Design and optimization of venlafaxine niosomes loaded thermosensitive in- situ gel for prolonging intranasal residence in depressive disorder

Design and optimization of venlafaxine niosomes loaded thermosensitive in- situ gel for prolonging intranasal residence in depressive disorder | 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 Design and optimization of venlafaxine niosomes loaded thermosensitive in- situ gel for prolonging intranasal residence in depressive disorder Purushottam Gangane, Mandar Thool, Sachin More, Amol Warokar, and 2 more This is a preprint; it has not been peer reviewed by a journal. https://doi.org/ 10.21203/rs.3.rs-5028833/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 Venlafaxine (VLF) is the most commonly used drug for the treatment of depressive disorder. The oral bioavailability of VLF is low. Therefore, the present study emphasized the development of niosomes formulation for solubility and permeation improvement. The niosome-VLF was formulated using a thin film hydration technique employing different molar ratios of Span 40 and cholesterol. The optimization of niosomes was performed using the Box-Behnken screening model, which employs numerical optimization. The optimized niosmoes-VLF showed Particle size: 264.2 ± 2.2 nm; Zeta potential: 49.2 ± 1.3 mV; Polydispersity Index: 0.265 ± 0.15; Entrapment efficiency: 70.25 ± 1.5%. The noisome-VLF (OF) was incorporated into the thermosensitive in situ gel (TISG). The noisome-VLF TISG (OF-A) showed gelling temperature: 37 ± 0.5 o C; gelling time: 23 ± 2.2s; viscosity: 4526 ± 142 cps; mucoadhesive strength: 3589 ± 65 dyne/cm, drug content: 88 ± 5.4%. The in vivo pharmacokinetic study revealed a higher concentration of VLF in developed noisome-VLF TISG (OF-A) formulation than VLF suspension. The higher and sustained concentration of VLF in brain and plasma suggested a better therapeutic approach to counteract a chronic depressive disorder. Further, the accelerated stability studies of noisome-VLF TISG (OF-A) indicated good physical and chemical attributes. Therefore, intranasal noisome-VLF TISG (OF-A) can be sorted as an alternative approach for targeting the brain for the effective management of CNS conditions like depression. Venlafaxine depression niosomes in-situ gel optimization Figures Figure 1 Figure 2 Figure 3 Figure 4 Figure 5 1. Introduction Depression is a feeling of sadness and is one of the most common major psychiatric disorders [ 1 ]. Depression frequently results in suicide, especially in men [ 2 ]. Each year, about 8.9 million adults in the USA suffer from major depressive disorder, and nearly 2.8 million people develop treatment-resistant depression [ 3 ]. In India, the number is even higher; nearly 45.7 million people suffer from depression [ 4 ]. However, treatment for the management of depression cannot resolve the problem of treatment-resistant depression at advanced stages. Moreover, the drugs available require a long time to show effect. To combat these challenges, developing new drug delivery systems is essential. Venlafaxine (VLF) is an anti-depressant drug of the category of serotonin-norepinephrine reuptake inhibitors (SNRIs). SNRIs are currently the most commonly used drugs for depression. VLF comes under the BCS class II category drug, having bioavailability of nearly 45% from the oral route. Furthermore, the administration of drug to the brain stays questionable owing to the blood-brain barrier, which restricts the passage of drugs [ 5 ]. A daily dose of VLF is 75 to 475 mg, and it is also associated with side effects like cardiac arrhythmias. Because of these issues, developing an intranasal formulation for VLF is an exciting move towards enhancing therapeutic efficiency and decreasing adverse effects. The intranasal route has been a very attractive route for administration of drug to the brain because of its advantages like rapid drug absorption, efficient targeting via the trigeminal and olfactory nerve pathways, avoidance of hepatic first-pass metabolism, extensive surface area enriched with blood capillaries and neuronal supply, ease of drug administration, and higher compliance by patients compared to IV route [ 6 ]. Furthermore, it is well-established that the therapeutic efficiency from the intranasal route is also higher than that with other routes. However, delivery of drugs to the brain is mostly restricted by several factors, such as the low oral bioavailability of drugs owing to less solubility, since the drugs developed for acting in CNS are mostly lipophilic drugs belonging to the BCS class II [ 7 ]. Further, suitable nanocarriers can be administered to increase the therapeutic efficiency of the drugs through the intranasal route[ 8 ]. Niosomes are bi-layered vesicles made up of non-ionic surfactant molecules with an aqueous core containing the drug [ 9 ]. Niosomes offer several advantages when given throughthe intranasal route, such as protection from enzymes, higher drug-targeting efficiency, higher permeability across the nasal mucosa, and dose reduction [ 10 ]. Drugs directly administered via the intranasal route are degraded by the enzymes or cleared off by mucociliary clearance, lowering the drug concentration in the brain[ 11 ], [ 12 ]. Furthermore, loading the niosomes into in-situ gel improves therapeutic efficiency by preventing mucociliary clearance and prolonging residence time in the nasal cavity for continuous absorption. Niosomes given by the intranasal route reduce the total dose administered since it is a well-established fact that the nasal dose is 1/10th of the oral dose[ 13 ]. Thus, nano-drug delivery carriers given by intranasal route serve as the best approach currently available for targeting drugs to the brain. Therefore, the present study aimed to design a formulation of niosomes loaded temperature-sensitive in-situ gel (TISG) to enhance VLF delivery to the brain and improve therapeutic efficiency. 2. Materials and methods Venlafaxine (VLF) was received as a gift sample from Enaltec Labs Pvt. Ltd., (Mumbai, India) Span 40, cholesterol, di-ethyl ether, mannitol, and benzalkonium chloride were purchased from Loba Chemie Pvt. Ltd, (Mumbai, India). Chitosan was ordered from Sigma Aldrich Pvt. Ltd. Chloroform was purchased from Genni Chem Healthcare Pvt. Ltd., (Mumbai, India). Poloxamer 188 and Poloxamer 407were purchased from Sigma Aldrich Pvt. Ltd. HPMC were procured from Colorcon Pvt. Ltd. Methanol and acetonitrile were of HPLC grade purchased from Loba Chemie Pvt. Ltd. 2.1. Preparation of VLF-loaded niosomes Niosomes were prepared using the thin film hydration method. For this, the desired quantity of Span 40 was accurately weighed and added to a mixture of chloroform: di-ethyl ether (1:2). Cholesterol was then added to this mixture until a clear solution was formed. The solution was then added to a rotary vacuum evaporator flask and operated for 30 min by heating under vacuum at a temperature of 65°C at120 rpm till a thin dried film was obtained on the inner wall of the flask. VLF was dissolved in10 mL of warm double distilled water and added to the flask for hydration. Hydration of the film was continued for 60 min at 65°C. The niosomal solution thus formed was then subjected to ultra-sonication in a bath sonicator for a desired period to reduce the size of particles. For coating, niosomal solution was added drop-wise in chitosan (0.5%w/v) solution (10 mL) under magnetic stirring for 30 min to impart positive charge on niosomes [ 14 – 16 ]. 2.2. Design of experiments Experiments were designed to employ a Box Behnken design using 3 levels and 3 factors for niosomes formulation to investigate the impact of the independent variables on the dependent variables. Herein, concentration of Span 40 (X 1 ), concentration of cholesterol (X 2 ), and sonication time (X 3 ) were chosen as the independent variables, whereas particle size (Y 1 ), zeta potential (mV)(Y 2 ), polydispersity index (Y 3 ), and entrapment efficiency (Y 4 )were selected as dependent variables ( Table 1 ). The design suggested a total of 13 experimental runs, as illustrated in Table 2 . Table 1 Box Behnken design for formulation of VLF-niosomes Independent variables Levels Dependent variables Low (-1) Medium (0) High (+ 1) X 1 : Concentration of span 40 (molar ratio) 1 2 3 Y 1 : Particle size (nm) Y 2 : Entrapment efficiency (%) Y 3 : In-vitro drug release (%) X 2 : Concentration of cholesterol (molar ratio) 1 1.25 1.5 X 3 : Sonication time (min) 15 30 45 Table 2 Formulation batches of niosomes-VLF as per Box Behnken design. Batch. No. Span 40 concentration (molar ratio) (X 1 ) Cholesterol concentration (molar ratio) (X 2 ) Sonication time (min) (X 3 ) Particle size (nm) (Y 1 ) Zeta potential (mv) (Y 2 ) PDI (Y 3 ) Entrapment efficiency (%) (Y 4 ) 1 1.0 1.0 30 477 ± 1.1 26.2 ± 0.28 0.349 ± 0.16 39.1 ± 1.6 2 3.0 1.0 30 176 ± 1.0 59.1 ± 0.32 0.224 ± 0.09 83.6 ± 1.9 3 1.0 1.5 30 447 ± 1.2 32.3 ± 0.35 0.339 ± 0.17 55.8 ± 2.5 4 3.0 1.5 30 146 ± 0.5 72.6 ± 0.25 0.203 ± 0.04 90.8 ± 1.7 5 1.0 1.25 15 462 ± 2.7 29.1 ± 1.09 0.345 ± 0.16 43.2 ± 1.4 6 3.0 1.25 15 173 ± 1.7 62.9 ± 0.20 0.220 ± 0.05 86.5 ± 1.3 7 1.0 1.25 45 236 ± 1.1 27.7 ± 0.05 0.296 ± 0.10 89.8 ± 0.9 8 3.0 1.25 45 268 ± 0.4 68.8 ± 1.10 0.215 ± 0.13 81.1 ± 1.2 9 2.0 1.0 15 293 ± 0.7 19.1 ± 1.15 0.298 ± 0.12 59.6 ± 2.2 10 2.0 1.5 15 224 ± 1.1 57.2 ± 0.28 0.229 ± 0.10 65.7 ± 1.2 11 2.0 1.0 45 288 ± 1.5 43.1 ± 0.15 0.249 ± 0.12 69.8 ± 1.4 12 2.0 1.5 45 245 ± 1.1 52.8 ± 0.20 0.236 ± 0.08 71.3 ± 1.6 13 2.0 1.25 30 267 ± 1.7 49.9 ± 0.46 0.242 ± 0.10 65.4 ± 1.5 All the results are expressed as mean ± standard deviation (n = 3). 2.3. Particle size, zeta potential, and PDI analysis Particle size, zeta potential, and PDI were measured by Litesizer-500 (Anton Paar, Austria), which works on the dynamic light scattering principle. The niosomal solution was accurately diluted 10 times with distilled water for particle size measurements. For zeta potential analysis, the sample was directly filled in the omega cuvettes and analyzed. Zeta potential measurement is based on the principle of laser doppler anemometry. All the procedures were done at room temperature [ 17 , 18 ]. 2.4. Entrapment efficiency (%) It was determined using an indirect method based on the previously reported literature [ 19 ]. For this, a defined volume of niosomal dispersion was added into the tube and centrifuged at 9000 rpm for 45 min at 4°C. After centrifugation, 1mL of the supernatant was diluted appropriately, and the absorbance of a diluted solution was recorded by UV spectrophotometer at 225nm. Entrapment efficiency was determined by using the following Eq. (1) [ 20 ], Entrapment Efficiency (%) = \(\:\frac{\text{T}\text{o}\text{t}\text{a}\text{l}\:\text{d}\text{r}\text{u}\text{g}\:\text{a}\text{d}\text{d}\text{e}\text{d}\:-\:\text{U}\text{n}\text{e}\text{n}\text{t}\text{r}\text{a}\text{p}\text{p}\text{e}\text{d}\:\text{d}\text{r}\text{u}\text{g}}{\text{T}\text{o}\text{t}\text{a}\text{l}\:\text{d}\text{r}\text{u}\text{g}\:\text{a}\text{d}\text{d}\text{e}\text{d}}\) * 100 …………………….. (1) 2.5. Optimization of formulation The optimization of the formulation was executed employing numerical optimization [ 21 ]. The criteria for optimized formulation were given by setting the goal in the desired range, such as particle size (Y 1 ): 146 to 300 nm; zeta potential (Y 2 ): 40 to 50 mV; polydispersity index (Y 3 ): 0.20 to 0.30; and entrapment efficiency (Y 4 ): 60 to 90%. The models suitability was ascertained by developing a new formulation based on the suggestion provided by the software. 2.6. Lyophillization of niosomes-VLF formulation The noisome-VLF (OF) was combined with mannitol (5%w/v) and stored at approximately − 40ºC for the entire night in a deep freezer (KCN 372, Blue Star Ltd., Mumbai). After that, the frozen noisome-VLF (OF) was freeze-dried for roughly 72 h using a freeze dryer (Virtis Ltd., USA) to produce lyophilized powder. 2.7. X-ray diffraction study X-ray diffractogram was recorded for VLF (pure), physical mixture (drug and excipients), and niosomes-VLF formulation. The experiment used an XRD instrument (BrukerAXS,Inc., Madison, WI, USA). Powder XRD patterns were recorded on the D2 Phaser 2nd generation-based diffractometer using a voltage of 30kV and a current of 10mA.The scanning rate was 50 min, over the 50 to 400 diffraction angle (2θ) range [ 22 ]. 2.8. Preparation of niosomes-VLF loaded thermosensitive in-situ gel (TISG) Niosomes-VLF TISG has been developed using the cold method. Briefly, definite concentrations of poloxamer–407 (16.5–18.5%) followed by poloxamer–188 (1%) and HPMC-K4M (0.5%) were slowly dissolved one by one by continuous stirring in niosomes-VLF(OF) kept over ice-bath [ 23 – 24 ]. After forming a clear solution, mannitol (4% w/v) was added as a release modifier, and cryoprotectant, benzalkonium chloride (1%w/v) was used as a preservative Table 3 . The solution was kept in the refrigerator until further use [ 25 – 26 ]. 2.9. Characterization of niosomes-VLFTISG The gelling temperature was determined using the method previously described [ 27 ]. A niosomal-VLF (OF) formulation was kept over a heating magnetic plate stirrer and stirred with a magnetic bead at a constant speed of 52 rpm. The plate temperature was slowly increased at a rate of 1 o C/min. The temperature was recorded as the gelling temperature where the magnetic bead stops spinning [ 27 ], and the time was noted as the gelling time of the formulation [ 28 ]. Viscosity is an important parameter governing the performance of the developed system. Viscosity was determined using a Brookfield viscometer at 10 RPM and spindle 63 under a constant temperature of 35 o C. About 10 mL of niosomes-VLF TISG was added to a cylindrical beaker and placed properly under the spindle of the viscometer. The mucoadhesive strength of all the developed gel formulations was determined as per the methodology mentioned in the previous literature [ 29 ] using the following equation; Mucoadhesive strength (dyne/cm 2 ) = M × g/A …………….……………………………… (2) A precise weight of VLF-TISG was mixed with a specified volume of Phosphate buffer saline (PBS) (30 mL, pH 7.4) and vigorously shaken for 12 h. After filtering the dispersion, the amount of drug in the filtrate was measured using spectrophotometry at a wavelength of λ max 225 nm. The drug content (%) was determined using following Eq. (3); Drug content (%) = \(\:\frac{\text{T}\text{h}\text{e}\text{r}\text{o}\text{t}\text{i}\text{c}\text{a}\text{l}\:\text{a}\text{m}\text{o}\text{u}\text{n}\text{t}\:\text{o}\text{f}\:\text{V}\text{L}\text{F}\:\text{i}\text{n}\:\text{n}\text{i}\text{o}\text{s}\text{o}\text{m}\text{s}\:}{\text{A}\text{m}\text{o}\text{u}\text{n}\text{t}\:\text{o}\text{f}\:\text{d}\text{r}\text{u}\text{g}\:\text{l}\text{o}\text{a}\text{d}\text{e}\text{d}\:\text{i}\text{n}\:\text{n}\text{i}\text{o}\text{s}\text{o}\text{m}\text{e}\text{s}}\) *100………………………………….. (3) 2.10. Ex-vivo drug diffusion studies The ex-vivo drug permeation study of prepared niosomal VLF TISG and VLF suspension was performed on Franz diffusion cell using goat nasal mucosa. Freshly cut nasal mucosa was procured from a local slaughter house and was cleaned properly using double distilled water. After cleaning, it was immediately kept in normal saline solution to keep the tissue alive. A piece of about 2 cm 2 was cut and placed correctly between the acceptor and donor compartment. PBS pH 6.4 was added in the acceptor compartment, and the temperature was maintained at 37 ± 0.2°C. 1 mL of niosomal VLF TISG containing 7.5 mg of the drug was kept over the mucosa. Because of the temperature of the system, the solution turned rapidly into a gel and remained adhered to the mucosa. At definite time intervals (0.25, 1, 2, 4, 6, 8, 10, 12, and 24 h), 1 mL of the solution was collected from the acceptor compartment and replaced with fresh buffer. This 1 mL solution was further diluted up to 9 mL using PBS pH 6.4, and the absorbance of this solution was recorded at 225 nm. PBS pH 6.4 was used as blank [ 30 ]. 2.11. HPLC Chromatographic separation of VLF was achieved by using a C 18 reverse phase column Shimadzu Shimpack Xbridge, 5µm particle size. Exactly 20 µL volume of sample was injected. The mobile phase selected was acetonitrile and 25 mM ammonium carbonate buffer pH 7.6 (85:15) at 1 mL/min flow rate. Sample run time was set at 10 min, and detection was done using a UV detector at 225 nm. The plasma sample was spiked with 1 mL of 10µg/mL celecoxib as the internal standard. Retention time was found to be 5.3 min. 2.12. Animals protocol The in-vivo studies were performed on Sparge Dwaley rats weighing 180 g to 300 g, 8–11 weeks old. Animal protocol was approved by the Institutional Animal Ethics Committee of Dadasaheb Balpande College of Pharmacy, Besa, Nagpur, India (Protocol approval number DBCOP/IAEC/1426/2022-23/P-10). Food and water were provided to rats ad libitum. The animal study protocol was followed as per the CPCSEA guidelines for laboratory animals. 2.13. In-vivo pharmacokinetics Pharmacokinetics analysis was done to access the VLF content in the brain and plasma after the installation of niosomal-VLF TISG and VLF (pure) suspension intra-nasally. For the pharmacokinetic estimations, rats were segregated into 2 groups. Each group contained 12 rats. Niosomal-VLF TISG (100 µL, 50 µL in each nostril) at a dose equivalent to 0.60 mg was instilled in Group I animals. Group II received standard VLF suspension intra-nasally at a dose equivalent to 0.60 mg. At time intervals of 1, 2, 3, 4, 6, and 15 h, the rats were euthanized with ketamine and xylazine. Blood was carefully collected by using a tuberculin syringe and filled immediately in EDTA tube to prevent clotting. Afterward, the brain was carefully removed and collected in a clean small container after washing it with double distilled water. For determining the concentration of VLF, brain tissue was accurately weighed and homogenized in acetonitrile. The supernatant was collected in a test tube and dried in nitrogen air. Acetonitrile (1 mL) was added to the test tube and sonicated for 10 min, followed by injecting into the HPLC system for detection. To determine the VLF amount in plasma, a collected blood sample was ultra-centrifuged at 9000 RPM for 15 min to separate blood and plasma. About 100µL of the plasma was collected from the supernatant and centrifuged again at 9000 RPM for 15 min with 1 mL of acetonitrile to precipitate plasma proteins[ 31 ]. After centrifugation, 100 µL of supernatant was dried in a nitrogen atmosphere. To this, 1 mL of acetonitrile was added and sonicated for 10 min, followed by injecting into the HPLC system. At each interval, 2 rats were sacrificed to minimize standard deviation [ 32 ]. 2.14. Accelerated stability studies Niosomal VLF TISG (OF-A) was subjected to stability studies at accelerated conditions (25 ± 2 o C/60 ± 5%) for a period of 6 months and evaluated for different quality parameters like gelling time, gelling temperature, mucoadhesive strength, and drug content. For stability studies, 15 mL of the final formulation was packed in clear, sealed 25 mL vials with rubber closures. Sampling was done at 0, 3, and 6 month intervals and formulation was then evaluated. A total of 3 vials were loaded for each time point. Results obtained were then compared with the initial formulation results. 2.15. Statistical analysis The obtained data were statistically examined using Graph Pad Prism version 4.00 to calculate the mean and standard deviation. In the pharmacokinetic study, a one-way ANOVA was used to determine the significant differences between VLF (pure) suspension and niosomal VLF TISG (OF-A). The predicted P-value of 0.005 was determined to be significant. 3. Results and discussion 3.1. Particle size, PDI, and zeta potential analysis Particle size of all the batches were between 173 ± 1.7 nm to 477 ± 1.1 nm. The influence of the independent variables on the selected responses was thoroughly investigated using a 3D response surface plots as illustrated in Fig. 1 . It was found that, as Span 40concentrationswas increased up-to the molar ration of 2 leading to smaller and uniform particle size. Further, the higher concentration of cholesterol resulted in more rigid bilayers causing a decrease in particle size. In the response surface plot, red color at apex of X 2 axis indicated negative impact on particle size with the higher level of concentrations of cholesterol and Span 40. The increase in the sonication time also reduces the particle size. The impact of each variable and their effects on the particle size (Y 1 ) could be best interpreted by the polynomial Eq. (4) in actual factors illustrated below; Particle size (Y 1 ) = 635.77-107.37X 1 -86.0X 2 -0.95X 3 ........................................................... (4) Zeta Potential (Y 2 ) = -38.95 + 18.51X 1 + 33.70X 2 + 0.20X 3 ...................................................... (5) Polydispersity Index (Y 3 ) = 0.47-0.058X 1 -0.056X 2 -8.0X 3 ................................................... (6) The above Eq. (4) describes that the Span 40 concentration (X 1 ), cholesterol concentration (X 2 ), and sonication time (X 3 ) reveals negative impact on the particle size. Zeta potential of all the batches ranged from 19.1 ± 1.15 to 68.8 ± 1.10 mV. The positive charge on the niosomes appeared owing to coating on the surface. The positive charge coating was applied on the niosomes due to the presence of the sialic acid on cell membrane of the nasal mucosa leading to the chemical intervention which could provide better muco-adhesion and prolong residence time[ 33 ]. All the selected variables showed the positive impact on the zeta potential as suggested by the polynomial Eq. (5), indicating more stabilization of niosomal bilayer and improved the surface characteristics. PDI also governs the stability of niosomal formulation as indicated in the Eq. (6), all the independent variable showed negative influence on the PDI. 3.2. Entrapment efficiency (%) Entrapment efficiency of all batch were between the ranges of 39.1 ± 1.6 to 90.8 ± 1.7.As the cholesterol concentration was increased the rigidity of the bilayer increases which decreases the fluidity of the bilayer and makes the vesicles less leaky leading to increase in the entrapment efficiency. Another reason for lower entrapment efficiency in some batches could be the smaller particle size of the formed niosomes which decreases the entrapment efficiency (%). The impact of each variable and their combination effects on entrapment efficiency (%) could be best interpreted by the polynomial equation in actual factor. Entrapment efficiency (Y 2 ) = 6.944 + 14.065X 1 + 15.75X 2 + 0.488X 3 ……………………… (7) Above equation describes that as the higher concentration Span 40(X 1 ) causes increase in entrapment efficiency owing to the membrane stabilization of niosome. Furthermore, the higher cholesterol concentration and prolonged sonication time also leads to increase entrapment efficiency.Table 4 represents a summary of the ANOVA study's findings and demonstrates that all models were significant (P < 0.5) for each response parameter investigated. Additionally, Fig. 2 shows desirability plot provided by Design-Expert 8.0.0.1 software indicating predication 1, suggesting all the target ranges were within the limit. Table 4 Summary of ANOVA for the response parameters Source Sum of squares d.f. a Mean square F-value p-value prob > F a) Particle size (nm) Model 97586.25 3 32528.75 5.97 0.0159 (S) X 1 92235.13 1 92235.13 16.94 0.0026 (S) X 2 3698.00 1 3698.00 0.68 0.4312 (NS) X 3 1653.13 1 1653.13 0.30 0.5950 (NS) b) Zeta Potential (mV) Model 3382.15 3 1127.38 24.10 0.0001(S) X 1 2741.70 1 2741.70 58.61 <0.0001(S) X 2 567.84 1 567.84 12.14 0.0069 (S) X 3 72.60 1 72.60 1.55 0.2443 (NS) c) PDI Model 0.030 3 0.010 22.82 0.0002 (S) X 1 0.027 1 0.027 62.20 < 0.0001(S) X 2 1.596 1 1.596 3.64 0.0887 (NS) X 3 1.152 1 1.152 2.63 0.1394 (NS) d) Entrapment efficiency (%) Model 2135.71 3 711.90 4.86 0.0280 (S) X 1 1582.88 1 1582.88 10.82 0.0094 (S) X 2 124.03 1 124.03 0.85 0.3813 (NS) X 3 428.81 1 428.81 2.93 0.1211 (NS) X 1 , X 2, & X 3 represents concentration of span 40, concentration of cholesterol, and sonication time, respectively. S indicate significant, NS indicate non significant; a d.f. Indicates degree of freedom; The values of "Prob > F" less than 0.0500 indicate model terms are significant, while, values greater than 0.1000 indicate the model terms are not significant. 3.3. Optimization of niosomes The numerical optimization was performed based on the desirability ranges, software has suggested batch comprising all the targeted ranges of response. Surfactant to cholesterol concentration in the molar ratio of 2:1.25 and sonication time of 30 min yielded the best possible outcomes and was suggested as the optimized batch. The optimized VLF-noisome (OF) formulation was evaluated for the desired response. The Table 5 represents the evaluation of noisome-VLF (OF) with the percentage of errors. The noisome-VLF (OF) exhibited the particle size (nm) = 264.2 ± 2.2, zeta potential (mV) = 49.2 ± 1.3, polydispersity index = 0.265 ± 0.15, and entrapment efficiency (%) = 70.25 ± 1.5. Further, the outcomes showed a strong correlation with the expected values, as shown with a low percentage of errors, suggesting the fitness of the model for the presented study. Table 5 Results of experiments for confirming optimization capability Mean ± SD; n = 3, a Percentage of error= (Actual value-predicted value)/ predicted value × 100 Batch Conc. of Span 40, X 1 Conc. of Cholesterol X 2 Sonication time (min) X 3 Responses Predicted values Actual values Error a (%) OF Particle size (nm) 260.7 264.2 ± 2.2 1.34 2.40 1.20 18.00 Zeta potential (mV) 48.7 49.2 ± 1.3 1.02 Polydispersity Index 0.255 0.265 ± 0.15 3.92 Entrapment efficiency (%) 67.88 70.25 ± 1.5 2.37 3.4. Powder x-ray diffraction (PXRD) PXRD spectra of VLF (pure), physical mixture, and niosomes-VLF are illustrated in Fig. 3 . PXRD spectra of VLF (pure) showed sharp peaks around 2 theta indicating crystalline nature of VLF. The PXRD spectrum of physical mixture shows slight decrease in the intensity and merging of the peaks. In physical mixture, there is mixing of the formulation excipients which reduces the purity of each excipients and drug and results in merging of peaks, but may not necessarily represent compatibility problems. The XRD spectra of niosomes show significant decrease in the total number of peaks. There was a reduction in the intensity of peaks along with merging of peaks. This signifies the formation of niosomes and encapsulation of drug inside the vesicles. The reduction in the intensity of peaks might indicate the VLF is converted from crystalline to semi-crystalline-amorphous form. In the XRD spectra of niosomes few merged peaks can be seen which may be of the un-encapsulated drug adsorbed over the niosomes surface. 3.5. Preparation of niosomes-VLF TISG Niosomes-VLF (OF) was added to a TISG formulation to make it easier to apply, extend the time the drug remained on the nasal mucosal surface after application, and ultimately increase drug penetration through the nasal mucosa. To achieve this, niosomes-VLF TISG were prepared using in situ gelling systems based on Poloxamer 407 at different concentrations, ranging from 16.5 to 18.5% w/w, in conjunction with Poloxamer 188 (1% w/w).Furthermore, an optimized formula for the niosomes-VLF TISG was obtained by examining and implementing the effect of co-polymer concentration on the formulation attributes. Notably, the co-polymer concentrations utilized were chosen in light of our initial experiments. 3.6. Characterization of niosomes-VLF TISG The characterization of niosomes-VLF TISG batches is illustrated in Table 3 . Gelling temperature of all three formulation batches were in the range of 37 ± 0.5 ° Cto 39 ± 0.5 ° C . Gelling temperature of niosomes-VLF TISG (OF-A) batch was found to be 37 ± 0.5 ° C which might be due to the presence of Poloxamer-407 in lowest concentration. Further, rise in Poloxamer-407 concentration leads to increase in gelation temperature [ 34 ],[ 27 ]. Higher numbers of the polymeric micelles are formed by increasing the Poloxamer-407 concentration[ 25 ].Gelling time of the niosomes-VLF TISG (OF-A) batch was found to be 23 ± 2.2s, less among the other batches and satisfactory and in accordance with the already available literature data [ 28 ].The viscosity of all three batches at 35 ° C (4526 ± 142 to 6274 ± 102 cps) was found to be good and in accordance with the reported number. The observation suggested increased in the viscosity with rise in Poloxamer-407 concentration. Mucoadhesive strength (dyne/cm) was found to 3589 ± 65 to 4222 ± 57 indicating higher mucoadhesion owing to chitosan coating which imparts positive charge over the surface of niosomes and interaction of electrostatic forces between the positive charge of chitosan and negative charge of sialic acid of the cells of mucosal membrane thereby enhancing muco-adhesion[ 35 ], [ 36 ], [ 37 ]. The drug content (%) was in the range of 83 ± 3.6 to 88 ± 5.4. The higher drug content was reported in the batch OF-A. The observation revealed that among all three batches, niosomes-VLF TISG (OF-A) batch was showed excellent properties and therefore selected for further study. Table 3 The formulation batches of niosomes-VLF TISG Batch No. Conc. of Poloxamer 407 (%) Conc. of Poloxamer 188 (%) Conc. of HPMC K4M (%) Gelling temperature ( o C) Gelling time (s) Viscosity at 35 o C (cps) Mucoadhesive strength (dyne/cm) Drug content (%) OF-A 16.5 1 0.5 37 ± 0.5 23 ± 2.2 4526 ± 142 3589 ± 65 88 ± 5.4 OF-B 17.5 1 0.5 38 ± 1.0 42 ± 2.5 5614 ± 156 3874 ± 34 84 ± 4.7 OF-C 18.5 1 0.5 39 ± 0.5 65 ± 1.5 6274 ± 102 4222 ± 57 83 ± 3.6 All the results are expressed as mean ± standard deviation (n = 3). OF-A; Selected batch (highlighted) 3.7. Ex-vivo drug diffusion tudy The ex-vivo drug diffusion of niosomes-VLF TISG (OF-A)and VLF suspension is illustrated in Fig. 4 .The drug diffusion (%) of niosomes-VLF TISG(OF-A) after 24 h was found to be 94.76%. Diffusion mechanism showed best fitting for Highuchi model suggested sustained drug delivery. The drug diffusion for VLF suspension was less owing to its low aqueous solubility. The higher diffusion of developed formulation indicated improvement in the solubility resulting into increment in the rate of diffusion. 3.8. In-vivo pharmacokinetics The VLF plasma concentration versus time profile after intranasal administration of niosomes-VLF TISG (OF-A) and VLF suspension are shown in the Fig. 5 . The pharmacokinetic parameters are elaborated in Table 6 . After the administration of a single niosomes-VLF TISG (OF-A), the total concentration of the drug in the brain was found to be 158.4 ± 4 ng/mL, which was significantly higher than VLF suspension (62.5 ± 1.53 ng/mL), (p value < 0.05). There was an increase in the total AUC value for intranasal niosomes (1046.59 ± 30.80ng/mL*h) as compared to VLF suspension (408.82 ± 7.27ng/mL*h). Mean residence time in the brain was also prolonged for the niosomes (7.10 h) as compared with the VLF suspension (4.06 h) contributing to higher half life. This higher C max of VLF from niosomes-VLF TISG (OF-A) formulation in the brain is due to higher permeability of the niosomes across cell membrane which facilitates passive diffusion and endocytosis across cells. This signifies the potential of the niosomal-VLF TSIG (OF) for targeting brain via intranasal route. T max for the VLF suspension was found to be 2 h which was less as compared to the VLF niosomes having T max of 3 h. This might be due to higher viscosity of the in-situ gel as compared to standard VLF suspension in the nasal cavity which hampers and delays release of the niosomes from the formulation [ 38 ]. The C max in the plasma for niosomes was found to be 91.70 ± 0.62 ng/mL at T max of 4h. This may be due to the removal of the gel from the nasal respiratory region by the sniffing action of rat before the gelation of the formulation contributing to lower C max value as compared to brain concentration. The total AUC in the plasma was 691.83 ± 30.95 ng/mL*h which was significantly higher as compared to standard VLF suspension (463.18 ± 11.56ng/mL*h) proving the enhanced permeability of the developed system across epithelial cell membrane. T max for standard intranasal VLF suspension was found to be 2 h. This could be due to higher viscosity of the in-situ gel formulation as compared to standard intranasal VLF suspension which delays the release of drug from gel. Table 6 Pharmacokinetic parameter of Niosomes VLF TSIG (OF-A) and VLF (pure) All the results are expressed as mean ± standard deviation (n = 3). Parameters Niosomes VLF TSIG (OF-A) VLF (Pure) Brain Plasma Brain Plasma C max (ng/ml) 158.4 ± 4 91.70 ± 0.62 62.5 ± 1.53 71.74 ± 2.14 T max (h) 3 4 2 1 AUC 0 − t (ng/ml*h) 1046.59 ± 30.80 691.83 ± 30.95 408.82 ± 7.27 463.18 ± 11.56 T 1/2 (h) 4.405 2.421 1.195 1.173 MRT (h) 7.10 5.23 4.06 3.986 Table 7 Accelerated stability studies of Niosome VLF TISG (OF-A) Sr. No. Parameters Months 0 3 6 1. Gelling time (s) 23 ± 2.2 22 ± 1.4 22 ± 0.8 2. Gelling temperature ( o C) 37 ± 0.5 36 ± 1.6 36 ± 0.8 3. Viscosity (cps) 4526 ± 142 4414 ± 120 4402 ± 65 4. Mucoadhesive Strength (dyne/cm) 3589 ± 65 3416 ± 42 3410 ± 32 5. Drug content (%) 88 ± 5.4 87 ± 2.3 87 ± 1.1 All the results are expressed as mean ± standard deviation (n = 3). 3.9. Accelerated stability studies From the stability studies of niosomes-VLF TISG (OF-A) under controlled conditions of 25 ± 2°C and 60 ± 5% RH over a period 6 months, the study concluded that the developed formulation was stable and maintained its physical and chemical characteristics over the period indicating its robustness. Quality characteristics evaluated, such as drug content, showed a slight decline from 88 ± 5.4% to 87 ± 1.1, suggesting a marginal loss of drug in the formulation. The other quality attributes such as gelling temperature, gelling time, viscosity, and mucoadhesive strength showed no significant changes from their original value, indicating stability over a period of 6 months. These results collectively suggested the robustness of the VLF-niosomal TISG (OF-A) and reliability as a drug delivery system. Conclusion Niosomes-VLF TISG (OF-A) was successfully developed and evaluated. Optimization of niosomes–VLF was using a Box-Behnken design. The numerical optimization assisted in getting the optimized noisome formulation based on the desirability criteria. The niosomes optimized formulation was successfully incorporated into the TISG. The niosomes-VLF TISG (OF-A) indicated a better characteristics. Ex-vivo diffusion study revealed 94.76% drug permeation across the goat nasal mucosa. The pharmacokinetics study revealed higher brain drug concentration following intranasal administration of niosomes-VLF TISG (OF-A) compared to standard intranasal VLF pure suspension. The developed niosomes-VLF TISG (OF-A) system showed a good alternative for sustained and targeted delivery of VLF to the brain for depressive disorder. Declarations Acknowledgment We are grateful to Enaltec Labs Pvt. Ltd., (Mumbai, India) andGenni Chem Healthcare Pvt. Ltd., (Mumbai, India) for the gift sample of drug and excipients. Ethical Approval Animal protocol was approved by the Institutional Animal Ethics Committee of Dadasaheb Balpande College of Pharmacy, Besa, Nagpur, India (Protocol approval number DBCOP/IAEC/1426/2022-23/P-10). The study protocol was followed as per the CPCSEA ARRIVE guidelines for laboratory animals. Competing interests The authors declare no competing interests. Conflict of Interest None Funding No funding received for this project Author Contribution Purushottam Gangane: Conceptualization, Supervision, Resources, Funding acquisition, Project administration, reviewing of original draft preparation, Mandar Tool: Methodology, Formal analysis, Data curation, Validation, Sachin More: Methodology, Formal analysis, Amol Warokar: Formal analysis, Kishor Salunkhe : Supervision, Formal analysis, Pankaj Dangre: Software, Formal analysis, Writing-reviewing and editing, Visualization, Investigation. References Hao, Y., Ge, H., Sun, M., & Gao, Y. (2019). 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International Journal of Biological Macromolecules . https://doi.org/10.1016/j.ijbiomac.2021.02.216 Rashki, S., Asgarpour, K., Tarrahimofrad, H., Hashemipour, M., Ebrahimi, M. S., Fathizadeh, H., … Mirzaei, H. (2021). Chitosan-based nanoparticles against bacterial infections. Carbohydrate Polymers . https://doi.org/10.1016/j.carbpol.2020.117108 Abdulla, N. A., Balata, G. F., El-ghamry, H. A., & Gomaa, E. (2021). Intranasal delivery of Clozapine using nanoemulsion-based in-situ gels: An approach for bioavailability enhancement. Saudi Pharmaceutical Journal , 29 (12). https://doi.org/10.1016/j.jsps.2021.11.006 Additional Declarations No competing interests reported. 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. 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08:04:22","extension":"png","order_by":2,"title":"Figure 2","display":"","copyAsset":false,"role":"figure","size":161422,"visible":true,"origin":"","legend":"\u003cp\u003eDesirability plot indicating the prediction point at the targeted ranges.\u003c/p\u003e","description":"","filename":"Figure2.png","url":"https://assets-eu.researchsquare.com/files/rs-5028833/v1/636ed8ab1c92cacfd3d3f0d8.png"},{"id":66152471,"identity":"cfd897c3-4f4b-41b0-9359-dbd2c4d04a3a","added_by":"auto","created_at":"2024-10-08 08:04:22","extension":"png","order_by":3,"title":"Figure 3","display":"","copyAsset":false,"role":"figure","size":38053,"visible":true,"origin":"","legend":"\u003cp\u003ePXRD spectra of VLF, physical mixture, and noisome-VLF.\u003c/p\u003e","description":"","filename":"Figure3.png","url":"https://assets-eu.researchsquare.com/files/rs-5028833/v1/2f40eea76f0857d5ced82163.png"},{"id":66153658,"identity":"9dcd73c1-e8ed-409b-8813-d9165fede06f","added_by":"auto","created_at":"2024-10-08 08:12:22","extension":"png","order_by":4,"title":"Figure 4","display":"","copyAsset":false,"role":"figure","size":58765,"visible":true,"origin":"","legend":"\u003cp\u003eEx vivo diffusion study of VLF suspension and VLF-niosomes TISG (OF-A)\u003c/p\u003e","description":"","filename":"Figure4.png","url":"https://assets-eu.researchsquare.com/files/rs-5028833/v1/fb6398e8502f482df7a1f522.png"},{"id":66152470,"identity":"681969c0-8bde-4a42-a790-318bb8ab0054","added_by":"auto","created_at":"2024-10-08 08:04:22","extension":"png","order_by":5,"title":"Figure 5","display":"","copyAsset":false,"role":"figure","size":95562,"visible":true,"origin":"","legend":"\u003cp\u003eVLF concentration in brain and plasma versus time profile after intranasal administration of VFL suspension and VLF-niosomes TISG (OF-A).\u003c/p\u003e","description":"","filename":"Figure5.png","url":"https://assets-eu.researchsquare.com/files/rs-5028833/v1/12b160556f5e08b078325dad.png"},{"id":67653830,"identity":"0f94849c-0e16-44d5-b543-aaca32535612","added_by":"auto","created_at":"2024-10-28 12:02:15","extension":"pdf","order_by":0,"title":"","display":"","copyAsset":false,"role":"manuscript-pdf","size":1619619,"visible":true,"origin":"","legend":"","description":"","filename":"manuscript.pdf","url":"https://assets-eu.researchsquare.com/files/rs-5028833/v1/710b8adf-dd10-4030-a0f4-80689a886c81.pdf"}],"financialInterests":"No competing interests reported.","formattedTitle":"Design and optimization of venlafaxine niosomes loaded thermosensitive in- situ gel for prolonging intranasal residence in depressive disorder","fulltext":[{"header":"1. Introduction","content":"\u003cp\u003eDepression is a feeling of sadness and is one of the most common major psychiatric disorders [\u003cspan citationid=\"CR1\" class=\"CitationRef\"\u003e1\u003c/span\u003e]. Depression frequently results in suicide, especially in men [\u003cspan citationid=\"CR2\" class=\"CitationRef\"\u003e2\u003c/span\u003e]. Each year, about 8.9\u0026nbsp;million adults in the USA suffer from major depressive disorder, and nearly 2.8\u0026nbsp;million people develop treatment-resistant depression [\u003cspan citationid=\"CR3\" class=\"CitationRef\"\u003e3\u003c/span\u003e]. In India, the number is even higher; nearly 45.7\u0026nbsp;million people suffer from depression [\u003cspan citationid=\"CR4\" class=\"CitationRef\"\u003e4\u003c/span\u003e]. However, treatment for the management of depression cannot resolve the problem of treatment-resistant depression at advanced stages. Moreover, the drugs available require a long time to show effect. To combat these challenges, developing new drug delivery systems is essential. Venlafaxine (VLF) is an anti-depressant drug of the category of serotonin-norepinephrine reuptake inhibitors (SNRIs). SNRIs are currently the most commonly used drugs for depression. VLF comes under the BCS class II category drug, having bioavailability of nearly 45% from the oral route. Furthermore, the administration of drug to the brain stays questionable owing to the blood-brain barrier, which restricts the passage of drugs [\u003cspan citationid=\"CR5\" class=\"CitationRef\"\u003e5\u003c/span\u003e]. A daily dose of VLF is 75 to 475 mg, and it is also associated with side effects like cardiac arrhythmias. Because of these issues, developing an intranasal formulation for VLF is an exciting move towards enhancing therapeutic efficiency and decreasing adverse effects. The intranasal route has been a very attractive route for administration of drug to the brain because of its advantages like rapid drug absorption, efficient targeting via the trigeminal and olfactory nerve pathways, avoidance of hepatic first-pass metabolism, extensive surface area enriched with blood capillaries and neuronal supply, ease of drug administration, and higher compliance by patients compared to IV route [\u003cspan citationid=\"CR6\" class=\"CitationRef\"\u003e6\u003c/span\u003e]. Furthermore, it is well-established that the therapeutic efficiency from the intranasal route is also higher than that with other routes. However, delivery of drugs to the brain is mostly restricted by several factors, such as the low oral bioavailability of drugs owing to less solubility, since the drugs developed for acting in CNS are mostly lipophilic drugs belonging to the BCS class II [\u003cspan citationid=\"CR7\" class=\"CitationRef\"\u003e7\u003c/span\u003e]. Further, suitable nanocarriers can be administered to increase the therapeutic efficiency of the drugs through the intranasal route[\u003cspan citationid=\"CR8\" class=\"CitationRef\"\u003e8\u003c/span\u003e]. Niosomes are bi-layered vesicles made up of non-ionic surfactant molecules with an aqueous core containing the drug [\u003cspan citationid=\"CR9\" class=\"CitationRef\"\u003e9\u003c/span\u003e]. Niosomes offer several advantages when given throughthe intranasal route, such as protection from enzymes, higher drug-targeting efficiency, higher permeability across the nasal mucosa, and dose reduction [\u003cspan citationid=\"CR10\" class=\"CitationRef\"\u003e10\u003c/span\u003e]. Drugs directly administered via the intranasal route are degraded by the enzymes or cleared off by mucociliary clearance, lowering the drug concentration in the brain[\u003cspan citationid=\"CR11\" class=\"CitationRef\"\u003e11\u003c/span\u003e], [\u003cspan citationid=\"CR12\" class=\"CitationRef\"\u003e12\u003c/span\u003e]. Furthermore, loading the niosomes into \u003cem\u003ein-situ\u003c/em\u003e gel improves therapeutic efficiency by preventing mucociliary clearance and prolonging residence time in the nasal cavity for continuous absorption. Niosomes given by the intranasal route reduce the total dose administered since it is a well-established fact that the nasal dose is 1/10th of the oral dose[\u003cspan citationid=\"CR13\" class=\"CitationRef\"\u003e13\u003c/span\u003e]. Thus, nano-drug delivery carriers given by intranasal route serve as the best approach currently available for targeting drugs to the brain. Therefore, the present study aimed to design a formulation of niosomes loaded temperature-sensitive \u003cem\u003ein-situ\u003c/em\u003e gel (TISG) to enhance VLF delivery to the brain and improve therapeutic efficiency.\u003c/p\u003e"},{"header":"2. Materials and methods","content":"\u003cp\u003eVenlafaxine (VLF) was received as a gift sample from Enaltec Labs Pvt. Ltd., (Mumbai, India) Span 40, cholesterol, di-ethyl ether, mannitol, and benzalkonium chloride were purchased from Loba Chemie Pvt. Ltd, (Mumbai, India). Chitosan was ordered from Sigma Aldrich Pvt. Ltd. Chloroform was purchased from Genni Chem Healthcare Pvt. Ltd., (Mumbai, India). Poloxamer 188 and Poloxamer 407were purchased from Sigma Aldrich Pvt. Ltd. HPMC were procured from Colorcon Pvt. Ltd. Methanol and acetonitrile were of HPLC grade purchased from Loba Chemie Pvt. Ltd.\u003c/p\u003e \u003cdiv id=\"Sec3\" class=\"Section2\"\u003e \u003ch2\u003e2.1. Preparation of VLF-loaded niosomes\u003c/h2\u003e \u003cp\u003eNiosomes were prepared using the thin film hydration method. For this, the desired quantity of Span 40 was accurately weighed and added to a mixture of chloroform: di-ethyl ether (1:2). Cholesterol was then added to this mixture until a clear solution was formed. The solution was then added to a rotary vacuum evaporator flask and operated for 30 min by heating under vacuum at a temperature of 65\u0026deg;C at120 rpm till a thin dried film was obtained on the inner wall of the flask. VLF was dissolved in10 mL of warm double distilled water and added to the flask for hydration. Hydration of the film was continued for 60 min at 65\u0026deg;C. The niosomal solution thus formed was then subjected to ultra-sonication in a bath sonicator for a desired period to reduce the size of particles. For coating, niosomal solution was added drop-wise in chitosan (0.5%w/v) solution (10 mL) under magnetic stirring for 30 min to impart positive charge on niosomes [\u003cspan additionalcitationids=\"CR15\" citationid=\"CR14\" class=\"CitationRef\"\u003e14\u003c/span\u003e\u0026ndash;\u003cspan citationid=\"CR16\" class=\"CitationRef\"\u003e16\u003c/span\u003e].\u003c/p\u003e \u003c/div\u003e \u003cdiv id=\"Sec4\" class=\"Section2\"\u003e \u003ch2\u003e2.2. Design of experiments\u003c/h2\u003e \u003cp\u003eExperiments were designed to employ a Box Behnken design using 3 levels and 3 factors for niosomes formulation to investigate the impact of the independent variables on the dependent variables. Herein, concentration of Span 40 (X\u003csub\u003e1\u003c/sub\u003e), concentration of cholesterol (X\u003csub\u003e2\u003c/sub\u003e), and sonication time (X\u003csub\u003e3\u003c/sub\u003e) were chosen as the independent variables, whereas particle size (Y\u003csub\u003e1\u003c/sub\u003e), zeta potential (mV)(Y\u003csub\u003e2\u003c/sub\u003e), polydispersity index (Y\u003csub\u003e3\u003c/sub\u003e), and entrapment efficiency (Y\u003csub\u003e4\u003c/sub\u003e)were selected as dependent variables \u003cb\u003e(\u003c/b\u003eTable\u0026nbsp;\u003cspan refid=\"Tab1\" class=\"InternalRef\"\u003e1\u003c/span\u003e\u003cb\u003e).\u003c/b\u003e The design suggested a total of 13 experimental runs, as illustrated in Table\u0026nbsp;\u003cspan refid=\"Tab2\" 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 1\u003c/div\u003e \u003cdiv class=\"CaptionContent\"\u003e \u003cp\u003eBox Behnken design for formulation of VLF-niosomes\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=\"left\" class=\"colspec\" colname=\"c3\" colnum=\"3\"\u003e\u003c/div\u003e \u003cdiv align=\"left\" class=\"colspec\" colname=\"c4\" colnum=\"4\"\u003e\u003c/div\u003e \u003cdiv align=\"left\" class=\"colspec\" colname=\"c5\" colnum=\"5\"\u003e\u003c/div\u003e \u003cthead\u003e \u003ctr\u003e \u003cth align=\"left\" colname=\"c1\"\u003e \u003cp\u003eIndependent variables\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colspan=\"3\" nameend=\"c4\" namest=\"c2\"\u003e \u003cp\u003eLevels\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c5\"\u003e \u003cp\u003eDependent variables\u003c/p\u003e \u003c/th\u003e \u003c/tr\u003e \u003ctr\u003e \u003cth align=\"left\" colname=\"c1\"\u003e\u0026nbsp;\u003c/th\u003e \u003cth align=\"left\" colname=\"c2\"\u003e \u003cp\u003eLow\u003c/p\u003e \u003cp\u003e(-1)\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c3\"\u003e \u003cp\u003eMedium\u003c/p\u003e \u003cp\u003e(0)\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c4\"\u003e \u003cp\u003eHigh\u003c/p\u003e \u003cp\u003e(+\u0026thinsp;1)\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c5\"\u003e\u0026nbsp;\u003c/th\u003e \u003c/tr\u003e \u003c/thead\u003e \u003ctbody\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eX\u003csub\u003e1\u003c/sub\u003e: Concentration of span 40 (molar ratio)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e \u003cp\u003e1\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e2\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e3\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\" morerows=\"2\" rowspan=\"3\"\u003e \u003cp\u003eY\u003csub\u003e1\u003c/sub\u003e: Particle size (nm)\u003c/p\u003e \u003cp\u003eY\u003csub\u003e2\u003c/sub\u003e: Entrapment efficiency (%)\u003c/p\u003e \u003cp\u003eY\u003csub\u003e3\u003c/sub\u003e: \u003cem\u003eIn-vitro\u003c/em\u003e drug release (%)\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eX\u003csub\u003e2\u003c/sub\u003e: Concentration of cholesterol (molar ratio)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e \u003cp\u003e1\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e1.25\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e1.5\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eX\u003csub\u003e3\u003c/sub\u003e: Sonication time (min)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e \u003cp\u003e15\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e30\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e45\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 \u003cdiv class=\"gridtable\"\u003e\u003ctable float=\"Yes\" id=\"Tab2\" border=\"1\"\u003e \u003ccaption language=\"En\"\u003e \u003cdiv class=\"CaptionNumber\"\u003eTable 2\u003c/div\u003e \u003cdiv class=\"CaptionContent\"\u003e \u003cp\u003eFormulation batches of niosomes-VLF as per Box Behnken design.\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=\"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=\"\u0026plusmn;\" 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 \u003cthead\u003e \u003ctr\u003e \u003cth align=\"left\" colname=\"c1\"\u003e \u003cp\u003eBatch. No.\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c2\"\u003e \u003cp\u003eSpan 40 concentration\u003c/p\u003e \u003cp\u003e(molar ratio)\u003c/p\u003e \u003cp\u003e(X\u003csub\u003e1\u003c/sub\u003e)\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c3\"\u003e \u003cp\u003eCholesterol concentration\u003c/p\u003e \u003cp\u003e(molar ratio)\u003c/p\u003e \u003cp\u003e(X\u003csub\u003e2\u003c/sub\u003e)\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c4\"\u003e \u003cp\u003eSonication time\u003c/p\u003e \u003cp\u003e(min)\u003c/p\u003e \u003cp\u003e(X\u003csub\u003e3\u003c/sub\u003e)\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c5\"\u003e \u003cp\u003eParticle size\u003c/p\u003e \u003cp\u003e(nm)\u003c/p\u003e \u003cp\u003e(Y\u003csub\u003e1\u003c/sub\u003e)\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c6\"\u003e \u003cp\u003eZeta potential (mv)\u003c/p\u003e \u003cp\u003e(Y\u003csub\u003e2\u003c/sub\u003e)\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c7\"\u003e \u003cp\u003ePDI\u003c/p\u003e \u003cp\u003e(Y\u003csub\u003e3\u003c/sub\u003e)\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c8\"\u003e \u003cp\u003eEntrapment efficiency\u003c/p\u003e \u003cp\u003e(%)\u003c/p\u003e \u003cp\u003e(Y\u003csub\u003e4\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=\"char\" char=\".\" colname=\"c2\"\u003e \u003cp\u003e1.0\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e \u003cp\u003e1.0\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e \u003cp\u003e30\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\"\u0026plusmn;\" colname=\"c5\"\u003e \u003cp\u003e477\u0026thinsp;\u0026plusmn;\u0026thinsp;1.1\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\"\u0026plusmn;\" colname=\"c6\"\u003e \u003cp\u003e26.2\u0026thinsp;\u0026plusmn;\u0026thinsp;0.28\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\"\u0026plusmn;\" colname=\"c7\"\u003e \u003cp\u003e0.349\u0026thinsp;\u0026plusmn;\u0026thinsp;0.16\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\"\u0026plusmn;\" colname=\"c8\"\u003e \u003cp\u003e39.1\u0026thinsp;\u0026plusmn;\u0026thinsp;1.6\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=\"char\" char=\".\" colname=\"c2\"\u003e \u003cp\u003e3.0\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e \u003cp\u003e1.0\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e \u003cp\u003e30\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\"\u0026plusmn;\" colname=\"c5\"\u003e \u003cp\u003e176\u0026thinsp;\u0026plusmn;\u0026thinsp;1.0\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\"\u0026plusmn;\" colname=\"c6\"\u003e \u003cp\u003e59.1\u0026thinsp;\u0026plusmn;\u0026thinsp;0.32\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\"\u0026plusmn;\" colname=\"c7\"\u003e \u003cp\u003e0.224\u0026thinsp;\u0026plusmn;\u0026thinsp;0.09\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\"\u0026plusmn;\" colname=\"c8\"\u003e \u003cp\u003e83.6\u0026thinsp;\u0026plusmn;\u0026thinsp;1.9\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=\"char\" char=\".\" colname=\"c2\"\u003e \u003cp\u003e1.0\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e \u003cp\u003e1.5\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e \u003cp\u003e30\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\"\u0026plusmn;\" colname=\"c5\"\u003e \u003cp\u003e447\u0026thinsp;\u0026plusmn;\u0026thinsp;1.2\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\"\u0026plusmn;\" colname=\"c6\"\u003e \u003cp\u003e32.3\u0026thinsp;\u0026plusmn;\u0026thinsp;0.35\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\"\u0026plusmn;\" colname=\"c7\"\u003e \u003cp\u003e0.339\u0026thinsp;\u0026plusmn;\u0026thinsp;0.17\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\"\u0026plusmn;\" colname=\"c8\"\u003e \u003cp\u003e55.8\u0026thinsp;\u0026plusmn;\u0026thinsp;2.5\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=\"char\" char=\".\" colname=\"c2\"\u003e \u003cp\u003e3.0\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e \u003cp\u003e1.5\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e \u003cp\u003e30\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\"\u0026plusmn;\" colname=\"c5\"\u003e \u003cp\u003e146\u0026thinsp;\u0026plusmn;\u0026thinsp;0.5\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\"\u0026plusmn;\" colname=\"c6\"\u003e \u003cp\u003e72.6\u0026thinsp;\u0026plusmn;\u0026thinsp;0.25\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\"\u0026plusmn;\" colname=\"c7\"\u003e \u003cp\u003e0.203\u0026thinsp;\u0026plusmn;\u0026thinsp;0.04\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\"\u0026plusmn;\" colname=\"c8\"\u003e \u003cp\u003e90.8\u0026thinsp;\u0026plusmn;\u0026thinsp;1.7\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=\"char\" char=\".\" colname=\"c2\"\u003e \u003cp\u003e1.0\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e \u003cp\u003e1.25\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e \u003cp\u003e15\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\"\u0026plusmn;\" colname=\"c5\"\u003e \u003cp\u003e462\u0026thinsp;\u0026plusmn;\u0026thinsp;2.7\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\"\u0026plusmn;\" colname=\"c6\"\u003e \u003cp\u003e29.1\u0026thinsp;\u0026plusmn;\u0026thinsp;1.09\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\"\u0026plusmn;\" colname=\"c7\"\u003e \u003cp\u003e0.345\u0026thinsp;\u0026plusmn;\u0026thinsp;0.16\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\"\u0026plusmn;\" colname=\"c8\"\u003e \u003cp\u003e43.2\u0026thinsp;\u0026plusmn;\u0026thinsp;1.4\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=\"char\" char=\".\" colname=\"c2\"\u003e \u003cp\u003e3.0\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e \u003cp\u003e1.25\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e \u003cp\u003e15\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\"\u0026plusmn;\" colname=\"c5\"\u003e \u003cp\u003e173\u0026thinsp;\u0026plusmn;\u0026thinsp;1.7\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\"\u0026plusmn;\" colname=\"c6\"\u003e \u003cp\u003e62.9\u0026thinsp;\u0026plusmn;\u0026thinsp;0.20\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\"\u0026plusmn;\" colname=\"c7\"\u003e \u003cp\u003e0.220\u0026thinsp;\u0026plusmn;\u0026thinsp;0.05\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\"\u0026plusmn;\" colname=\"c8\"\u003e \u003cp\u003e86.5\u0026thinsp;\u0026plusmn;\u0026thinsp;1.3\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=\"char\" char=\".\" colname=\"c2\"\u003e \u003cp\u003e1.0\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e \u003cp\u003e1.25\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e \u003cp\u003e45\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\"\u0026plusmn;\" colname=\"c5\"\u003e \u003cp\u003e236\u0026thinsp;\u0026plusmn;\u0026thinsp;1.1\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\"\u0026plusmn;\" colname=\"c6\"\u003e \u003cp\u003e27.7\u0026thinsp;\u0026plusmn;\u0026thinsp;0.05\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\"\u0026plusmn;\" colname=\"c7\"\u003e \u003cp\u003e0.296\u0026thinsp;\u0026plusmn;\u0026thinsp;0.10\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\"\u0026plusmn;\" colname=\"c8\"\u003e \u003cp\u003e89.8\u0026thinsp;\u0026plusmn;\u0026thinsp;0.9\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=\"char\" char=\".\" colname=\"c2\"\u003e \u003cp\u003e3.0\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e \u003cp\u003e1.25\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e \u003cp\u003e45\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\"\u0026plusmn;\" colname=\"c5\"\u003e \u003cp\u003e268\u0026thinsp;\u0026plusmn;\u0026thinsp;0.4\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\"\u0026plusmn;\" colname=\"c6\"\u003e \u003cp\u003e68.8\u0026thinsp;\u0026plusmn;\u0026thinsp;1.10\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\"\u0026plusmn;\" colname=\"c7\"\u003e \u003cp\u003e0.215\u0026thinsp;\u0026plusmn;\u0026thinsp;0.13\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\"\u0026plusmn;\" colname=\"c8\"\u003e \u003cp\u003e81.1\u0026thinsp;\u0026plusmn;\u0026thinsp;1.2\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=\"char\" char=\".\" colname=\"c2\"\u003e \u003cp\u003e2.0\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e \u003cp\u003e1.0\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e \u003cp\u003e15\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\"\u0026plusmn;\" colname=\"c5\"\u003e \u003cp\u003e293\u0026thinsp;\u0026plusmn;\u0026thinsp;0.7\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\"\u0026plusmn;\" colname=\"c6\"\u003e \u003cp\u003e19.1\u0026thinsp;\u0026plusmn;\u0026thinsp;1.15\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\"\u0026plusmn;\" colname=\"c7\"\u003e \u003cp\u003e0.298\u0026thinsp;\u0026plusmn;\u0026thinsp;0.12\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\"\u0026plusmn;\" colname=\"c8\"\u003e \u003cp\u003e59.6\u0026thinsp;\u0026plusmn;\u0026thinsp;2.2\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=\"char\" char=\".\" colname=\"c2\"\u003e \u003cp\u003e2.0\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e \u003cp\u003e1.5\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e \u003cp\u003e15\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\"\u0026plusmn;\" colname=\"c5\"\u003e \u003cp\u003e224\u0026thinsp;\u0026plusmn;\u0026thinsp;1.1\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\"\u0026plusmn;\" colname=\"c6\"\u003e \u003cp\u003e57.2\u0026thinsp;\u0026plusmn;\u0026thinsp;0.28\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\"\u0026plusmn;\" colname=\"c7\"\u003e \u003cp\u003e0.229\u0026thinsp;\u0026plusmn;\u0026thinsp;0.10\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\"\u0026plusmn;\" colname=\"c8\"\u003e \u003cp\u003e65.7\u0026thinsp;\u0026plusmn;\u0026thinsp;1.2\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=\"char\" char=\".\" colname=\"c2\"\u003e \u003cp\u003e2.0\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e \u003cp\u003e1.0\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e \u003cp\u003e45\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\"\u0026plusmn;\" colname=\"c5\"\u003e \u003cp\u003e288\u0026thinsp;\u0026plusmn;\u0026thinsp;1.5\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\"\u0026plusmn;\" colname=\"c6\"\u003e \u003cp\u003e43.1\u0026thinsp;\u0026plusmn;\u0026thinsp;0.15\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\"\u0026plusmn;\" colname=\"c7\"\u003e \u003cp\u003e0.249\u0026thinsp;\u0026plusmn;\u0026thinsp;0.12\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\"\u0026plusmn;\" colname=\"c8\"\u003e \u003cp\u003e69.8\u0026thinsp;\u0026plusmn;\u0026thinsp;1.4\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=\"char\" char=\".\" colname=\"c2\"\u003e \u003cp\u003e2.0\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e \u003cp\u003e1.5\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e \u003cp\u003e45\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\"\u0026plusmn;\" colname=\"c5\"\u003e \u003cp\u003e245\u0026thinsp;\u0026plusmn;\u0026thinsp;1.1\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\"\u0026plusmn;\" colname=\"c6\"\u003e \u003cp\u003e52.8\u0026thinsp;\u0026plusmn;\u0026thinsp;0.20\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\"\u0026plusmn;\" colname=\"c7\"\u003e \u003cp\u003e0.236\u0026thinsp;\u0026plusmn;\u0026thinsp;0.08\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\"\u0026plusmn;\" colname=\"c8\"\u003e \u003cp\u003e71.3\u0026thinsp;\u0026plusmn;\u0026thinsp;1.6\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=\"char\" char=\".\" colname=\"c2\"\u003e \u003cp\u003e2.0\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e \u003cp\u003e1.25\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e \u003cp\u003e30\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\"\u0026plusmn;\" colname=\"c5\"\u003e \u003cp\u003e267\u0026thinsp;\u0026plusmn;\u0026thinsp;1.7\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\"\u0026plusmn;\" colname=\"c6\"\u003e \u003cp\u003e49.9\u0026thinsp;\u0026plusmn;\u0026thinsp;0.46\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\"\u0026plusmn;\" colname=\"c7\"\u003e \u003cp\u003e0.242\u0026thinsp;\u0026plusmn;\u0026thinsp;0.10\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\"\u0026plusmn;\" colname=\"c8\"\u003e \u003cp\u003e65.4\u0026thinsp;\u0026plusmn;\u0026thinsp;1.5\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003c/tbody\u003e \u003c/colgroup\u003e \u003ctfoot\u003e \u003ctr\u003e\u003ctd colspan=\"8\"\u003eAll the results are expressed as mean\u0026thinsp;\u0026plusmn;\u0026thinsp;standard deviation (n\u0026thinsp;=\u0026thinsp;3).\u003c/td\u003e\u003c/tr\u003e \u003c/tfoot\u003e \u003c/table\u003e\u003c/div\u003e \u003c/p\u003e \u003c/div\u003e \u003cdiv id=\"Sec5\" class=\"Section2\"\u003e \u003ch2\u003e2.3. Particle size, zeta potential, and PDI analysis\u003c/h2\u003e \u003cp\u003eParticle size, zeta potential, and PDI were measured by Litesizer-500 (Anton Paar, Austria), which works on the dynamic light scattering principle. The niosomal solution was accurately diluted 10 times with distilled water for particle size measurements. For zeta potential analysis, the sample was directly filled in the omega cuvettes and analyzed. Zeta potential measurement is based on the principle of laser doppler anemometry. All the procedures were done at room temperature [\u003cspan citationid=\"CR17\" class=\"CitationRef\"\u003e17\u003c/span\u003e, \u003cspan citationid=\"CR18\" class=\"CitationRef\"\u003e18\u003c/span\u003e].\u003c/p\u003e \u003c/div\u003e \u003cdiv id=\"Sec6\" class=\"Section2\"\u003e \u003ch2\u003e2.4. Entrapment efficiency (%)\u003c/h2\u003e \u003cp\u003eIt was determined using an indirect method based on the previously reported literature [\u003cspan citationid=\"CR19\" class=\"CitationRef\"\u003e19\u003c/span\u003e]. For this, a defined volume of niosomal dispersion was added into the tube and centrifuged at 9000 rpm for 45 min at 4\u0026deg;C. After centrifugation, 1mL of the supernatant was diluted appropriately, and the absorbance of a diluted solution was recorded by UV spectrophotometer at 225nm. Entrapment efficiency was determined by using the following Eq.\u0026nbsp;(1) [\u003cspan citationid=\"CR20\" class=\"CitationRef\"\u003e20\u003c/span\u003e],\u003c/p\u003e \u003cp\u003eEntrapment Efficiency (%) = \u003cspan class=\"InlineEquation\"\u003e\u003cspan class=\"mathinline\"\u003e\\(\\:\\frac{\\text{T}\\text{o}\\text{t}\\text{a}\\text{l}\\:\\text{d}\\text{r}\\text{u}\\text{g}\\:\\text{a}\\text{d}\\text{d}\\text{e}\\text{d}\\:-\\:\\text{U}\\text{n}\\text{e}\\text{n}\\text{t}\\text{r}\\text{a}\\text{p}\\text{p}\\text{e}\\text{d}\\:\\text{d}\\text{r}\\text{u}\\text{g}}{\\text{T}\\text{o}\\text{t}\\text{a}\\text{l}\\:\\text{d}\\text{r}\\text{u}\\text{g}\\:\\text{a}\\text{d}\\text{d}\\text{e}\\text{d}}\\)\u003c/span\u003e\u003c/span\u003e * 100 \u0026hellip;\u0026hellip;\u0026hellip;\u0026hellip;\u0026hellip;\u0026hellip;\u0026hellip;\u0026hellip;.. (1)\u003c/p\u003e \u003c/div\u003e \u003cdiv id=\"Sec7\" class=\"Section2\"\u003e \u003ch2\u003e2.5. Optimization of formulation\u003c/h2\u003e \u003cp\u003eThe optimization of the formulation was executed employing numerical optimization [\u003cspan citationid=\"CR21\" class=\"CitationRef\"\u003e21\u003c/span\u003e]. The criteria for optimized formulation were given by setting the goal in the desired range, such as particle size (Y\u003csub\u003e1\u003c/sub\u003e): 146 to 300 nm; zeta potential (Y\u003csub\u003e2\u003c/sub\u003e): 40 to 50 mV; polydispersity index (Y\u003csub\u003e3\u003c/sub\u003e): 0.20 to 0.30; and entrapment efficiency (Y\u003csub\u003e4\u003c/sub\u003e): 60 to 90%. The models suitability was ascertained by developing a new formulation based on the suggestion provided by the software.\u003c/p\u003e \u003c/div\u003e \u003cdiv id=\"Sec8\" class=\"Section2\"\u003e \u003ch2\u003e2.6. Lyophillization of niosomes-VLF formulation\u003c/h2\u003e \u003cp\u003eThe noisome-VLF (OF) was combined with mannitol (5%w/v) and stored at approximately \u0026minus;\u0026thinsp;40\u0026ordm;C for the entire night in a deep freezer (KCN 372, Blue Star Ltd., Mumbai). After that, the frozen noisome-VLF (OF) was freeze-dried for roughly 72 h using a freeze dryer (Virtis Ltd., USA) to produce lyophilized powder.\u003c/p\u003e \u003c/div\u003e \u003cdiv id=\"Sec9\" class=\"Section2\"\u003e \u003ch2\u003e2.7. X-ray diffraction study\u003c/h2\u003e \u003cp\u003eX-ray diffractogram was recorded for VLF (pure), physical mixture (drug and excipients), and niosomes-VLF formulation. The experiment used an XRD instrument (BrukerAXS,Inc., Madison, WI, USA). Powder XRD patterns were recorded on the D2 Phaser 2nd generation-based diffractometer using a voltage of 30kV and a current of 10mA.The scanning rate was 50 min, over the 50 to 400 diffraction angle (2θ) range [\u003cspan citationid=\"CR22\" class=\"CitationRef\"\u003e22\u003c/span\u003e].\u003c/p\u003e \u003c/div\u003e \u003cdiv id=\"Sec10\" class=\"Section2\"\u003e \u003ch2\u003e2.8. Preparation of niosomes-VLF loaded thermosensitive \u003cem\u003ein-situ\u003c/em\u003e gel (TISG)\u003c/h2\u003e \u003cp\u003eNiosomes-VLF TISG has been developed using the cold method. Briefly, definite concentrations of poloxamer\u0026ndash;407 (16.5\u0026ndash;18.5%) followed by poloxamer\u0026ndash;188 (1%) and HPMC-K4M (0.5%) were slowly dissolved one by one by continuous stirring in niosomes-VLF(OF) kept over ice-bath [\u003cspan citationid=\"CR23\" class=\"CitationRef\"\u003e23\u003c/span\u003e\u0026ndash;\u003cspan citationid=\"CR24\" class=\"CitationRef\"\u003e24\u003c/span\u003e]. After forming a clear solution, mannitol (4% w/v) was added as a release modifier, and cryoprotectant, benzalkonium chloride (1%w/v) was used as a preservative\u003cb\u003eTable 3\u003c/b\u003e. The solution was kept in the refrigerator until further use [\u003cspan citationid=\"CR25\" class=\"CitationRef\"\u003e25\u003c/span\u003e\u0026ndash;\u003cspan citationid=\"CR26\" class=\"CitationRef\"\u003e26\u003c/span\u003e].\u003c/p\u003e \u003c/div\u003e \u003cdiv id=\"Sec11\" class=\"Section2\"\u003e \u003ch2\u003e2.9. Characterization of niosomes-VLFTISG\u003c/h2\u003e \u003cp\u003eThe gelling temperature was determined using the method previously described [\u003cspan citationid=\"CR27\" class=\"CitationRef\"\u003e27\u003c/span\u003e]. A niosomal-VLF (OF) formulation was kept over a heating magnetic plate stirrer and stirred with a magnetic bead at a constant speed of 52 rpm. The plate temperature was slowly increased at a rate of 1\u003csup\u003eo\u003c/sup\u003eC/min. The temperature was recorded as the gelling temperature where the magnetic bead stops spinning [\u003cspan citationid=\"CR27\" class=\"CitationRef\"\u003e27\u003c/span\u003e], and the time was noted as the gelling time of the formulation [\u003cspan citationid=\"CR28\" class=\"CitationRef\"\u003e28\u003c/span\u003e]. Viscosity is an important parameter governing the performance of the developed system. Viscosity was determined using a Brookfield viscometer at 10 RPM and spindle 63 under a constant temperature of 35\u003csup\u003eo\u003c/sup\u003eC. About 10 mL of niosomes-VLF TISG was added to a cylindrical beaker and placed properly under the spindle of the viscometer. The mucoadhesive strength of all the developed gel formulations was determined as per the methodology mentioned in the previous literature [\u003cspan citationid=\"CR29\" class=\"CitationRef\"\u003e29\u003c/span\u003e] using the following equation;\u003c/p\u003e \u003cp\u003eMucoadhesive strength (dyne/cm\u003csup\u003e2\u003c/sup\u003e)\u0026thinsp;=\u0026thinsp;M \u0026times; g/A \u0026hellip;\u0026hellip;\u0026hellip;\u0026hellip;\u0026hellip;.\u0026hellip;\u0026hellip;\u0026hellip;\u0026hellip;\u0026hellip;\u0026hellip;\u0026hellip;\u0026hellip;\u0026hellip;\u0026hellip;\u0026hellip;\u0026hellip; (2)\u003c/p\u003e \u003cp\u003eA precise weight of VLF-TISG was mixed with a specified volume of Phosphate buffer saline (PBS) (30 mL, pH 7.4) and vigorously shaken for 12 h. After filtering the dispersion, the amount of drug in the filtrate was measured using spectrophotometry at a wavelength of λ\u003csub\u003emax\u003c/sub\u003e225 nm. The drug content (%) was determined using following Eq.\u0026nbsp;(3);\u003c/p\u003e \u003cp\u003eDrug content (%) =\u003cspan class=\"InlineEquation\"\u003e\u003cspan class=\"mathinline\"\u003e\\(\\:\\frac{\\text{T}\\text{h}\\text{e}\\text{r}\\text{o}\\text{t}\\text{i}\\text{c}\\text{a}\\text{l}\\:\\text{a}\\text{m}\\text{o}\\text{u}\\text{n}\\text{t}\\:\\text{o}\\text{f}\\:\\text{V}\\text{L}\\text{F}\\:\\text{i}\\text{n}\\:\\text{n}\\text{i}\\text{o}\\text{s}\\text{o}\\text{m}\\text{s}\\:}{\\text{A}\\text{m}\\text{o}\\text{u}\\text{n}\\text{t}\\:\\text{o}\\text{f}\\:\\text{d}\\text{r}\\text{u}\\text{g}\\:\\text{l}\\text{o}\\text{a}\\text{d}\\text{e}\\text{d}\\:\\text{i}\\text{n}\\:\\text{n}\\text{i}\\text{o}\\text{s}\\text{o}\\text{m}\\text{e}\\text{s}}\\)\u003c/span\u003e\u003c/span\u003e*100\u0026hellip;\u0026hellip;\u0026hellip;\u0026hellip;\u0026hellip;\u0026hellip;\u0026hellip;\u0026hellip;\u0026hellip;\u0026hellip;\u0026hellip;\u0026hellip;\u0026hellip;.. (3)\u003c/p\u003e \u003c/div\u003e \u003cdiv id=\"Sec12\" class=\"Section2\"\u003e \u003ch2\u003e2.10. \u003cem\u003eEx-vivo\u003c/em\u003e drug diffusion studies\u003c/h2\u003e \u003cp\u003eThe \u003cem\u003eex-vivo\u003c/em\u003e drug permeation study of prepared niosomal VLF TISG and VLF suspension was performed on Franz diffusion cell using goat nasal mucosa. Freshly cut nasal mucosa was procured from a local slaughter house and was cleaned properly using double distilled water. After cleaning, it was immediately kept in normal saline solution to keep the tissue alive. A piece of about 2 cm\u003csup\u003e2\u003c/sup\u003e was cut and placed correctly between the acceptor and donor compartment. PBS pH 6.4 was added in the acceptor compartment, and the temperature was maintained at 37\u0026thinsp;\u0026plusmn;\u0026thinsp;0.2\u0026deg;C. 1 mL of niosomal VLF TISG containing 7.5 mg of the drug was kept over the mucosa. Because of the temperature of the system, the solution turned rapidly into a gel and remained adhered to the mucosa. At definite time intervals (0.25, 1, 2, 4, 6, 8, 10, 12, and 24 h), 1 mL of the solution was collected from the acceptor compartment and replaced with fresh buffer. This 1 mL solution was further diluted up to 9 mL using PBS pH 6.4, and the absorbance of this solution was recorded at 225 nm. PBS pH 6.4 was used as blank [\u003cspan citationid=\"CR30\" class=\"CitationRef\"\u003e30\u003c/span\u003e].\u003c/p\u003e \u003c/div\u003e \u003cdiv id=\"Sec13\" class=\"Section2\"\u003e \u003ch2\u003e2.11. HPLC\u003c/h2\u003e \u003cp\u003eChromatographic separation of VLF was achieved by using a C\u003csub\u003e18\u003c/sub\u003e reverse phase column Shimadzu Shimpack Xbridge, 5\u0026micro;m particle size. Exactly 20 \u0026micro;L volume of sample was injected. The mobile phase selected was acetonitrile and 25 mM ammonium carbonate buffer pH 7.6 (85:15) at 1 mL/min flow rate. Sample run time was set at 10 min, and detection was done using a UV detector at 225 nm. The plasma sample was spiked with 1 mL of 10\u0026micro;g/mL celecoxib as the internal standard. Retention time was found to be 5.3 min.\u003c/p\u003e \u003c/div\u003e \u003cdiv id=\"Sec14\" class=\"Section2\"\u003e \u003ch2\u003e2.12. Animals protocol\u003c/h2\u003e \u003cp\u003eThe \u003cem\u003ein-vivo\u003c/em\u003e studies were performed on Sparge Dwaley rats weighing 180 g to 300 g, 8\u0026ndash;11 weeks old. Animal protocol was approved by the Institutional Animal Ethics Committee of Dadasaheb Balpande College of Pharmacy, Besa, Nagpur, India (Protocol approval number DBCOP/IAEC/1426/2022-23/P-10). Food and water were provided to rats ad libitum. The animal study protocol was followed as per the CPCSEA guidelines for laboratory animals.\u003c/p\u003e \u003c/div\u003e \u003cdiv id=\"Sec15\" class=\"Section2\"\u003e \u003ch2\u003e2.13. \u003cem\u003eIn-vivo\u003c/em\u003e pharmacokinetics\u003c/h2\u003e \u003cp\u003ePharmacokinetics analysis was done to access the VLF content in the brain and plasma after the installation of niosomal-VLF TISG and VLF (pure) suspension intra-nasally. For the pharmacokinetic estimations, rats were segregated into 2 groups. Each group contained 12 rats. Niosomal-VLF TISG (100 \u0026micro;L, 50 \u0026micro;L in each nostril) at a dose equivalent to 0.60 mg was instilled in Group I animals. Group II received standard VLF suspension intra-nasally at a dose equivalent to 0.60 mg. At time intervals of 1, 2, 3, 4, 6, and 15 h, the rats were euthanized with ketamine and xylazine. Blood was carefully collected by using a tuberculin syringe and filled immediately in EDTA tube to prevent clotting. Afterward, the brain was carefully removed and collected in a clean small container after washing it with double distilled water. For determining the concentration of VLF, brain tissue was accurately weighed and homogenized in acetonitrile. The supernatant was collected in a test tube and dried in nitrogen air. Acetonitrile (1 mL) was added to the test tube and sonicated for 10 min, followed by injecting into the HPLC system for detection. To determine the VLF amount in plasma, a collected blood sample was ultra-centrifuged at 9000 RPM for 15 min to separate blood and plasma. About 100\u0026micro;L of the plasma was collected from the supernatant and centrifuged again at 9000 RPM for 15 min with 1 mL of acetonitrile to precipitate plasma proteins[\u003cspan citationid=\"CR31\" class=\"CitationRef\"\u003e31\u003c/span\u003e]. After centrifugation, 100 \u0026micro;L of supernatant was dried in a nitrogen atmosphere. To this, 1 mL of acetonitrile was added and sonicated for 10 min, followed by injecting into the HPLC system. At each interval, 2 rats were sacrificed to minimize standard deviation [\u003cspan citationid=\"CR32\" class=\"CitationRef\"\u003e32\u003c/span\u003e].\u003c/p\u003e \u003c/div\u003e \u003cdiv id=\"Sec16\" class=\"Section2\"\u003e \u003ch2\u003e2.14. Accelerated stability studies\u003c/h2\u003e \u003cp\u003eNiosomal VLF TISG (OF-A) was subjected to stability studies at accelerated conditions (25\u0026thinsp;\u0026plusmn;\u0026thinsp;2\u003csup\u003eo\u003c/sup\u003eC/60\u0026thinsp;\u0026plusmn;\u0026thinsp;5%) for a period of 6 months and evaluated for different quality parameters like gelling time, gelling temperature, mucoadhesive strength, and drug content. For stability studies, 15 mL of the final formulation was packed in clear, sealed 25 mL vials with rubber closures. Sampling was done at 0, 3, and 6 month intervals and formulation was then evaluated. A total of 3 vials were loaded for each time point. Results obtained were then compared with the initial formulation results.\u003c/p\u003e \u003c/div\u003e \u003cdiv id=\"Sec17\" class=\"Section2\"\u003e \u003ch2\u003e2.15. Statistical analysis\u003c/h2\u003e \u003cp\u003eThe obtained data were statistically examined using Graph Pad Prism version 4.00 to calculate the mean and standard deviation. In the pharmacokinetic study, a one-way ANOVA was used to determine the significant differences between VLF (pure) suspension and niosomal VLF TISG (OF-A). The predicted P-value of 0.005 was determined to be significant.\u003c/p\u003e \u003c/div\u003e"},{"header":"3. Results and discussion","content":"\u003cdiv id=\"Sec19\" class=\"Section2\"\u003e \u003ch2\u003e3.1. Particle size, PDI, and zeta potential analysis\u003c/h2\u003e \u003cp\u003eParticle size of all the batches were between 173 ± 1.7 nm to 477 ± 1.1 nm. The influence of the independent variables on the selected responses was thoroughly investigated using a 3D response surface plots as illustrated in Fig.\u0026nbsp;\u003cspan refid=\"Fig1\" class=\"InternalRef\"\u003e1\u003c/span\u003e. It was found that, as Span 40concentrationswas increased up-to the molar ration of 2 leading to smaller and uniform particle size. Further, the higher concentration of cholesterol resulted in more rigid bilayers causing a decrease in particle size. In the response surface plot, red color at apex of X\u003csub\u003e2\u003c/sub\u003eaxis indicated negative impact on particle size with the higher level of concentrations of cholesterol and Span 40. The increase in the sonication time also reduces the particle size. The impact of each variable and their effects on the particle size (Y\u003csub\u003e1\u003c/sub\u003e) could be best interpreted by the polynomial Eq.\u0026nbsp;(4) in actual factors illustrated below;\u003c/p\u003e \u003cp\u003e \u003c/p\u003e\u003cdiv class=\"BlockQuote\"\u003e \u003cp\u003eParticle size (Y\u003csub\u003e1\u003c/sub\u003e) = 635.77-107.37X\u003csub\u003e1\u003c/sub\u003e-86.0X\u003csub\u003e2\u003c/sub\u003e-0.95X\u003csub\u003e3\u003c/sub\u003e........................................................... (4)\u003c/p\u003e \u003cp\u003eZeta Potential (Y\u003csub\u003e2\u003c/sub\u003e) = -38.95 + 18.51X\u003csub\u003e1\u003c/sub\u003e + 33.70X\u003csub\u003e2\u003c/sub\u003e + 0.20X\u003csub\u003e3\u003c/sub\u003e...................................................... (5)\u003c/p\u003e \u003cp\u003ePolydispersity Index (Y\u003csub\u003e3\u003c/sub\u003e) = 0.47-0.058X\u003csub\u003e1\u003c/sub\u003e-0.056X\u003csub\u003e2\u003c/sub\u003e-8.0X\u003csub\u003e3\u003c/sub\u003e................................................... (6)\u003c/p\u003e \u003c/div\u003e \u003cp\u003e\u003c/p\u003e \u003cp\u003eThe above Eq.\u0026nbsp;(4) describes that the Span 40 concentration (X\u003csub\u003e1\u003c/sub\u003e), cholesterol concentration (X\u003csub\u003e2\u003c/sub\u003e), and sonication time (X\u003csub\u003e3\u003c/sub\u003e) reveals negative impact on the particle size. Zeta potential of all the batches ranged from 19.1 ± 1.15 to 68.8 ± 1.10 mV. The positive charge on the niosomes appeared owing to coating on the surface. The positive charge coating was applied on the niosomes due to the presence of the sialic acid on cell membrane of the nasal mucosa leading to the chemical intervention which could provide better muco-adhesion and prolong residence time[\u003cspan citationid=\"CR33\" class=\"CitationRef\"\u003e33\u003c/span\u003e]. All the selected variables showed the positive impact on the zeta potential as suggested by the polynomial Eq.\u0026nbsp;(5), indicating more stabilization of niosomal bilayer and improved the surface characteristics. PDI also governs the stability of niosomal formulation as indicated in the Eq.\u0026nbsp;(6), all the independent variable showed negative influence on the PDI.\u003c/p\u003e \u003c/div\u003e \u003cdiv id=\"Sec20\" class=\"Section2\"\u003e \u003ch2\u003e3.2. Entrapment efficiency (%)\u003c/h2\u003e \u003cp\u003eEntrapment efficiency of all batch were between the ranges of 39.1 ± 1.6 to 90.8 ± 1.7.As the cholesterol concentration was increased the rigidity of the bilayer increases which decreases the fluidity of the bilayer and makes the vesicles less leaky leading to increase in the entrapment efficiency. Another reason for lower entrapment efficiency in some batches could be the smaller particle size of the formed niosomes which decreases the entrapment efficiency (%). The impact of each variable and their combination effects on entrapment efficiency (%) could be best interpreted by the polynomial equation in actual factor.\u003c/p\u003e \u003cp\u003eEntrapment efficiency (Y\u003csub\u003e2\u003c/sub\u003e) = 6.944 + 14.065X\u003csub\u003e1\u003c/sub\u003e + 15.75X\u003csub\u003e2\u003c/sub\u003e + 0.488X\u003csub\u003e3\u003c/sub\u003e……………………… (7)\u003c/p\u003e \u003cp\u003eAbove equation describes that as the higher concentration Span 40(X\u003csub\u003e1\u003c/sub\u003e) causes increase in entrapment efficiency owing to the membrane stabilization of niosome. Furthermore, the higher cholesterol concentration and prolonged sonication time also leads to increase entrapment efficiency.Table\u0026nbsp;\u003cspan refid=\"Tab3\" class=\"InternalRef\"\u003e4\u003c/span\u003e represents a summary of the ANOVA study's findings and demonstrates that all models were significant (P \u0026lt; 0.5) for each response parameter investigated. Additionally, Fig.\u0026nbsp;\u003cspan refid=\"Fig2\" class=\"InternalRef\"\u003e2\u003c/span\u003e shows desirability plot provided by Design-Expert 8.0.0.1 software indicating predication 1, suggesting all the target ranges were within the limit.\u003c/p\u003e \u003cp\u003e \u003c/p\u003e\u003cdiv class=\"gridtable\"\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\u003cdiv align=\"left\" class=\"colspec\" colname=\"c6\" colnum=\"6\"\u003e\u003c/div\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\u003eSummary of ANOVA for the response parameters\u003c/p\u003e \u003c/div\u003e \u003c/caption\u003e\u003ccolgroup cols=\"6\"\u003e\u003c/colgroup\u003e\u003cthead\u003e\u003ctr\u003e\u003cth align=\"left\" colname=\"c1\"\u003e \u003cp\u003eSource\u003c/p\u003e \u003c/th\u003e\u003cth align=\"left\" colname=\"c2\"\u003e \u003cp\u003eSum of squares\u003c/p\u003e \u003c/th\u003e\u003cth align=\"left\" colname=\"c3\"\u003e \u003cp\u003ed.f.\u003csup\u003ea\u003c/sup\u003e\u003c/p\u003e \u003c/th\u003e\u003cth align=\"left\" colname=\"c4\"\u003e \u003cp\u003eMean square\u003c/p\u003e \u003c/th\u003e\u003cth align=\"left\" colname=\"c5\"\u003e \u003cp\u003eF-value\u003c/p\u003e \u003c/th\u003e\u003cth align=\"left\" colname=\"c6\"\u003e \u003cp\u003ep-value\u003c/p\u003e \u003cp\u003eprob \u0026gt; F\u003c/p\u003e \u003c/th\u003e\u003c/tr\u003e\u003ctr\u003e\u003cth align=\"left\" colname=\"c1\"\u003e \u003cp\u003ea) Particle size\u003c/p\u003e \u003cp\u003e(nm)\u003c/p\u003e \u003c/th\u003e\u003cth align=\"left\" colname=\"c2\"\u003e\u0026nbsp;\u003c/th\u003e\u003cth align=\"left\" colname=\"c3\"\u003e\u0026nbsp;\u003c/th\u003e\u003cth align=\"left\" colname=\"c4\"\u003e\u0026nbsp;\u003c/th\u003e\u003cth align=\"left\" colname=\"c5\"\u003e\u0026nbsp;\u003c/th\u003e\u003cth align=\"left\" colname=\"c6\"\u003e\u0026nbsp;\u003c/th\u003e\u003c/tr\u003e\u003c/thead\u003e\u003ctbody\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eModel\u003c/p\u003e \u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e \u003cp\u003e97586.25\u003c/p\u003e \u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e \u003cp\u003e3\u003c/p\u003e \u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e \u003cp\u003e32528.75\u003c/p\u003e \u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c5\"\u003e \u003cp\u003e5.97\u003c/p\u003e \u003c/td\u003e\u003ctd align=\"left\" colname=\"c6\"\u003e \u003cp\u003e0.0159 (S)\u003c/p\u003e \u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eX\u003csub\u003e1\u003c/sub\u003e\u003c/p\u003e \u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e \u003cp\u003e92235.13\u003c/p\u003e \u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e \u003cp\u003e1\u003c/p\u003e \u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e \u003cp\u003e92235.13\u003c/p\u003e \u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c5\"\u003e \u003cp\u003e16.94\u003c/p\u003e \u003c/td\u003e\u003ctd align=\"left\" colname=\"c6\"\u003e \u003cp\u003e0.0026 (S)\u003c/p\u003e \u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eX\u003csub\u003e2\u003c/sub\u003e\u003c/p\u003e \u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e \u003cp\u003e3698.00\u003c/p\u003e \u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e \u003cp\u003e1\u003c/p\u003e \u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e \u003cp\u003e3698.00\u003c/p\u003e \u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c5\"\u003e \u003cp\u003e0.68\u003c/p\u003e \u003c/td\u003e\u003ctd align=\"left\" colname=\"c6\"\u003e \u003cp\u003e0.4312 (NS)\u003c/p\u003e \u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eX\u003csub\u003e3\u003c/sub\u003e\u003c/p\u003e \u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e \u003cp\u003e1653.13\u003c/p\u003e \u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e \u003cp\u003e1\u003c/p\u003e \u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e \u003cp\u003e1653.13\u003c/p\u003e \u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c5\"\u003e \u003cp\u003e0.30\u003c/p\u003e \u003c/td\u003e\u003ctd align=\"left\" colname=\"c6\"\u003e \u003cp\u003e0.5950 (NS)\u003c/p\u003e \u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003e\u003cb\u003eb) Zeta Potential (mV)\u003c/b\u003e\u003c/p\u003e \u003c/td\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u0026nbsp;\u003c/td\u003e\u003ctd align=\"left\" colname=\"c3\"\u003e\u0026nbsp;\u003c/td\u003e\u003ctd align=\"left\" colname=\"c4\"\u003e\u0026nbsp;\u003c/td\u003e\u003ctd align=\"left\" colname=\"c5\"\u003e\u0026nbsp;\u003c/td\u003e\u003ctd align=\"left\" colname=\"c6\"\u003e\u0026nbsp;\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eModel\u003c/p\u003e \u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e \u003cp\u003e3382.15\u003c/p\u003e \u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e \u003cp\u003e3\u003c/p\u003e \u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e \u003cp\u003e1127.38\u003c/p\u003e \u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c5\"\u003e \u003cp\u003e24.10\u003c/p\u003e \u003c/td\u003e\u003ctd align=\"left\" colname=\"c6\"\u003e \u003cp\u003e0.0001(S)\u003c/p\u003e \u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eX\u003csub\u003e1\u003c/sub\u003e\u003c/p\u003e \u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e \u003cp\u003e2741.70\u003c/p\u003e \u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e \u003cp\u003e1\u003c/p\u003e \u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e \u003cp\u003e2741.70\u003c/p\u003e \u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c5\"\u003e \u003cp\u003e58.61\u003c/p\u003e \u003c/td\u003e\u003ctd align=\"left\" colname=\"c6\"\u003e \u003cp\u003e\u0026lt;0.0001(S)\u003c/p\u003e \u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eX\u003csub\u003e2\u003c/sub\u003e\u003c/p\u003e \u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e \u003cp\u003e567.84\u003c/p\u003e \u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e \u003cp\u003e1\u003c/p\u003e \u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e \u003cp\u003e567.84\u003c/p\u003e \u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c5\"\u003e \u003cp\u003e12.14\u003c/p\u003e \u003c/td\u003e\u003ctd align=\"left\" colname=\"c6\"\u003e \u003cp\u003e0.0069 (S)\u003c/p\u003e \u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eX\u003csub\u003e3\u003c/sub\u003e\u003c/p\u003e \u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e \u003cp\u003e72.60\u003c/p\u003e \u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e \u003cp\u003e1\u003c/p\u003e \u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e \u003cp\u003e72.60\u003c/p\u003e \u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c5\"\u003e \u003cp\u003e1.55\u003c/p\u003e \u003c/td\u003e\u003ctd align=\"left\" colname=\"c6\"\u003e \u003cp\u003e0.2443 (NS)\u003c/p\u003e \u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003e\u003cb\u003ec) PDI\u003c/b\u003e\u003c/p\u003e \u003c/td\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u0026nbsp;\u003c/td\u003e\u003ctd align=\"left\" colname=\"c3\"\u003e\u0026nbsp;\u003c/td\u003e\u003ctd align=\"left\" colname=\"c4\"\u003e\u0026nbsp;\u003c/td\u003e\u003ctd align=\"left\" colname=\"c5\"\u003e\u0026nbsp;\u003c/td\u003e\u003ctd align=\"left\" colname=\"c6\"\u003e\u0026nbsp;\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eModel\u003c/p\u003e \u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e \u003cp\u003e0.030\u003c/p\u003e \u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e \u003cp\u003e3\u003c/p\u003e \u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e \u003cp\u003e0.010\u003c/p\u003e \u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c5\"\u003e \u003cp\u003e22.82\u003c/p\u003e \u003c/td\u003e\u003ctd align=\"left\" colname=\"c6\"\u003e \u003cp\u003e0.0002 (S)\u003c/p\u003e \u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eX\u003csub\u003e1\u003c/sub\u003e\u003c/p\u003e \u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e \u003cp\u003e0.027\u003c/p\u003e \u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e \u003cp\u003e1\u003c/p\u003e \u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e \u003cp\u003e0.027\u003c/p\u003e \u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c5\"\u003e \u003cp\u003e62.20\u003c/p\u003e \u003c/td\u003e\u003ctd align=\"left\" colname=\"c6\"\u003e \u003cp\u003e\u0026lt; 0.0001(S)\u003c/p\u003e \u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eX\u003csub\u003e2\u003c/sub\u003e\u003c/p\u003e \u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e \u003cp\u003e1.596\u003c/p\u003e \u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e \u003cp\u003e1\u003c/p\u003e \u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e \u003cp\u003e1.596\u003c/p\u003e \u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c5\"\u003e \u003cp\u003e3.64\u003c/p\u003e \u003c/td\u003e\u003ctd align=\"left\" colname=\"c6\"\u003e \u003cp\u003e0.0887 (NS)\u003c/p\u003e \u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eX\u003csub\u003e3\u003c/sub\u003e\u003c/p\u003e \u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e \u003cp\u003e1.152\u003c/p\u003e \u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e \u003cp\u003e1\u003c/p\u003e \u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e \u003cp\u003e1.152\u003c/p\u003e \u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c5\"\u003e \u003cp\u003e2.63\u003c/p\u003e \u003c/td\u003e\u003ctd align=\"left\" colname=\"c6\"\u003e \u003cp\u003e0.1394 (NS)\u003c/p\u003e \u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003e\u003cb\u003ed) Entrapment efficiency (%)\u003c/b\u003e\u003c/p\u003e \u003c/td\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u0026nbsp;\u003c/td\u003e\u003ctd align=\"left\" colname=\"c3\"\u003e\u0026nbsp;\u003c/td\u003e\u003ctd align=\"left\" colname=\"c4\"\u003e\u0026nbsp;\u003c/td\u003e\u003ctd align=\"left\" colname=\"c5\"\u003e\u0026nbsp;\u003c/td\u003e\u003ctd align=\"left\" colname=\"c6\"\u003e\u0026nbsp;\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eModel\u003c/p\u003e \u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e \u003cp\u003e2135.71\u003c/p\u003e \u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e \u003cp\u003e3\u003c/p\u003e \u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e \u003cp\u003e711.90\u003c/p\u003e \u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c5\"\u003e \u003cp\u003e4.86\u003c/p\u003e \u003c/td\u003e\u003ctd align=\"left\" colname=\"c6\"\u003e \u003cp\u003e0.0280 (S)\u003c/p\u003e \u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eX\u003csub\u003e1\u003c/sub\u003e\u003c/p\u003e \u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e \u003cp\u003e1582.88\u003c/p\u003e \u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e \u003cp\u003e1\u003c/p\u003e \u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e \u003cp\u003e1582.88\u003c/p\u003e \u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c5\"\u003e \u003cp\u003e10.82\u003c/p\u003e \u003c/td\u003e\u003ctd align=\"left\" colname=\"c6\"\u003e \u003cp\u003e0.0094 (S)\u003c/p\u003e \u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eX\u003csub\u003e2\u003c/sub\u003e\u003c/p\u003e \u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e \u003cp\u003e124.03\u003c/p\u003e \u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e \u003cp\u003e1\u003c/p\u003e \u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e \u003cp\u003e124.03\u003c/p\u003e \u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c5\"\u003e \u003cp\u003e0.85\u003c/p\u003e \u003c/td\u003e\u003ctd align=\"left\" colname=\"c6\"\u003e \u003cp\u003e0.3813 (NS)\u003c/p\u003e \u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eX\u003csub\u003e3\u003c/sub\u003e\u003c/p\u003e \u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e \u003cp\u003e428.81\u003c/p\u003e \u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e \u003cp\u003e1\u003c/p\u003e \u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e \u003cp\u003e428.81\u003c/p\u003e \u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c5\"\u003e \u003cp\u003e2.93\u003c/p\u003e \u003c/td\u003e\u003ctd align=\"left\" colname=\"c6\"\u003e \u003cp\u003e0.1211 (NS)\u003c/p\u003e \u003c/td\u003e\u003c/tr\u003e\u003c/tbody\u003e\u003ctfoot\u003e\u003ctr\u003e\u003ctd colspan=\"6\"\u003eX\u003csub\u003e1\u003c/sub\u003e, X\u003csub\u003e2,\u003c/sub\u003e \u0026amp; X\u003csub\u003e3\u003c/sub\u003e represents concentration of span 40, concentration of cholesterol, and sonication time, respectively. S indicate significant, NS indicate non significant; \u003csup\u003ea\u003c/sup\u003ed.f. Indicates degree of freedom; The values of \"Prob \u0026gt; F\" less than 0.0500 indicate model terms are significant, while, values greater than 0.1000 indicate the model terms are not significant.\u003c/td\u003e\u003c/tr\u003e\u003c/tfoot\u003e\u003c/table\u003e\u003c/div\u003e \u003cp\u003e\u003c/p\u003e \u003cp\u003e \u003c/p\u003e \u003c/div\u003e \u003cdiv id=\"Sec21\" class=\"Section2\"\u003e \u003ch2\u003e3.3. Optimization of niosomes\u003c/h2\u003e \u003cp\u003eThe numerical optimization was performed based on the desirability ranges, software has suggested batch comprising all the targeted ranges of response. Surfactant to cholesterol concentration in the molar ratio of 2:1.25 and sonication time of 30 min yielded the best possible outcomes and was suggested as the optimized batch. The optimized VLF-noisome (OF) formulation was evaluated for the desired response. The Table\u0026nbsp;\u003cspan refid=\"Tab4\" class=\"InternalRef\"\u003e5\u003c/span\u003e represents the evaluation of noisome-VLF (OF) with the percentage of errors. The noisome-VLF (OF) exhibited the particle size (nm) = 264.2 ± 2.2, zeta potential (mV) = 49.2 ± 1.3, polydispersity index = 0.265 ± 0.15, and entrapment efficiency (%) = 70.25 ± 1.5. Further, the outcomes showed a strong correlation with the expected values, as shown with a low percentage of errors, suggesting the fitness of the model for the presented study.\u003c/p\u003e \u003cp\u003e \u003c/p\u003e\u003cdiv class=\"gridtable\"\u003e\u003cdiv align=\"left\" class=\"colspec\" colname=\"c1\" colnum=\"1\"\u003e\u003c/div\u003e\u003cdiv align=\"left\" class=\"colspec\" colname=\"c2\" colnum=\"2\"\u003e\u003c/div\u003e\u003cdiv align=\"left\" class=\"colspec\" colname=\"c3\" colnum=\"3\"\u003e\u003c/div\u003e\u003cdiv align=\"left\" class=\"colspec\" colname=\"c4\" colnum=\"4\"\u003e\u003c/div\u003e\u003cdiv align=\"left\" class=\"colspec\" colname=\"c5\" colnum=\"5\"\u003e\u003c/div\u003e\u003cdiv align=\"char\" char=\".\" class=\"colspec\" colname=\"c6\" colnum=\"6\"\u003e\u003c/div\u003e\u003cdiv align=\"char\" char=\"±\" class=\"colspec\" colname=\"c7\" colnum=\"7\"\u003e\u003c/div\u003e\u003cdiv align=\"char\" char=\".\" class=\"colspec\" colname=\"c8\" colnum=\"8\"\u003e\u003c/div\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\u003eResults of experiments for confirming optimization capability Mean ± SD; n = 3, \u003csup\u003ea\u003c/sup\u003ePercentage of error= (Actual value-predicted value)/ predicted value × 100\u003c/p\u003e \u003c/div\u003e \u003c/caption\u003e\u003ccolgroup cols=\"8\"\u003e\u003c/colgroup\u003e\u003cthead\u003e\u003ctr\u003e\u003cth align=\"left\" colname=\"c1\"\u003e \u003cp\u003eBatch\u003c/p\u003e \u003c/th\u003e\u003cth align=\"left\" colname=\"c2\"\u003e \u003cp\u003eConc. of Span 40,\u003c/p\u003e \u003cp\u003eX\u003csub\u003e1\u003c/sub\u003e\u003c/p\u003e \u003c/th\u003e\u003cth align=\"left\" colname=\"c3\"\u003e \u003cp\u003eConc. of Cholesterol\u003c/p\u003e \u003cp\u003eX\u003csub\u003e2\u003c/sub\u003e\u003c/p\u003e \u003c/th\u003e\u003cth align=\"left\" colname=\"c4\"\u003e \u003cp\u003eSonication time (min)\u003c/p\u003e \u003cp\u003eX\u003csub\u003e3\u003c/sub\u003e\u003c/p\u003e \u003c/th\u003e\u003cth align=\"left\" colname=\"c5\"\u003e \u003cp\u003eResponses\u003c/p\u003e \u003c/th\u003e\u003cth align=\"left\" colname=\"c6\"\u003e \u003cp\u003ePredicted\u003c/p\u003e \u003cp\u003evalues\u003c/p\u003e \u003c/th\u003e\u003cth align=\"left\" colname=\"c7\"\u003e \u003cp\u003eActual\u003c/p\u003e \u003cp\u003evalues\u003c/p\u003e \u003c/th\u003e\u003cth align=\"left\" colname=\"c8\"\u003e \u003cp\u003eError\u003csup\u003ea\u003c/sup\u003e\u003c/p\u003e \u003cp\u003e(%)\u003c/p\u003e \u003c/th\u003e\u003c/tr\u003e\u003c/thead\u003e\u003ctbody\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\" morerows=\"3\" rowspan=\"4\"\u003e \u003cp\u003e\u003cb\u003eOF\u003c/b\u003e\u003c/p\u003e \u003c/td\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u0026nbsp;\u003c/td\u003e\u003ctd align=\"left\" colname=\"c3\"\u003e\u0026nbsp;\u003c/td\u003e\u003ctd align=\"left\" colname=\"c4\"\u003e\u0026nbsp;\u003c/td\u003e\u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003eParticle size\u003c/p\u003e \u003cp\u003e(nm)\u003c/p\u003e \u003c/td\u003e\u003ctd align=\"left\" colname=\"c6\"\u003e \u003cp\u003e260.7\u003c/p\u003e \u003c/td\u003e\u003ctd align=\"left\" colname=\"c7\"\u003e \u003cp\u003e264.2 ± 2.2\u003c/p\u003e \u003c/td\u003e\u003ctd align=\"left\" colname=\"c8\"\u003e \u003cp\u003e1.34\u003c/p\u003e \u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e2.40\u003c/p\u003e \u003c/td\u003e\u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e1.20\u003c/p\u003e \u003c/td\u003e\u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e18.00\u003c/p\u003e \u003c/td\u003e\u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003eZeta potential (mV)\u003c/p\u003e \u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c6\"\u003e \u003cp\u003e48.7\u003c/p\u003e \u003c/td\u003e\u003ctd align=\"char\" char=\"±\" colname=\"c7\"\u003e \u003cp\u003e49.2 ± 1.3\u003c/p\u003e \u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c8\"\u003e \u003cp\u003e1.02\u003c/p\u003e \u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u0026nbsp;\u003c/td\u003e\u003ctd align=\"left\" colname=\"c3\"\u003e\u0026nbsp;\u003c/td\u003e\u003ctd align=\"left\" colname=\"c4\"\u003e\u0026nbsp;\u003c/td\u003e\u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003ePolydispersity Index\u003c/p\u003e \u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c6\"\u003e \u003cp\u003e0.255\u003c/p\u003e \u003c/td\u003e\u003ctd align=\"char\" char=\"±\" colname=\"c7\"\u003e \u003cp\u003e0.265 ± 0.15\u003c/p\u003e \u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c8\"\u003e \u003cp\u003e3.92\u003c/p\u003e \u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u0026nbsp;\u003c/td\u003e\u003ctd align=\"left\" colname=\"c3\"\u003e\u0026nbsp;\u003c/td\u003e\u003ctd align=\"left\" colname=\"c4\"\u003e\u0026nbsp;\u003c/td\u003e\u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003eEntrapment efficiency (%)\u003c/p\u003e \u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c6\"\u003e \u003cp\u003e67.88\u003c/p\u003e \u003c/td\u003e\u003ctd align=\"char\" char=\"±\" colname=\"c7\"\u003e \u003cp\u003e70.25 ± 1.5\u003c/p\u003e \u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c8\"\u003e \u003cp\u003e2.37\u003c/p\u003e \u003c/td\u003e\u003c/tr\u003e\u003c/tbody\u003e\u003c/table\u003e\u003c/div\u003e \u003cp\u003e\u003c/p\u003e \u003c/div\u003e \u003cdiv id=\"Sec22\" class=\"Section2\"\u003e \u003ch2\u003e3.4. Powder x-ray diffraction (PXRD)\u003c/h2\u003e \u003cp\u003ePXRD spectra of VLF (pure), physical mixture, and niosomes-VLF are illustrated in Fig.\u0026nbsp;\u003cspan refid=\"Fig3\" class=\"InternalRef\"\u003e3\u003c/span\u003e. PXRD spectra of VLF (pure) showed sharp peaks around 2 theta indicating crystalline nature of VLF. The PXRD spectrum of physical mixture shows slight decrease in the intensity and merging of the peaks. In physical mixture, there is mixing of the formulation excipients which reduces the purity of each excipients and drug and results in merging of peaks, but may not necessarily represent compatibility problems. The XRD spectra of niosomes show significant decrease in the total number of peaks. There was a reduction in the intensity of peaks along with merging of peaks. This signifies the formation of niosomes and encapsulation of drug inside the vesicles. The reduction in the intensity of peaks might indicate the VLF is converted from crystalline to semi-crystalline-amorphous form. In the XRD spectra of niosomes few merged peaks can be seen which may be of the un-encapsulated drug adsorbed over the niosomes surface.\u003c/p\u003e \u003cp\u003e \u003c/p\u003e \u003c/div\u003e \u003cdiv id=\"Sec23\" class=\"Section2\"\u003e \u003ch2\u003e3.5. Preparation of niosomes-VLF TISG\u003c/h2\u003e \u003cp\u003eNiosomes-VLF (OF) was added to a TISG formulation to make it easier to apply, extend the time the drug remained on the nasal mucosal surface after application, and ultimately increase drug penetration through the nasal mucosa. To achieve this, niosomes-VLF TISG were prepared using \u003cem\u003ein situ\u003c/em\u003e gelling systems based on Poloxamer 407 at different concentrations, ranging from 16.5 to 18.5% w/w, in conjunction with Poloxamer 188 (1% w/w).Furthermore, an optimized formula for the niosomes-VLF TISG was obtained by examining and implementing the effect of co-polymer concentration on the formulation attributes. Notably, the co-polymer concentrations utilized were chosen in light of our initial experiments.\u003c/p\u003e \u003c/div\u003e \u003cdiv id=\"Sec24\" class=\"Section2\"\u003e \u003ch2\u003e3.6. Characterization of niosomes-VLF TISG\u003c/h2\u003e \u003cp\u003eThe characterization of niosomes-VLF TISG batches is illustrated in Table\u0026nbsp;\u003cspan refid=\"Tab5\" class=\"InternalRef\"\u003e3\u003c/span\u003e. Gelling temperature of all three formulation batches were in the range of 37 ± 0.5\u003csup\u003e°\u003c/sup\u003eCto 39 ± 0.5\u003csup\u003e\u003cb\u003e°\u003c/b\u003e\u003c/sup\u003e\u003cb\u003eC\u003c/b\u003e. Gelling temperature of niosomes-VLF TISG (OF-A) batch was found to be 37 ± 0.5\u003csup\u003e°\u003c/sup\u003eC which might be due to the presence of Poloxamer-407 in lowest concentration. Further, rise in Poloxamer-407 concentration leads to increase in gelation temperature [\u003cspan citationid=\"CR34\" class=\"CitationRef\"\u003e34\u003c/span\u003e],[\u003cspan citationid=\"CR27\" class=\"CitationRef\"\u003e27\u003c/span\u003e]. Higher numbers of the polymeric micelles are formed by increasing the Poloxamer-407 concentration[\u003cspan citationid=\"CR25\" class=\"CitationRef\"\u003e25\u003c/span\u003e].Gelling time of the niosomes-VLF TISG (OF-A) batch was found to be 23 ± 2.2s, less among the other batches and satisfactory and in accordance with the already available literature data [\u003cspan citationid=\"CR28\" class=\"CitationRef\"\u003e28\u003c/span\u003e].The viscosity of all three batches at 35\u003csup\u003e°\u003c/sup\u003eC (4526 ± 142 to 6274 ± 102 cps) was found to be good and in accordance with the reported number. The observation suggested increased in the viscosity with rise in Poloxamer-407 concentration. Mucoadhesive strength (dyne/cm) was found to 3589 ± 65 to 4222 ± 57 indicating higher mucoadhesion owing to chitosan coating which imparts positive charge over the surface of niosomes and interaction of electrostatic forces between the positive charge of chitosan and negative charge of sialic acid of the cells of mucosal membrane thereby enhancing muco-adhesion[\u003cspan citationid=\"CR35\" class=\"CitationRef\"\u003e35\u003c/span\u003e], [\u003cspan citationid=\"CR36\" class=\"CitationRef\"\u003e36\u003c/span\u003e], [\u003cspan citationid=\"CR37\" class=\"CitationRef\"\u003e37\u003c/span\u003e]. The drug content (%) was in the range of 83 ± 3.6 to 88 ± 5.4. The higher drug content was reported in the batch OF-A. The observation revealed that among all three batches, niosomes-VLF TISG (OF-A) batch was showed excellent properties and therefore selected for further study.\u003c/p\u003e \u003cp\u003e \u003c/p\u003e\u003cdiv class=\"gridtable\"\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\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\u003cdiv align=\"char\" char=\"±\" class=\"colspec\" colname=\"c8\" colnum=\"8\"\u003e\u003c/div\u003e\u003cdiv align=\"char\" char=\"±\" class=\"colspec\" colname=\"c9\" colnum=\"9\"\u003e\u003c/div\u003e\u003ctable float=\"Yes\" id=\"Tab5\" border=\"1\"\u003e\u003ccaption language=\"En\"\u003e \u003cdiv class=\"CaptionNumber\"\u003eTable 3\u003c/div\u003e \u003cdiv class=\"CaptionContent\"\u003e \u003cp\u003eThe formulation batches of niosomes-VLF TISG\u003c/p\u003e \u003c/div\u003e \u003c/caption\u003e\u003ccolgroup cols=\"9\"\u003e\u003c/colgroup\u003e\u003cthead\u003e\u003ctr\u003e\u003cth align=\"left\" colname=\"c1\"\u003e \u003cp\u003eBatch No.\u003c/p\u003e \u003c/th\u003e\u003cth align=\"left\" colname=\"c2\"\u003e \u003cp\u003eConc. of Poloxamer 407 (%)\u003c/p\u003e \u003c/th\u003e\u003cth align=\"left\" colname=\"c3\"\u003e \u003cp\u003eConc. of Poloxamer 188 (%)\u003c/p\u003e \u003c/th\u003e\u003cth align=\"left\" colname=\"c4\"\u003e \u003cp\u003eConc. of HPMC K4M (%)\u003c/p\u003e \u003c/th\u003e\u003cth align=\"left\" colname=\"c5\"\u003e \u003cp\u003eGelling temperature (\u003csup\u003eo\u003c/sup\u003eC)\u003c/p\u003e \u003c/th\u003e\u003cth align=\"left\" colname=\"c6\"\u003e \u003cp\u003eGelling time (s)\u003c/p\u003e \u003c/th\u003e\u003cth align=\"left\" colname=\"c7\"\u003e \u003cp\u003eViscosity at 35 \u003csup\u003eo\u003c/sup\u003eC (cps)\u003c/p\u003e \u003c/th\u003e\u003cth align=\"left\" colname=\"c8\"\u003e \u003cp\u003eMucoadhesive strength (dyne/cm)\u003c/p\u003e \u003c/th\u003e\u003cth align=\"left\" colname=\"c9\"\u003e \u003cp\u003eDrug content (%)\u003c/p\u003e \u003c/th\u003e\u003c/tr\u003e\u003ctr\u003e\u003cth align=\"left\" colname=\"c1\"\u003e \u003cp\u003eOF-A\u003c/p\u003e \u003c/th\u003e\u003cth align=\"left\" colname=\"c2\"\u003e \u003cp\u003e16.5\u003c/p\u003e \u003c/th\u003e\u003cth align=\"left\" colname=\"c3\"\u003e \u003cp\u003e1\u003c/p\u003e \u003c/th\u003e\u003cth align=\"left\" colname=\"c4\"\u003e \u003cp\u003e0.5\u003c/p\u003e \u003c/th\u003e\u003cth align=\"left\" colname=\"c5\"\u003e \u003cp\u003e37 ± 0.5\u003c/p\u003e \u003c/th\u003e\u003cth align=\"left\" colname=\"c6\"\u003e \u003cp\u003e23 ± 2.2\u003c/p\u003e \u003c/th\u003e\u003cth align=\"left\" colname=\"c7\"\u003e \u003cp\u003e4526 ± 142\u003c/p\u003e \u003c/th\u003e\u003cth align=\"left\" colname=\"c8\"\u003e \u003cp\u003e3589 ± 65\u003c/p\u003e \u003c/th\u003e\u003cth align=\"left\" colname=\"c9\"\u003e \u003cp\u003e88 ± 5.4\u003c/p\u003e \u003c/th\u003e\u003c/tr\u003e\u003c/thead\u003e\u003ctbody\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eOF-B\u003c/p\u003e \u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e \u003cp\u003e17.5\u003c/p\u003e \u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e \u003cp\u003e1\u003c/p\u003e \u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e \u003cp\u003e0.5\u003c/p\u003e \u003c/td\u003e\u003ctd align=\"char\" char=\"±\" colname=\"c5\"\u003e \u003cp\u003e38 ± 1.0\u003c/p\u003e \u003c/td\u003e\u003ctd align=\"char\" char=\"±\" colname=\"c6\"\u003e \u003cp\u003e42 ± 2.5\u003c/p\u003e \u003c/td\u003e\u003ctd align=\"char\" char=\"±\" colname=\"c7\"\u003e \u003cp\u003e5614 ± 156\u003c/p\u003e \u003c/td\u003e\u003ctd align=\"char\" char=\"±\" colname=\"c8\"\u003e \u003cp\u003e3874 ± 34\u003c/p\u003e \u003c/td\u003e\u003ctd align=\"char\" char=\"±\" colname=\"c9\"\u003e \u003cp\u003e84 ± 4.7\u003c/p\u003e \u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eOF-C\u003c/p\u003e \u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e \u003cp\u003e18.5\u003c/p\u003e \u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e \u003cp\u003e1\u003c/p\u003e \u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e \u003cp\u003e0.5\u003c/p\u003e \u003c/td\u003e\u003ctd align=\"char\" char=\"±\" colname=\"c5\"\u003e \u003cp\u003e39 ± 0.5\u003c/p\u003e \u003c/td\u003e\u003ctd align=\"char\" char=\"±\" colname=\"c6\"\u003e \u003cp\u003e65 ± 1.5\u003c/p\u003e \u003c/td\u003e\u003ctd align=\"char\" char=\"±\" colname=\"c7\"\u003e \u003cp\u003e6274 ± 102\u003c/p\u003e \u003c/td\u003e\u003ctd align=\"char\" char=\"±\" colname=\"c8\"\u003e \u003cp\u003e4222 ± 57\u003c/p\u003e \u003c/td\u003e\u003ctd align=\"char\" char=\"±\" colname=\"c9\"\u003e \u003cp\u003e83 ± 3.6\u003c/p\u003e \u003c/td\u003e\u003c/tr\u003e\u003c/tbody\u003e\u003ctfoot\u003e\u003ctr\u003e\u003ctd colspan=\"9\"\u003eAll the results are expressed as mean ± standard deviation (n = 3).\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd colspan=\"9\"\u003eOF-A; Selected batch (highlighted)\u003c/td\u003e\u003c/tr\u003e\u003c/tfoot\u003e\u003c/table\u003e\u003c/div\u003e \u003cp\u003e\u003c/p\u003e \u003c/div\u003e \u003cdiv id=\"Sec25\" class=\"Section2\"\u003e \u003ch2\u003e3.7. \u003cem\u003eEx-vivo\u003c/em\u003e drug diffusion tudy\u003c/h2\u003e \u003cp\u003eThe \u003cem\u003eex-vivo\u003c/em\u003e drug diffusion of niosomes-VLF TISG (OF-A)and VLF suspension is illustrated in Fig.\u0026nbsp;\u003cspan refid=\"Fig4\" class=\"InternalRef\"\u003e4\u003c/span\u003e.The drug diffusion (%) of niosomes-VLF TISG(OF-A) after 24 h was found to be 94.76%. Diffusion mechanism showed best fitting for Highuchi model suggested sustained drug delivery. The drug diffusion for VLF suspension was less owing to its low aqueous solubility. The higher diffusion of developed formulation indicated improvement in the solubility resulting into increment in the rate of diffusion.\u003c/p\u003e \u003cp\u003e \u003c/p\u003e \u003c/div\u003e \u003cdiv id=\"Sec26\" class=\"Section2\"\u003e \u003ch2\u003e3.8. \u003cem\u003eIn-vivo\u003c/em\u003e pharmacokinetics\u003c/h2\u003e \u003cp\u003eThe VLF plasma concentration versus time profile after intranasal administration of niosomes-VLF TISG (OF-A) and VLF suspension are shown in the Fig.\u0026nbsp;\u003cspan refid=\"Fig5\" class=\"InternalRef\"\u003e5\u003c/span\u003e. The pharmacokinetic parameters are elaborated in Table\u0026nbsp;\u003cspan refid=\"Tab6\" class=\"InternalRef\"\u003e6\u003c/span\u003e. After the administration of a single niosomes-VLF TISG (OF-A), the total concentration of the drug in the brain was found to be 158.4 ± 4 ng/mL, which was significantly higher than VLF suspension (62.5 ± 1.53 ng/mL), (p value \u0026lt; 0.05). There was an increase in the total AUC value for intranasal niosomes (1046.59 ± 30.80ng/mL*h) as compared to VLF suspension (408.82 ± 7.27ng/mL*h). Mean residence time in the brain was also prolonged for the niosomes (7.10 h) as compared with the VLF suspension (4.06 h) contributing to higher half life. This higher C\u003csub\u003emax\u003c/sub\u003eof VLF from niosomes-VLF TISG (OF-A) formulation in the brain is due to higher permeability of the niosomes across cell membrane which facilitates passive diffusion and endocytosis across cells. This signifies the potential of the niosomal-VLF TSIG (OF) for targeting brain via intranasal route. T\u003csub\u003emax\u003c/sub\u003e for the VLF suspension was found to be 2 h which was less as compared to the VLF niosomes having T\u003csub\u003emax\u003c/sub\u003e of 3 h. This might be due to higher viscosity of the \u003cem\u003ein-situ\u003c/em\u003e gel as compared to standard VLF suspension in the nasal cavity which hampers and delays release of the niosomes from the formulation [\u003cspan citationid=\"CR38\" class=\"CitationRef\"\u003e38\u003c/span\u003e]. The C\u003csub\u003emax\u003c/sub\u003e in the plasma for niosomes was found to be 91.70 ± 0.62 ng/mL at T\u003csub\u003emax\u003c/sub\u003e of 4h. This may be due to the removal of the gel from the nasal respiratory region by the sniffing action of rat before the gelation of the formulation contributing to lower C\u003csub\u003emax\u003c/sub\u003e value as compared to brain concentration. The total AUC in the plasma was 691.83 ± 30.95 ng/mL*h which was significantly higher as compared to standard VLF suspension (463.18 ± 11.56ng/mL*h) proving the enhanced permeability of the developed system across epithelial cell membrane. T\u003csub\u003emax\u003c/sub\u003e for standard intranasal VLF suspension was found to be 2 h. This could be due to higher viscosity of the \u003cem\u003ein-situ\u003c/em\u003e gel formulation as compared to standard intranasal VLF suspension which delays the release of drug from gel.\u003c/p\u003e \u003cp\u003e \u003c/p\u003e \u003cp\u003e \u003c/p\u003e\u003cdiv class=\"gridtable\"\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\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\u003ePharmacokinetic parameter of Niosomes VLF TSIG (OF-A) and VLF (pure) All the results are expressed as mean ± standard deviation (n = 3).\u003c/p\u003e \u003c/div\u003e \u003c/caption\u003e\u003ccolgroup cols=\"5\"\u003e\u003c/colgroup\u003e\u003cthead\u003e\u003ctr\u003e\u003cth align=\"left\" colname=\"c1\"\u003e \u003cp\u003eParameters\u003c/p\u003e \u003c/th\u003e\u003cth align=\"left\" colspan=\"2\" nameend=\"c3\" namest=\"c2\"\u003e \u003cp\u003eNiosomes VLF TSIG (OF-A)\u003c/p\u003e \u003c/th\u003e\u003cth align=\"left\" colspan=\"2\" nameend=\"c5\" namest=\"c4\"\u003e \u003cp\u003eVLF (Pure)\u003c/p\u003e \u003c/th\u003e\u003c/tr\u003e\u003c/thead\u003e\u003ctbody\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u0026nbsp;\u003c/td\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003eBrain\u003c/p\u003e \u003c/td\u003e\u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003ePlasma\u003c/p\u003e \u003c/td\u003e\u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003eBrain\u003c/p\u003e \u003c/td\u003e\u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003ePlasma\u003c/p\u003e \u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eC\u003csub\u003emax\u003c/sub\u003e (ng/ml)\u003c/p\u003e \u003c/td\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e158.4 ± 4\u003c/p\u003e \u003c/td\u003e\u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e91.70 ± 0.62\u003c/p\u003e \u003c/td\u003e\u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e62.5 ± 1.53\u003c/p\u003e \u003c/td\u003e\u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e71.74 ± 2.14\u003c/p\u003e \u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eT\u003csub\u003emax\u003c/sub\u003e (h)\u003c/p\u003e \u003c/td\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e3\u003c/p\u003e \u003c/td\u003e\u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e4\u003c/p\u003e \u003c/td\u003e\u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e2\u003c/p\u003e \u003c/td\u003e\u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e1\u003c/p\u003e \u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eAUC\u003csub\u003e0 − t\u003c/sub\u003e (ng/ml*h)\u003c/p\u003e \u003c/td\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e1046.59 ± 30.80\u003c/p\u003e \u003c/td\u003e\u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e691.83 ± 30.95\u003c/p\u003e \u003c/td\u003e\u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e408.82 ± 7.27\u003c/p\u003e \u003c/td\u003e\u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e463.18 ± 11.56\u003c/p\u003e \u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eT\u003csub\u003e1/2\u003c/sub\u003e(h)\u003c/p\u003e \u003c/td\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e4.405\u003c/p\u003e \u003c/td\u003e\u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e2.421\u003c/p\u003e \u003c/td\u003e\u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e1.195\u003c/p\u003e \u003c/td\u003e\u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e1.173\u003c/p\u003e \u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eMRT (h)\u003c/p\u003e \u003c/td\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e7.10\u003c/p\u003e \u003c/td\u003e\u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e5.23\u003c/p\u003e \u003c/td\u003e\u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e4.06\u003c/p\u003e \u003c/td\u003e\u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e3.986\u003c/p\u003e \u003c/td\u003e\u003c/tr\u003e\u003c/tbody\u003e\u003c/table\u003e\u003c/div\u003e \u003cp\u003e\u003c/p\u003e \u003cp\u003e \u003c/p\u003e\u003cdiv class=\"gridtable\"\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\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\u003eAccelerated stability studies of Niosome VLF TISG (OF-A)\u003c/p\u003e \u003c/div\u003e \u003c/caption\u003e\u003ccolgroup cols=\"5\"\u003e\u003c/colgroup\u003e\u003cthead\u003e\u003ctr\u003e\u003cth align=\"left\" colname=\"c1\"\u003e \u003cp\u003eSr. No.\u003c/p\u003e \u003c/th\u003e\u003cth align=\"left\" colname=\"c2\"\u003e \u003cp\u003eParameters\u003c/p\u003e \u003c/th\u003e\u003cth align=\"left\" colspan=\"3\" nameend=\"c5\" namest=\"c3\"\u003e \u003cp\u003eMonths\u003c/p\u003e \u003c/th\u003e\u003c/tr\u003e\u003ctr\u003e\u003cth align=\"left\" colname=\"c1\"\u003e\u0026nbsp;\u003c/th\u003e\u003cth align=\"left\" colname=\"c2\"\u003e\u0026nbsp;\u003c/th\u003e\u003cth align=\"left\" colname=\"c3\"\u003e \u003cp\u003e0\u003c/p\u003e \u003c/th\u003e\u003cth align=\"left\" colname=\"c4\"\u003e \u003cp\u003e3\u003c/p\u003e \u003c/th\u003e\u003cth align=\"left\" colname=\"c5\"\u003e \u003cp\u003e6\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\u003eGelling time (s)\u003c/p\u003e \u003c/td\u003e\u003ctd align=\"char\" char=\"±\" colname=\"c3\"\u003e \u003cp\u003e23 ± 2.2\u003c/p\u003e \u003c/td\u003e\u003ctd align=\"char\" char=\"±\" colname=\"c4\"\u003e \u003cp\u003e22 ± 1.4\u003c/p\u003e \u003c/td\u003e\u003ctd align=\"char\" char=\"±\" colname=\"c5\"\u003e \u003cp\u003e22 ± 0.8\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\u003eGelling temperature (\u003csup\u003eo\u003c/sup\u003eC)\u003c/p\u003e \u003c/td\u003e\u003ctd align=\"char\" char=\"±\" colname=\"c3\"\u003e \u003cp\u003e37 ± 0.5\u003c/p\u003e \u003c/td\u003e\u003ctd align=\"char\" char=\"±\" colname=\"c4\"\u003e \u003cp\u003e36 ± 1.6\u003c/p\u003e \u003c/td\u003e\u003ctd align=\"char\" char=\"±\" colname=\"c5\"\u003e \u003cp\u003e36 ± 0.8\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\u003eViscosity (cps)\u003c/p\u003e \u003c/td\u003e\u003ctd align=\"char\" char=\"±\" colname=\"c3\"\u003e \u003cp\u003e4526 ± 142\u003c/p\u003e \u003c/td\u003e\u003ctd align=\"char\" char=\"±\" colname=\"c4\"\u003e \u003cp\u003e4414 ± 120\u003c/p\u003e \u003c/td\u003e\u003ctd align=\"char\" char=\"±\" colname=\"c5\"\u003e \u003cp\u003e4402 ± 65\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\u003eMucoadhesive Strength (dyne/cm)\u003c/p\u003e \u003c/td\u003e\u003ctd align=\"char\" char=\"±\" colname=\"c3\"\u003e \u003cp\u003e3589 ± 65\u003c/p\u003e \u003c/td\u003e\u003ctd align=\"char\" char=\"±\" colname=\"c4\"\u003e \u003cp\u003e3416 ± 42\u003c/p\u003e \u003c/td\u003e\u003ctd align=\"char\" char=\"±\" colname=\"c5\"\u003e \u003cp\u003e3410 ± 32\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\u003eDrug content (%)\u003c/p\u003e \u003c/td\u003e\u003ctd align=\"char\" char=\"±\" colname=\"c3\"\u003e \u003cp\u003e88 ± 5.4\u003c/p\u003e \u003c/td\u003e\u003ctd align=\"char\" char=\"±\" colname=\"c4\"\u003e \u003cp\u003e87 ± 2.3\u003c/p\u003e \u003c/td\u003e\u003ctd align=\"char\" char=\"±\" colname=\"c5\"\u003e \u003cp\u003e87 ± 1.1\u003c/p\u003e \u003c/td\u003e\u003c/tr\u003e\u003c/tbody\u003e\u003ctfoot\u003e\u003ctr\u003e\u003ctd colspan=\"5\"\u003eAll the results are expressed as mean ± standard deviation (n = 3).\u003c/td\u003e\u003c/tr\u003e\u003c/tfoot\u003e\u003c/table\u003e\u003c/div\u003e \u003cp\u003e\u003c/p\u003e \u003c/div\u003e \u003cdiv id=\"Sec27\" class=\"Section2\"\u003e \u003ch2\u003e3.9. Accelerated stability studies\u003c/h2\u003e \u003cp\u003eFrom the stability studies of niosomes-VLF TISG (OF-A) under controlled conditions of 25 ± 2°C and 60 ± 5% RH over a period 6 months, the study concluded that the developed formulation was stable and maintained its physical and chemical characteristics over the period indicating its robustness. Quality characteristics evaluated, such as drug content, showed a slight decline from 88 ± 5.4% to 87 ± 1.1, suggesting a marginal loss of drug in the formulation. The other quality attributes such as gelling temperature, gelling time, viscosity, and mucoadhesive strength showed no significant changes from their original value, indicating stability over a period of 6 months. These results collectively suggested the robustness of the VLF-niosomal TISG (OF-A) and reliability as a drug delivery system.\u003c/p\u003e \u003c/div\u003e"},{"header":"Conclusion","content":"\u003cp\u003eNiosomes-VLF TISG (OF-A) was successfully developed and evaluated. Optimization of niosomes–VLF was using a Box-Behnken design. The numerical optimization assisted in getting the optimized noisome formulation based on the desirability criteria. The niosomes optimized formulation was successfully incorporated into the TISG. The niosomes-VLF TISG (OF-A) indicated a better characteristics. \u003cem\u003eEx-vivo\u003c/em\u003e diffusion study revealed 94.76% drug permeation across the goat nasal mucosa. The pharmacokinetics study revealed higher brain drug concentration following intranasal administration of niosomes-VLF TISG (OF-A) compared to standard intranasal VLF pure suspension. The developed niosomes-VLF TISG (OF-A) system showed a good alternative for sustained and targeted delivery of VLF to the brain for depressive disorder.\u003c/p\u003e"},{"header":"Declarations","content":"\u003cp\u003e\u003cstrong\u003eAcknowledgment\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003eWe are grateful to Enaltec Labs Pvt. Ltd., (Mumbai, India) andGenni Chem Healthcare Pvt. Ltd., (Mumbai, India) for the gift sample of drug and excipients.\u0026nbsp;\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eEthical Approval\u0026nbsp;\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003eAnimal protocol was approved by the Institutional Animal Ethics Committee of Dadasaheb Balpande College of Pharmacy, Besa, Nagpur, India (Protocol approval number\u0026nbsp;DBCOP/IAEC/1426/2022-23/P-10). The study protocol was followed as per the CPCSEA ARRIVE guidelines for laboratory animals.\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eCompeting interests\u003c/strong\u003e The authors declare no competing interests.\u0026nbsp;\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eConflict of Interest\u003c/strong\u003e None\u003c/p\u003e\n\u003ch2\u003eFunding\u003c/h2\u003e \u003cp\u003eNo funding received for this project\u003c/p\u003e\u003ch2\u003eAuthor Contribution\u003c/h2\u003e\u003cp\u003ePurushottam Gangane: Conceptualization, Supervision, Resources, Funding acquisition, Project administration, reviewing of original draft preparation, Mandar Tool: Methodology, Formal analysis, Data curation, Validation, Sachin More: Methodology, Formal analysis, Amol Warokar: Formal analysis, Kishor Salunkhe : Supervision, Formal analysis, Pankaj Dangre: Software, Formal analysis, Writing-reviewing and editing, Visualization, Investigation.\u003c/p\u003e"},{"header":"References","content":"\u003col\u003e\n\u003cli\u003eHao, Y., Ge, H., Sun, M., \u0026amp; Gao, Y. (2019). Selecting an Appropriate Animal Model of Depression. \u003cem\u003eInternational Journal of Molecular Sciences\u003c/em\u003e, \u003cem\u003e20\u003c/em\u003e(19), 4827. https://doi.org/10.3390/ijms20194827\u003c/li\u003e\n\u003cli\u003eSwetlitz, N. (2021). Depression\u0026rsquo;s problem with men. \u003cem\u003eAMA Journal of Ethics\u003c/em\u003e. https://doi.org/10.1001/amajethics.2021.586\u003c/li\u003e\n\u003cli\u003eZhdanava, M., Pilon, D., Ghelerter, I., Chow, W., Joshi, K., Lefebvre, P., \u0026amp; Sheehan, J. J. (2021). The Prevalence and National Burden of Treatment-Resistant Depression and Major Depressive Disorder in the United States. \u003cem\u003eJournal of Clinical Psychiatry\u003c/em\u003e, \u003cem\u003e82\u003c/em\u003e(2). https://doi.org/10.4088/JCP.20M13699\u003c/li\u003e\n\u003cli\u003eSagar, R., Dandona, R., Gururaj, G., Dhaliwal, R. S., Singh, A., Ferrari, A., \u0026hellip; Dandona, L. (2020). 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Intranasal delivery of Clozapine using nanoemulsion-based in-situ gels: An approach for bioavailability enhancement. \u003cem\u003eSaudi Pharmaceutical Journal\u003c/em\u003e, \u003cem\u003e29\u003c/em\u003e(12). https://doi.org/10.1016/j.jsps.2021.11.006\u003c/li\u003e\n\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":"Venlafaxine, depression, niosomes, in-situ gel, optimization","lastPublishedDoi":"10.21203/rs.3.rs-5028833/v1","lastPublishedDoiUrl":"https://doi.org/10.21203/rs.3.rs-5028833/v1","license":{"name":"CC BY 4.0","url":"https://creativecommons.org/licenses/by/4.0/"},"manuscriptAbstract":"\u003cp\u003eVenlafaxine (VLF) is the most commonly used drug for the treatment of depressive disorder. The oral bioavailability of VLF is low. Therefore, the present study emphasized the development of niosomes formulation for solubility and permeation improvement. The niosome-VLF was formulated using a thin film hydration technique employing different molar ratios of Span 40 and cholesterol. The optimization of niosomes was performed using the Box-Behnken screening model, which employs numerical optimization. The optimized niosmoes-VLF showed Particle size: 264.2\u0026thinsp;\u0026plusmn;\u0026thinsp;2.2 nm; Zeta potential: 49.2\u0026thinsp;\u0026plusmn;\u0026thinsp;1.3 mV; Polydispersity Index: 0.265\u0026thinsp;\u0026plusmn;\u0026thinsp;0.15; Entrapment efficiency: 70.25\u0026thinsp;\u0026plusmn;\u0026thinsp;1.5%. The noisome-VLF (OF) was incorporated into the thermosensitive \u003cem\u003ein situ\u003c/em\u003e gel (TISG). The noisome-VLF TISG (OF-A) showed gelling temperature: 37\u0026thinsp;\u0026plusmn;\u0026thinsp;0.5\u003csup\u003eo\u003c/sup\u003eC; gelling time: 23\u0026thinsp;\u0026plusmn;\u0026thinsp;2.2s; viscosity: 4526\u0026thinsp;\u0026plusmn;\u0026thinsp;142 cps; mucoadhesive strength: 3589\u0026thinsp;\u0026plusmn;\u0026thinsp;65 dyne/cm, drug content: 88\u0026thinsp;\u0026plusmn;\u0026thinsp;5.4%. The \u003cem\u003ein vivo\u003c/em\u003e pharmacokinetic study revealed a higher concentration of VLF in developed noisome-VLF TISG (OF-A) formulation than VLF suspension. The higher and sustained concentration of VLF in brain and plasma suggested a better therapeutic approach to counteract a chronic depressive disorder. Further, the accelerated stability studies of noisome-VLF TISG (OF-A) indicated good physical and chemical attributes. Therefore, intranasal noisome-VLF TISG (OF-A) can be sorted as an alternative approach for targeting the brain for the effective management of CNS conditions like depression.\u003c/p\u003e","manuscriptTitle":"Design and optimization of venlafaxine niosomes loaded thermosensitive in- situ gel for prolonging intranasal residence in depressive disorder","msid":"","msnumber":"","nonDraftVersions":[{"code":1,"date":"2024-10-08 08:04:17","doi":"10.21203/rs.3.rs-5028833/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":"8a2d0eab-598d-4f8e-a468-c9d33aaf4013","owner":[],"postedDate":"October 8th, 2024","published":true,"recentEditorialEvents":[],"rejectedJournal":[],"revision":"","amendment":"","status":"posted","subjectAreas":[],"tags":[],"updatedAt":"2024-10-31T02:53:26+00:00","versionOfRecord":[],"versionCreatedAt":"2024-10-08 08:04:17","video":"","vorDoi":"","vorDoiUrl":"","workflowStages":[]},"version":"v1","identity":"rs-5028833","journalConfig":"researchsquare"},"__N_SSP":true},"page":"/article/[identity]/[[...version]]","query":{"redirect":"/article/rs-5028833","identity":"rs-5028833","version":["v1"]},"buildId":"qtupq5eGEP_6zYnWcrvyt","isFallback":false,"isExperimentalCompile":false,"dynamicIds":[84888],"gssp":true,"scriptLoader":[]}