stability. In SNEDDS characterization, percent transmittance closer to 100% has been used as an indicator of successful nanoemulsion formation and is considered a critical quality attribute of optimized formulations [ 55 ]. Higher transmittance also suggests reduced potential for drug precipitation and enhanced surface area available for drug release. Thus, the observed transmittance results, 88.12% confirmed the formation of a uniform and optically clear ELAG-SNEDDS, supporting the formulation’s potential to improve dissolution and bioavailability. 3.5.4 Dispersibility Test The ability of a nanoemulsion to maintain a stable, uniform dispersion of water and oil droplets in the sub-micron size range because of surfactants and co-surfactants that reduce interfacial tension is considered as an important parameter to evaluate nanoemulsion. By avoiding sedimentation, creaming, and coalescence, this stability guarantees that the tiny droplet sizes during shelf life, offering advantages including optical transparency, increased bioavailability of low bioavailable therapeutic agents. Within one minute of completing the dispersibility test, All ELAG-SNEDDS formulations in this study were verified to be clear. They are very noticeable and of excellent quality. In order to produce nanoemulsions with in the GI fluid, the SNEDDS are distributed throughout the GIT lumen. Under GIT conditions, this dispersibility must happen fully and swiftly. The ELAG-SNEDDS formulation is Grade A, which forms a clear or bluish nanoemulsion quickly (within 1 min). 3.5.5 Self-Nanoemulsification Efficiency Test Self-emulsification time measures the time to spontaneously emulsify a pre-concentrate when diffused in an aqueous medium, typically under mild agitation, is a crucial parameter in assessing the effectiveness of self-emulsifying drug delivery systems. A more effective formulation that quickly turns into a stable emulsion is indicated by a shorter self-emulsification time, which is ideal for enhancing drug miscibility and bioavailability [ 12 ]. Visual examination was used to evaluate the self-emulsification time of ELAG-SNEDDS. SE timings that was determined for the developed optimized formulation was found to be 37.33 ± 2.5 sec. As evidenced by the fact that the time of formulation was emulsified in less than a minute, predicting good self-emulsification time. 3.5.6 Thermodynamic Stability and pH robustness Finding formulations that demonstrate metastability is the main goal of the thermodynamic stability study. The emulsions remained stable throughout the heating–cooling and freeze–thaw cycles, as well as during 30 minutes of centrifugation at 5000 rpm, suggesting neither phase separation nor precipitation occurred. The thermodynamic stability evaluation of SNEDDS produced positive results (Table 9 ), showing strong stability even under stressful circumstances. Notably, the results showed no evidence of flocculation, crystallization, or phase separation. Determining the cloud point is essential for forecasting the prepared SNEDDS's stability and precipitation patterns. It acts as an indicator for the possibility of surfactant precipitation at high temperatures. This concern emerges because higher temperatures may cause surfactant molecules to lose water, which would cause the formulation to gel and lose some of its emulsifying qualities. When ice crystals develop and then melt, a freeze-thaw cycle on emulsions can lead to instability, which can result in phase separation, droplet coalescence, and a permanent increase in particle size. Nanoemulsion droplets may congregate during freezing due to ice crystals, and the melted liquid may not fully re-disperse them upon thawing, producing an unstable and separated product [ 56 ]. Present ELAG-SNEDDS formulation showed stability after a freeze–thaw testing at − 20°C and 25°C. Emulsions are studied and controlled by cycles of heating and cooling that alter their physical stability, break the emulsion completely, or produce smaller droplets through freezing and melting processes [ 57 ]. After a heating-cooling cycle of at 4°C and 45°C showed thermodynamic stability. The optimized nanoemulsion was diluted with distilled water, 0.1 N HCl, and phosphate buffer (pH 6.8); no phase separation and coalescence was observed. Table 9 Thermodynamic stability and pH dilution results S. No Name of formulation Centrifugation (5000 rpm for 30 min) Freeze–thaw cycling between − 20°C and 25°C Heating–cooling cycles between 4°C and 45°C pH dilution Distilled water 0.1N HCL pH 6.8 buffer 1 Optimized ELAG-SNEDDS √ √ √ √ √ √ 3.5.7 Determination of Cloud Point Essentially, cloud points of the developed ELAG-SNEDDS were determined to be more than 70°C, suggesting that the nanoemulsion area would remain stable at physiologic temperature, hence removing the chance of precipitation or phase separation. At 78°C ELAG-SNEDDS formulation becomes hazy. The stability of emulsions containing nonionic surfactants, such as Tween 80, can be assessed using cloud point analysis. Since the surfactant begins to lose its hydrophilicity because of dehydration occurring in the polyoxyethylene chain of Tween 80, its HLB value shifts (toward lipophilicity) as the temperature rises [ 12 ]. This causes the inversion of emulsion from oil in water to water to oil, moreover droplet size of the emulsion increased, which ultimately causes the transparent emulsion to become hazy. In order to avoid phase inversion and phase separation of the SNEDDS at the physiological temperature of the gastrointestinal tract, the nanoemulsion formulation must have a cloud point above 37°C. This would prevent precipitation of drug and cloudy appearance [ 58 ]. The outcome indicated that at room temperature, the emulsion may exhibit the maximum likelihood of stability. 3.5.8 Morphological study Transmission electron microscopy (TEM) was performed to characterize the morphology and structural features of the ELAG-SNEDDS formulation. The TEM images demonstrated well-defined, spherical nano-droplets with smooth surfaces, indicating the formation of a uniform nanoemulsion system (Fig. 5 ). The droplets appeared discrete and evenly dispersed, reflecting good colloidal stability without signs of aggregation or coalescence. The observed particle dimensions were consistent with the nanometric size range obtained, confirming the reliability of the formulation’s size distribution. Furthermore, the clarity and uniformity of the droplets supported the efficiency of the selected surfactant–co-surfactant blend in stabilizing the nanoemulsion. Overall, the TEM results validate the morphological integrity, stability, and nanoscale characteristics of the prepared ELAG-SNEDDS. 3.6 In-Vitro Drug Release Study Using a modified dialysis technique, the in-vitro assessment of the drug release behaviour from ELAG-SNEDDS and the pure drug suspension was examined in 0.1 N HCL and PBS media (pH 6.8) maintained at 37°C for 12 h. Figure 6 illustrates the comparative drug release profiles of the pure drug suspension and the optimized ELAG-SNEDDS. The in vitro drug release profiles of ELAG-SNEDDS showed consistently improved release in PBS 6.8, whereas drug release was significantly reduced in 0.1 N HCL when compared to the suspension of pure drug, as shown in Fig. 6 . In the first two hours, approximately 48.08% of elagolix sodium was released from the pure drug suspension in PBS 6.8, whereas 71.5% of elagolix sodium was quick initial released from the optimized SNEDDS formulation in the same medium. Approximately 40.02% of elagolix sodium was released from the pure drug suspension in 0.1 N HCl, compared to 63.7% quick initial released from the optimized SNEDDS formulation in first two hours, respectively. In the continuous 12 hours study, ELAG-SNEDDS showed enhanced drug release of 94.4%, compared to the pure drug suspension released only 61.63% of the drug in PBS (pH 6.8) over 12 hours. In contrast, drug release of 53.91% was observed in 0.1 N HCl for the pure drug suspension, compared to 79.44% for the ELAG-SNEDDS formulation, respectively. This pattern of drug release from ELAG-SNEDDS that transports the encapsulated drug in the form of fine emulsion to the site of uptake is beneficial in boosting bioavailability [ 59 ]. 3.7 Drug Permeation Using Ex Vivo Model An ex vivo drug permeation study was conducted to evaluate the ability of elagolix sodium to permeate through the non-everted gut sac method. The results demonstrated that the total drug permeation through the SD rat intestine was higher for ELAG-SNEDDS than for the pure drug suspension. After 6 hours, the apparent permeability coefficient as well as the steady-state flux associated with the pure drug suspension was evaluated as 1.43 × 10⁻ 4 cm²/s and 0.00043µg/min, ELAG-SNEDDS showed a measurable apparent permeability of 2.08 × 10⁻⁴ cm²/s and a steady-state flux of 0.00063 µg/min. The permeation profile demonstrated enhanced flux and cumulative drug transport for the optimized SNEDDS formulation compared to the pure drug. This improvement indicates improved membrane permeability and supports the potential of SNEDDS to enhance oral absorption of elagolix sodium. 3.8 Pharmacokinetic Study Clinical efficacy trials of the commercial product are included in the majority of them. In essence, elagolix sodium is a BCS class III drug that exhibited reduced permeability. Two groups participated in the in vivo investigations: one was fed ELAG-SNEDDS, and the other was given ELAG in the form of suspension. After oral delivery of the ELAG-SNEDDS and ELAG alone to Sprague Dawley (SD) female rats (n = 8) at a dosing level of 15.42 mg/kg through gavage, the concentration-time profile of ELAG were assessed in plasma (Fig. 7 ). ELAG and ELAG-SNEDDS pharmacokinetic parameters were derived from the plasma drug concentration and time (h) data (Table 10 ). The C max and AUC of ELAG-SNEDDS were higher than those of ELAG suspension alone. For ELAG-SNEDDS, the AUC 0–24 and C max values were found to be 137.5 ± 44.1 µg.h/mL and 16.4 ± 2.6 µg.h/mL, In comparison to 82.8 ± 14.3 µg.h/mL and 10.7 ± 1.5 µg.h/mL of the ELAG alone, respectively. The ELAG-SNEDDS enhanced the C max and AUC 0–24 by 1.66 and 1.53 times, respectively. Bile salts, phospholipids, and other colloidal systems produced by lipid based products may have enhanced diffusion over the undisturbed water layer and enhanced lymphatic absorption, which could account for this outcome [ 60 ]. Because ELAG-SNEDDS entered the bloodstream relatively faster after intestinal and lymphatic uptake, a lower t max was achieved. The t max for the ELAG and ELAG-SNEDDS was 1.8 and 1.2 hours respectively. Due to the higher permeability of ELAG-SNEDDS, it achieved maximum plasma concentration more rapidly, which resulted in a decrease in t max . The literature contains very little in vivo pharmacokinetic research for elagolix sodium. The bioavailability has been improved with ELAG-SNEDDS. The augmentation of oral uptake of ELAG, the outcomes of which have been shown in Table 10 . The bioavailability of ELAG-SNEDDS may be enhanced by spontaneous nanoemulsion formation within the GI tract, along with the extensive surface area generated by these nanosized globules. The gut epithelium layer is the only barrier that slows down the diffusion or absorption of drugs. When the formulation's high surfactant content goes through the intestine, it exhibit an interplay with the lipid bilayer's polar groups, breaking down the structure and boosting absoption and extent of uptake [ 31 ]. Lipid-based formulations undergo digestion by gastric and pancreatic lipases, breaking down triglycerides into monoglycerides and fatty acids, which form micelles and mixed micelles that facilitate drug absorption. In response to the presence of dietary lipids, bile salts secreted from the gall bladder form micelles and vesicular structure, facilitating the solubilization and subsequent absorption of lipid digestion products and drugs in the small intestine. Drug absorption in the intestine is mediated by specific transport proteins, such as solute carriers and ABC transporters, located on the apical and basolateral membranes of enterocytes. Several strategies facilitate drug transport to the lymphatic system after oral administration, such as transcellular absorption due to increase the membrane fluidity, paracellular transport by loosening tight junctions [ 61 , 62 ], uptake via Peyer's patch M cells [ 63 ] (Fig. 8 ). Lipids induced the formation of lipoproteins/chylomicrons [ 64 , 65 ] that enables lipophilic drug compounds to be solubilized by embedding them within the hydrophobic core and promote the absorption through intestinal lymphatics [ 66 ]. Moreover, surfactants and cosurfactant work by causing to open the tight junctions [ 67 ]. In this study Tween 80 and Transcutol P, which are utilized as surfactants and co-surfactants in the formulation might have caused to boost bioavailability because of their intermediate HLB value (Tween 80 = 15), which is also inhibit the P-gp substrate leads to enhance intracellular concentration of drug via intestinal lymphatic system [ 68 ]. Fundamentally, certain unique surfactants are considered to aid in opening tight junctions by interplay with proteins like Factin and actin anchoring protein at tight junctions [ 69 ]. Furthermore it is believed that nanoemulsions get taken up from the small intestine and delivered to the blood via the lymphatic pathway because of the globule size of nm, whereas the raw elagolix sodium entered into the enterohepatic circulation following peroral administration and eliminated [ 70 ]. According to the literature, the majority of lipidbased drug carriers that contain medium- and long-chain fatty acids, as well as high HLB value of surfactants amount bypass the portal vein and enters the lymphatic system of the intestine [ 62 , 71 ]. As SNEDDS, which was prepared to improve the therapeutic efficacy by enhancing the weakly bioavailable elagolix sodium, offers hope for the potential therapeutic strategy and treatment; however, more research is needed to determine whether this is clinically significant. Table 10 Evaluation of pharmacokinetic parameters (mean ± SD) of ELAG-SNEDDS and pure drug suspension in female SD rats (n = 8) after oral administration Pharmacokinetic Parameters ELAG-SNEDDS Pure drug suspension Value St dev. Value St dev. t½ (hr) 6.7 1.37 4.7 1.17 AUC 0 − 24 (µg.mL-1*hr) 137.5 44.11 82.8 14.33 AUC 0 − inf (µg.mL-1*hr) 149.1 46.4 86.4 15.56 t max (hr) 1.2 0.27 1.8 0.26 C max (µM) 16.4 2.64 10.7 1.47 4. Conclusions The optimum formulation of liquid SNEDDS, which are consists of Labrafil M2125-CS (125.92 mg) w/w, Tween 80 (665.81 mg) w/w and Transcutol-P (185.86 mg) w/w was chosen because it produces a nanoemulsion, with droplet size (216.8 ± 1.044 nm) zeta potential (-38.04 ± 0.372 mV), PDI (0.439 ± 0.024), and % transmittance (88.12 ± 0.14%), and considerable thermodynamic stability when dispersed in water. According to pharmacokinetic studies, the enhanced oral bioavailability may be because of the mechanism of nanoemulsification with increased surface area resulting into enhanced drug permeability. The optimized ELAG-SNEDDS may be a capable method for the oral absorption augmentation of low permeable drugs, as evidenced by the notable increase in elagolix sodium bioavailability. Declarations Conflict of Interest The authors declare no conflicts of interest or personal relationships that could have influenced the work reported in this paper. Ethics Declarations Ethical Approval Before starting the animal experiments, this research granted the approval by the Institutional Animal Ethics Committee (IAEC) of IIMT College of Medical Sciences, IIMT University, Meerut, Uttar Pradesh, India, which was established under the committee for the Purpose of Control and Supervision of Experiments on Animals (CPCSEA). Consent For Publication The final text and submission of the work have been approved by all authors. Funding This study was conducted without financial support from public, commercial, or not-for-profit funding agencies. Author Contribution Author 1 (Jonee Panwar)Did this experiment, wrote main manuscript, data collection, create figures and tablesAuthor 2 (Garima Garg)Validation of data, editing of manuscript and interpretation of resultsAuthor 3 (Hasan Ali)Validation of data, editing of manuscript and interpretation of results References Mohs, R. C., & Greig, N. H. (2017). 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Lipid Based Nanocarriers: Promising Drug Delivery System for Topical Application. European Journal of Lipid Science and Technology , 123 (5). https://doi.org/10.1002/ejlt.202000264 Zhang, H., Yao, M., Morrison, R. A., & Chong, S. (2003). Commonly used surfactant, Tween 80, improves absorption of P-glycoprotein substrate, digoxin, in rats. Archives of Pharmacal Research , 26 (9), 768–772. https://doi.org/10.1007/BF02976689 Zhang, Z., Lu, Y., Qi, J., & Wu, W. (2021). An update on oral drug delivery via intestinal lymphatic transport. Acta Pharmaceutica Sinica B , 11 (8), 2449–2468. https://doi.org/10.1016/j.apsb.2020.12.022 Caliph, S. M., Charman, W. N., & Porter, C. J. H. (2000). Effect of Short-, Medium‐, and Long‐Chain Fatty Acid‐Based Vehicles on the Absolute Oral Bioavailability and Intestinal Lymphatic Transport of Halofantrine and Assessment of Mass Balance in Lymph‐Cannulated and Non‐cannulated Rats. Journal of Pharmaceutical Sciences , 89 (8), 1073–1084. https://doi.org/10.1002/1520-6017(200008)89:8%3C1073::AID-JPS12%3E3.0.CO;2-V Beg, S., Sandhu, P. S., Batra, R. S., Khurana, R. K., & Singh, B. (2015). QbD-based systematic development of novel optimized solid self-nanoemulsifying drug delivery systems (SNEDDS) of lovastatin with enhanced biopharmaceutical performance. Drug Delivery , 22 (6), 765–784. https://doi.org/10.3109/10717544.2014.900154 Additional Declarations No competing interests reported. Supplementary Files Graphicalabstract.jpg Graphical abstract Cite Share Download PDF Status: Posted Version 1 posted 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. As a division of Research Square Company, we’re committed to making research communication faster, fairer, and more useful. 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Also discoverable on Platform About Our Team In Review Editorial Policies Advisory Board Help Center Resources Author Services Accessibility API Access RSS feed Manage Cookie Preferences © Research Square 2026 | ISSN 2693-5015 (online) Privacy Policy Terms of Service Do Not Sell My Personal Information {"props":{"pageProps":{"initialData":{"identity":"rs-8424074","acceptedTermsAndConditions":true,"allowDirectSubmit":true,"archivedVersions":[],"articleType":"Research Article","associatedPublications":[],"authors":[{"id":570749601,"identity":"cc215d79-5f0f-41ac-b06c-81ae9bcb4f80","order_by":0,"name":"Jonee 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06:35:51","extension":"html","order_by":24,"title":"","display":"","copyAsset":false,"role":"acdc-reference","size":287209,"visible":true,"origin":"","legend":"","description":"","filename":"earlyproof.html","url":"https://assets-eu.researchsquare.com/files/rs-8424074/v1/67cf8a63fe13d4f61db9eeaf.html"},{"id":99858804,"identity":"8313ecc5-8d2d-48b6-bdb3-c81874288443","added_by":"auto","created_at":"2026-01-09 06:35:51","extension":"jpg","order_by":1,"title":"Figure 1","display":"","copyAsset":false,"role":"figure","size":80406,"visible":true,"origin":"","legend":"\u003cp\u003eSolubility behavior of elagolix sodium in different oils (a), surfactants (b), and co-surfactants (c)\u003c/p\u003e","description":"","filename":"1.jpg","url":"https://assets-eu.researchsquare.com/files/rs-8424074/v1/50fa613ec27b37de10fcdb2a.jpg"},{"id":99858806,"identity":"fcb6d477-d5a1-47df-8e9f-39e469cbf8bf","added_by":"auto","created_at":"2026-01-09 06:35:51","extension":"jpg","order_by":2,"title":"Figure 2","display":"","copyAsset":false,"role":"figure","size":126966,"visible":true,"origin":"","legend":"\u003cp\u003e\u003cstrong\u003e(A) \u003c/strong\u003ePseudo-ternary phase diagrams depicting the o/w nanoemulsion (transparent region) of Labrafil M2125-CS, Tween 80, Transcutol P, and water at 25 °C for S\u003csub\u003emix\u003c/sub\u003e ratios: (a) 1:1, (b) 2:1, (c) 3:1, and (d) 4:1.\u003cstrong\u003e(B) \u003c/strong\u003eFormation of optically transparent emulsions during water titration for constructing the pseudo-ternary phase diagram\u003c/p\u003e","description":"","filename":"2.jpg","url":"https://assets-eu.researchsquare.com/files/rs-8424074/v1/f1c06386ce39f9b9bfdf93c6.jpg"},{"id":100357673,"identity":"033b631b-d49c-44e2-9d46-fe866471a5bb","added_by":"auto","created_at":"2026-01-16 07:20:10","extension":"jpg","order_by":3,"title":"Figure 3","display":"","copyAsset":false,"role":"figure","size":192323,"visible":true,"origin":"","legend":"\u003cp\u003eResponse surface plots illustrating the effects of X\u003csub\u003e1\u003c/sub\u003e and X\u003csub\u003e2\u003c/sub\u003e on the measured responses at the mid-level of X\u003csub\u003e3\u003c/sub\u003e: (a) Globule size (Y\u003csub\u003e1\u003c/sub\u003e), (b) Polydispersity index (Y\u003csub\u003e2\u003c/sub\u003e), (c) Zeta potential (Y\u003csub\u003e3\u003c/sub\u003e), and (d) Percent transmittance (Y\u003csub\u003e4\u003c/sub\u003e).\u003c/p\u003e","description":"","filename":"3.jpg","url":"https://assets-eu.researchsquare.com/files/rs-8424074/v1/84f0a76d6970da0ebff14bce.jpg"},{"id":100357958,"identity":"c1f51a96-f8c8-4c8f-bcc6-f07b60bf9267","added_by":"auto","created_at":"2026-01-16 07:20:31","extension":"jpg","order_by":4,"title":"Figure 4","display":"","copyAsset":false,"role":"figure","size":47950,"visible":true,"origin":"","legend":"\u003cp\u003eGlobule size (a) and zeta potential (b) of optimized ELAG-SNEDDS\u003c/p\u003e","description":"","filename":"4.jpg","url":"https://assets-eu.researchsquare.com/files/rs-8424074/v1/8ea7ae665309107e581a5a37.jpg"},{"id":100357665,"identity":"d73ce1b9-53db-46b3-a0f1-9a08aa6c7e7a","added_by":"auto","created_at":"2026-01-16 07:20:09","extension":"jpg","order_by":5,"title":"Figure 5","display":"","copyAsset":false,"role":"figure","size":181127,"visible":true,"origin":"","legend":"\u003cp\u003eTEM images of optimized ELAG-SNEDDS\u003c/p\u003e","description":"","filename":"5.jpg","url":"https://assets-eu.researchsquare.com/files/rs-8424074/v1/5cb8e5f02592c1f36ac7a194.jpg"},{"id":100357555,"identity":"b1c960a7-9d3c-4e87-880a-235fa90d5ee6","added_by":"auto","created_at":"2026-01-16 07:20:02","extension":"jpg","order_by":6,"title":"Figure 6","display":"","copyAsset":false,"role":"figure","size":63535,"visible":true,"origin":"","legend":"\u003cp\u003eDrug release profiles of pure drug and optimized ELAG-SNEDDS in 0.1 N HCl (A) and PBS pH 6.8 (B).\u003c/p\u003e","description":"","filename":"6.jpg","url":"https://assets-eu.researchsquare.com/files/rs-8424074/v1/1203ce3b93edced920631147.jpg"},{"id":100357400,"identity":"c270e960-ca84-4b2d-b836-f09c08e5f9e4","added_by":"auto","created_at":"2026-01-16 07:19:51","extension":"jpg","order_by":7,"title":"Figure 7","display":"","copyAsset":false,"role":"figure","size":67570,"visible":true,"origin":"","legend":"\u003cp\u003ePlasma concentration-time profile of elagolix sodium after oral administration of the pure drug and optimized ELAG-SNEDDS\u003c/p\u003e","description":"","filename":"7.jpg","url":"https://assets-eu.researchsquare.com/files/rs-8424074/v1/5cf17d8ae45f58922789deb8.jpg"},{"id":99858818,"identity":"57c7e651-2ea4-408e-a73b-d85e239ab51a","added_by":"auto","created_at":"2026-01-09 06:35:51","extension":"jpg","order_by":8,"title":"Figure 8","display":"","copyAsset":false,"role":"figure","size":188434,"visible":true,"origin":"","legend":"\u003cp\u003eMechanism of drug absorption via oral administration using nanoemulsion (NE)\u003c/p\u003e","description":"","filename":"8.jpg","url":"https://assets-eu.researchsquare.com/files/rs-8424074/v1/27ca9b904bac34dbd4f1bfb7.jpg"},{"id":107485937,"identity":"1f00868b-4a4c-4486-bd5e-b12c8af5e6cd","added_by":"auto","created_at":"2026-04-22 02:36:54","extension":"pdf","order_by":0,"title":"","display":"","copyAsset":false,"role":"manuscript-pdf","size":2167131,"visible":true,"origin":"","legend":"","description":"","filename":"manuscript.pdf","url":"https://assets-eu.researchsquare.com/files/rs-8424074/v1/624e00a8-3bf5-450b-9186-2cb60e67a669.pdf"},{"id":100357519,"identity":"a2bc2926-21bd-4dbf-8a28-905c8b160cdf","added_by":"auto","created_at":"2026-01-16 07:19:59","extension":"jpg","order_by":1,"title":"","display":"","copyAsset":false,"role":"supplement","size":138758,"visible":true,"origin":"","legend":"\u003cp\u003eGraphical abstract\u003c/p\u003e","description":"","filename":"Graphicalabstract.jpg","url":"https://assets-eu.researchsquare.com/files/rs-8424074/v1/2cf45cbbbb9568c6ffc82d1d.jpg"}],"financialInterests":"No competing interests reported.","formattedTitle":"Development, Optimization Using Box–Behnken, In Vitro, Ex Vivo Characterization \u0026 Pharmacokinetic Evaluation of Elagolix Sodium Loaded Self-Nano Emulsifying Drug Delivery System","fulltext":[{"header":"1 Introduction","content":"\u003cp\u003eThe purpose of drug development is to balance the activity of a drug at its target with absorption, distribution, metabolism and excretion (ADME) properties, ensuring it can successfully treat the disease [\u003cspan citationid=\"CR1\" class=\"CitationRef\"\u003e1\u003c/span\u003e]. The Biopharmaceutics Classification System (BCS) was established to provide a scientifically rigorous framework for classifying oral immediate-release drug formulations on the basis of their aqueous solubility and intestinal permeability, in conjunction with their dissolution behavior [\u003cspan citationid=\"CR2\" class=\"CitationRef\"\u003e2\u003c/span\u003e]. BCS Class III pharmaceutical compounds are largely hydrophilic and exhibit poor intestinal permeability. This classification may also be applied to new chemical entities (NCEs) possessing similar characteristics. Although such molecules typically show robust pharmacological activity in vitro, they pose considerable challenges in the development of oral dosage forms due to their limited permeability emerges as the key limitation in achieving adequate bioavailability [\u003cspan citationid=\"CR3\" class=\"CitationRef\"\u003e3\u003c/span\u003e]. Poor oral absorption due to low permeability prevents many drug candidates from being commercialized, despite the fact that this suggests possible pharmacodynamic effect. Furthermore, therapeutic agents with poor permeability are occasionally administered at much greater single doses than required to reach targeted plasma drug concentrations [\u003cspan citationid=\"CR4\" class=\"CitationRef\"\u003e4\u003c/span\u003e]. Pharmaceutical techniques to overcome poororal permeability and bioavailability of less permeable drugs include pH modification within the microenvironment, development of solute\u0026ndash;solvent interactions, solid dispersion, micronization, lipid-based systems, permeation enhancerand cyclodextrin-based molecular encapsulation using solvent deposition [\u003cspan citationid=\"CR5\" class=\"CitationRef\"\u003e5\u003c/span\u003e, \u003cspan citationid=\"CR6\" class=\"CitationRef\"\u003e6\u003c/span\u003e]. Lipid-based systems offer great potential as a promising technology, including microemulsion, nanoemulsion, self-nanoemulsifying drug delivery systems (SNEDDS) and related approaches for improving the oral bioavailability of low-permeability drugs [\u003cspan citationid=\"CR7\" class=\"CitationRef\"\u003e7\u003c/span\u003e].\u003c/p\u003e \u003cp\u003eSelf-nanoemulsifying drug delivery systems (SNEDDS) are isotropic, thermodynamically stable formulations that contain a drug, oils, surfactants, and co-surfactants. They are administered as oil-in-water (O/W) emulsions, with their characteristics influenced by the selected components and formulation method, they can produce coarse, micro-, or nano-size emulsions when they come into contact with stomach contents. The GI system naturally produces an emulsion as a result of mild agitation brought on by stomach motility, which is the main mechanism that helps SNEDDS increase the rate of permeability. The interfacial area expands as a result of the droplets' decreasing size, which facilitates drug uptake. As a result, SNEDDS makes less permeable drugs more permeate. It has been demonstrated that the usage of SNEDDS improves drug permeability, and lymphatic uptake, which increase drug absorption [\u003cspan citationid=\"CR8\" class=\"CitationRef\"\u003e8\u003c/span\u003e].\u003c/p\u003e \u003cp\u003eElagolix sodium (ELAG) exhibits poor permeability and high solubility, making it a BCS class III pharmacological agent [\u003cspan citationid=\"CR9\" class=\"CitationRef\"\u003e9\u003c/span\u003e]. According to in vitro research, elagolix sodium has a log D 7.4 value of 4.6, indicating a high level of lipophilicity. An increased affinity for lipids (fats) and a decreased affinity for water are indicated by a higher log D value. This characteristic is important for the body's absorption and distribution of drugs [\u003cspan citationid=\"CR10\" class=\"CitationRef\"\u003e10\u003c/span\u003e]. ELAG showed quick absorption with a (T\u003csub\u003emax\u003c/sub\u003e) of roughly one hour. ELAG exposure (maximum concentration [C\u003csub\u003emax\u003c/sub\u003e] and area under the curve [AUC] is higher than dosage proportional with single doses of 600\u0026ndash;1200 mg and dose proportional between 100 and 400 mg twice day. However, oral darunavir administration suffers from low oral bioavailability (37%) due to high low permeability, which is the primary reason of the low bioavailability [\u003cspan citationid=\"CR9\" class=\"CitationRef\"\u003e9\u003c/span\u003e, \u003cspan citationid=\"CR11\" class=\"CitationRef\"\u003e11\u003c/span\u003e] substantial first pass metabolism.\u003c/p\u003e \u003cp\u003eAn exhaustive analysis of the literature showed dearth of knowledge regarding the use of SNEDDS developed to enhance the bioavailability of poorly permeable drug of elagolix sodium. In order to improve oral bioavailability, the present study set out to create a SNEDDS. The solubility of elagolix sodium was examined in a range of oils, surfactants, and co-surfactants. The selected oils, surfactants, and co-surfactants showing the highest solubility for elagolix sodium were chosen to be the components of SNEDDS. Nanoemulsion (NEs) optimization, droplet size, in vitro drug release, self-emulsification time, thermodynamic stability, ex vivo studies and oral bioavailability of the proposed ELAG -SNEDDS formulations were tested in female SD rats.\u003c/p\u003e"},{"header":"2 Chemical Reagents, Animals and Methods","content":"\u003cdiv id=\"Sec3\" class=\"Section2\"\u003e \u003ch2\u003e2.1 Chemical Reagents\u003c/h2\u003e \u003cp\u003eElagolix sodium was a kind gift sample from Alkem Laboratory, Mumbai, India. Transcutol P (Diethylene glycol monoethyl ether), Labrafil M2125-CS (Linoleoyl Polyoxyl-6 glycerides), were received as gift samples from Gattefosse (France). Castor oil, Span-20 (sorbitan monolaurate), Span-80 (sorbitan monooleate), caprylic acid, Propylene glycol, Tryethylamain, Tetraglycol were purchased by Central Drug House (CDH), Gujarat, India. Tween 80 (Polyoxyethylene 20 sorbitan monooleate), Tween-20 (polyoxyethylene sorbitan monolaurate), sodium hydroxide, potassium dihydrogen phosphate, and sodium dihydrogen phosphate were purchased by Sisco Research Laboratories, Mumbai, India. Acetonitrile purchased from Thermofisher Scientific, Mumbai, India. Methanol was purchased from Sigma Aldrich (Merck) Germany. Ethanol was procured from MSB Chemical Limited, India. Coconut oil was purchased from local vender. Dialysis membrane (DM\u0026ndash;50, LA387-1MT) was sourced from HI-MEDIA, Mumbai, India. All other chemicals and reagents employed in this research were of high-purity grade and HPLC grade. Throughout the experimental investigations, freshly prepared double-distilled water was utilized.\u003c/p\u003e \u003c/div\u003e \u003cdiv id=\"Sec4\" class=\"Section2\"\u003e \u003ch2\u003e2.2 Animals\u003c/h2\u003e \u003cp\u003eAbout 8\u0026ndash;12 weeks old, 24 Female Sprague Dawley (SD) rats, with a body weight of 250\u0026ndash;300 g, were used in this research model. The animals were sourced from National Institute of Biologicals (NIB), Noida, Uttar Pradesh, India. The Sprague Dawley (SD) rats resided in cages with plastic bottoms and provided access to the food and water. The rats were kept at a temperature of 25\u0026thinsp;\u0026plusmn;\u0026thinsp;2\u0026deg;C and a relative humidity of 45\u0026thinsp;\u0026plusmn;\u0026thinsp;5%, respectively. All animals were given a ten-day adaptation period at the experimental facility prior to the actual testing.\u003c/p\u003e \u003c/div\u003e \u003cdiv id=\"Sec5\" class=\"Section2\"\u003e \u003ch2\u003e2.3 Methods\u003c/h2\u003e \u003cdiv id=\"Sec6\" class=\"Section3\"\u003e \u003ch2\u003e2.3.1 Solubility Studies\u003c/h2\u003e \u003cp\u003eThe solubility of elagolix sodium was studied in various excipients (oils, surfactants, and co-surfactants) and selection of the excipients was performed based on their capacity to dissolve the highest amount of drug. Various oils (Coconut oil, Labrafil M2125-CS, Castor oil and Caprylic acid), surfactants (Tween 80, Span-20, Span-80 and Tween-20) and co-surfactants (Tetraglycol, Propylene glycol, Transcutol P, and Ethanol) were studied by using shake flask method. A surplusquantity of drug was added to 2 ml of each oil, surfactant and co-surfactant possessing vortexing (V1000, Benchmark Scientific, Inc.) until homogeneous a mixture of drug was achieved. The capped vials were then shaken in a water bath at 37\u0026deg;C for 72 hours. The collected test samples were subsequently centrifuged at 1500 rpm for 15 minutes, and the supernatant layer was carefully collected for further analysis. Concentration of drug was calculated using a UV-visible spectrophotometer (UV-1900i, Shimadzu Corporation, Tokyo, Japan) at 275 nm following an appropriate dilution with methanol, in comparison to a blank (methanol).\u003c/p\u003e \u003c/div\u003e \u003cdiv id=\"Sec7\" class=\"Section3\"\u003e \u003ch2\u003e2.3.2 Phase Diagram\u003c/h2\u003e \u003cp\u003eThe solubility data were used to choose the appropriate components for the preparation of SNEDDS in order to construct the pseudo-ternary phase diagram and to find out a maximum self-emulsifying region. Labrafil M2125-CS as an oil phase, Tween 80 served as a surfactant and Transcutol P employed as a co-surfactant were selected for the construction of pseudo-ternary diagram. In order to identify self-emulsification area and choose the optimal concentration of Labrafil M2125-CS, Tween 80 and Transcutol P for the development of the most successful SNEDDS by using water titration method at room temperature. A selected surfactant and co-surfactant (S\u003csub\u003emix\u003c/sub\u003e) were mixed by using various weighed proportions (1:1, 2:1, 3:1, and 4:1) for each experimental group. Subsequently, the oil and certain S\u003csub\u003emix\u003c/sub\u003e proportions were rigorously mixed in different ratios of (1:9, 2:8, 3:7, 4:6, 5:5, 6:4, 7:3, 8:2, and 9:1), sequentially. A certain amount of aqueous phase volume (100\u0026micro;L) was steadily added to each combination, while gently stirring continuously on a magnetic stirrer and maintaining the temperature at 37\u0026deg;C and its physical characteristics were recorded. If it remained transparent, increasing volumes of the aqueous phase were added until the system turned cloud and lost its clarity. At this stage, the weight fraction of oil, surfactant, co-surfactant, and aqueous phase of this combination was calculated on a weight-by-weight basis for all S\u003csub\u003emix\u003c/sub\u003e ratios. The phase diagrams were developed using the grapher software version to depict the results and an area of the clear micro-emulsion was chosen as the suitable region.\u003c/p\u003e \u003c/div\u003e \u003cdiv id=\"Sec8\" class=\"Section3\"\u003e \u003ch2\u003e2.3.3 Experimental Model\u003c/h2\u003e \u003cp\u003eExperimental models were developed using a three-factor, three-level Box\u0026ndash;Behnken design (BBD) via Design Expert\u003cb\u003e\u0026reg;\u003c/b\u003e V.13.5 (Stat-Ease Inc., Minneapolis, MN). Among various several response surface methodologies, BBD is considered as an effective method for identifying effect of formulation variables (independent factors) with respect to the response variable. Essentially, The Box\u0026ndash;Behnken design (BBD) is a response surface methodology involving three factors, each at three levels used in the current research to analyze both the primary and interaction impacts of the independent factors (amount of oil, surfactant, and co-surfactants) on the behavior of the prepared SNEDDS and the appropriateness of the desirability function for formulation optimization [\u003cspan citationid=\"CR12\" class=\"CitationRef\"\u003e12\u003c/span\u003e]. To estimate the experimental error and evaluate the accuracy of the design, Box\u0026ndash;Behnken design needs fifteen experimental trials with three center points. A non-linear quadratic model equation as follows (Eq.\u0026nbsp;1):\u003c/p\u003e \u003cp\u003e \u003cem\u003eY\u003c/em\u003e\u0026thinsp;=\u0026thinsp;α\u003csub\u003e0\u003c/sub\u003e\u0026thinsp;+\u0026thinsp;α\u003csub\u003e1\u003c/sub\u003e\u003cem\u003eX\u003c/em\u003e\u003csub\u003e1\u003c/sub\u003e\u0026thinsp;+\u0026thinsp;α\u003csub\u003e2\u003c/sub\u003e\u003cem\u003eX\u003c/em\u003e\u003csub\u003e2\u003c/sub\u003e\u0026thinsp;+\u0026thinsp;α\u003csub\u003e3\u003c/sub\u003e\u003cem\u003eX\u003c/em\u003e\u003csub\u003e3\u003c/sub\u003e\u0026thinsp;+\u0026thinsp;α\u003csub\u003e4\u003c/sub\u003e\u003cem\u003eX\u003c/em\u003e\u003csub\u003e1\u003c/sub\u003e\u003cem\u003eX\u003c/em\u003e\u003csub\u003e2\u003c/sub\u003e\u0026thinsp;+\u0026thinsp;α\u003csub\u003e5\u003c/sub\u003e\u003cem\u003eX\u003c/em\u003e\u003csub\u003e2\u003c/sub\u003e\u003cem\u003eX\u003c/em\u003e\u003csub\u003e3\u003c/sub\u003e\u0026thinsp;+\u0026thinsp;α\u003csub\u003e6\u003c/sub\u003e\u003cem\u003eX\u003c/em\u003e\u003csub\u003e1\u003c/sub\u003e\u003cem\u003eX\u003c/em\u003e\u003csub\u003e3\u003c/sub\u003e\u0026thinsp;+\u0026thinsp;α\u003csub\u003e7\u003c/sub\u003e\u003cem\u003eX\u003c/em\u003e\u003csub\u003e1\u003c/sub\u003e\u003csup\u003e2\u003c/sup\u003e\u0026thinsp;+\u0026thinsp;α\u003csub\u003e8\u003c/sub\u003e\u003cem\u003eX\u003c/em\u003e\u003csub\u003e2\u003c/sub\u003e\u003csup\u003e2\u003c/sup\u003e\u0026thinsp;+\u0026thinsp;α\u003csub\u003e9\u003c/sub\u003eX\u003csub\u003e3\u003c/sub\u003e\u003csup\u003e2\u003c/sup\u003e \u0026hellip;\u0026hellip;\u0026hellip;.[\u003cspan citationid=\"CR1\" class=\"CitationRef\"\u003e1\u003c/span\u003e]\u003c/p\u003e \u003cp\u003eWhere, Y is the response variable for every combination of factors and the factor level; α0 represents the intercept, while α1\u0026ndash;α9 are the regression coefficients; X\u003csub\u003e1\u003c/sub\u003e, X\u003csub\u003e2\u003c/sub\u003e, and X\u003csub\u003e3\u003c/sub\u003e represent the independent variables employed in the research given in Table\u0026nbsp;\u003cspan refid=\"Tab1\" class=\"InternalRef\"\u003e2\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 2\u003c/div\u003e \u003cdiv class=\"CaptionContent\"\u003e \u003cp\u003eDifferent Types of variables used in Box\u0026ndash;Behnken design\u003c/p\u003e \u003c/div\u003e \u003c/caption\u003e \u003ccolgroup cols=\"4\"\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 \u003cthead\u003e \u003ctr\u003e \u003cth align=\"left\" colname=\"c1\"\u003e \u003cp\u003eS. No.\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c2\"\u003e \u003cp\u003eDependent variables\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c3\"\u003e \u003cp\u003eIndependent variables\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c4\"\u003e \u003cp\u003eObjectives for dependent variables\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\u003eGlobule size (Y\u003csub\u003e1\u003c/sub\u003e)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003eQuantity of oil added (X\u003csub\u003e1\u003c/sub\u003e)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003eMinimize\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\u003ePolydispersity index (Y\u003csub\u003e2\u003c/sub\u003e)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003eQuantity of surfactant added (X\u003csub\u003e2\u003c/sub\u003e)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003eMinimize\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\u003eZeta Potential (Y\u003csub\u003e3\u003c/sub\u003e)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003eQuantity of co-surfactant added (X\u003csub\u003e3\u003c/sub\u003e)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003eMaximize\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\u003ePercentage transmittance (Y\u003csub\u003e4\u003c/sub\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\u003eMaximize\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\u003eThe proportion of independent variables, primarily the percentage of oil phase (Labrafil M2125-CS; X\u003csub\u003e1\u003c/sub\u003e; 5\u0026ndash;20%), surfactant percentage (Tween 80; X\u003csub\u003e2\u003c/sub\u003e; 64\u0026ndash;76%), and percentage of co-surfactant (Transcutol P; X\u003csub\u003e3\u003c/sub\u003e; 16\u0026ndash;19%) were chosen in accordance with the findings of pseudo-ternary diagram. The preferred response variables included average droplet size (\u003cem\u003eY\u003c/em\u003e\u003csub\u003e\u003cem\u003e1\u003c/em\u003e\u003c/sub\u003e), polydispersity index (\u003cem\u003eY\u003c/em\u003e\u003csub\u003e\u003cem\u003e2\u003c/em\u003e\u003c/sub\u003e), zeta potential (\u003cem\u003eY\u003c/em\u003e\u003csub\u003e\u003cem\u003e3\u003c/em\u003e\u003c/sub\u003e), % transmittance (\u003cem\u003eY\u003c/em\u003e\u003csub\u003e\u003cem\u003e4\u003c/em\u003e\u003c/sub\u003e). The standardized coded variables chosen based on preliminary trials and pseudo-ternary phase diagrams presented in Table\u0026nbsp;\u003cspan refid=\"Tab2\" class=\"InternalRef\"\u003e3\u003c/span\u003e and were run experimentally with the coded factor levels in Table\u0026nbsp;\u003cspan refid=\"Tab3\" class=\"InternalRef\"\u003e4\u003c/span\u003e. ANOVA, multiple correlation coefficients (R\u003csup\u003e2\u003c/sup\u003e), and lack of fit tests were applied to validate the models.\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 3\u003c/div\u003e \u003cdiv class=\"CaptionContent\"\u003e \u003cp\u003eTransformation of coded variables into real measurements\u003c/p\u003e \u003c/div\u003e \u003c/caption\u003e \u003ccolgroup cols=\"4\"\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 \u003cthead\u003e \u003ctr\u003e \u003cth align=\"left\" colname=\"c1\"\u003e \u003cp\u003eStandardized levels\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c2\"\u003e \u003cp\u003eLow* (-1)\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c3\"\u003e \u003cp\u003eMiddle* (0)\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c4\"\u003e \u003cp\u003eHigh* (1)\u003c/p\u003e \u003c/th\u003e \u003c/tr\u003e \u003c/thead\u003e \u003ctbody\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eFactor 1 (X\u003csub\u003e1\u003c/sub\u003e) - Quantity of oil\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e5%\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e12.5%\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e20%\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eFactor 2 (X\u003csub\u003e2\u003c/sub\u003e) - Quantity of surfactant\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e64%\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e70%\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e76%\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eFactor 3 (X\u003csub\u003e3\u003c/sub\u003e) - Quantity of co-surfactant\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e16%\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e17.5%\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e19%\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003c/tbody\u003e \u003c/colgroup\u003e \u003ctfoot\u003e \u003ctr\u003e\u003ctd colspan=\"4\"\u003e*The independent variables were assigned low, medium, and high coded levels based on preliminary trials and pseudo-ternary phase diagrams developed to achieve optimal percentage transmittance.\u003c/td\u003e\u003c/tr\u003e \u003c/tfoot\u003e \u003c/table\u003e\u003c/div\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 4\u003c/div\u003e \u003cdiv class=\"CaptionContent\"\u003e \u003cp\u003eExperimental runs with coded factor levels for three variables\u003c/p\u003e \u003c/div\u003e \u003c/caption\u003e \u003ccolgroup cols=\"4\"\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 \u003cthead\u003e \u003ctr\u003e \u003cth align=\"left\" colname=\"c1\"\u003e \u003cp\u003eRun Number\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c2\"\u003e \u003cp\u003eFactor 1 (X\u003csub\u003e1\u003c/sub\u003e)\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c3\"\u003e \u003cp\u003eFactor 2 (X\u003csub\u003e2\u003c/sub\u003e)\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c4\"\u003e \u003cp\u003eFactor 3 (X\u003csub\u003e3\u003c/sub\u003e)\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\u003e0\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e0\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e0\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\u003e0\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e1\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e-1\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\u003e1\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e-1\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e0\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\u003e0\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e-1\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e-1\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\u003e0\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e0\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e0\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\u003e1\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e0\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e-1\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\u003e1\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e0\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e1\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\u003e-1\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e1\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e0\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\u003e1\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e1\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e0\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003e10\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e-1\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e0\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e-1\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003e11\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e0\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e0\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e0\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003e12\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e-1\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e0\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e1\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003e13\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e0\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e1\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e1\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003e14\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e0\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e-1\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e1\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003e15\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e-1\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e-1\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e0\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=\"Sec9\" class=\"Section2\"\u003e \u003ch2\u003e2.4 Preparation of Elagolix Sodium-Loaded SNEDDS\u003c/h2\u003e \u003cp\u003eConsidering the findings on saturated solubility and pseudo-ternary diagram of the development of a SNEDDS of elagolix sodium, different amount of oil, surfactant, and co-surfactant were employed. A number of combinations of SNEDDS (Table\u0026nbsp;\u003cspan refid=\"Tab4\" class=\"InternalRef\"\u003e5\u003c/span\u003e) have been developed by varying the proportions of the oil phase, surfactant, and co-surfactant. Firstly the required amount of co-surfactant (Transcutol P) and surfactant (Tween 80) were mixed together separately to produce the surfactant to the co-surfactant combination (S\u003csub\u003emix\u003c/sub\u003e). The drug (150 mg) was added to oil (Labrafil M2125-CS) in a clean glass vials under the continuous stirring using vortex (V1000, Benchmark Scientific, Inc.). The S-mix was incorporated to the drug-containing oil phase and stirred continuously for ten to fifteen minutes by means of a vortex mixer at 37\u0026deg;C to obtain a homogenous and transparent formulation [\u003cspan citationid=\"CR12\" class=\"CitationRef\"\u003e12\u003c/span\u003e]. A total of fifteen formulations of elagolix sodium were developed as per BBD model.\u003c/p\u003e \u003cp\u003e \u003cdiv class=\"gridtable\"\u003e\u003ctable float=\"Yes\" id=\"Tab4\" border=\"1\"\u003e \u003ccaption language=\"En\"\u003e \u003cdiv class=\"CaptionNumber\"\u003eTable 5\u003c/div\u003e \u003cdiv class=\"CaptionContent\"\u003e \u003cp\u003eObserved responses obtained from the randomized Box\u0026ndash;Behnken experimental runs\u003c/p\u003e \u003c/div\u003e \u003c/caption\u003e \u003ccolgroup cols=\"9\"\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=\"\u0026plusmn;\" class=\"colspec\" colname=\"c6\" colnum=\"6\"\u003e\u003c/div\u003e \u003cdiv align=\"char\" char=\"\u0026plusmn;\" class=\"colspec\" colname=\"c7\" colnum=\"7\"\u003e\u003c/div\u003e \u003cdiv align=\"char\" char=\"\u0026plusmn;\" class=\"colspec\" colname=\"c8\" colnum=\"8\"\u003e\u003c/div\u003e \u003cdiv align=\"char\" char=\"\u0026plusmn;\" class=\"colspec\" colname=\"c9\" colnum=\"9\"\u003e\u003c/div\u003e \u003cthead\u003e \u003ctr\u003e \u003cth align=\"left\" colname=\"c1\" morerows=\"1\" rowspan=\"2\"\u003e \u003cp\u003eExperimental run\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c2\" morerows=\"1\" rowspan=\"2\"\u003e \u003cp\u003eFormulation code\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colspan=\"3\" nameend=\"c5\" namest=\"c3\"\u003e \u003cp\u003eIndependent Variables\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colspan=\"4\" nameend=\"c9\" namest=\"c6\"\u003e \u003cp\u003eDependent Variables\u003c/p\u003e \u003c/th\u003e \u003c/tr\u003e \u003ctr\u003e \u003cth align=\"left\" colname=\"c3\"\u003e \u003cp\u003eX\u003csub\u003e1\u003c/sub\u003eLabrafil M2125 CS (mg)\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c4\"\u003e \u003cp\u003eX\u003csub\u003e2\u003c/sub\u003e Tween 80 (mg)\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c5\"\u003e \u003cp\u003eX\u003csub\u003e3\u003c/sub\u003e Transcutol P (mg)\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c6\"\u003e \u003cp\u003eY\u003csub\u003e1\u003c/sub\u003e Globule size (nm)\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c7\"\u003e \u003cp\u003eY\u003csub\u003e2\u003c/sub\u003e Polydispersity index (PDI)\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c8\"\u003e \u003cp\u003eY\u003csub\u003e3\u003c/sub\u003e Zeta potential (mV)\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c9\"\u003e \u003cp\u003eY\u003csub\u003e4\u003c/sub\u003e Transmittance (%)\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\u003eF1\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e \u003cp\u003e125\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e \u003cp\u003e700\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c5\"\u003e \u003cp\u003e175\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\"\u0026plusmn;\" colname=\"c6\"\u003e \u003cp\u003e204\u0026thinsp;\u0026plusmn;\u0026thinsp;1.61\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\"\u0026plusmn;\" colname=\"c7\"\u003e \u003cp\u003e0.382\u0026thinsp;\u0026plusmn;\u0026thinsp;0.028\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\"\u0026plusmn;\" colname=\"c8\"\u003e \u003cp\u003e-38.04\u0026thinsp;\u0026plusmn;\u0026thinsp;0.37\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\"\u0026plusmn;\" colname=\"c9\"\u003e \u003cp\u003e91.17\u0026thinsp;\u0026plusmn;\u0026thinsp;0.62\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\u003eF2\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e \u003cp\u003e125\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e \u003cp\u003e760\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c5\"\u003e \u003cp\u003e160\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\"\u0026plusmn;\" colname=\"c6\"\u003e \u003cp\u003e181.1\u0026thinsp;\u0026plusmn;\u0026thinsp;2.05\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\"\u0026plusmn;\" colname=\"c7\"\u003e \u003cp\u003e0.51\u0026thinsp;\u0026plusmn;\u0026thinsp;0.047\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\"\u0026plusmn;\" colname=\"c8\"\u003e \u003cp\u003e-35.91\u0026thinsp;\u0026plusmn;\u0026thinsp;0.48\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\"\u0026plusmn;\" colname=\"c9\"\u003e \u003cp\u003e92.34\u0026thinsp;\u0026plusmn;\u0026thinsp;0.3\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\u003eF3\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e \u003cp\u003e200\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e \u003cp\u003e640\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c5\"\u003e \u003cp\u003e175\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\"\u0026plusmn;\" colname=\"c6\"\u003e \u003cp\u003e241.8\u0026thinsp;\u0026plusmn;\u0026thinsp;1.96\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\"\u0026plusmn;\" colname=\"c7\"\u003e \u003cp\u003e0.498\u0026thinsp;\u0026plusmn;\u0026thinsp;0.032\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\"\u0026plusmn;\" colname=\"c8\"\u003e \u003cp\u003e-5.82\u0026thinsp;\u0026plusmn;\u0026thinsp;0.22\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\"\u0026plusmn;\" colname=\"c9\"\u003e \u003cp\u003e79.73\u0026thinsp;\u0026plusmn;\u0026thinsp;0.44\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\u003eF4\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e \u003cp\u003e125\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e \u003cp\u003e640\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c5\"\u003e \u003cp\u003e160\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\"\u0026plusmn;\" colname=\"c6\"\u003e \u003cp\u003e235.9\u0026thinsp;\u0026plusmn;\u0026thinsp;2.69\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\"\u0026plusmn;\" colname=\"c7\"\u003e \u003cp\u003e0.531\u0026thinsp;\u0026plusmn;\u0026thinsp;0.052\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\"\u0026plusmn;\" colname=\"c8\"\u003e \u003cp\u003e-35.25\u0026thinsp;\u0026plusmn;\u0026thinsp;0.5\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\"\u0026plusmn;\" colname=\"c9\"\u003e \u003cp\u003e82.85\u0026thinsp;\u0026plusmn;\u0026thinsp;0.74\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\u003eF5\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e \u003cp\u003e125\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e \u003cp\u003e700\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c5\"\u003e \u003cp\u003e175\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\"\u0026plusmn;\" colname=\"c6\"\u003e \u003cp\u003e204\u0026thinsp;\u0026plusmn;\u0026thinsp;1.61\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\"\u0026plusmn;\" colname=\"c7\"\u003e \u003cp\u003e0.382\u0026thinsp;\u0026plusmn;\u0026thinsp;0.028\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\"\u0026plusmn;\" colname=\"c8\"\u003e \u003cp\u003e-38.04\u0026thinsp;\u0026plusmn;\u0026thinsp;0.37\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\"\u0026plusmn;\" colname=\"c9\"\u003e \u003cp\u003e91.17\u0026thinsp;\u0026plusmn;\u0026thinsp;0.62\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\u003eF6\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e \u003cp\u003e200\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e \u003cp\u003e700\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c5\"\u003e \u003cp\u003e160\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\"\u0026plusmn;\" colname=\"c6\"\u003e \u003cp\u003e181\u0026thinsp;\u0026plusmn;\u0026thinsp;1.61\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\"\u0026plusmn;\" colname=\"c7\"\u003e \u003cp\u003e0.361\u0026thinsp;\u0026plusmn;\u0026thinsp;0.044\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\"\u0026plusmn;\" colname=\"c8\"\u003e \u003cp\u003e-2\u0026thinsp;\u0026plusmn;\u0026thinsp;0.26\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\"\u0026plusmn;\" colname=\"c9\"\u003e \u003cp\u003e89.75\u0026thinsp;\u0026plusmn;\u0026thinsp;0.45\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\u003eF7\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e \u003cp\u003e200\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e \u003cp\u003e700\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c5\"\u003e \u003cp\u003e190\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\"\u0026plusmn;\" colname=\"c6\"\u003e \u003cp\u003e176.5\u0026thinsp;\u0026plusmn;\u0026thinsp;3.72\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\"\u0026plusmn;\" colname=\"c7\"\u003e \u003cp\u003e0.373\u0026thinsp;\u0026plusmn;\u0026thinsp;0.034\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\"\u0026plusmn;\" colname=\"c8\"\u003e \u003cp\u003e-2.04\u0026thinsp;\u0026plusmn;\u0026thinsp;0.34\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\"\u0026plusmn;\" colname=\"c9\"\u003e \u003cp\u003e91.6\u0026thinsp;\u0026plusmn;\u0026thinsp;0.56\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\u003eF8\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\u003e760\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c5\"\u003e \u003cp\u003e175\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\"\u0026plusmn;\" colname=\"c6\"\u003e \u003cp\u003e169.5\u0026thinsp;\u0026plusmn;\u0026thinsp;1.4\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\"\u0026plusmn;\" colname=\"c7\"\u003e \u003cp\u003e0.366\u0026thinsp;\u0026plusmn;\u0026thinsp;0.048\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\"\u0026plusmn;\" colname=\"c8\"\u003e \u003cp\u003e-21.9\u0026thinsp;\u0026plusmn;\u0026thinsp;0.3\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\"\u0026plusmn;\" colname=\"c9\"\u003e \u003cp\u003e96.33\u0026thinsp;\u0026plusmn;\u0026thinsp;0.55\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\u003eF9\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e \u003cp\u003e200\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e \u003cp\u003e760\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c5\"\u003e \u003cp\u003e175\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\"\u0026plusmn;\" colname=\"c6\"\u003e \u003cp\u003e171.5\u0026thinsp;\u0026plusmn;\u0026thinsp;0.64\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\"\u0026plusmn;\" colname=\"c7\"\u003e \u003cp\u003e0.389\u0026thinsp;\u0026plusmn;\u0026thinsp;0.057\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\"\u0026plusmn;\" colname=\"c8\"\u003e \u003cp\u003e-1.88\u0026thinsp;\u0026plusmn;\u0026thinsp;0.22\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\"\u0026plusmn;\" colname=\"c9\"\u003e \u003cp\u003e91.88\u0026thinsp;\u0026plusmn;\u0026thinsp;0.69\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003e10\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003eF10\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\u003e700\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c5\"\u003e \u003cp\u003e160\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\"\u0026plusmn;\" colname=\"c6\"\u003e \u003cp\u003e186.7\u0026thinsp;\u0026plusmn;\u0026thinsp;1.99\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\"\u0026plusmn;\" colname=\"c7\"\u003e \u003cp\u003e0.424\u0026thinsp;\u0026plusmn;\u0026thinsp;0.042\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\"\u0026plusmn;\" colname=\"c8\"\u003e \u003cp\u003e-24.07\u0026thinsp;\u0026plusmn;\u0026thinsp;0.65\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\"\u0026plusmn;\" colname=\"c9\"\u003e \u003cp\u003e91.47\u0026thinsp;\u0026plusmn;\u0026thinsp;0.37\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003e11\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003eF11\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e \u003cp\u003e125\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e \u003cp\u003e700\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c5\"\u003e \u003cp\u003e175\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\"\u0026plusmn;\" colname=\"c6\"\u003e \u003cp\u003e204\u0026thinsp;\u0026plusmn;\u0026thinsp;1.61\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\"\u0026plusmn;\" colname=\"c7\"\u003e \u003cp\u003e0.382\u0026thinsp;\u0026plusmn;\u0026thinsp;0.028\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\"\u0026plusmn;\" colname=\"c8\"\u003e \u003cp\u003e-38.04\u0026thinsp;\u0026plusmn;\u0026thinsp;0.37\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\"\u0026plusmn;\" colname=\"c9\"\u003e \u003cp\u003e91.17\u0026thinsp;\u0026plusmn;\u0026thinsp;0.62\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003e12\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003eF12\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\u003e700\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c5\"\u003e \u003cp\u003e190\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\"\u0026plusmn;\" colname=\"c6\"\u003e \u003cp\u003e179.8\u0026thinsp;\u0026plusmn;\u0026thinsp;1.26\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\"\u0026plusmn;\" colname=\"c7\"\u003e \u003cp\u003e0.407\u0026thinsp;\u0026plusmn;\u0026thinsp;0.058\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\"\u0026plusmn;\" colname=\"c8\"\u003e \u003cp\u003e-24.72\u0026thinsp;\u0026plusmn;\u0026thinsp;0.61\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\"\u0026plusmn;\" colname=\"c9\"\u003e \u003cp\u003e92.43\u0026thinsp;\u0026plusmn;\u0026thinsp;0.75\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003e13\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003eF13\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e \u003cp\u003e125\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e \u003cp\u003e760\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c5\"\u003e \u003cp\u003e190\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\"\u0026plusmn;\" colname=\"c6\"\u003e \u003cp\u003e176.3\u0026thinsp;\u0026plusmn;\u0026thinsp;2.76\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\"\u0026plusmn;\" colname=\"c7\"\u003e \u003cp\u003e0.579\u0026thinsp;\u0026plusmn;\u0026thinsp;0.032\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\"\u0026plusmn;\" colname=\"c8\"\u003e \u003cp\u003e-37.97\u0026thinsp;\u0026plusmn;\u0026thinsp;0.38\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\"\u0026plusmn;\" colname=\"c9\"\u003e \u003cp\u003e93.34\u0026thinsp;\u0026plusmn;\u0026thinsp;0.61\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003e14\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003eF14\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e \u003cp\u003e125\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e \u003cp\u003e640\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c5\"\u003e \u003cp\u003e190\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\"\u0026plusmn;\" colname=\"c6\"\u003e \u003cp\u003e227.5\u0026thinsp;\u0026plusmn;\u0026thinsp;0.96\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\"\u0026plusmn;\" colname=\"c7\"\u003e \u003cp\u003e0.515\u0026thinsp;\u0026plusmn;\u0026thinsp;0.092\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\"\u0026plusmn;\" colname=\"c8\"\u003e \u003cp\u003e-36.88\u0026thinsp;\u0026plusmn;\u0026thinsp;0.59\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\"\u0026plusmn;\" colname=\"c9\"\u003e \u003cp\u003e85.16\u0026thinsp;\u0026plusmn;\u0026thinsp;0.84\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003e15\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003eF15\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\u003e640\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c5\"\u003e \u003cp\u003e175\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\"\u0026plusmn;\" colname=\"c6\"\u003e \u003cp\u003e195.6\u0026thinsp;\u0026plusmn;\u0026thinsp;1.6\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\"\u0026plusmn;\" colname=\"c7\"\u003e \u003cp\u003e0.372\u0026thinsp;\u0026plusmn;\u0026thinsp;0.049\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\"\u0026plusmn;\" colname=\"c8\"\u003e \u003cp\u003e-25.91\u0026thinsp;\u0026plusmn;\u0026thinsp;0.61\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\"\u0026plusmn;\" colname=\"c9\"\u003e \u003cp\u003e90.7\u0026thinsp;\u0026plusmn;\u0026thinsp;0.56\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=\"Sec10\" class=\"Section2\"\u003e \u003ch2\u003e2.5 Characterization of Elagolix Sodium SNEDDS\u003c/h2\u003e \u003cdiv id=\"Sec11\" class=\"Section3\"\u003e \u003ch2\u003e2.5.1 Droplet Size and PDI\u003c/h2\u003e \u003cp\u003eGlobule size was measured at room temperature employing dynamic light scattering. Droplet size and distribution uniformity are represented by the polydispersity index. The low PDI results show that the droplet diameter of prepared formulation is relatively uniform [\u003cspan citationid=\"CR13\" class=\"CitationRef\"\u003e13\u003c/span\u003e]. It is preferred that the PDI value for self-nanoemulsifying systems should be less than 0.3 [\u003cspan citationid=\"CR14\" class=\"CitationRef\"\u003e14\u003c/span\u003e]. The Malvern Zetasizer, version-3.3.1.5 (MAL1278078, Malvern Panalytical Ltd.) was used to assess the PDI and droplet size of a SNEDDS after incorporating 250 mL of water to 1 mL of elagolix sodium-loaded SNEDDS formulation at 25\u0026thinsp;\u0026plusmn;\u0026thinsp;0.5\u0026deg;C. All of the information obtained was the mean of triplicate.\u003c/p\u003e \u003c/div\u003e \u003cdiv id=\"Sec12\" class=\"Section3\"\u003e \u003ch2\u003e2.5.2 Zeta Potential\u003c/h2\u003e \u003cp\u003eTo assess the stability of SNEDDS pre-concentrate, the electrical charge of the tiny particles in liquid SNEDDS was confirmed by using Malvern Zetasizer, version-3.3.1.5 (MAL1278078, Malvern Panalytical Ltd.) employing a 50 mV laser at room temperature with a fixed 90 degree angle. Particles with the value of zeta potential more than 30 millivolt (mV) are often seen as stable irrespective of their charge [\u003cspan citationid=\"CR15\" class=\"CitationRef\"\u003e15\u003c/span\u003e].\u003c/p\u003e \u003c/div\u003e \u003cdiv id=\"Sec13\" class=\"Section3\"\u003e \u003ch2\u003e2.5.3 Percent Transmittance\u003c/h2\u003e \u003cp\u003eThe percent transmittance of ELAG-SNEDDS formulations was determined spectrophotometrically (UV-1900i, Shimadzu Corporation, Tokyo, Japan) at a wavelength of 275 nm after diluting a 0.1 mL of nanoemulsion up to 10 mL of distilled water against water as a blank reference (configured at transmittance 100%)[\u003cspan additionalcitationids=\"CR17\" citationid=\"CR16\" class=\"CitationRef\"\u003e16\u003c/span\u003e\u0026ndash;\u003cspan citationid=\"CR18\" class=\"CitationRef\"\u003e18\u003c/span\u003e].\u003c/p\u003e \u003c/div\u003e \u003cdiv id=\"Sec14\" class=\"Section3\"\u003e \u003ch2\u003e2.5.4 Self-Nanoemulsification Efficiency Test\u003c/h2\u003e \u003cp\u003eIn order to assess the self emulsification efficiency, the precipitation of drug or separation of phase after dilution with water was examined visually [\u003cspan citationid=\"CR19\" class=\"CitationRef\"\u003e19\u003c/span\u003e]. Dispersibility and self-emulsification times were determined in order to evaluate the efficiency of self-nanoemulsification (Table\u0026nbsp;\u003cspan refid=\"Tab5\" class=\"InternalRef\"\u003e1\u003c/span\u003e).\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 1\u003c/div\u003e \u003cdiv class=\"CaptionContent\"\u003e \u003cp\u003eA grading system used to evaluate the emulsions formed after dilution.\u003c/p\u003e \u003c/div\u003e \u003c/caption\u003e \u003ccolgroup cols=\"4\"\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 \u003cthead\u003e \u003ctr\u003e \u003cth align=\"left\" colname=\"c1\"\u003e \u003cp\u003eS. No.\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c2\"\u003e \u003cp\u003eGrade\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c3\"\u003e \u003cp\u003ePhysical appearance of the formed emulsion\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c4\"\u003e \u003cp\u003eTime required\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\u003eA\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003eA transparent, clear and rapid forming nanoemulsion with bluish appearance\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e\u0026lt;\u0026thinsp;1 minutes\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\u003eB\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003eThe emulsion forms rapidly and exhibits reduced clarity with a bluish-white appearance\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e\u0026lt;\u0026thinsp;2 minutes\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\u003eC\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003eFormation of a cloudy or finely dispersed milky emulsion\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003eApproximately 2 minutes\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\u003eD\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003eSlow emulsification, producing a dull, grayish to faintly white appearance with a mild oily character.\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e\u0026gt;\u0026thinsp;2 minutes\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=\"Sec15\" class=\"Section3\"\u003e \u003ch2\u003e2.5.5 Dispersibility\u003c/h2\u003e \u003cp\u003eIn order to determine the unpredictability of the nanoemulsion development process, the dispersibility of the developed SNEDDS is evaluated. The optimized SNEDDS (1 mL) was introduced into 500 mL of aqueous medium at 37\u0026thinsp;\u0026plusmn;\u0026thinsp;0.5\u0026deg;C and stirred at 50 rpm using a magnetic stirrer (REMI 2MLH, India) that delivered mild agitation. The in vitro efficiency of optimized SNEDDS was typically determined based on the grading methodology [\u003cspan citationid=\"CR20\" class=\"CitationRef\"\u003e20\u003c/span\u003e].\u003c/p\u003e \u003c/div\u003e \u003cdiv id=\"Sec16\" class=\"Section3\"\u003e \u003ch2\u003e2.5.6 Time Required for Self-Emulsification\u003c/h2\u003e \u003cp\u003eIn order to calculate required time for emulsification (period of time required toform a stable emulsion, after dilution, a single-phase and clear colloidal suspension was obtained), A 1 mL aliquot of the drug-loaded premix was admixed to 250 mL of aqueous medium at 37\u0026thinsp;\u0026plusmn;\u0026thinsp;0.5\u0026deg;C and stirred at 50 rpm by using magnetic stirrer (REMI 2MLH, India). The period of time required to achieve self-emulsification was visually observed and assessed [\u003cspan citationid=\"CR21\" class=\"CitationRef\"\u003e21\u003c/span\u003e]. Based on the emulsification time SNEDDS were graded on a grading system of A, B, C.\u003c/p\u003e \u003c/div\u003e \u003cdiv id=\"Sec17\" class=\"Section3\"\u003e \u003ch2\u003e2.5.7 Thermodynamic Stability Studies\u003c/h2\u003e \u003cp\u003eThe phase integrity and thermal stability of the optimized SNEDDS was examined under different centrifugal force and temperature conditions [\u003cspan citationid=\"CR22\" class=\"CitationRef\"\u003e22\u003c/span\u003e]. The optimized formulation was run into the following test in order to explore the impact of temperature and centrifugal force [\u003cspan citationid=\"CR23\" class=\"CitationRef\"\u003e23\u003c/span\u003e].\u003c/p\u003e \u003c/div\u003e \u003cdiv id=\"Sec18\" class=\"Section3\"\u003e \u003ch2\u003e2.5.8 Centrifugal Test\u003c/h2\u003e \u003cp\u003eIn this test, the optimized SNEDDS was centrifuged at 5000 rpm (Singhla scientific India) for 30 minutes. A visual assessment was done in order to check for instabilities such as cracking, creaming, or phase separation. Further tests were conducted on the stable formulations [\u003cspan citationid=\"CR24\" class=\"CitationRef\"\u003e24\u003c/span\u003e].\u003c/p\u003e \u003c/div\u003e \u003cdiv id=\"Sec19\" class=\"Section3\"\u003e \u003ch2\u003e2.5.9 Heating and Cooling Cycle\u003c/h2\u003e \u003cp\u003eThe SNEDDS preconcentrate was surpassed through three cooling cycles and heating by maintaining the formulation was maintained at 4\u0026deg;C and 45\u0026deg;C, with a minimum duration of 48 hours at each temperature. SNEDDS preconcentrate was then diluted with distilled water after being initially evaluated to assess drug precipitation and phase separation. The stability of the resulting nanoemulsion was subsequent evaluated [\u003cspan citationid=\"CR25\" class=\"CitationRef\"\u003e25\u003c/span\u003e].\u003c/p\u003e \u003c/div\u003e \u003cdiv id=\"Sec20\" class=\"Section3\"\u003e \u003ch2\u003e2.5.10 Freeze\u0026thinsp;\u0026minus;\u0026thinsp;Thaw Stress Cycle\u003c/h2\u003e \u003cp\u003eThree cycles of freezing and thawing were carried out over the course of 48 hours at a temperature between (-21 and 25\u0026deg;C). After centrifuging at 5000 rpm by using centrifuge (Singhla scientific India) for 5\u0026ndash;10 minutes, all SNEDDS were checked for instability, such as phase separation, cracking, and creaming [\u003cspan citationid=\"CR24\" class=\"CitationRef\"\u003e24\u003c/span\u003e, \u003cspan citationid=\"CR26\" class=\"CitationRef\"\u003e26\u003c/span\u003e]. The optimized SNEDDS was determined by these studies.\u003c/p\u003e \u003c/div\u003e \u003cdiv id=\"Sec21\" class=\"Section3\"\u003e \u003ch2\u003e2.5.11 Impact of pH and Dilution Robustness\u003c/h2\u003e \u003cp\u003eThe optimized SNEDDS formulation was diluted 10, 100, and 1000 times using various dissolution media, including distilled water, 0.1N HCl (pH 1.2), and phosphate buffer (pH 6.8). After being stored for 24 hours, After dilution, the samples were inspected for signs of instability, including the occurrence of precipitation or phase separation [\u003cspan citationid=\"CR12\" class=\"CitationRef\"\u003e12\u003c/span\u003e].\u003c/p\u003e \u003c/div\u003e \u003cdiv id=\"Sec22\" class=\"Section3\"\u003e \u003ch2\u003e2.5.12 Determination of Cloud Point\u003c/h2\u003e \u003cp\u003eWhen the clear formulation becomes cloudy it is considered to have attained its cloud point [\u003cspan citationid=\"CR27\" class=\"CitationRef\"\u003e27\u003c/span\u003e]. The purpose of the cloud point study was to evaluate the stability of the optimized SNEDDS at physiological temperature, similar as in vivo. Cloud point determination was performed for the optimized SNEDDS using water bath (UQ \u0026ndash; 2021101940, Singhla scientific India). Upon dilution of optimized SNEDDS with 100 times with aqueous medium, the formulation was then placed on a water bath that steadily raised the temperature (between 25\u0026deg;C and 80\u0026deg;C, or about 2\u0026deg;C per minute). The temperature at which visible cloudiness first appeared was recorded as the cloud point [\u003cspan citationid=\"CR28\" class=\"CitationRef\"\u003e28\u003c/span\u003e].\u003c/p\u003e \u003c/div\u003e \u003cdiv id=\"Sec23\" class=\"Section3\"\u003e \u003ch2\u003e2.5.13 Morphological Study\u003c/h2\u003e \u003cp\u003eThe optimized SNEDDS was analyzed by transmission electron microscope (TEM) to evaluate its morphological features. To accomplish this, a transmission electron microscope (Talos L120C, Thermo Scientific \u0026trade;) set up at the Jamia Hamdard University, New Delhi, India, was employed. The optimized SNEDDS was diluted with aqueous medium in a 1:10 ratio [\u003cspan citationid=\"CR29\" class=\"CitationRef\"\u003e29\u003c/span\u003e]. One or two drops of diluted SNEDDS were applied to the carbon grid, and the TEM was utilized to examine particles at the ideal magnifications [\u003cspan citationid=\"CR30\" class=\"CitationRef\"\u003e30\u003c/span\u003e].\u003c/p\u003e \u003c/div\u003e \u003c/div\u003e \u003cdiv id=\"Sec24\" class=\"Section2\"\u003e \u003ch2\u003e2.6 In Vitro Release Study of the Elagolix Sodium\u003c/h2\u003e \u003cp\u003eThe in vitro release studies of elagolix sodium from optimized SNEDDS as well as the pure drug suspension was examined using USP Type II (paddle) dissolution apparatus (ECO-2070, Singhla Scientific India) which rotated at 100 rpm and maintained at 37\u0026deg;C. Drug release was evaluated using the dialysis bag method to ensure that only unbound drugs reached the dissolving media, limiting the effect of the unreleased drug. The dialysis membranes were kept in the dissolution media (PBS 6.8) at room temperature for about 12 h. About 150 mg of drug loaded SNEDDS were diluted with 10 mL of aqueous medium, and then 5 mL of pure drug suspensions and diluted SNEDDS were poured into dialysis membranes (DM \u0026ndash; 50, LA387-1MT, Hi-media, Mumbai, India) that were already presoaked. After securely tying the dialysis membranes at both ends to prevent leakage, they were attached to the rotating paddles and immersed in the dissolving media. The sample was withdrawn using a pipette from the dissolution vessel in aliquots of 5 mL at regular intervals of 0.25, 0.5, 1, 1.5, 2, 3, 4, 5, 6, and 12 hours. Five mL of fresh dissolving medium were introduced to keep the sink condition stable. The samples were examined for drug release at a wavelength of 275 nm using a UV spectrophotometer (UV-1900i, Shimadzu Corporation, Tokyo, Japan) [\u003cspan citationid=\"CR31\" class=\"CitationRef\"\u003e31\u003c/span\u003e].\u003c/p\u003e \u003c/div\u003e \u003cdiv id=\"Sec25\" class=\"Section2\"\u003e \u003ch2\u003e2.7 Evaluation of Drug Permeation Using Ex Vivo Model\u003c/h2\u003e \u003cp\u003ePermeation assessment of elagolix sodium from the suspension of pure drug and optimized SNEDDS was studied using a non-everted gut sac approach. Approval for the study protocol was obtained from the Ethical Review Committee. For this research, a female Sprague Dawley rat, weighing between 150 and 250 g, was euthanized, followed by careful removal of the small intestine. A 5\u0026ndash;6 cm portion of the duodenum was carefully removed and washed sequentially using Krebs\u0026ndash;Ringer phosphate buffer and normal saline at a controlled temperature of 37\u0026deg;C. The duodenal sac was filled with suspension (1 mL) of pure drug and the optimized SNEDDS using a 3 mL syringe, and both ends were tightly secured with cotton thread. The intestinal sacs were immersed in 250 mL of phosphate-buffered saline (PBS, pH 6.8) with 0.2% polysorbate, stirred at 50 rpm, and maintained at 37\u0026deg;C. At predetermined time points 0.25, 0.5, 1, 1.5, 2,3, 4, 5 and 6 h, 3 mL of the sample was withdrawn from the beaker and immediately replaced with an equal volume of PBS (pH 6.8). The absorbance of each collected sample was determined by UV\u0026ndash;visible spectrophotometric analysis (UV-1900i, Shimadzu Corporation, Tokyo, Japan) set to 275 nm. The percentage of drug transport across the membrane into the receptor compartment, the apparent permeability (Papp), and the permeation flux (\u0026micro;g/min) were calculated. The equation below was used to determine the Papp, which is represented in cm/min [\u003cspan citationid=\"CR32\" class=\"CitationRef\"\u003e32\u003c/span\u003e]:\u003c/p\u003e \u003cp\u003eP\u003csub\u003eapp\u003c/sub\u003e value\u0026thinsp;=\u0026thinsp;d\u003cem\u003eQ/\u003c/em\u003ed\u003cem\u003et\u003c/em\u003e\u0026times; 1\u003cem\u003e/\u003c/em\u003eAC\u003csub\u003eo\u003c/sub\u003e\u003c/p\u003e \u003cp\u003eWhere \u0026#119889;\u0026#119876;/\u0026#119889;\u0026#119905; represents the permeation rate of the drug, \u0026#119862;\u003csub\u003e0\u003c/sub\u003e is the initial concentration of drug in the mucosal compartment at \u0026#119905;=0 and \u0026#119860; denotes the cross-sectional surface area of the tissue, calculated as 2\u0026#120587;\u0026#119903;ℎ. The slope of the linear segment of the graph depicting cumulative drug permeation per unit area (\u0026micro;g/cm\u0026sup2;) versus time (min) was used to calculate the permeability flux.\u003c/p\u003e \u003c/div\u003e \u003cdiv id=\"Sec26\" class=\"Section2\"\u003e \u003ch2\u003e2.8 Pharmacokinetic Study\u003c/h2\u003e \u003cp\u003eIn the current study, female Sprague Dawley (SD) rats, weighing 200\u0026thinsp;\u0026plusmn;\u0026thinsp;50 g were utilized as the experimental animals. Prior to the study, there were no restrictions on their intake of food or drink. Elagolix sodium (15 mg/kg) was given orally to each rat as a single dosage and at 0, 0.33, 0.67, 1, 1.5, 2, 3, 4, 6, 9, 12, 24, and 36 h post delivery, we collected the blood samples (300 \u0026micro;L) from each rat into EP tubes containing the anticoagulant heparin. Immediately after collection, plasma samples were separated by centrifugation for 8 minutes at 4000 rpm and stored in refrigerator until required. Data on the primary pharmacokinetic parameters of elagolix sodium in rats were assessed using the Drug and Statistics (DAS) Version 3.0 program.\u003c/p\u003e \u003c/div\u003e \u003cdiv id=\"Sec27\" class=\"Section2\"\u003e \u003ch2\u003e2.9 Statistical Evaluation\u003c/h2\u003e \u003cp\u003eIn the current study, Statistical analyses were carried out using SPSS\u0026reg; software V 22 (SPSS Inc., Chicago, IL, USA). The statistical comparisons were conducted using ANOVA. The difference was deemed significant when the p-value was less than 0.05.\u003c/p\u003e \u003c/div\u003e"},{"header":"3 Results","content":"\u003cdiv id=\"Sec29\" class=\"Section2\"\u003e \u003ch2\u003e3.1 Solubility\u003c/h2\u003e \u003cp\u003eThe excipients used to make SNEDDS should have a wider self-nanoemulsification area within the pseudo-ternary phase diagram and be able to solubilize a large quantity of drug. The excipients were selected based on the compatibility with the added drug, safety and solubilizing capacity. The solubility of elagolix sodium in various excipients at equilibrium is showed in Fig.\u0026nbsp;\u003cspan refid=\"Fig1\" class=\"InternalRef\"\u003e1\u003c/span\u003e. Four oils were investigated in our study for potential use as the oil phase in the preparation of SNEDDS. The study clearly showed that elagolix sodium was most soluble in Labrafil M2125-CS (92.12\u0026thinsp;\u0026plusmn;\u0026thinsp;1.93 mg/mL), followed by Castor oil (46.39\u0026thinsp;\u0026plusmn;\u0026thinsp;2.34 mg/mL), Coconut oil (22.28\u0026thinsp;\u0026plusmn;\u0026thinsp;2.72 mg/mL), and Caprylic acid (5.50 mg/mL\u0026thinsp;\u0026plusmn;\u0026thinsp;0.86 mg/mL). Furthermore, Tween 80, a non-ionic surfactant, was employed in the current investigation as Tween 80 exhibited a good HLB value (15) and an adequate solubilizing capacity for elagolix sodium (129.59\u0026thinsp;\u0026plusmn;\u0026thinsp;4.82 mg/mL), followed by Tween 20 (37.77\u0026thinsp;\u0026plusmn;\u0026thinsp;2.77 mg/mL), Span 80 (23.21\u0026thinsp;\u0026plusmn;\u0026thinsp;1.09 mg/mL) and Span 20 (15.94\u0026thinsp;\u0026plusmn;\u0026thinsp;1.92 mg/mL). Tween 80 was used as a surfactant for the preparation of SNEDDS to define a stable nanoemulsion area. The material that exhibits the highest solubility of elagolix sodium was selected because co-surfactants aid the surfactants in drug solubilization. Transcutol P was chosen among the co-surfactants because it exhibited the highest elagolix sodium solubility (97.59\u0026thinsp;\u0026plusmn;\u0026thinsp;1.97 mg/mL), followed by tetraglycol (55.91\u0026thinsp;\u0026plusmn;\u0026thinsp;1.50 mg/mL), propylene glycol (36.37\u0026thinsp;\u0026plusmn;\u0026thinsp;1.67 mg/mL) and ethanol (9.58\u0026thinsp;\u0026plusmn;\u0026thinsp;0.86 mg/mL).\u003c/p\u003e \u003cp\u003e \u003c/p\u003e \u003cp\u003eThe complex composition of linoleoyl polyoxyl-6 glycerides (PEG-6 linoleate) and different mono-, di-, and triglycerides of linoleic acid may have contributed to the high solubility of elagolix sodium in Labrafil M2125-CS [\u003cspan citationid=\"CR33\" class=\"CitationRef\"\u003e33\u003c/span\u003e]. It is commonly known that adding mixed glycerides to a lipid formulation increases its emulsifying and solubilizing capacity. Furthermore, they are beneficial to use in lipid formulations due to their resemblance to the lipid digestion product [\u003cspan citationid=\"CR33\" class=\"CitationRef\"\u003e33\u003c/span\u003e]. Since the therapeutic agent mustalways remain in dissolved form in SNEDDS, forming a concentration gradient that initiates the transport of drug molecules from areas of higher concentration to areas of lower concentration (in the blood) the penetration of therapeutic agent through the GI tract, the higher solubility of therapeutic agent in the oil phase is critical for self-nanoemulsification [\u003cspan citationid=\"CR34\" class=\"CitationRef\"\u003e34\u003c/span\u003e]. Hence because of its excellent solubilization capacity, Labrafil M2125-CS was chosen as the oil phase for further studies. Since surfactants are primarily play an important role in the stabilizing the emulsion, their choice is crucial in the lipid system. Compared to ionic surfactants, non-ionic surfactants are less irritable, safer, and show better emulsion stability across a wide range of ionic strength and pH. Additionally, they cause a reversible change in intestinal mucosal permeability, which speeds up drug adsorption [\u003cspan citationid=\"CR35\" class=\"CitationRef\"\u003e35\u003c/span\u003e]. Consequently, Tween 80, a non-ionic surfactant, was used for the SNEDDS preparation. Micellization of surfactant molecules in water, play an important role in SNEDDS. Furthermore, they decrease the chances of drug precipitation and improve drug solubility in micelles [\u003cspan citationid=\"CR36\" class=\"CitationRef\"\u003e36\u003c/span\u003e]. In this study, Transcutol P was selected because it dissolved the highest amount of the elagolix sodium.\u003c/p\u003e \u003c/div\u003e \u003cdiv id=\"Sec30\" class=\"Section2\"\u003e \u003ch2\u003e3.2 Phase diagram\u003c/h2\u003e \u003cp\u003eTo make out the self-emulsifying zone and choose the best lipid phase and S\u003csub\u003emix\u003c/sub\u003e ratios (surfactant: co-surfactant) for SNEDDS were determined through the construction of pseudo-ternary phase diagrams. These pseudo-ternary phase diagrams are essential for optimizing the preconcentrate for SNEDDS and comprehending the phase behaviour of nanoemulsions. Three elements make up the ternary diagram: S-mix, water, and oil. One of the components with a 100% concentration is represented by each corner (Fig.\u0026nbsp;\u003cspan refid=\"Fig2\" class=\"InternalRef\"\u003e2\u003c/span\u003e). Here in this study, Tween 80 served as the surfactant, Transcutol P as the co-surfactant, and Labrafil M2125-CS as the oily phase in this study. This experiment employed the following surfactant-to-co-surfactant (S\u003csub\u003emix\u003c/sub\u003e) ratios: 1:1, 2:1, 3:1, and 4:1.\u003c/p\u003e \u003cp\u003e \u003c/p\u003e \u003cp\u003eVarious S\u003csub\u003emix\u003c/sub\u003e ratios were mixed with various ratios of Labrafil M2125-CS; thereafter resulting mixtures were titrated using a predetermined volume of water [\u003cspan citationid=\"CR25\" class=\"CitationRef\"\u003e25\u003c/span\u003e]. The inclusion of Tween 80 within the self-emulsion region was observed to enhance the spontaneous nature of the self-emulsification process. When the S\u003csub\u003emix\u003c/sub\u003e in the SNEDDS exceeded 80%, the emulsification efficiency was noticeably favourable. It is evident from the experimental set-up that when the surfactant level in the SNEDDS was less than 50%, spontaneous emulsion formation was ineffective. These findings were included to the grapher software version to generating pseudo-ternary phase diagrams following the aqueous titration. The various areas of the triangles are depicted in Fig.\u0026nbsp;\u003cspan refid=\"Fig2\" class=\"InternalRef\"\u003e2\u003c/span\u003e, where the transparent region stands in for the nanoemulsion area. This specific zone is distinguished by its clear and homogenous nature in Fig.\u0026nbsp;\u003cspan refid=\"Fig2\" class=\"InternalRef\"\u003e2\u003c/span\u003e. A wide nanoemulsion region is indicated by the maximum self-nanoemulsifying activity and optimal intermolecular interaction between water and pre-concentrate (S\u003csub\u003emix\u003c/sub\u003e and oil). Additionally, Fig.\u0026nbsp;\u003cspan refid=\"Fig2\" class=\"InternalRef\"\u003e2\u003c/span\u003e shows that a larger nanoemulsion area results from an increase in S\u003csub\u003emix\u003c/sub\u003e ratio. However, the water titration revealed oil streaks as the oil ratio increased [\u003cspan citationid=\"CR37\" class=\"CitationRef\"\u003e37\u003c/span\u003e]. Tween 80 and Transcutol P were utilized as the S\u003csub\u003emix\u003c/sub\u003e in pseudo-ternary phase diagram assessments using Labrafil M2125-CS as oil. As a result, the S\u003csub\u003emix\u003c/sub\u003e ratio of 4:1 was selected because it provides the biggest nanoemulsion area for SNEDDS and the highest water uptake capacity for Tween 80 and Transcutol P. In order to get stable formulations and smaller droplet sizes, a greater S\u003csub\u003emix\u003c/sub\u003e concentration is necessary. This results in a bigger surface area and a smaller droplet size. As a result, more S\u003csub\u003emix\u003c/sub\u003e is needed to stabilize the oil droplets [\u003cspan citationid=\"CR38\" class=\"CitationRef\"\u003e38\u003c/span\u003e, \u003cspan citationid=\"CR39\" class=\"CitationRef\"\u003e39\u003c/span\u003e].\u003c/p\u003e \u003c/div\u003e \u003cdiv id=\"Sec31\" class=\"Section2\"\u003e \u003ch2\u003e3.3 Experimental Model\u003c/h2\u003e \u003cp\u003eIn order to examine the impact of three independent variables on response variables, BBD was chosen and used in the current study. Tables\u0026nbsp;\u003cspan refid=\"Tab1\" class=\"InternalRef\"\u003e2\u003c/span\u003e and \u003cspan refid=\"Tab2\" class=\"InternalRef\"\u003e3\u003c/span\u003e show the limitations of the independent and dependent factors. In this research, fifteen formulations were developed in accordance with the BBD, and their response variables\u0026mdash;globule size (Y\u003csub\u003e1\u003c/sub\u003e), PDI (Y\u003csub\u003e2\u003c/sub\u003e), zeta potential (Y\u003csub\u003e3\u003c/sub\u003e), and percentage transmittance (Y\u003csub\u003e4\u003c/sub\u003e) were evaluated (Table\u0026nbsp;\u003cspan refid=\"Tab4\" class=\"InternalRef\"\u003e5\u003c/span\u003e). Malvern Zetasizer, version-3.3.1.5 (MAL1278078, Malvern Panalytical Ltd.) was used to collect all of the data. ANOVA, the multiple correlation (R\u003csup\u003e2\u003c/sup\u003e) tests, lack of fit test, were used to evaluate the model's significance after every response was fitted independently to a complete polynomial equation. A model p-value of less than 0.05 was considered statistically significant. The lack-of-fit test is employed to evaluate the fluctuation of data around the fitted value, and it must be negligible (p\u0026thinsp;\u0026lt;\u0026thinsp;0.05). The multiple correlation coefficients (R\u003csup\u003e2\u003c/sup\u003e value) indicate the extent of variance around the mean. An R\u003csup\u003e2\u003c/sup\u003e value near 1 is ideal [\u003cspan citationid=\"CR12\" class=\"CitationRef\"\u003e12\u003c/span\u003e].\u003c/p\u003e \u003cdiv id=\"Sec32\" class=\"Section3\"\u003e \u003ch2\u003e3.3.1 Globule Size\u003c/h2\u003e \u003cp\u003eThe BBD revealed that the selected independent factor arrangement of Labrafil M2125-CS (X\u003csub\u003e1\u003c/sub\u003e), Tween 80 (X\u003csub\u003e2\u003c/sub\u003e) and Transcutol P (X\u003csub\u003e3\u003c/sub\u003e) produced distinct effects for the globule size (Y\u003csub\u003e1\u003c/sub\u003e). The following equation [\u003cspan citationid=\"CR2\" class=\"CitationRef\"\u003e2\u003c/span\u003e] describes the polynomial equation that represents the mathematical relationship of independent factors with response (Y\u003csub\u003e1\u003c/sub\u003e).\u003c/p\u003e \u003cp\u003e \u003cem\u003eY\u003c/em\u003e \u003csub\u003e1\u003c/sub\u003e (Globule size)\u0026thinsp;=\u0026thinsp;204\u0026thinsp;+\u0026thinsp;4.9 \u003cem\u003eX\u003c/em\u003e\u003csub\u003e1\u003c/sub\u003e \u0026ndash; 25.3 \u003cem\u003eX\u003c/em\u003e\u003csub\u003e2\u003c/sub\u003e \u0026ndash; 3.075 \u003cem\u003eX\u003c/em\u003e\u003csub\u003e3\u003c/sub\u003e \u0026ndash; 11.05 \u003cem\u003eX\u003c/em\u003e\u003csub\u003e1\u003c/sub\u003e\u003cem\u003eX\u003c/em\u003e\u003csub\u003e2\u003c/sub\u003e\u0026thinsp;+\u0026thinsp;0.6 \u003cem\u003eX\u003c/em\u003e\u003csub\u003e2\u003c/sub\u003e\u003cem\u003eX\u003c/em\u003e\u003csub\u003e3\u003c/sub\u003e\u0026thinsp;+\u0026thinsp;0.9 \u003cem\u003eX\u003c/em\u003e\u003csub\u003e1\u003c/sub\u003e\u003cem\u003eX\u003c/em\u003e\u003csub\u003e3\u003c/sub\u003e \u0026ndash; 16.8 \u003cem\u003eX\u003c/em\u003e\u003csub\u003e1\u003c/sub\u003e\u003csup\u003e2\u003c/sup\u003e\u0026thinsp;+\u0026thinsp;7.4 \u003cem\u003eX\u003c/em\u003e\u003csub\u003e2\u003c/sub\u003e\u003csup\u003e2\u003c/sup\u003e \u0026ndash; 6.2 \u003cem\u003eX\u003c/em\u003e\u003csub\u003e3\u003c/sub\u003e\u003csup\u003e2\u003c/sup\u003e \u0026hellip;\u0026hellip;\u0026hellip;\u0026hellip;\u0026hellip;\u0026hellip;\u0026hellip; [\u003cspan citationid=\"CR2\" class=\"CitationRef\"\u003e2\u003c/span\u003e]\u003c/p\u003e \u003cp\u003eThe quadratic model provided a good fit for globule size (Y\u003csub\u003e1\u003c/sub\u003e). The multiple correlation tests (R\u003csup\u003e2\u003c/sup\u003e) and ANOVA were used to confirm the model's effectiveness. Along with larger coefficients (Eq.\u0026nbsp;2), the polynomial term (X\u003csub\u003e1\u003c/sub\u003e\u003csup\u003e2\u003c/sup\u003e) and surfactant concentration (X\u003csub\u003e2\u003c/sub\u003e) have a substantial impact (p\u0026thinsp;\u0026lt;\u0026thinsp;0.05). However, the concentration of lipids (X\u003csub\u003e1\u003c/sub\u003e), co-surfactants (X\u003csub\u003e3\u003c/sub\u003e), interactive terms (X\u003csub\u003e1\u003c/sub\u003eX\u003csub\u003e2\u003c/sub\u003e), (X\u003csub\u003e2\u003c/sub\u003eX\u003csub\u003e3\u003c/sub\u003e), (X\u003csub\u003e1\u003c/sub\u003eX\u003csub\u003e3\u003c/sub\u003e), and X\u003csub\u003e2\u003c/sub\u003e\u003csup\u003e2\u003c/sup\u003e, X\u003csub\u003e3\u003c/sub\u003e\u003csup\u003e2\u003c/sup\u003e, had a low coefficient indicating no significant influence (p\u0026thinsp;\u0026gt;\u0026thinsp;0.05) on the response Y\u003csub\u003e1\u003c/sub\u003e. Consequently, the globule size rose marginally as the lipid levels in the SNEDDS increased. The significant effect of independent factors in predicting the response variable (Y\u003csub\u003e1\u003c/sub\u003e) was corroborated by the observed R\u003csup\u003e2\u003c/sup\u003e value (0.9456) and p value (\u0026lt;\u0026thinsp;0.05). Since there is an insufficient surfactant to emulsify and break the interfacial barrier between the lipid micro globular surface and the water phase, the higher amount of lipid depicted the increase in droplet size [\u003cspan citationid=\"CR12\" class=\"CitationRef\"\u003e12\u003c/span\u003e, \u003cspan citationid=\"CR40\" class=\"CitationRef\"\u003e40\u003c/span\u003e]. On the other hand, a significant reduction in particle size was shown as the level of surfactant increased, indicating the opposite impact (negative). This fact supported the single (X\u003csub\u003e2\u003c/sub\u003e) negative effect of the X\u003csub\u003e2\u003c/sub\u003e factor; thus, an excessive quantity of surfactant significantly emulsified the oil and substantially decreased the interfacial energy between the oil phase and aqueous phase, which led to a notable reduction in the droplet size [\u003cspan citationid=\"CR41\" class=\"CitationRef\"\u003e41\u003c/span\u003e]. Co-surfactants in SNEDDS, generally, break the interfacial film and reduce the interfacial tension as a result help in the reduction of droplet size, enabling the production of nanoemulsion, however, in the current study co-surfactant (X\u003csub\u003e3\u003c/sub\u003e) reduces emulsion droplet size insignificantly (p\u0026thinsp;\u0026gt;\u0026thinsp;0.05). Additionally, the binary interaction term (X\u003csub\u003e1\u003c/sub\u003eX\u003csub\u003e2\u003c/sub\u003e) and a negative coefficient indicated that in a mutual setting, the emulsion droplet size is mostly influenced by the quantity of surfactant (X\u003csub\u003e2\u003c/sub\u003e) rather than the lipid content (X\u003csub\u003e1\u003c/sub\u003e) (Eq.\u0026nbsp;2). In this experimental setup, a positive but an in-significant effect (p\u0026thinsp;\u0026gt;\u0026thinsp;0.05) of binary interaction (X\u003csub\u003e2\u003c/sub\u003eX\u003csub\u003e3\u003c/sub\u003e) was noted. A noteworthy decrease in the droplet size was suggested by the significant effect (p\u0026thinsp;\u0026lt;\u0026thinsp;0.05) of the lipid's exponential additions (X\u003csub\u003e1\u003c/sub\u003e\u003csup\u003e2\u003c/sup\u003e). However, a smaller positive effect was seen when exponential effect (X\u003csub\u003e1\u003c/sub\u003e\u003csup\u003e2\u003c/sup\u003e) compared with the single effect (X\u003csub\u003e1\u003c/sub\u003e) (Eq.\u0026nbsp;2). A certain degree of the lipid's self-emulsification property is predicted by the decrease in the coefficient [\u003cspan citationid=\"CR12\" class=\"CitationRef\"\u003e12\u003c/span\u003e]. However, the surfactant concentration's additive impact (X\u003csub\u003e2\u003c/sub\u003e\u003csup\u003e2\u003c/sup\u003e) has a positive effect and demonstrated a notable rise in droplet size. This could be because a long-chain surfactant molecule is overcrowded, or has several layers [\u003cspan citationid=\"CR21\" class=\"CitationRef\"\u003e21\u003c/span\u003e]. The current investigation found that the globule size of the nanoemulsion was not markedly affected by an exponential increase in the co-surfactant concentration (X\u003csub\u003e3\u003c/sub\u003e\u003csup\u003e2\u003c/sup\u003e). The response surface plot for droplet size is depicted in Fig.\u0026nbsp;\u003cspan refid=\"Fig3\" class=\"InternalRef\"\u003e3\u003c/span\u003e.\u003c/p\u003e \u003cp\u003e \u003c/p\u003e \u003c/div\u003e \u003cdiv id=\"Sec33\" class=\"Section3\"\u003e \u003ch2\u003e3.3.2 PDI\u003c/h2\u003e \u003cp\u003eThe BBD revealed that the combinations of Labrafil M2125-CS (X\u003csub\u003e1\u003c/sub\u003e), Tween 80 (X\u003csub\u003e2\u003c/sub\u003e), and Transcutol P (X\u003csub\u003e3\u003c/sub\u003e) varied effect on dependent factor PDI (Y\u003csub\u003e2\u003c/sub\u003e). The following equation [\u003cspan citationid=\"CR3\" class=\"CitationRef\"\u003e3\u003c/span\u003e] describes the mathematical relationship between the observed responses (PDI), Y\u003csub\u003e2\u003c/sub\u003e and various effects of independent variables:\u003c/p\u003e \u003cp\u003e \u003cem\u003eY\u003c/em\u003e \u003csub\u003e2\u003c/sub\u003e (PDI)\u0026thinsp;=\u0026thinsp;0.382\u0026thinsp;+\u0026thinsp;0.0065 \u003cem\u003eX\u003c/em\u003e\u003csub\u003e1\u003c/sub\u003e \u0026ndash; 0.009 \u003cem\u003eX\u003c/em\u003e\u003csub\u003e2\u003c/sub\u003e\u0026thinsp;+\u0026thinsp;0.006 \u003cem\u003eX\u003c/em\u003e\u003csub\u003e3\u003c/sub\u003e \u0026ndash; 0.0258 \u003cem\u003eX\u003c/em\u003e\u003csub\u003e1\u003c/sub\u003e\u003cem\u003eX\u003c/em\u003e\u003csub\u003e2\u003c/sub\u003e\u0026thinsp;+\u0026thinsp;0.00725 \u003cem\u003eX\u003c/em\u003e\u003csub\u003e2\u003c/sub\u003e\u003cem\u003eX\u003c/em\u003e\u003csub\u003e3\u003c/sub\u003e\u0026thinsp;+\u0026thinsp;0.02125 \u003cem\u003eX\u003c/em\u003e\u003csub\u003e1\u003c/sub\u003e\u003cem\u003eX\u003c/em\u003e\u003csub\u003e3\u003c/sub\u003e \u0026ndash; 0.0591 \u003cem\u003eX\u003c/em\u003e\u003csub\u003e1\u003c/sub\u003e\u003csup\u003e2\u003c/sup\u003e\u0026thinsp;+\u0026thinsp;0.08338 \u003cem\u003eX\u003c/em\u003e\u003csub\u003e2\u003c/sub\u003e\u003csup\u003e2\u003c/sup\u003e\u0026thinsp;+\u0026thinsp;0.06838 \u003cem\u003eX\u003c/em\u003e\u003csub\u003e3\u003c/sub\u003e\u003csup\u003e2\u003c/sup\u003e\u0026hellip;\u0026hellip;\u0026hellip;\u0026hellip;\u0026hellip;\u0026hellip;\u0026hellip; [\u003cspan citationid=\"CR3\" class=\"CitationRef\"\u003e3\u003c/span\u003e]\u003c/p\u003e \u003cp\u003eAccording to the above equation, the response Y\u003csub\u003e2\u003c/sub\u003e is quantitatively affected by the independent variables X\u003csub\u003e1\u003c/sub\u003e (amount of oil), X\u003csub\u003e2\u003c/sub\u003e (amount of surfactant), and amount of co-surfactant (X\u003csub\u003e3\u003c/sub\u003e), as well as by their binary interactions and exponential effects. Their significant impact on Y\u003csub\u003e2\u003c/sub\u003e was demonstrated by the coefficient's p value (Table\u0026nbsp;\u003cspan refid=\"Tab6\" class=\"InternalRef\"\u003e6\u003c/span\u003e). The coefficient's negative value indicates that independent factors work together to lower PDI, whereas its positive sign indicates that independent variables have a positive impact on PDI. Their significant impact on the outcome is indicated by the factor's higher coefficient value. The quadratic model was well-fitted for PDI response. Multiple correlation and ANOVA were used to confirm the effectiveness of the model. The quadratic model's multiple correlation test (R\u003csup\u003e2\u003c/sup\u003e) and ANOVA results are exhibited in Table\u0026nbsp;\u003cspan refid=\"Tab7\" class=\"InternalRef\"\u003e7\u003c/span\u003e. The significant effect of independent factors in predicting the response variable (Y\u003csub\u003e2\u003c/sub\u003e) was corroborated by the observed R\u003csup\u003e2\u003c/sup\u003e value (0.8524) and p value (\u0026lt;\u0026thinsp;0.05).\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\u003eRegression coefficient values and corresponding p-values for each measured response.\u003c/p\u003e \u003c/div\u003e \u003c/caption\u003e \u003ccolgroup cols=\"11\"\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=\"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 \u003cthead\u003e \u003ctr\u003e \u003cth align=\"left\" colname=\"c1\" morerows=\"1\" rowspan=\"2\"\u003e\u0026nbsp;\u003c/th\u003e \u003cth align=\"left\" colname=\"c2\"\u003e \u003cp\u003eIntercept\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c3\"\u003e \u003cp\u003eX\u003csub\u003e1\u003c/sub\u003e\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c4\"\u003e \u003cp\u003eX\u003csub\u003e2\u003c/sub\u003e\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c5\"\u003e \u003cp\u003eX\u003csub\u003e3\u003c/sub\u003e\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c6\"\u003e \u003cp\u003eX\u003csub\u003e1\u003c/sub\u003eX\u003csub\u003e2\u003c/sub\u003e\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c7\"\u003e \u003cp\u003eX\u003csub\u003e1\u003c/sub\u003eX\u003csub\u003e3\u003c/sub\u003e\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c8\"\u003e \u003cp\u003eX\u003csub\u003e2\u003c/sub\u003eX\u003csub\u003e3\u003c/sub\u003e\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c9\"\u003e \u003cp\u003eX\u003csub\u003e1\u003c/sub\u003e\u003csup\u003e2\u003c/sup\u003e\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c10\"\u003e \u003cp\u003eX\u003csub\u003e2\u003c/sub\u003e\u003csup\u003e2\u003c/sup\u003e\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c11\"\u003e \u003cp\u003eX\u003csub\u003e3\u003c/sub\u003e\u003csup\u003e2\u003c/sup\u003e\u003c/p\u003e \u003c/th\u003e \u003c/tr\u003e \u003ctr\u003e \u003cth align=\"left\" colname=\"c2\"\u003e \u003cp\u003eα0\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c3\"\u003e \u003cp\u003eα1\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c4\"\u003e \u003cp\u003eα2\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c5\"\u003e \u003cp\u003eα3\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c6\"\u003e \u003cp\u003eα4\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c7\"\u003e \u003cp\u003eα5\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c8\"\u003e \u003cp\u003eα6\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c9\"\u003e \u003cp\u003eα7\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c10\"\u003e \u003cp\u003eα8\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c11\"\u003e \u003cp\u003eα9\u003c/p\u003e \u003c/th\u003e \u003c/tr\u003e \u003c/thead\u003e \u003ctbody\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eGlobule size (Y\u003csub\u003e1\u003c/sub\u003e)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e204\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e \u003cp\u003e4.9\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e \u003cp\u003e-25.3\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c5\"\u003e \u003cp\u003e-3.075\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e \u003cp\u003e-11.05\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c7\"\u003e \u003cp\u003e0.6\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c8\"\u003e \u003cp\u003e0.9\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c9\"\u003e \u003cp\u003e-16.8\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c10\"\u003e \u003cp\u003e7.4\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c11\"\u003e \u003cp\u003e-6.2\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003ep-values\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e \u003cp\u003e0.1913\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e \u003cp\u003e0.0006\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c5\"\u003e \u003cp\u003e0.3867\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e \u003cp\u003e0.061\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c7\"\u003e \u003cp\u003e0.901\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c8\"\u003e \u003cp\u003e0.8522\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c9\"\u003e \u003cp\u003e0.0169\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c10\"\u003e \u003cp\u003e0.1819\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c11\"\u003e \u003cp\u003e0.2508\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003ePolydispersity index (Y\u003csub\u003e2\u003c/sub\u003e)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e0.382\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e \u003cp\u003e0.0065\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e \u003cp\u003e-0.009\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c5\"\u003e \u003cp\u003e0.006\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e \u003cp\u003e-0.0258\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c7\"\u003e \u003cp\u003e0.00725\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c8\"\u003e \u003cp\u003e0.02125\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c9\"\u003e \u003cp\u003e-0.0591\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c10\"\u003e \u003cp\u003e0.08338\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c11\"\u003e \u003cp\u003e0.06838\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003ep-values\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e \u003cp\u003e0.7125\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e \u003cp\u003e0.6123\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c5\"\u003e \u003cp\u003e0.7335\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e \u003cp\u003e0.3243\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c7\"\u003e \u003cp\u003e0.7707\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c8\"\u003e \u003cp\u003e0.4085\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c9\"\u003e \u003cp\u003e0.0608\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c10\"\u003e \u003cp\u003e0.0193\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c11\"\u003e \u003cp\u003e0.0385\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eZeta Potential (Y\u003csub\u003e3\u003c/sub\u003e)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e-38.04\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e \u003cp\u003e10.6075\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e \u003cp\u003e0.775\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c5\"\u003e \u003cp\u003e-0.5475\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e \u003cp\u003e-0.0175\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c7\"\u003e \u003cp\u003e0.1525\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c8\"\u003e \u003cp\u003e-0.1075\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c9\"\u003e \u003cp\u003e23.7288\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c10\"\u003e \u003cp\u003e0.43375\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c11\"\u003e \u003cp\u003e1.10375\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003ep-values\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e \u003cp\u003e\u0026lt;\u0026thinsp;0.0001\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e \u003cp\u003e0.2694\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c5\"\u003e \u003cp\u003e0.4205\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e \u003cp\u003e0.9849\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c7\"\u003e \u003cp\u003e0.8696\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c8\"\u003e \u003cp\u003e0.9078\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c9\"\u003e \u003cp\u003e\u0026lt;\u0026thinsp;0.0001\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c10\"\u003e \u003cp\u003e0.6567\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c11\"\u003e \u003cp\u003e0.2833\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003ePercentage transmittance (Y\u003csub\u003e4\u003c/sub\u003e)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e90.0727\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e \u003cp\u003e-2.24625\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e \u003cp\u003e4.43125\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c5\"\u003e \u003cp\u003e0.765\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e \u003cp\u003e\u003cb\u003e-\u003c/b\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c7\"\u003e \u003cp\u003e\u003cb\u003e-\u003c/b\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c8\"\u003e \u003cp\u003e\u003cb\u003e-\u003c/b\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c9\"\u003e \u003cp\u003e\u003cb\u003e-\u003c/b\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c10\"\u003e \u003cp\u003e\u003cb\u003e-\u003c/b\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c11\"\u003e \u003cp\u003e\u003cb\u003e-\u003c/b\u003e\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003ep-values\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e \u003cp\u003e0.0148\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e \u003cp\u003e0.0001\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c5\"\u003e \u003cp\u003e0.3469\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e \u003cp\u003e\u003cb\u003e-\u003c/b\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c7\"\u003e \u003cp\u003e\u003cb\u003e-\u003c/b\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c8\"\u003e \u003cp\u003e\u003cb\u003e-\u003c/b\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c9\"\u003e \u003cp\u003e\u003cb\u003e-\u003c/b\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c10\"\u003e \u003cp\u003e\u003cb\u003e-\u003c/b\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c11\"\u003e \u003cp\u003e\u003cb\u003e-\u003c/b\u003e\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003c/tbody\u003e \u003c/colgroup\u003e \u003ctfoot\u003e \u003ctr\u003e\u003ctd colspan=\"11\"\u003e*p\u0026thinsp;\u0026lt;\u0026thinsp;0.05 indicate significant terms\u003c/td\u003e\u003c/tr\u003e \u003c/tfoot\u003e \u003c/table\u003e\u003c/div\u003e \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\u003eStatistical analysis of the measured responses\u003c/p\u003e \u003c/div\u003e \u003c/caption\u003e \u003ccolgroup cols=\"7\"\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=\"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 \u003cdiv align=\"char\" char=\".\" class=\"colspec\" colname=\"c7\" colnum=\"7\"\u003e\u003c/div\u003e \u003cthead\u003e \u003ctr\u003e \u003cth align=\"left\" colname=\"c1\"\u003e \u003cp\u003eS. No.\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c2\"\u003e \u003cp\u003eVariables Responses\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c3\"\u003e \u003cp\u003eModel\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c4\"\u003e \u003cp\u003ePredicted R\u003csup\u003e2\u003c/sup\u003e\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c5\"\u003e \u003cp\u003eAdjusted R\u003csup\u003e2\u003c/sup\u003e\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c6\"\u003e \u003cp\u003eModel p value\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c7\"\u003e \u003cp\u003eLack of fit P value\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\u003eY\u003csub\u003e1\u003c/sub\u003e (nm)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003eQuadratic\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e \u003cp\u003e0.9456\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c5\"\u003e \u003cp\u003e0.8477\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c6\"\u003e \u003cp\u003e0.0214\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c7\"\u003e \u003cp\u003e0.489\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\u003eY\u003csub\u003e2\u003c/sub\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003eQuadratic\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e \u003cp\u003e0.8524\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c5\"\u003e \u003cp\u003e0.7868\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c6\"\u003e \u003cp\u003e0.0197\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c7\"\u003e \u003cp\u003e0.253\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\u003eY\u003csub\u003e3\u003c/sub\u003e (mV)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003eQuadratic\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e \u003cp\u003e0.9948\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c5\"\u003e \u003cp\u003e0.9855\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c6\"\u003e \u003cp\u003e\u0026lt;\u0026thinsp;0.0001\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c7\"\u003e \u003cp\u003e0.562\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\u003eY\u003csub\u003e4\u003c/sub\u003e (%)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003eLinear\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e \u003cp\u003e0.9713\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c5\"\u003e \u003cp\u003e0.8343\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c6\"\u003e \u003cp\u003e0.0005\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c7\"\u003e \u003cp\u003e0.324\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003c/tbody\u003e \u003c/colgroup\u003e \u003ctfoot\u003e \u003ctr\u003e\u003ctd colspan=\"7\"\u003eP\u0026thinsp;\u0026lt;\u0026thinsp;0.05 indicate significant terms\u003c/td\u003e\u003c/tr\u003e \u003c/tfoot\u003e \u003c/table\u003e\u003c/div\u003e \u003c/p\u003e \u003cp\u003ePDI increased non-significantly (p\u0026thinsp;\u0026gt;\u0026thinsp;0.05) with the X\u003csub\u003e1\u003c/sub\u003e factor (amount of oil) (Table\u0026nbsp;\u003cspan refid=\"Tab6\" class=\"InternalRef\"\u003e6\u003c/span\u003e). Furthermore, a non significant drop in PDI value was seen at the higher lipid content (X\u003csub\u003e1\u003c/sub\u003e\u003csup\u003e2\u003c/sup\u003e). This slight variation in PDI could be the result of adding oil at a constant surfactant concentration, which raises PDI. The PDI also exhibited a not statistically significant decline when the X\u003csub\u003e2\u003c/sub\u003e factor (surfactant concentration) was taken into account. The droplet size reduces and homogeneity improves as a result of the surfactant's known ability to break interfacial tension [\u003cspan citationid=\"CR42\" class=\"CitationRef\"\u003e42\u003c/span\u003e]. As a result, the PDI decreased negligibly as the amount of surfactant (X\u003csub\u003e2\u003c/sub\u003e) increased. PDI increased non-significantly (p\u0026thinsp;\u0026gt;\u0026thinsp;0.05) as a result of the single effect of co-surfactant (X\u003csub\u003e3\u003c/sub\u003e). An increase in the surfactant's intrinsic emulsifying properties combined with the emulsifying properties of the mono- and diglycerides present in the oil phase may be the cause of the not demonstrating statistical significance (p\u0026thinsp;\u0026gt;\u0026thinsp;0.05) reduction in PDI shown by the interactive impact of the quantity of oil and surfactant (X\u003csub\u003e1\u003c/sub\u003eX\u003csub\u003e2\u003c/sub\u003e). A not statistically significant (p\u0026thinsp;\u0026gt;\u0026thinsp;0.05) rise in PDI was seen as a result of the interrelationship between the amount of surfactant and co-surfactant (X\u003csub\u003e2\u003c/sub\u003eX\u003csub\u003e3\u003c/sub\u003e). The amount of oil and co-surfactant (X\u003csub\u003e1\u003c/sub\u003eX\u003csub\u003e3\u003c/sub\u003e) had an interactive impact that increased the PDI non-significantly (p\u0026thinsp;\u0026gt;\u0026thinsp;0.05). However, the emulsifying properties of the mono- and diglycerides present in the oil phase may be the cause of the reduction in PDI shown by the exponential effect of the amount of oil (X\u003csub\u003e1\u003c/sub\u003e\u003csup\u003e2\u003c/sup\u003e). The relatively small size micelle of Tween 80 (CMC of Tween 80 is (13\u0026ndash;15 mg/L) may be the cause of the considerable (p\u0026thinsp;\u0026lt;\u0026thinsp;0.05) increase in PDI that was observed in the exponential effect of the amount of surfactant (X\u003csub\u003e2\u003c/sub\u003e\u003csup\u003e2\u003c/sup\u003e). PDI may therefore rise as a result of using too much surfactant (Tween 80). Because a higher concentration of Transcutol P can interfere with the emulsifier's ability to stabilize the emulsion, an exponential effect of the quantity of co-surfactant (X\u003csub\u003e3\u003c/sub\u003e\u003csup\u003e2\u003c/sup\u003e) also exhibited a substantial (p\u0026thinsp;\u0026lt;\u0026thinsp;0.05) increase in PDI. The surfactant molecules may be dislodged or form micelles in place of stable films around droplets, increasing the size of the distribution [\u003cspan citationid=\"CR43\" class=\"CitationRef\"\u003e43\u003c/span\u003e]. The response surface plot for PDI is depicted in Fig.\u0026nbsp;\u003cspan refid=\"Fig3\" class=\"InternalRef\"\u003e3\u003c/span\u003e.\u003c/p\u003e \u003c/div\u003e \u003cdiv id=\"Sec34\" class=\"Section3\"\u003e \u003ch2\u003e3.3.3 Zeta Potential\u003c/h2\u003e \u003cp\u003eZeta potential was examined to determine the stability of the resulting nanoemulsion. Zeta potential results vary from \u0026minus;\u0026thinsp;1.88 mV to -38.04 mV. Labrafil M2125-CS (X\u003csub\u003e1\u003c/sub\u003e), Tween 80 (X\u003csub\u003e2\u003c/sub\u003e), and Transcutol P (X\u003csub\u003e3\u003c/sub\u003e), their interactive exponential effects showed variable on zeta potential (Y\u003csub\u003e3\u003c/sub\u003e). The following equation [\u003cspan citationid=\"CR4\" class=\"CitationRef\"\u003e4\u003c/span\u003e] describes the mathematical relationship between observed response (zeta potential), Y\u003csub\u003e3\u003c/sub\u003e, and various independent factors.\u003c/p\u003e \u003cp\u003e \u003cem\u003eY\u003c/em\u003e \u003csub\u003e3\u003c/sub\u003e (Zeta potential) = \u0026minus;\u0026thinsp;38.4\u0026thinsp;+\u0026thinsp;10.6075 \u003cem\u003eX\u003c/em\u003e\u003csub\u003e1\u003c/sub\u003e\u0026thinsp;+\u0026thinsp;0.775 \u003cem\u003eX\u003c/em\u003e\u003csub\u003e2\u003c/sub\u003e \u0026ndash; 0.5475 \u003cem\u003eX\u003c/em\u003e\u003csub\u003e3\u003c/sub\u003e \u0026ndash; 0.0175 \u003cem\u003eX\u003c/em\u003e\u003csub\u003e1\u003c/sub\u003e\u003cem\u003eX\u003c/em\u003e\u003csub\u003e2\u003c/sub\u003e\u0026thinsp;+\u0026thinsp;0.1525 \u003cem\u003eX\u003c/em\u003e\u003csub\u003e2\u003c/sub\u003e\u003cem\u003eX\u003c/em\u003e\u003csub\u003e3\u003c/sub\u003e \u0026ndash; 0.1075 \u003cem\u003eX\u003c/em\u003e\u003csub\u003e1\u003c/sub\u003e\u003cem\u003eX\u003c/em\u003e\u003csub\u003e3\u003c/sub\u003e\u0026thinsp;+\u0026thinsp;23.7288 \u003cem\u003eX\u003c/em\u003e\u003csub\u003e1\u003c/sub\u003e\u003csup\u003e2\u003c/sup\u003e\u0026thinsp;+\u0026thinsp;0.43375 \u003cem\u003eX\u003c/em\u003e\u003csub\u003e2\u003c/sub\u003e\u003csup\u003e2\u003c/sup\u003e\u0026thinsp;+\u0026thinsp;1.10375 X\u003csub\u003e3\u003c/sub\u003e\u003csup\u003e2\u003c/sup\u003e\u0026hellip;\u0026hellip;\u0026hellip;\u0026hellip;\u0026hellip;\u0026hellip;\u0026hellip; [\u003cspan citationid=\"CR4\" class=\"CitationRef\"\u003e4\u003c/span\u003e]\u003c/p\u003e \u003cp\u003eThe measurable effects of the independent variables X\u003csub\u003e1\u003c/sub\u003e (amount of oil), X\u003csub\u003e2\u003c/sub\u003e (amount of surfactant), and amount of co-surfactant (X\u003csub\u003e3\u003c/sub\u003e) on the zeta potential (Y\u003csub\u003e3\u003c/sub\u003e) are depicted in the equation above, along with their binary interactions and exponential effects. Collegial effect is shown by a positive coefficient sign, whereas the opposite effect of the independent variables on response is indicated by a negative coefficient sign. Their significant impact on the response is indicated by the higher coefficient value of the factor. The quadratic model showed a good fit in this setting. The multiple correlation tests (R\u003csup\u003e2\u003c/sup\u003e) and ANOVA were used to confirm the effectiveness of the model. The results showed that the R\u003csup\u003e2\u003c/sup\u003e value was 0.9948 and the p value was p\u0026thinsp;\u0026lt;\u0026thinsp;0.05. The R\u003csup\u003e2\u003c/sup\u003e value (0.9948) and observed p value (p\u0026thinsp;\u0026lt;\u0026thinsp;0.05) verified that independent variables significantly influenced the response (Y\u003csub\u003e3\u003c/sub\u003e) prediction. The zeta potential increased significantly (p\u0026thinsp;\u0026lt;\u0026thinsp;0.05) with the X\u003csub\u003e1\u003c/sub\u003e factor (amount of oil) (Table\u0026nbsp;\u003cspan refid=\"Tab6\" class=\"InternalRef\"\u003e6\u003c/span\u003e). The zeta potential values significantly decreased at the greater lipid content (X\u003csub\u003e1\u003c/sub\u003e). Oil droplets dilute the stabilizing surfactant at the interface, lowering the concentration of charge carriers and shifting the shear plane, consequently adding more oil to an oil-in-water emulsion generally lowers the zeta potential [\u003cspan citationid=\"CR44\" class=\"CitationRef\"\u003e44\u003c/span\u003e]. Additionally, there was also a not statistically significant drop in the zeta potential for the X\u003csub\u003e2\u003c/sub\u003e factor (surfactant concentration). Zeta potential increased insignificantly (p\u0026thinsp;\u0026gt;\u0026thinsp;0.05) as a result of the co-surfactant (X\u003csub\u003e3\u003c/sub\u003e) single effect. The amount of oil and surfactant (X\u003csub\u003e1\u003c/sub\u003eX\u003csub\u003e2\u003c/sub\u003e) had an interactive influence that increased zeta potential did not reach statistical significance (p\u0026thinsp;\u0026gt;\u0026thinsp;0.05). Zeta potential decreased in a not statistically significant (p\u0026thinsp;\u0026gt;\u0026thinsp;0.05) way as a result of the interaction between the amount of surfactant and co-surfactant (X\u003csub\u003e2\u003c/sub\u003eX\u003csub\u003e3\u003c/sub\u003e). The amount of oil and co-surfactant (X\u003csub\u003e1\u003c/sub\u003eX\u003csub\u003e3\u003c/sub\u003e) had an interactive impact that increased zeta potential in a not significant at the statistical level (p\u0026thinsp;\u0026gt;\u0026thinsp;0.05). Nevertheless, the zeta potential showed a significant reduction (p\u0026thinsp;\u0026lt;\u0026thinsp;0.05) as a result of the exponential effect of the oil amount (X\u003csub\u003e1\u003c/sub\u003e\u003csup\u003e2\u003c/sup\u003e). Conversely, the zeta potential decreased not significantly (p\u0026thinsp;\u0026gt;\u0026thinsp;0.05) when the amount of surfactant (X\u003csub\u003e2\u003c/sub\u003e\u003csup\u003e2\u003c/sup\u003e) increased exponentially. Therefore, a reduction in zeta potential may result from using too much surfactant (Tween 80). Similarly, an insignificant (p\u0026thinsp;\u0026gt;\u0026thinsp;0.05) drop in zeta potential was seen as the quantity of co-surfactant (X\u003csub\u003e3\u003c/sub\u003e\u003csup\u003e2\u003c/sup\u003e) increased exponentially. The response surface plot for zeta potential is depicted in Fig.\u0026nbsp;\u003cspan refid=\"Fig3\" class=\"InternalRef\"\u003e3\u003c/span\u003e.\u003c/p\u003e \u003c/div\u003e \u003cdiv id=\"Sec35\" class=\"Section3\"\u003e \u003ch2\u003e3.3.4 Percentage Transmittance\u003c/h2\u003e \u003cp\u003eTo make sure the resulting nanoemulsion was clear and transparent, % transmittance was examined. Higher transmittance is produced by clear solutions and dispersions, whereas lower transmittance is produced by cloudier or turbid solutions and dispersions because the latter scatter more incident radiation. The transmittance percentage ranges from 79.73% to 96.33%. It was found that when the amount of oil in the mixture decreased and the amount of surfactant and co-surfactant increased, correspondingly, the percentage transmittance increased. Below is the complete quadratic equation [\u003cspan citationid=\"CR5\" class=\"CitationRef\"\u003e5\u003c/span\u003e] for the observed response variable.\u003c/p\u003e \u003cp\u003e \u003cem\u003eY\u003c/em\u003e \u003csub\u003e4\u003c/sub\u003e (% Transmittance)\u0026thinsp;=\u0026thinsp;90.0727\u0026ndash;2.24625 \u003cem\u003eX\u003c/em\u003e\u003csub\u003e1\u003c/sub\u003e\u0026thinsp;+\u0026thinsp;4.43125 \u003cem\u003eX\u003c/em\u003e\u003csub\u003e2\u003c/sub\u003e\u0026thinsp;+\u0026thinsp;0.765 \u003cem\u003eX\u003c/em\u003e\u003csub\u003e3\u003c/sub\u003e\u0026hellip;\u0026hellip;\u0026hellip;\u0026hellip;\u0026hellip;\u0026hellip;\u0026hellip; [\u003cspan citationid=\"CR5\" class=\"CitationRef\"\u003e5\u003c/span\u003e]\u003c/p\u003e \u003cp\u003eThe measurable effects of the independent variables X\u003csub\u003e1\u003c/sub\u003e (amount of oil), X\u003csub\u003e2\u003c/sub\u003e (amount of surfactant), and amount of co-surfactant (X\u003csub\u003e3\u003c/sub\u003e) on the % Transmittance (Y\u003csub\u003e4\u003c/sub\u003e) are depicted in the equation above. Collegial effect is shown by a positive coefficient sign; whereas the opposite effect was observed for the independent variables on response is indicated by a negative coefficient sign. Their significant impact on the response is indicated by the higher coefficient value of the factor. The linear model showed a good fit in this setting. The multiple correlation tests (R\u003csup\u003e2\u003c/sup\u003e) and ANOVA were used to confirm the effectiveness of the model. Given that the R\u003csup\u003e2\u003c/sup\u003e value (0.9713) and p value (p\u0026thinsp;\u0026lt;\u0026thinsp;0.05) showed the model was well-fitted to the linear model, the aforementioned equation [\u003cspan citationid=\"CR5\" class=\"CitationRef\"\u003e5\u003c/span\u003e] demonstrated a strong fit to the response variable (Y\u003csub\u003e4\u003c/sub\u003e). The percentage transmittance showed a significant (p\u0026thinsp;\u0026lt;\u0026thinsp;0.05) decline with the X\u003csub\u003e1\u003c/sub\u003e factor (quantity of oil) (Table\u0026nbsp;\u003cspan refid=\"Tab6\" class=\"InternalRef\"\u003e6\u003c/span\u003e). The extent of the percentage transmittance significantly decreased at the increased amount of lipid (X\u003csub\u003e1\u003c/sub\u003e). Since the oil droplets dilute the stabilizing surfactant at the interface, lowering the concentration of surfactant at the interface and producing a more turbid emulsion because of larger droplet size, adding more oil to an oil-in-water emulsion typically lowers the transmittance percentage [\u003cspan citationid=\"CR12\" class=\"CitationRef\"\u003e12\u003c/span\u003e, \u003cspan citationid=\"CR45\" class=\"CitationRef\"\u003e45\u003c/span\u003e]. However, the X\u003csub\u003e2\u003c/sub\u003e factor (surfactant amount) demonstrated a notable rise (p\u0026thinsp;\u0026lt;\u0026thinsp;0.05) in the transmittance percentage because of decrease droplet size. The co-surfactant (X\u003csub\u003e3\u003c/sub\u003e) single effect showed a not statistically significant (p\u0026thinsp;\u0026gt;\u0026thinsp;0.05) increase in transmittance percentage. According to this, the percentage transmittance raises when the concentration of Tween 80 (surfactant) and Transcutol P (co-surfactant) rises and the amount of oil (Labrafil M2125-CS) decreases. The response surface plot for percentage transmittance is depicted in Fig.\u0026nbsp;\u003cspan refid=\"Fig3\" class=\"InternalRef\"\u003e3\u003c/span\u003e.\u003c/p\u003e \u003c/div\u003e \u003c/div\u003e \u003cdiv id=\"Sec36\" class=\"Section2\"\u003e \u003ch2\u003e3.4 Optimization\u003c/h2\u003e \u003cp\u003eThe range of the percent bias is +\u0026thinsp;0.789 to -1.616%. The desirability function is based on transforming each response into a dimensionless desirability value. The desirability function has a value between 0 and 1. A number of 1 indicates the best response for the elements being studied, while a value of 0 is seen when the factors produce undesired consequences [\u003cspan citationid=\"CR12\" class=\"CitationRef\"\u003e12\u003c/span\u003e]. Using this method, the intended robust formulation that satisfies the highest requirements of all responses within the specified limitations is produced.\u003c/p\u003e \u003cp\u003eIn this study, Design Expert\u0026reg; V.13.5 (Stat-Ease Inc., Minneapolis, MN) was used to implement the desirability function process. All of the responses were subject to the constraints. Table\u0026nbsp;\u003cspan refid=\"Tab2\" class=\"InternalRef\"\u003e3\u003c/span\u003e shows the range in which the independent variables (factors) were specified. Every response received the same weight (1) and importance (+++), which are determined by the software's constraints. The default setting for weight and importance is three pluses (+++), which denotes that every response is equally important. To combine all of the responses into a single measurement, the desirability function approach requires the calculation of an individual desirability function. This will assist in predicting the independent factors optimum amounts [\u003cspan citationid=\"CR12\" class=\"CitationRef\"\u003e12\u003c/span\u003e].\u003c/p\u003e \u003cp\u003eThe desirability function was utilized to optimize the process after a polynomial equation was generated and the impacts of independent factors on responses were examined. The optimal formulation that satisfied the greatest number of response variable requirements and had the best desirability function was chosen. The overall desirability of the chosen optimized formulation, which has X\u003csub\u003e1\u003c/sub\u003e\u0026thinsp;=\u0026thinsp;125.92 mg w/w, X\u003csub\u003e2\u003c/sub\u003e\u0026thinsp;=\u0026thinsp;665.81 mg w/w, and X\u003csub\u003e3\u003c/sub\u003e\u0026thinsp;=\u0026thinsp;185.86 mg w/w, was determined to be 0.794. For the responses Y\u003csub\u003e1\u003c/sub\u003e, Y\u003csub\u003e2\u003c/sub\u003e, Y\u003csub\u003e3\u003c/sub\u003e and Y\u003csub\u003e4\u003c/sub\u003e, the optimum formulation predicted values of 216.8\u0026thinsp;\u0026plusmn;\u0026thinsp;1.044 nm, 0.439\u0026thinsp;\u0026plusmn;\u0026thinsp;0.024, -38.04\u0026thinsp;\u0026plusmn;\u0026thinsp;0.372 mV, and 88.12\u0026thinsp;\u0026plusmn;\u0026thinsp;0.14%, respectively. The optimized formulation was created in triplicate to verify and validate the optimization. Every response was assessed using observed values for every formulation. (Table\u0026nbsp;\u003cspan refid=\"Tab8\" class=\"InternalRef\"\u003e8\u003c/span\u003e) displays a comparison between the observed and predicted values. All of the obtained values were in agreement with the expected values, as was evident from the findings, suggesting that BBD in conjunction with the desirability function is a potential method for SNEDDS optimization and assessment.\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\u003eQuantitative analysis of the predicted and observed value\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=\"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 \u003cthead\u003e \u003ctr\u003e \u003cth align=\"left\" colname=\"c1\"\u003e \u003cp\u003eS. No.\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c2\"\u003e \u003cp\u003eVariables Responses\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c3\"\u003e \u003cp\u003ePredicted Value\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c4\"\u003e \u003cp\u003eObserved Value\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c5\"\u003e \u003cp\u003e% Bias\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\u003eGlobule size (Y\u003csub\u003e1\u003c/sub\u003e)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e \u003cp\u003e215.1\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e \u003cp\u003e216.8\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c5\"\u003e \u003cp\u003e0.789\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\u003ePolydispersity index (Y\u003csub\u003e2\u003c/sub\u003e)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e \u003cp\u003e0.446\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e \u003cp\u003e0.439\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c5\"\u003e \u003cp\u003e-1.616\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\u003eZeta potential (Y\u003csub\u003e3\u003c/sub\u003e)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e \u003cp\u003e-37.98\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e \u003cp\u003e-38.04\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c5\"\u003e \u003cp\u003e0.167\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\u003ePercentage transmittance (Y\u003csub\u003e4\u003c/sub\u003e)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e \u003cp\u003e88.08\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e \u003cp\u003e88.12\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c5\"\u003e \u003cp\u003e0.051\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=\"Sec37\" class=\"Section2\"\u003e \u003ch2\u003e3.5 Characterization of Elagolix Sodium SNEDDS\u003c/h2\u003e \u003cdiv id=\"Sec38\" class=\"Section3\"\u003e \u003ch2\u003e3.5.1 Droplet Size and PDI\u003c/h2\u003e \u003cp\u003eThe developed ELAG-SNEDDS formulation was characterized by measuring droplet size and polydispersity index (PDI) to assess the quality and uniformity of the nanoemulsion system. In SNEDDS studies, acceptable PDI values are typically considered to be below 0.5, indicating narrow size distribution and formulation uniformity. Droplet size is a critical parameter in SNEDDS characterization because nanoscale droplets provide a high interfacial area that can enhance drug dissolution and absorption in the gastrointestinal tract, contributing to improved oral bioavailability [\u003cspan citationid=\"CR46\" class=\"CitationRef\"\u003e46\u003c/span\u003e]. Smaller and uniform droplets also favour consistent drug release profiles during in vitro and in vivo performance evaluation. Polydispersity index reflects the breadth of the droplet size distribution, with lower values indicating a more homogeneous population and stable nanoemulsion dispersion [\u003cspan citationid=\"CR47\" class=\"CitationRef\"\u003e47\u003c/span\u003e]. Thus, the observed droplet size and PDI results, 216.8 nm and 0.439 (Fig.\u0026nbsp;\u003cspan refid=\"Fig4\" class=\"InternalRef\"\u003e4\u003c/span\u003e) confirmed that the optimized ELAG-SNEDDS possess desirable physicochemical properties that support efficient drug delivery and stability.\u003c/p\u003e \u003cp\u003e \u003c/p\u003e \u003c/div\u003e \u003cdiv id=\"Sec39\" class=\"Section3\"\u003e \u003ch2\u003e3.5.2 Zeta potential\u003c/h2\u003e \u003cp\u003eZeta potential is an important physical quantity in the determination of stability of emulsion. The potential of a nanoparticle applied to the shear plane under an electric field is known as the zeta potential [\u003cspan citationid=\"CR48\" class=\"CitationRef\"\u003e48\u003c/span\u003e]. According to several publications, zeta potential values between \u0026plusmn;\u0026thinsp;0 and 10 mV are considered extremely unstable, between \u0026plusmn;\u0026thinsp;10 and 20 mV are quite stable, between \u0026plusmn;\u0026thinsp;20 and 30 mV are considered reasonably stable, whereas those exceeding\u0026thinsp;\u0026plusmn;\u0026thinsp;30 mV are regarded as highly stable [\u003cspan citationid=\"CR49\" class=\"CitationRef\"\u003e49\u003c/span\u003e]. Nevertheless, according to DLVO theory, some colloids have lower Zeta (\u003cb\u003eζ)\u003c/b\u003e-potential but remain stable, which could be attributed to the combined action of the electrical double layer's electrostatic repulsive and van der Waals attractive forces [\u003cspan citationid=\"CR50\" class=\"CitationRef\"\u003e50\u003c/span\u003e]. The colloidal stability is also caused by certain steric interactions [\u003cspan citationid=\"CR51\" class=\"CitationRef\"\u003e51\u003c/span\u003e]. Since the hydrocarbon tail of the Tween 80 (surfactant) and lipid phase surface put forth a lipophilic interaction that create higher energy barriers among the dispersed globules, hence colloidal formulations will not exhibit any instability (coalescence) due to the steric effects and negative zeta potential. Furthermore, certain non-DLVO factors, such as the high concentration of nonionic surfactant and the hydration of its polar group, contribute to the system's inherent stability [\u003cspan citationid=\"CR52\" class=\"CitationRef\"\u003e52\u003c/span\u003e]. In the current setting zeta potential was found to be -38.04\u0026thinsp;\u0026plusmn;\u0026thinsp;0.37 mV (Fig.\u0026nbsp;\u003cspan refid=\"Fig4\" class=\"InternalRef\"\u003e4\u003c/span\u003e). The negative zeta potential in this study may be caused by the esters and free fatty acids\u0026rsquo; negative charge over the oil droplets [\u003cspan citationid=\"CR53\" class=\"CitationRef\"\u003e53\u003c/span\u003e].\u003c/p\u003e \u003c/div\u003e \u003cdiv id=\"Sec40\" class=\"Section3\"\u003e \u003ch2\u003e3.5.3 Percent Transmittance\u003c/h2\u003e \u003cp\u003eThe percent transmittance of the ELAG-SNEDDS was determined to assess the optical clarity and homogeneity of the nanoemulsion upon dilution. Percent transmittance represents the amount of light transmitted through the sample, with high values indicating a clear and transparent nanoemulsion with minimal light scattering due to small droplet size [\u003cspan citationid=\"CR54\" class=\"CitationRef\"\u003e54\u003c/span\u003e]. High transmittance values are often correlated with droplet sizes in the nanometric range and uniform dispersion of droplets throughout the continuous phase, reflecting efficient self‑emulsification and formulation stability. In SNEDDS characterization, percent transmittance closer to 100% has been used as an indicator of successful nanoemulsion formation and is considered a critical quality attribute of optimized formulations [\u003cspan citationid=\"CR55\" class=\"CitationRef\"\u003e55\u003c/span\u003e]. Higher transmittance also suggests reduced potential for drug precipitation and enhanced surface area available for drug release. Thus, the observed transmittance results, 88.12% confirmed the formation of a uniform and optically clear ELAG-SNEDDS, supporting the formulation\u0026rsquo;s potential to improve dissolution and bioavailability.\u003c/p\u003e \u003c/div\u003e \u003cdiv id=\"Sec41\" class=\"Section3\"\u003e \u003ch2\u003e3.5.4 Dispersibility Test\u003c/h2\u003e \u003cp\u003eThe ability of a nanoemulsion to maintain a stable, uniform dispersion of water and oil droplets in the sub-micron size range because of surfactants and co-surfactants that reduce interfacial tension is considered as an important parameter to evaluate nanoemulsion. By avoiding sedimentation, creaming, and coalescence, this stability guarantees that the tiny droplet sizes during shelf life, offering advantages including optical transparency, increased bioavailability of low bioavailable therapeutic agents. Within one minute of completing the dispersibility test, All ELAG-SNEDDS formulations in this study were verified to be clear. They are very noticeable and of excellent quality. In order to produce nanoemulsions with in the GI fluid, the SNEDDS are distributed throughout the GIT lumen. Under GIT conditions, this dispersibility must happen fully and swiftly. The ELAG-SNEDDS formulation is Grade A, which forms a clear or bluish nanoemulsion quickly (within 1 min).\u003c/p\u003e \u003c/div\u003e \u003cdiv id=\"Sec42\" class=\"Section3\"\u003e \u003ch2\u003e3.5.5 Self-Nanoemulsification Efficiency Test\u003c/h2\u003e \u003cp\u003eSelf-emulsification time measures the time to spontaneously emulsify a pre-concentrate when diffused in an aqueous medium, typically under mild agitation, is a crucial parameter in assessing the effectiveness of self-emulsifying drug delivery systems. A more effective formulation that quickly turns into a stable emulsion is indicated by a shorter self-emulsification time, which is ideal for enhancing drug miscibility and bioavailability [\u003cspan citationid=\"CR12\" class=\"CitationRef\"\u003e12\u003c/span\u003e]. Visual examination was used to evaluate the self-emulsification time of ELAG-SNEDDS. SE timings that was determined for the developed optimized formulation was found to be 37.33\u0026thinsp;\u0026plusmn;\u0026thinsp;2.5 sec. As evidenced by the fact that the time of formulation was emulsified in less than a minute, predicting good self-emulsification time.\u003c/p\u003e \u003c/div\u003e \u003cdiv id=\"Sec43\" class=\"Section3\"\u003e \u003ch2\u003e3.5.6 Thermodynamic Stability and pH robustness\u003c/h2\u003e \u003cp\u003eFinding formulations that demonstrate metastability is the main goal of the thermodynamic stability study. The emulsions remained stable throughout the heating\u0026ndash;cooling and freeze\u0026ndash;thaw cycles, as well as during 30 minutes of centrifugation at 5000 rpm, suggesting neither phase separation nor precipitation occurred. The thermodynamic stability evaluation of SNEDDS produced positive results (Table\u0026nbsp;\u003cspan refid=\"Tab9\" class=\"InternalRef\"\u003e9\u003c/span\u003e), showing strong stability even under stressful circumstances. Notably, the results showed no evidence of flocculation, crystallization, or phase separation. Determining the cloud point is essential for forecasting the prepared SNEDDS's stability and precipitation patterns. It acts as an indicator for the possibility of surfactant precipitation at high temperatures. This concern emerges because higher temperatures may cause surfactant molecules to lose water, which would cause the formulation to gel and lose some of its emulsifying qualities. When ice crystals develop and then melt, a freeze-thaw cycle on emulsions can lead to instability, which can result in phase separation, droplet coalescence, and a permanent increase in particle size. Nanoemulsion droplets may congregate during freezing due to ice crystals, and the melted liquid may not fully re-disperse them upon thawing, producing an unstable and separated product [\u003cspan citationid=\"CR56\" class=\"CitationRef\"\u003e56\u003c/span\u003e]. Present ELAG-SNEDDS formulation showed stability after a freeze\u0026ndash;thaw testing at \u0026minus;\u0026thinsp;20\u0026deg;C and 25\u0026deg;C. Emulsions are studied and controlled by cycles of heating and cooling that alter their physical stability, break the emulsion completely, or produce smaller droplets through freezing and melting processes [\u003cspan citationid=\"CR57\" class=\"CitationRef\"\u003e57\u003c/span\u003e]. After a heating-cooling cycle of at 4\u0026deg;C and 45\u0026deg;C showed thermodynamic stability. The optimized nanoemulsion was diluted with distilled water, 0.1 N HCl, and phosphate buffer (pH 6.8); no phase separation and coalescence was observed.\u003c/p\u003e \u003cp\u003e \u003cdiv class=\"gridtable\"\u003e\u003ctable float=\"Yes\" id=\"Tab9\" border=\"1\"\u003e \u003ccaption language=\"En\"\u003e \u003cdiv class=\"CaptionNumber\"\u003eTable 9\u003c/div\u003e \u003cdiv class=\"CaptionContent\"\u003e \u003cp\u003eThermodynamic stability and pH dilution results\u003c/p\u003e \u003c/div\u003e \u003c/caption\u003e \u003ccolgroup cols=\"8\"\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 \u003cthead\u003e \u003ctr\u003e \u003cth align=\"left\" colname=\"c1\" morerows=\"1\" rowspan=\"2\"\u003e \u003cp\u003eS. No\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c2\" morerows=\"1\" rowspan=\"2\"\u003e \u003cp\u003eName of formulation\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c3\" morerows=\"1\" rowspan=\"2\"\u003e \u003cp\u003eCentrifugation (5000 rpm for\u003c/p\u003e \u003cp\u003e30 min)\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c4\" morerows=\"1\" rowspan=\"2\"\u003e \u003cp\u003eFreeze\u0026ndash;thaw cycling between \u0026minus;\u0026thinsp;20\u0026deg;C and 25\u0026deg;C\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c5\" morerows=\"1\" rowspan=\"2\"\u003e \u003cp\u003eHeating\u0026ndash;cooling cycles between 4\u0026deg;C and 45\u0026deg;C\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colspan=\"3\" nameend=\"c8\" namest=\"c6\"\u003e \u003cp\u003epH dilution\u003c/p\u003e \u003c/th\u003e \u003c/tr\u003e \u003ctr\u003e \u003cth align=\"left\" colname=\"c6\"\u003e \u003cp\u003eDistilled water\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c7\"\u003e \u003cp\u003e0.1N HCL\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c8\"\u003e \u003cp\u003epH 6.8 buffer\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\u003eOptimized ELAG-SNEDDS\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e\u003cb\u003e\u0026radic;\u003c/b\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e\u003cb\u003e\u0026radic;\u003c/b\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e\u003cb\u003e\u0026radic;\u003c/b\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e \u003cp\u003e\u003cb\u003e\u0026radic;\u003c/b\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c7\"\u003e \u003cp\u003e\u003cb\u003e\u0026radic;\u003c/b\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c8\"\u003e \u003cp\u003e\u003cb\u003e\u0026radic;\u003c/b\u003e\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=\"Sec44\" class=\"Section3\"\u003e \u003ch2\u003e3.5.7 Determination of Cloud Point\u003c/h2\u003e \u003cp\u003eEssentially, cloud points of the developed ELAG-SNEDDS were determined to be more than 70\u0026deg;C, suggesting that the nanoemulsion area would remain stable at physiologic temperature, hence removing the chance of precipitation or phase separation. At 78\u0026deg;C ELAG-SNEDDS formulation becomes hazy. The stability of emulsions containing nonionic surfactants, such as Tween 80, can be assessed using cloud point analysis. Since the surfactant begins to lose its hydrophilicity because of dehydration occurring in the polyoxyethylene chain of Tween 80, its HLB value shifts (toward lipophilicity) as the temperature rises [\u003cspan citationid=\"CR12\" class=\"CitationRef\"\u003e12\u003c/span\u003e]. This causes the inversion of emulsion from oil in water to water to oil, moreover droplet size of the emulsion increased, which ultimately causes the transparent emulsion to become hazy. In order to avoid phase inversion and phase separation of the SNEDDS at the physiological temperature of the gastrointestinal tract, the nanoemulsion formulation must have a cloud point above 37\u0026deg;C. This would prevent precipitation of drug and cloudy appearance [\u003cspan citationid=\"CR58\" class=\"CitationRef\"\u003e58\u003c/span\u003e]. The outcome indicated that at room temperature, the emulsion may exhibit the maximum likelihood of stability.\u003c/p\u003e \u003c/div\u003e \u003cdiv id=\"Sec45\" class=\"Section3\"\u003e \u003ch2\u003e3.5.8 Morphological study\u003c/h2\u003e \u003cp\u003eTransmission electron microscopy (TEM) was performed to characterize the morphology and structural features of the ELAG-SNEDDS formulation. The TEM images demonstrated well-defined, spherical nano-droplets with smooth surfaces, indicating the formation of a uniform nanoemulsion system (Fig.\u0026nbsp;\u003cspan refid=\"Fig5\" class=\"InternalRef\"\u003e5\u003c/span\u003e). The droplets appeared discrete and evenly dispersed, reflecting good colloidal stability without signs of aggregation or coalescence. The observed particle dimensions were consistent with the nanometric size range obtained, confirming the reliability of the formulation\u0026rsquo;s size distribution. Furthermore, the clarity and uniformity of the droplets supported the efficiency of the selected surfactant\u0026ndash;co-surfactant blend in stabilizing the nanoemulsion. Overall, the TEM results validate the morphological integrity, stability, and nanoscale characteristics of the prepared ELAG-SNEDDS.\u003c/p\u003e \u003cp\u003e \u003c/p\u003e \u003c/div\u003e \u003c/div\u003e \u003cdiv id=\"Sec46\" class=\"Section2\"\u003e \u003ch2\u003e3.6 In-Vitro Drug Release Study\u003c/h2\u003e \u003cp\u003eUsing a modified dialysis technique, the in-vitro assessment of the drug release behaviour from ELAG-SNEDDS and the pure drug suspension was examined in 0.1 N HCL and PBS media (pH 6.8) maintained at 37\u0026deg;C for 12 h. Figure\u0026nbsp;\u003cspan refid=\"Fig6\" class=\"InternalRef\"\u003e6\u003c/span\u003e illustrates the comparative drug release profiles of the pure drug suspension and the optimized ELAG-SNEDDS. The in vitro drug release profiles of ELAG-SNEDDS showed consistently improved release in PBS 6.8, whereas drug release was significantly reduced in 0.1 N HCL when compared to the suspension of pure drug, as shown in Fig.\u0026nbsp;\u003cspan refid=\"Fig6\" class=\"InternalRef\"\u003e6\u003c/span\u003e. In the first two hours, approximately 48.08% of elagolix sodium was released from the pure drug suspension in PBS 6.8, whereas 71.5% of elagolix sodium was quick initial released from the optimized SNEDDS formulation in the same medium. Approximately 40.02% of elagolix sodium was released from the pure drug suspension in 0.1 N HCl, compared to 63.7% quick initial released from the optimized SNEDDS formulation in first two hours, respectively. In the continuous 12 hours study, ELAG-SNEDDS showed enhanced drug release of 94.4%, compared to the pure drug suspension released only 61.63% of the drug in PBS (pH 6.8) over 12 hours. In contrast, drug release of 53.91% was observed in 0.1 N HCl for the pure drug suspension, compared to 79.44% for the ELAG-SNEDDS formulation, respectively. This pattern of drug release from ELAG-SNEDDS that transports the encapsulated drug in the form of fine emulsion to the site of uptake is beneficial in boosting bioavailability [\u003cspan citationid=\"CR59\" class=\"CitationRef\"\u003e59\u003c/span\u003e].\u003c/p\u003e \u003cp\u003e \u003c/p\u003e \u003c/div\u003e \u003cdiv id=\"Sec47\" class=\"Section2\"\u003e \u003ch2\u003e3.7 Drug Permeation Using Ex Vivo Model\u003c/h2\u003e \u003cp\u003eAn ex vivo drug permeation study was conducted to evaluate the ability of elagolix sodium to permeate through the non-everted gut sac method. The results demonstrated that the total drug permeation through the SD rat intestine was higher for ELAG-SNEDDS than for the pure drug suspension. After 6 hours, the apparent permeability coefficient as well as the steady-state flux associated with the pure drug suspension was evaluated as 1.43 \u0026times; 10⁻\u003csup\u003e4\u003c/sup\u003ecm\u0026sup2;/s and 0.00043\u0026micro;g/min, ELAG-SNEDDS showed a measurable apparent permeability of 2.08 \u0026times; 10⁻⁴ cm\u0026sup2;/s and a steady-state flux of 0.00063 \u0026micro;g/min. The permeation profile demonstrated enhanced flux and cumulative drug transport for the optimized SNEDDS formulation compared to the pure drug. This improvement indicates improved membrane permeability and supports the potential of SNEDDS to enhance oral absorption of elagolix sodium.\u003c/p\u003e \u003c/div\u003e \u003cdiv id=\"Sec48\" class=\"Section2\"\u003e \u003ch2\u003e3.8 Pharmacokinetic Study\u003c/h2\u003e \u003cp\u003eClinical efficacy trials of the commercial product are included in the majority of them. In essence, elagolix sodium is a BCS class III drug that exhibited reduced permeability. Two groups participated in the in vivo investigations: one was fed ELAG-SNEDDS, and the other was given ELAG in the form of suspension. After oral delivery of the ELAG-SNEDDS and ELAG alone to Sprague Dawley (SD) female rats (n\u0026thinsp;=\u0026thinsp;8) at a dosing level of 15.42 mg/kg through gavage, the concentration-time profile of ELAG were assessed in plasma (Fig.\u0026nbsp;\u003cspan refid=\"Fig7\" class=\"InternalRef\"\u003e7\u003c/span\u003e). ELAG and ELAG-SNEDDS pharmacokinetic parameters were derived from the plasma drug concentration and time (h) data (Table\u0026nbsp;\u003cspan refid=\"Tab10\" class=\"InternalRef\"\u003e10\u003c/span\u003e). The C\u003csub\u003emax\u003c/sub\u003e and AUC of ELAG-SNEDDS were higher than those of ELAG suspension alone. For ELAG-SNEDDS, the AUC\u003csub\u003e0\u0026ndash;24\u003c/sub\u003e and C\u003csub\u003emax\u003c/sub\u003e values were found to be 137.5\u0026thinsp;\u0026plusmn;\u0026thinsp;44.1 \u0026micro;g.h/mL and 16.4\u0026thinsp;\u0026plusmn;\u0026thinsp;2.6 \u0026micro;g.h/mL, In comparison to 82.8\u0026thinsp;\u0026plusmn;\u0026thinsp;14.3 \u0026micro;g.h/mL and 10.7\u0026thinsp;\u0026plusmn;\u0026thinsp;1.5 \u0026micro;g.h/mL of the ELAG alone, respectively. The ELAG-SNEDDS enhanced the C\u003csub\u003emax\u003c/sub\u003e and AUC\u003csub\u003e0\u0026ndash;24\u003c/sub\u003e by 1.66 and 1.53 times, respectively. Bile salts, phospholipids, and other colloidal systems produced by lipid based products may have enhanced diffusion over the undisturbed water layer and enhanced lymphatic absorption, which could account for this outcome [\u003cspan citationid=\"CR60\" class=\"CitationRef\"\u003e60\u003c/span\u003e]. Because ELAG-SNEDDS entered the bloodstream relatively faster after intestinal and lymphatic uptake, a lower t\u003csub\u003emax\u003c/sub\u003e was achieved. The t\u003csub\u003emax\u003c/sub\u003e for the ELAG and ELAG-SNEDDS was 1.8 and 1.2 hours respectively. Due to the higher permeability of ELAG-SNEDDS, it achieved maximum plasma concentration more rapidly, which resulted in a decrease in t\u003csub\u003emax\u003c/sub\u003e. The literature contains very little in vivo pharmacokinetic research for elagolix sodium. The bioavailability has been improved with ELAG-SNEDDS. The augmentation of oral uptake of ELAG, the outcomes of which have been shown in Table\u0026nbsp;\u003cspan refid=\"Tab10\" class=\"InternalRef\"\u003e10\u003c/span\u003e. The bioavailability of ELAG-SNEDDS may be enhanced by spontaneous nanoemulsion formation within the GI tract, along with the extensive surface area generated by these nanosized globules. The gut epithelium layer is the only barrier that slows down the diffusion or absorption of drugs. When the formulation's high surfactant content goes through the intestine, it exhibit an interplay with the lipid bilayer's polar groups, breaking down the structure and boosting absoption and extent of uptake [\u003cspan citationid=\"CR31\" class=\"CitationRef\"\u003e31\u003c/span\u003e]. Lipid-based formulations undergo digestion by gastric and pancreatic lipases, breaking down triglycerides into monoglycerides and fatty acids, which form micelles and mixed micelles that facilitate drug absorption. In response to the presence of dietary lipids, bile salts secreted from the gall bladder form micelles and vesicular structure, facilitating the solubilization and subsequent absorption of lipid digestion products and drugs in the small intestine. Drug absorption in the intestine is mediated by specific transport proteins, such as solute carriers and ABC transporters, located on the apical and basolateral membranes of enterocytes. Several strategies facilitate drug transport to the lymphatic system after oral administration, such as transcellular absorption due to increase the membrane fluidity, paracellular transport by loosening tight junctions [\u003cspan citationid=\"CR61\" class=\"CitationRef\"\u003e61\u003c/span\u003e, \u003cspan citationid=\"CR62\" class=\"CitationRef\"\u003e62\u003c/span\u003e], uptake via Peyer's patch M cells [\u003cspan citationid=\"CR63\" class=\"CitationRef\"\u003e63\u003c/span\u003e] (Fig.\u0026nbsp;\u003cspan refid=\"Fig8\" class=\"InternalRef\"\u003e8\u003c/span\u003e). Lipids induced the formation of lipoproteins/chylomicrons [\u003cspan citationid=\"CR64\" class=\"CitationRef\"\u003e64\u003c/span\u003e, \u003cspan citationid=\"CR65\" class=\"CitationRef\"\u003e65\u003c/span\u003e] that enables lipophilic drug compounds to be solubilized by embedding them within the hydrophobic core and promote the absorption through intestinal lymphatics [\u003cspan citationid=\"CR66\" class=\"CitationRef\"\u003e66\u003c/span\u003e]. Moreover, surfactants and cosurfactant work by causing to open the tight junctions [\u003cspan citationid=\"CR67\" class=\"CitationRef\"\u003e67\u003c/span\u003e]. In this study Tween 80 and Transcutol P, which are utilized as surfactants and co-surfactants in the formulation might have caused to boost bioavailability because of their intermediate HLB value (Tween 80\u0026thinsp;=\u0026thinsp;15), which is also inhibit the P-gp substrate leads to enhance intracellular concentration of drug via intestinal lymphatic system [\u003cspan citationid=\"CR68\" class=\"CitationRef\"\u003e68\u003c/span\u003e]. Fundamentally, certain unique surfactants are considered to aid in opening tight junctions by interplay with proteins like Factin and actin anchoring protein at tight junctions [\u003cspan citationid=\"CR69\" class=\"CitationRef\"\u003e69\u003c/span\u003e]. Furthermore it is believed that nanoemulsions get taken up from the small intestine and delivered to the blood via the lymphatic pathway because of the globule size of nm, whereas the raw elagolix sodium entered into the enterohepatic circulation following peroral administration and eliminated [\u003cspan citationid=\"CR70\" class=\"CitationRef\"\u003e70\u003c/span\u003e]. According to the literature, the majority of lipidbased drug carriers that contain medium- and long-chain fatty acids, as well as high HLB value of surfactants amount bypass the portal vein and enters the lymphatic system of the intestine [\u003cspan citationid=\"CR62\" class=\"CitationRef\"\u003e62\u003c/span\u003e, \u003cspan citationid=\"CR71\" class=\"CitationRef\"\u003e71\u003c/span\u003e]. As SNEDDS, which was prepared to improve the therapeutic efficacy by enhancing the weakly bioavailable elagolix sodium, offers hope for the potential therapeutic strategy and treatment; however, more research is needed to determine whether this is clinically significant.\u003c/p\u003e \u003cp\u003e \u003c/p\u003e \u003cp\u003e \u003cdiv class=\"gridtable\"\u003e\u003ctable float=\"Yes\" id=\"Tab10\" border=\"1\"\u003e \u003ccaption language=\"En\"\u003e \u003cdiv class=\"CaptionNumber\"\u003eTable 10\u003c/div\u003e \u003cdiv class=\"CaptionContent\"\u003e \u003cp\u003eEvaluation of pharmacokinetic parameters (mean\u0026thinsp;\u0026plusmn;\u0026thinsp;SD) of ELAG-SNEDDS and pure drug suspension in female SD rats (n\u0026thinsp;=\u0026thinsp;8) after oral administration\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=\"char\" char=\".\" 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 \u003cthead\u003e \u003ctr\u003e \u003cth align=\"left\" colname=\"c1\" morerows=\"1\" rowspan=\"2\"\u003e \u003cp\u003ePharmacokinetic Parameters\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colspan=\"2\" nameend=\"c3\" namest=\"c2\"\u003e \u003cp\u003eELAG-SNEDDS\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colspan=\"2\" nameend=\"c5\" namest=\"c4\"\u003e \u003cp\u003ePure drug suspension\u003c/p\u003e \u003c/th\u003e \u003c/tr\u003e \u003ctr\u003e \u003cth align=\"left\" colname=\"c2\"\u003e \u003cp\u003eValue\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c3\"\u003e \u003cp\u003eSt dev.\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c4\"\u003e \u003cp\u003eValue\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c5\"\u003e \u003cp\u003eSt dev.\u003c/p\u003e \u003c/th\u003e \u003c/tr\u003e \u003c/thead\u003e \u003ctbody\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003et\u0026frac12; (hr)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e \u003cp\u003e6.7\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e \u003cp\u003e1.37\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e \u003cp\u003e4.7\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c5\"\u003e \u003cp\u003e1.17\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eAUC\u003csub\u003e0\u0026thinsp;\u0026minus;\u0026thinsp;24\u003c/sub\u003e (\u0026micro;g.mL-1*hr)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e \u003cp\u003e137.5\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e \u003cp\u003e44.11\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e \u003cp\u003e82.8\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c5\"\u003e \u003cp\u003e14.33\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eAUC\u003csub\u003e0\u0026thinsp;\u0026minus;\u0026thinsp;inf\u003c/sub\u003e (\u0026micro;g.mL-1*hr)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e \u003cp\u003e149.1\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e \u003cp\u003e46.4\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e \u003cp\u003e86.4\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c5\"\u003e \u003cp\u003e15.56\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003et\u003csub\u003emax\u003c/sub\u003e (hr)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e \u003cp\u003e1.2\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e \u003cp\u003e0.27\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e \u003cp\u003e1.8\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c5\"\u003e \u003cp\u003e0.26\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eC\u003csub\u003emax\u003c/sub\u003e (\u0026micro;M)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e \u003cp\u003e16.4\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e \u003cp\u003e2.64\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e \u003cp\u003e10.7\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c5\"\u003e \u003cp\u003e1.47\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"},{"header":"4. Conclusions","content":"\u003cp\u003eThe optimum formulation of liquid SNEDDS, which are consists of Labrafil M2125-CS (125.92 mg) w/w, Tween 80 (665.81 mg) w/w and Transcutol-P (185.86 mg) w/w was chosen because it produces a nanoemulsion, with droplet size (216.8\u0026thinsp;\u0026plusmn;\u0026thinsp;1.044 nm) zeta potential (-38.04\u0026thinsp;\u0026plusmn;\u0026thinsp;0.372 mV), PDI (0.439\u0026thinsp;\u0026plusmn;\u0026thinsp;0.024), and % transmittance (88.12\u0026thinsp;\u0026plusmn;\u0026thinsp;0.14%), and considerable thermodynamic stability when dispersed in water. According to pharmacokinetic studies, the enhanced oral bioavailability may be because of the mechanism of nanoemulsification with increased surface area resulting into enhanced drug permeability. The optimized ELAG-SNEDDS may be a capable method for the oral absorption augmentation of low permeable drugs, as evidenced by the notable increase in elagolix sodium bioavailability.\u003c/p\u003e"},{"header":"Declarations","content":"\u003cp\u003e \u003ch2\u003eConflict of Interest\u003c/h2\u003e \u003cp\u003eThe authors declare no conflicts of interest or personal relationships that could have influenced the work reported in this paper.\u003c/p\u003e \u003c/p\u003e\u003ch2\u003e \u003cb\u003eEthics Declarations\u003c/b\u003e \u003c/h2\u003e \u003cp\u003e \u003cstrong\u003eEthical Approval\u003c/strong\u003e \u003cp\u003e Before starting the animal experiments, this research granted the approval by the Institutional Animal Ethics Committee (IAEC) of IIMT College of Medical Sciences, IIMT University, Meerut, Uttar Pradesh, India, which was established under the committee for the Purpose of Control and Supervision of Experiments on Animals (CPCSEA).\u003c/p\u003e \u003c/p\u003e \u003cp\u003e \u003cstrong\u003eConsent For Publication\u003c/strong\u003e \u003cp\u003e The final text and submission of the work have been approved by all authors.\u003c/p\u003e \u003c/p\u003e\u003ch2\u003eFunding\u003c/h2\u003e \u003cp\u003eThis study was conducted without financial support from public, commercial, or not-for-profit funding agencies.\u003c/p\u003e\u003ch2\u003eAuthor Contribution\u003c/h2\u003e\u003cp\u003eAuthor 1 (Jonee Panwar)Did this experiment, wrote main manuscript, data collection, create figures and tablesAuthor 2 (Garima Garg)Validation of data, editing of manuscript and interpretation of resultsAuthor 3 (Hasan Ali)Validation of data, editing of manuscript and interpretation of results\u003c/p\u003e"},{"header":"References","content":"\u003col\u003e\u003cli\u003e\u003cspan\u003eMohs, R. 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QbD-based systematic development of novel optimized solid self-nanoemulsifying drug delivery systems (SNEDDS) of lovastatin with enhanced biopharmaceutical performance. \u003cem\u003eDrug Delivery\u003c/em\u003e, \u003cem\u003e22\u003c/em\u003e(6), 765\u0026ndash;784. \u003cspan class=\"ExternalRef\"\u003e\u003cspan class=\"RefSource\"\u003ehttps://doi.org/10.3109/10717544.2014.900154\u003c/span\u003e\u003cspan address=\"10.3109/10717544.2014.900154\" targettype=\"DOI\" class=\"RefTarget\"\u003e\u003c/span\u003e\u003c/span\u003e\u003c/span\u003e\u003c/li\u003e\u003c/ol\u003e"}],"fulltextSource":"","fullText":"","funders":[],"hasAdminPriorityOnWorkflow":false,"hasManuscriptDocX":true,"hasOptedInToPreprint":true,"hasPassedJournalQc":"","hasAnyPriority":false,"hideJournal":true,"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":"researchsquare","isNatureJournal":false,"hasQc":true,"allowDirectSubmit":true,"externalIdentity":"","sideBox":"","snPcode":"","submissionUrl":"/submission","title":"Research Square","twitterHandle":"researchsquare","acdcEnabled":true,"dfaEnabled":false,"editorialSystem":"","reportingPortfolio":"","inReviewEnabled":false,"inReviewRevisionsEnabled":true},"keywords":"Elagolix sodium, SNEDDS, Permeability, Oral bioavailability, Optimization","lastPublishedDoi":"10.21203/rs.3.rs-8424074/v1","lastPublishedDoiUrl":"https://doi.org/10.21203/rs.3.rs-8424074/v1","license":{"name":"CC BY 4.0","url":"https://creativecommons.org/licenses/by/4.0/"},"manuscriptAbstract":"\u003cp\u003eElagolix sodium is a novel, orally active non-peptide GnRH receptor antagonist used to treat endometriosis and uterine fibroids, as a BCS class III drug, it exhibits high solubility but low permeability, leading to poor and variable oral absorption. Limited permeability restricts bioavailability, but strategies such as lipid-based systems (nanoemulsions, SEDDS/SNEDDS), solid lipid or nanostructured lipid nanoparticles (SLN/NLC), permeation enhancers and cyclodextrin complexes can enhance oral absorption. The current research focused on the formulation and optimization of a self-nanoemulsifying drug delivery system (SNEDDS) for elagolix sodium aims to overcome low permeability and enhance its oral bioavailability. ELAG-SNEDDS were prepared using Labrafil M2125 CS, Tween 80, and Transcutol P at various ratios (1:1 to 4:1), and nanoemulsion region was determined using pseudo-ternary phase diagrams. The evidence from our research shows that optimized ELAG-SNEDDS was stable under thermodynamic conditions and possessed a droplet size of 216.8\u0026thinsp;\u0026plusmn;\u0026thinsp;1.044 nm, zeta (ζ) potential \u0026minus;\u0026thinsp;38.04\u0026thinsp;\u0026plusmn;\u0026thinsp;0.372 mV, exhibited a PDI of 0.439\u0026thinsp;\u0026plusmn;\u0026thinsp;0.024 and time of emulsification\u0026thinsp;\u0026lt;\u0026thinsp;1 minute. In pharmacokinetic study, ELAG-SNEDDS substantially increased drug absorption in the female Sprague Dawley (SD) rats, producing a higher C\u003csub\u003emax\u003c/sub\u003e and lower T\u003csub\u003emax\u003c/sub\u003e compared with the raw drug. These early outcomes suggest that ELAG-SNEDDS has the potential to serve as an effective delivery system of improving the permeability, absorption and oral bioavailability of elagolix sodium and may offer therapeutic potential in the treatment of endometriosis.\u003c/p\u003e","manuscriptTitle":"Development, Optimization Using Box–Behnken, In Vitro, Ex Vivo Characterization \u0026amp; Pharmacokinetic Evaluation of Elagolix Sodium Loaded Self-Nano Emulsifying Drug Delivery System","msid":"","msnumber":"","nonDraftVersions":[{"code":1,"date":"2026-01-09 06:35:46","doi":"10.21203/rs.3.rs-8424074/v1","editorialEvents":[{"type":"communityComments","content":0}],"status":"published","journal":{"display":true,"email":"[email protected]","identity":"researchsquare","isNatureJournal":false,"hasQc":true,"allowDirectSubmit":true,"externalIdentity":"","sideBox":"","snPcode":"","submissionUrl":"/submission","title":"Research Square","twitterHandle":"researchsquare","acdcEnabled":true,"dfaEnabled":false,"editorialSystem":"","reportingPortfolio":"","inReviewEnabled":false,"inReviewRevisionsEnabled":true}}],"origin":"","ownerIdentity":"bb3a5896-45c5-4922-925d-73bc9f22af4f","owner":[],"postedDate":"January 9th, 2026","published":true,"recentEditorialEvents":[],"rejectedJournal":[],"revision":"","amendment":"","status":"posted","subjectAreas":[],"tags":[],"updatedAt":"2026-04-20T06:42:50+00:00","versionOfRecord":[],"versionCreatedAt":"2026-01-09 06:35:46","video":"","vorDoi":"","vorDoiUrl":"","workflowStages":[]},"version":"v1","identity":"rs-8424074","journalConfig":"researchsquare"},"__N_SSP":true},"page":"/article/[identity]/[[...version]]","query":{"redirect":"/article/rs-8424074","identity":"rs-8424074","version":["v1"]},"buildId":"XKTyCvWXoU3ODBz1xrDgd","isFallback":false,"isExperimentalCompile":false,"dynamicIds":[84888],"gssp":true,"scriptLoader":[]}