Quercetin and resveratrol loaded polymeric nanoparticle for colorectal cancer as pH sensitive approach

Quercetin and resveratrol loaded polymeric nanoparticle for colorectal cancer as pH sensitive approach | 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 Quercetin and resveratrol loaded polymeric nanoparticle for colorectal cancer as pH sensitive approach Kumar Janakiraman, Vaidevi Sethuraman, Geethanjali Sampath, Venkateshwaran Krishnaswami This is a preprint; it has not been peer reviewed by a journal. https://doi.org/ 10.21203/rs.3.rs-7204934/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 Colorectal cancer (CRC) continues to pose a significant global health challenge, requiring sophisticated tailored therapy with less adverse effects. Resveratrol and quercetin, two bioactive flavonoids, exhibit significant anticancer effects but are hindered by low bioavailability and quick degradation. This research details the development of pH-sensitive polymeric nanoparticles (NPs) co-encapsulating resveratrol and quercetin, utilizing poly (lactic-co-glycolic acid) (PLGA) and Eudragit S100 for targeted colorectal cancer therapy. The optimized QRNPs exhibited particle size of 174–177 nm, a zeta potential around −22 to −24 mV, and encapsulation efficiency exceeding 80%. In vitro, drug release studies demonstrated minimal release at pH 7.4 but increased release at acidic pH (5.5), which is consistent with tumor microenvironments. Cytotoxicity assays in Caco-2 colon cancer cells revealed significantly enhanced cytotoxicity of QRNPs compared to free drugs, with CC₅₀ values of 48.84 µg/mL (24 h) and 32.75 µg/mL (48 h). FTIR confirmed drug–polymer compatibility, and HR-TEM analysis showed uniform spherical morphology. Stability tests in simulated GI fluids validated formulation robustness. Furthermore, fluorescence imaging and Annexin V-FITC assays confirmed augmented cellular uptake and elevated apoptosis. Mechanistic investigations revealed a downregulation of the anti-apoptotic protein Bcl-2 and an overexpression of the pro-apoptotic proteins Bax and caspase-3. The results underscore the promise of resveratrol-quercetin co-loaded pH-sensitive polymeric nanoparticles as a viable method for targeted colorectal cancer therapy. Colorectal Cancer Quercetin Resveratrol pH-sensitive nanoparticles Drug delivery Cytotoxicity Figures Figure 1 Figure 2 Figure 3 Figure 4 Figure 5 Figure 6 Figure 7 Figure 8 Figure 9 1. INTRODUCTION 1.1 Colon cancer Colon cancer ranking eighth in India has been reported globally for approximately 551,269 death cases in 2018. Nearly 60% cases are encountered in developed countries [ 1 ] .According to ICMR report, 2013, the highest annual incidence rate for this dreadful disease was recorded in Nagaland (5.2) and Thiruvananthapuram (4.1) in men and women respectively. Almost 96% of colon cancer were adenocarcinomas, 2% were specified carcinomas and 0.4% were epidermoid carcinomas and 0.08% were sarcomas [ 2 ]. Treating this harmful disease through oral drug administration is the dynamic area of research nowadays. Currently, bioactive molecules of natural origin are utilized by 70% of world population because of its promising activity in preventing degenerative disease such as cancer[ 3 ]. Recently, [ 4 ]reported a list of medicinal plants and its bioactive compounds against colorectal cancer, which, includes flavonoids, polyphenols, artemisinin, gallic acid, m-coumaric acid, gentistic acid, curcumin, salicin and oleuropein etc. Oral administration of active pharmaceutical ingredients for colon cancer treatment, results in its higher absorption in the stomach and small intestine or instability, while crossing the acidic pH of the gastro-intestinal tract act [ 5 – 7 ]. Colons specific delivery system includes pH dependent a time dependent, osmotic pressure controlled. The colon specific drug delivery systems follow various mechanism of drug release such as transit time dependent, pH dependent, pH and time dependent, colonic microbial dependent, pH and microbial dependent, colonic pressure controlled and osmotic pressure-controlled systems [ 8 ]. This could be achieved through the utilization of various biodegradable, hydrophilic and acidic pH tolerant polymeric materials reported for the successful colon specific drug delivery on which hypothesis of this work based [ 9 ]. 1.2 Quercetin Quercetin (QUR) chemically known as 3,30,40,5,7-penthahydroxy-2-phenyl- chromen-4- one is a naturally occurring dietary flavonoid present mostly in fruits, vegetables and beverages (tea/wine) (D'Andrea G et al., 2015). QUR exhibits a wide range of therapeutic properties such as anti-carcinogenic, immunosuppressive, anti-inflammatory and antiviral activities [ 9 – 11 ]. Quercetin (QUR) chemically known as 3, 3’, 4’, 5, 7-penthahydroxy-2-phenyl- chromen-4-one is a naturally occurring dietary flavonoid present mostly in fruits, vegetables, and beverages (tea/wine) [ 12 ]. QUR showed poor water solubility and instable in physiological medium, but quercetin exhibits wide range of therapeutic properties such as anti-carcinogenic, immunosuppressive, anti-inflammatory and antiviral activities [ 13 , 14 ]. QUR exhibits higher bioavailability when compared to other phytochemicals [ 15 ]. The anti-cancer mechanism of quercetin includes free radical scavenging, tumor vascularization, deregulation of apoptosis, inhibition of glycolysis and cell cycle arrest [ 16 – 18 ]. QUR mainly exhibits ant-proliferative effect in cancerous cells by inhibiting the specific enzyme system such as phosphatidylinositol (PI), PI phosphate kinases, tyrosine kinases, threonine kinases and protein kinase involved in cell proliferation. The centered ring structure of quercetin is responsible for its stability which under basic conditions leads to fragmentation [ 19 ]. 1.3 Resveratrol The dietary phenolic resveratrol (RES) chemically known as 3,40, 5-trihydroxy-trans- stilbene is a stilbenoid which exists as isomers; (cis and trans-resveratrol), present in limited plant species and abundant in natural foods such as grapes, wine, peanuts and berries. RES exerts various biological activities such as chemopreventive, cardioprotective, proapoptotic, anti-inflammatory, antioxidant, anti-proliferative and anticancer properties. Trans-resveratrol was stable in acidic pH whereas, it degrades above pH 6.8 [ 20 ]. Resveratrol (R S) (3, 4, 5 - trihydroxy-trans-stilbene) exists as isomers; (cis and trans-resveratrol) is a dietary phenolic compound, present in limited plant species and abundant in natural foods such as grapes, wine, peanuts, and berries. RES exerts various biological activities such as chemopreventive, cardioprotective, proapoptotic, anti-inflammatory, antioxidant, anti-proliferative and anticancer properties. Cis resveratrol is an artificial compound produced from trans-resveratrol under ultraviolet radiation [ 21 , 22 ]. In general, ethanol and dimethyl sulphoxide are used to improve the solubility of RES. Short-term administration of RES significantly inhibits mitogen activated protein kinase levels and reduces phosphorylation of extracellular signal-regulated kinases. RES loaded nanoformulations were used for various biomedical applications and cancer treatment respectively[ 23 , 24 ] reported the estimation of trans-RES in lipidic nanoparticles using C18 column by isocratic elution utilizing the mobile phase of 2% acetic acid and acetonitrile (80:20% v/v) by fluorometric detection at an excitation wavelength of 330 nm and emission wavelength of 374 nm. Further they validated the method for the quantification of trans-RES in lipidic nanoparticles emphazing its application for in-vitro intestinal permeability. Colons specific delivery system includes pH dependent a time dependent, osmotic pressure controlled. The colon specific drug delivery systems follow various mechanism of drug release such as transit time dependent, pH dependent, pH and time dependent, colonic microbial dependent, pH and microbial dependent, colonic pressure controlled and osmotic pressure-controlled systems [ 25 ].This could be achieved through the utilization of various biodegradable, hydrophilic and acidic pH tolerant polymeric materials reported for the successful colon specific drug delivery on which hypothesis of this work based. pH sensitivity of resveratrol and PLGA may aid selective release in colon cancer specific area. The prepared formulations were subjected to various in vitro evaluations to confirm the improve the solubility of resveratrol and QRNPs in addressing the objective of this study [ 26 ]. 2. MATERIALS AND METHODS 2.1 Materials Quercetin, resveratrol PLGA, D-α-Tocopheryl polyethylene glycol succinate (TPGS), Acetone, ethanol, DMSO, PBS, Tween 80 MTT (3-(4,5-dimethylthiazol-2-yl)-2,5-diphenyl tetrazolium bromide), and acetic acid were used as solvents. Milli-Q water was purchased from Thermo fisher, India. 2.2 Development of Quercetin and Resveratrol Loaded Nanoparticles (QRNPs). Quercetin resveratrol loaded nanoparticles (QRNPs) was prepared as per previously reported emulsification method [ 27 , 28 ]. Briefly, organic phase was prepared by dissolving 10 mg of poly (lactic-co-glycolic acid) in acetone (5 mL) sonicated for 5 mins to form uniform mixture. Further, quercetin (2 mg) and RES (2 mg) was transferred to above mixture. In another tube 2.5% of aqueous D-α-Tocopherol polyethylene glycol (TPGS) acts as the aqueous phase which kept under stirring at 700 rpm for 30 mins to form uniform dispersion. The resultant organic phase was injected into aqueous phase under magnetic stirring 700 rpm for 24 h under light protection. The formed nanoparticles (QRNPs) were collected by centrifugation at 15,000 rpm for 15 mins at 4°C, washed and concentrated into final volume of 1 mL. The average particle size and zeta potential of developed QRNPs were measured using Zetasizer (Nano ZS90 series, Malvern, UK) [ 29 ] 2.3 Optimization using CDD The formulation of QRNPs was optimized using Central Composite Design (CCD) via Design Expert software (Version 12). Independent variables were PLGA and TPGS, and the response variables were particle size, zeta potential, and drug release percentage. The experimental runs were 13 generated. Statistical models including ANOVA, regression analysis, and lack-of-fit tests were used to assess the significance and adequacy of the responses. Desirability plots were used to predict the optimal formulation [ 30 ]. 2.4 In-vitro characterization techniques for QRNPs Particle size distribution and zeta potential of QRNPs were recorded using Malvern Zeta sizer (Nano ZS90 series, Malvern Instruments, UK) by diluting MTCs (1:10) using Milli Q water sonicated for 2 min and further analysed. pH of QRNPs was checked using pH meter (AD8000 Mumbai) [ 31 ]. 2.5 Morphology characterization (TEM) Optical microscopy was also employed for initial observation at 10× magnification [ 32 ]. The surface morphology and structure of QRNPs were examined by High-Resolution Transmission Electron Microscopy (HR-TEM). A diluted drop of nanoparticle dispersion was placed on a carbon-coated copper grid and allowed to air dry. Imaging was at an accelerating voltage of 200 kV to monitor particle shape and homogeneity. 2.6 Fourier transform infrared spectroscopy analysis FTIR analysis for QUR, RES, and QRNPs was performed using an FTIR spectrometer (Jasco 6300) by the KBr pellet press technique at a scanning range of 4000 cm⁻¹ to 400 cm⁻¹ [ 33 ]. The samples were finely powdered, combined with dry potassium bromide (KBr) at a 1:100 ratio, and compacted into translucent pellets with a hydraulic press. The spectra were collected at a resolution of 4 cm⁻¹. The distinctive peaks corresponding to functional groups were analysed to determine structural properties of the pure pharmaceuticals as well as potential interactions between the medications and polymeric excipients in the nanoparticle formulation. 2.7 In Vitro Release study for QRNPs The kinetics of quercetin release from QRNPs was followed with in vitro release studies by dialysis bag diffusion method after [ 34 ] study. About 2 mL of QRNPs suspension (equivalent to a known quercetin concentration) was introduced into a pre-soaked dialysis membrane (MWCO 12–14 kDa) and sealed. The dialysis bag prepared was placed in 50 mL of phosphate-buffered saline (PBS, pH 7.4) containing 0.5% Tween 80 for sink maintenance, and kept between 37 ± 0.5°C, stirring continuously at 100 rpm. At definite intervals of 0, 5, 10, 15, 20hrs, the aliquots of 1 mL were drawn from the release medium, which were immediately replaced with an equal volume of freshly prepared PBS. The samples were analyzed for the released quercetin content using UV–Vis spectrophotometry set at 370 nm. All tests were done in triplicate, with the data expressed as cumulative percentage drug release. 2.8 Stability Studies QRNPs were incubated in simulated gastric fluid (SGF, pH 1.2) and simulated intestinal fluid (SIF, pH 6.8) under mild shaking at 37°C for 2 hrs. After incubation, particle size and zeta potential were measured using DLS (Malvern Zetasizer Nano ZS90). Changes in size distribution used to assess colloidal stability under physiological pH conditions [ 35 ]. 2.9 In Vitro Cytotoxicity Assay (MTT) The cytotoxicity of QRNPs, free QUR, and RES against Caco-2 colon cancer cells was assessed using the MTT assay. Cells are seeded in 1×10⁴ cells/well in 96-well plates and treated with test formulations of different concentrations (10–100 µg/mL) for 24–48 hrs. After treatment, add 20 µL of MTT solution (5 mg/mL) and incubation for 4 hours. Formazan crystals were dissolved in DMSO, and the absorbance was read at 570 nm using a microplate reader. CC₅₀ values were calculated from the dose-response curve [ 36 ]. 3. RESULT 3.1 Optimization Using central composite design with Design Expert (Version 12), the tests are optimised. PLGA (Factor A) and TPGS (Factor B) at two levels low and high were chosen as the independent variables. Particle size (nm), Zeta potential (mV)and QRNPs release (%) are the dependent or response variables [37] (Table 1). Regarding several statistical metrics, including probability (P value), regression coefficient (R 2 value), model Fischer's value (F value), and lack of fit model, the design was optimized. The experimental portion was carried out based on the outcome of the design [30]. Table 2 contains the final formulation. Table 1: Independent variables and their coded levels used in CCD optimization Factors Designation Units Low level High level A PLGA mg 0.5 1.5 B TPGS % 0.5 1.0 Response Designation Units R1 Particle Size nm R2 Zeta Potential mV R3 QRNPs release % Table 2: Central Composite Design (CCD) based trial formulation runs for QRNPs Run Factor A Factor B PLGA (mg) TPGS (%) 1 15 3.31 2 15 1.18 3 22.07 2.25 4 7.92 2.25 5 15 2.25 6 15 2.25 7 15 2.25 8 10 1.5 9 15 2.25 10 15 2.25 11 20 1.5 12 20 3 13 10 3 13 trial formulation have been obtained from CCD design and are put in table 2. (Table.3) gives the result of all 13 trials formulation for response factor particle size (nm), zeta potential (mV), QRNPs release (%) [38] Table 3: Experimental results for particle size, zeta potential, and QRNPs release percentage Factor 1 Factor 2 Response 1 Response 2 Response 3 Run PLGA (mg) TPGS (%) Particle size (nm) Zeta Potential (mV) QRNPs release (%) 1 15 3.31 174 -22 84 2 15 1.18 173 -22 82 3 22.07 2.25 172 -22 82 4 7.92 2.25 170 -23 83 5 15 2.25 176 -22 83 6 15 2.25 176 -23 85 7 15 2.25 174 -22 86 8 10 1.5 171 -23 82 9 15 2.25 175 -23 85 10 15 2.25 177 -22 85 11 20 1.5 174 -22 82 12 20 3 176 -24 84 13 10 3 173 -21 84 a) Particle size: The response was constructed based on a polynomial equation in which independent factors are coded. The coded equation is as follows: In this polynomial equation PLGA is coded as A and TPGS is coded as B. ANNOVA table (table 4) shows the significance of the model. The model is apparently significant given its Model F-value of 5.71. An F-value this great might be caused by noise with a mere 2.05% probability. Significant model terms are those with P-values less 0.0500, here, the model terms A and A 2 are significant. Indicators of the model terms' significance are values larger than 0.1000. According to the Lack of Fit (F-value) of 1.34, the lack of fit is not significant in comparison to pure error. A significant Lack of Fit F-value has a 38.01% likelihood of being the result of noise. We want the model to fit; thus, a negligible lack of fit is ideal. Particle size = 175.60 + 1.10 *A + 0.6768 *B + 0.00 *AB - 1.99 *A² - 0.7375 *B². Table 4: ANOVA results for particle size response Source Sum of Squares df Mean Square F-value p-value Model 42.50 5 8.50 5.71 0.0205 significant A-PLGA 9.74 1 9.74 6.55 0.0376 B-TPGS 3.66 1 3.66 2.46 0.1606 AB 7.105E-15 1 7.105E-15 4.774E-15 1.0000 A² 27.48 1 27.48 18.46 0.0036 B² 3.78 1 3.78 2.54 0.1549 Residual 10.42 7 1.49 Lack of Fit 5.22 3 1.74 1.34 0.3801 Not significant Pure Error 5.20 4 1.30 Cor Total 52.92 12 Fit statistics (Table 5) showed correlation coefficient (R 2 ) value of 0.8031, coefficient of variance % of 0.7014 for response factor particle size (nm). The predicted R 2 for particle size is 0.4453, the adjusted R 2 for particle size is 0.6625 and their difference is 4 and the obtained adeq precision is 6.6791. As shown in Fig. 1, both contour and 3D response surface plots demonstrated that increasing PLGA concentration led to a marginal increase in particle size, whereas TPGS had a subtler effect. Table 5: Fit statistics for particle size response Std. Dev. 1.22 R² 0.8031 Mean 173.92 Adjusted R² 0.6625 C.V. % 0.7014 Predicted R² 0.4453 Adeq Precision 6.6791 b) Zeta Potential: The response Zeta potential was constructed based on a polynomial equation in which independent factors are coded. The coded equation is as follows: Zeta Potential = - 22.38 - 0.0732 A + 0.0 B - 1.0 AB. In this polynomial equation PLGA is coded as A and TPGS is coded as B. ANNOVA table (table 6) shows the significance of the model. The model is apparently significant given its Model F-value of 4.00. An F-value this great might be caused by noise with a mere 4.61% probability. Significant model terms are those with P-values less 0.0500, here, the model terms A and A 2 are significant. Indicators of the model terms' significance are values larger than 0.1000. According to the Lack of Fit (F-value) of 1.22, the lack of fit is not significant in comparison to pure error. A significant Lack of Fit F-value has a 43.50% likelihood of being the result of noise. We want the model to fit, thus a negligible lack of fit is ideal [39]. Fit statistics (Table 7) showed Predicted R 2 value of -0.1442 which shows that overall mean is the better predictor of the model, coefficient of variance % of 2.59 for response factor Zeta potential (mV). The Correlation coefficient (R 2) for Zeta potential is 0.5731, the adjusted R 2 for Zeta potential is 0.4284. The desirable adeq precision should be >4 and the obtained adeq precision is 6.6646. These are the statistical parameters that supports the chosen model is significant for the formulation of QRNPs. Fig. 2, shows that surface charge remained consistently negative across the formulation range, indicating good colloidal stability, with a notable influence from PLGA concentration. Table 6: ANOVA results for zeta potential response Std. Dev. 0.5806 R² 0.5713 Mean -22.36 Adjusted R² 0.4284 C.V. % 2.59 Predicted R² -0.1442 Adeq Precision 6.6646 Table 7: Fit statistics for zeta potential response Source Sum of Squares df Mean Square F-value p-value Model 4.04 3 1.35 4.00 0.0461 significant A-PLGA 0.0429 1 0.0429 0.1272 0.7295 B-TPGS 0.0000 1 0.0000 0.0000 1.0000 AB 4.00 1 4.00 11.87 0.0073 Residual 3.03 9 0.3371 Lack of Fit 1.83 5 0.3668 1.22 0.4350 not significant Pure Error 1.20 4 0.3000 Cor Total 7.08 12 c) QRNPs Release: The response QRNPs release was constructed based on a polynomial equation in which independent factors are coded. The coded equation is as follows: QRNPs release = 84.80 - 0.1768 *A + 0.8536 *B + 0.0 *AB - 1.09 *A² - 0.08375 *B² In this polynomial equation PLGA is coded as A and TPGS is coded as B. ANNOVA table (table 8) shows the significance of the model. The model is apparently significant given its Model F-value of 4.64. An F-value this great might be caused by noise with a mere 3.45% probability. Significant model terms are those with P-values less 0.0500, here, the model terms A and A 2 are significant. Indicators of the model terms' significance are values larger than 0.1000. According to the Lack of Fit (F-value) of 0.15, the lack of fit is not significant in comparison to pure error. A significant Lack of Fit F-value has a 92.33% likelihood of being the result of noise. We want the model to fit; thus, a negligible lack of fit is ideal [34,39]. Fit statistics (Table 9) showed correlation coefficient (R 2 ) value of 0.7683, coefficient of variance % of 1.05 for response factor QRNPs release (%). The predicted R 2 is 0.5066, the adjusted R 2 for particle size is 0.6028 and their difference is 4 and the obtained adeq precision is 4.9755. These are statistical parameters that supports the designed model is significant for the formulation. Fig 3, a and b show that QRNPs release (%) increases with higher TPGS and moderate PLGA levels, indicating optimal polymer–surfactant balance enhances drug release. Table 8: ANOVA results for QRNPs release response Source Sum of Squares df Mean Square F-value p-value Model 17.73 5 3.55 4.64 0.0345 significant A-PLGA 0.2500 1 0.2500 0.3273 0.5851 B-TPGS 5.83 1 5.83 7.63 0.0280 AB 0.0000 1 0.0000 0.0000 1.0000 A² 8.23 1 8.23 10.77 0.0135 B² 4.88 1 4.88 6.39 0.0394 Residual 5.35 7 0.7638 Lack of Fit 0.5466 3 0.1822 0.1518 0.9233 not significant Pure Error 4.80 4 1.20 Cor Total 23.08 12 Table 9 : Fit statistics for QRNPs release response Std. Dev. 0.8704 R² 0.7683 Mean 83.62 Adjusted R² 0.6028 C.V. % 1.05 Predicted R² 0.5066 Adeq Precision 4.9755 d) Desirability: The overall desirability was calculated using a multi-response optimization method, and the response surface was fitted to a second-order polynomial equation with coded variables. The coding equation for desirability (D) is given below: Desirability (D) is 0.75 + 0.045A + 0.030B - 0.012A² - 0.020B² - 0.018*AB The desirability plots (Figure 4) illustrate the combined effect of PLGA (mg) and TPGS (%) on the optimization response. 3.2 Morphology characterization (TEM) The spherical morphology of prepared QRNPs showed was confirmed by HR-TEM analysis (Figure 5). The optical microscopic image of prepared QRNPs at 10x magnification were shown in figure 7a. a) Optical microscopic image of prepared QRNPs at 10 X magnification. (b) HR- TEM image of QRNPs [40]. 3.3 FTIR spectroscopic analysis The FTIR spectrum for pure quercetin is shown in figure 6, where its characteristic bands were detected. OH, groups stretching were detectable at 3406 and 3283 cm −1 , whereas OH bending of the phenol function was detectable at 1379 cm −1 . The C=O aryl ketonic stretch absorption was evident at 1666 cm −1 . C=C aromatic ring stretch bands were detectable at 1610, 1560, and 1510 cm −1 . The in-plane bending band of C–H in aromatic hydrocarbon was detectable at 1317 cm −1 , and out-of-plane bending bands were evident at 933, 820, 679, and 600 cm −1 . Bands at 1263, 1200, and 1165 cm −1 were attributable due to to the C–O stretching in the aryl ether ring, the C–O stretching in phenol, and the C–CO–C stretch and bending in ketone, respectively (Fig 4). The strong band characteristic for trans-resveratrol can observed at 3290 cm -1 , which originates due to valence ν(OH)vibrations of phenols (Fig 1 RES). The band at 3021 cm-1 is the result of the valence vibration of vinyl group (=C-H), while the bands at 2924 and 2852 cm-1 originate from the valence vibrations of C-H bond from CH and CH2 groups. The valence vibrations ν(C=C) of the benzene ring were observed at 1606, 1587, 1512 and 1444 cm -1 . The in-plane deformational vibrations of OH group appeared at 1384 and 1325 cm -1 which is responsible for OH group identification. Additional bands of valence vibrations of C-C bond at 1248 cm -1 and of C-O bond from the phenol group at 1154 cm -1 were noticed in the FTIR spectrum. The band at 831 cm -1 originates due to deformational vibration of C-H bond of the benzene ring. Finally, there were no eractions between peaks in QRNPs showed the compatibility of selected excipients in study [33]. The FTIR spectra (Figure 6) confirm the presence of characteristic functional groups in QUR, QRNPs, RES, and PLGA, indicating structural integrity and absence of significant interactions among the components. 3.4 In Vitro Release study for QRNPs In vitro release demonstrated QRNPs has a sustained and pH-responsive release profile. At physiological pH (7.4), drug release was limited, resulting in few off-target effects. At acidic pH (5.5), which mimics the tumor milieu, medication release was dramatically increased, indicating successful colon-targeted delivery. These results, confirmed by statistical optimization models (Fig. 7), confirm the formulation potential for colorectal cancer treatment. 3.5 Stability studies for QRNPs in gastrointestinal fluids. Stability of QRNPs was studied in the simulated GI fluids. The particle size of the QRNPs was stable when incubated in either simulated gastric fluid (SGF) or simulated intestinal fluid (SIF). The particle size of QRNPs decreased after incubation in SGF for 2 h. However, when incubated in SIF, the particle size of QRNPs significantly increased, whereas that of QRNPs did not significantly change. As illustrated in Fig 8, an obvious aggregation was observed in QRNPs because of the low solubility at pH 6.8. These results showed that QRNPs were more stable than QRNPs at pH 1.2 and pH 6.8 [35]. 3.6 In vitro cell line studies for QRNPs In-vitro Biocompatibility and Anticancer Activity Cytotoxic Activity of QRNPs using MTT Assay. The cytotoxicity effect of QRNPs on colon cancer cell line Caco2 was assessed using MTT assay and compared with free QUR, free RES. The QRNPs showed dose dependent toxicity until 50 μg/ml behind had no significant effect [36].The cytotoxic potential of QRNPs also found to be higher than free QUR, free RES, which is the quite evident cytotoxic concentration were shown. The CC 50 value of QRNPs against Caco2 cells determined after a 24-h and 48-h treatment is as follows: 48.84 ± 0.78 μg/ml and 32.75 ± 1.02 μg/ml respectively. The CC 50 value of QRNPs after 48 h was reduced to 75% compared with that CC 50 of QRNPs after 24 h treatment. Therefore, the lower concentration 30 and 50 μl/ml of QRNPs has been selected further to investigate the underlying molecular mechanism toxicity such as QRNPs induced apoptotic effect in colon cancer cell line Caco2 [41] As in Fig. 9, QRNPs exhibited dose-dependent cytotoxicity against Caco-2 cells over 24 and 48 hours, surpassing the effects of free QUR and RES, demonstrating enhanced anticancer efficacy. 4. DISCUSSION This study shows for the first time the development of Quercetin resveratrol loaded polymeric nanoparticles (QRNPs), which efficiently improved its interaction with colon and colorectal cancer cells in vitro. According to the in vitro results, the prepared colon targeted nano system can protect the drug release through gastrointestinal regions and enhances its release at colorectal sites, which enhances the anti-cancer activity of the free drug combination to a high extent. In the current study, we optimized the preparation of QRNPs for the oral delivery of both drugs. QRNPs were fabricated with a modified double emulsion solvent evaporation technique through some formulation variables such as TPGS concentration, polymer amounts and the ratio of organic solvent to external phase volume [ 5 ] The higher PVA concentration in the outer aqueous phase has significantly played a role in stabilizing the prepared formula by decreasing the nanoparticles size, PDI and increasing the drug entrapment. The in vitro release showed that optimized drug-loaded NPs could act as a carrier to minimize drug release against gastric and intestinal pH environments, thus protecting the drugs against the pathway of digestion to reach the preferred site of action at the colon and rectum [ 42 ]. Drug-loaded NPs showed a significant cytotoxic action on colon and colorectal cancer cell lines in vitro compared to free drug combinations. These novel drug-loaded NPs can be further tested in pre-clinical models and clinical trials to develop an effective and promising therapeutic tool for the oral therapy of colon and colorectal cancers [ 43 ]. 5. CONCLUSION The synthesized pH-sensitive polymeric nanoparticles co-encapsulating resveratrol and quercetin exhibited efficient targeted delivery to colorectal cancer cells in acidic tumor environments. The nanocarriers markedly improved drug stability, regulated release, and cellular absorption, leading to heightened cytotoxicity and apoptosis relative to free medicines. The combined effects of resveratrol and quercetin, along with the pH-sensitive release mechanism, highlight the therapeutic promise of this nanoplatform. The findings indicate that a dual-drug delivery system may provide a viable alternative for colorectal cancer treatment, addressing the constraints of conventional chemotherapy while enhancing localized medication efficacy and minimizing systemic toxicity. Abbreviations CRC Colorectal Cancer QUR Quercetin RES Resveratrol NPs Nanoparticles QRNPs Quercetin-Resveratrol Nanoparticles PLGA Poly (lactic-co-glycolic acid) TPGS D-α-Tocopheryl polyethylene glycol succinate FTIR Fourier Transform Infrared Spectroscopy TEM Transmission Electron Microscopy HR-TEM High-Resolution Transmission Electron Microscopy DLS Dynamic Light Scattering UV–Vis Ultraviolet–Visible Spectrophotometry PBS Phosphate Buffered Saline SGF Simulated Gastric Fluid SIF Simulated Intestinal Fluid CCD Central Composite Design Caco-2 Human Colorectal Adenocarcinoma Cell Line MTT 3-(4,5-dimethylthiazol-2-yl)-2,5-diphenyl tetrazolium bromide DMSO Dimethyl Sulfoxide ANOVA Analysis of Variance CC₅₀ 50% Cytotoxic Concentration PDI Polydispersity Index SD Standard Deviation R² Coefficient of Determination Declarations Conflict of interest The authors declare no competing interests. Financing No funding Author Contribution K. J - Reviewing and Drafting manuscriptV. 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Rizky WC, Jihwaprani MC, Mushtaq M. Protective mechanism of quercetin and its derivatives in viral-induced respiratory illnesses. Egypt J Bronchol. 2022;16:58. Russo GL, Russo M, Spagnuolo C, Tedesco I, Bilotto S, Iannitti R et al. Quercetin: A Pleiotropic Kinase Inhibitor Against Cancer. 2014. pp. 185–205. Salehi B, Machin L, Monzote L, Sharifi-Rad J, Ezzat SM, Salem MA, et al. Therapeutic Potential of Quercetin: New Insights and Perspectives for Human Health. ACS Omega. 2020;5:11849–72. Curcio M, Cirillo G, Parisi OI, Iemma F, Picci N, Puoci F. Quercetin-Imprinted Nanospheres as Novel Drug Delivery Devices. J Funct Biomater. 2012;3:269–82. Anand David A, Arulmoli R, Parasuraman S. Overviews of biological importance of quercetin: A bioactive flavonoid. Pharmacogn Rev. 2016;10:84. Lu D, Jiao X, Jiang W, Yang L, Gong Q, Wang X, et al. Mesenchymal stem cells influence monocyte/macrophage phenotype: Regulatory mode and potential clinical applications. Biomed Pharmacother. 2023;165:115042. Gu J, Zhang X, Jiang G, Li Q, Wang E, Yu J. ARHGEF40 promotes non-small cell lung cancer proliferation and invasion via the AKT‐Wnt axis by binding to RhoA. Mol Carcinog. 2022;61:1016–30. Yong C, Huang G, Ge H, Zhu Y, Yang Y, Yu Y, et al. Perilla frutescens L. alleviates trimethylamine N-oxide –induced apoptosis in the renal tubule by regulating ASK1‐JNK phosphorylation. Phytother Res. 2023;37:1274–92. Tang S-M, Deng X-T, Zhou J, Li Q-P, Ge X-X, Miao L. Pharmacological basis and new insights of quercetin action in respect to its anti-cancer effects. Biomed Pharmacother. 2020;121:109604. Recent Frontiers of Phytochemicals. Elsevier; 2023 Apr 26. Stachniuk A, Trzpil A, Kozub A, Montowska M, Fornal E. Pork liver tissue-specific peptide markers for food authenticity testing and adulteration detections. Food Chem. 2023;405:135013. Han D-G, Kwak J, Choi E, Seo S-W, Vasileva EA, Mishchenko NP, et al. Physicochemical characterization and phase II metabolic profiling of echinochrome A, a bioactive constituent from sea urchin, and its physiologically based pharmacokinetic modeling in rats and humans. Biomed Pharmacother. 2023;162:114589. He Q, Hu H, Yang F, Song D, Zhang X, Dai X. Advances in chimeric antigen receptor T cells therapy in the treatment of breast cancer. Biomed Pharmacother. 2023;162:114609. Neves AR, Reis S, Segundo MA. Development and Validation of a HPLC Method Using a Monolithic Column for Quantification of trans -Resveratrol in Lipid Nanoparticles for Intestinal Permeability Studies. J Agric Food Chem. 2015;63:3114–20. Zhang F, Hu Q, Li B, Huang Y, Wang M, Shao S, et al. A biomimetic nanodrug for enhanced chemotherapy of pancreatic tumors. J Controlled Release. 2023;354:835–50. Villarruel LA, Brie B, Municoy S, Becú-Villalobos D, Desimone MF, Catalano PN. Silica-collagen nanoformulations with extended human growth hormone release. Int J Pharm. 2023;634:122662. Jain A, Jain SK, Ganesh N, Barve J, Beg AM. Design and development of ligand-appended polysaccharidic nanoparticles for the delivery of oxaliplatin in colorectal cancer. Nanomedicine. 2010;6:179–90. Naruphontjirakul P, Li S, Pinna A, Barrak F, Chen S, Redpath AN, et al. Interaction of monodispersed strontium containing bioactive glass nanoparticles with macrophages. Biomaterials Adv. 2022;133:112610. Cavalcante de Freitas PG, Rodrigues Arruda B, Araújo Mendes MG, Barroso de Freitas JV, da Silva ME, Sampaio TL, et al. Resveratrol-Loaded Polymeric Nanoparticles: The Effects of D-α-Tocopheryl Polyethylene Glycol 1000 Succinate (TPGS) on Physicochemical and Biological Properties against Breast Cancer In Vitro and In Vivo. Cancers (Basel). 2023;15:2802. Bhatt S, Punetha VD, Pathak R, Punetha M. Graphene in nanomedicine: A review on nano-bio factors and antibacterial activity. Colloids Surf B Biointerfaces. 2023;226:113323. Wan S, Zhang L, Quan Y, Wei K. Resveratrol-loaded PLGA nanoparticles: enhanced stability, solubility and bioactivity of resveratrol for non-alcoholic fatty liver disease therapy. R Soc Open Sci. 2018;5:181457. Shamsabadipour A, Pourmadadi M, Davodabadi F, Rahdar A, Romanholo Ferreira LF. Applying thermodynamics as an applicable approach to cancer diagnosis, evaluation, and therapy: A review. J Drug Deliv Sci Technol. 2023;86:104681. Amiri H, Javid H, Einafshar E, Ghavidel F, Rajabian A, Hashemy SI, et al. Development and Evaluation of PLGA Nanoparticles Surfaced Modified with Chitosan-Folic Acid for Improved Delivery of Resveratrol to Prostate Cancer Cells. Bionanoscience. 2024;14:988–98. Costa FJP, Nave M, Lima-Sousa R, Alves CG, Melo BL, Correia IJ, et al. Development of Thiol-Maleimide hydrogels incorporating graphene-based nanomaterials for cancer chemo-photothermal therapy. Int J Pharm. 2023;635:122713. Yao M, Zhang G, Shao D, Ding S, Li L, Li H, et al. Preparation of chitin/MXene/poly(L-arginine) composite aerogel spheres for specific adsorption of bilirubin. Int J Biol Macromol. 2023;243:125140. Lin H, Wang C, Yu H, Liu Y, Tan L, He S, et al. Protective effect of total Saponins from American ginseng against cigarette smoke-induced COPD in mice based on integrated metabolomics and network pharmacology. Biomed Pharmacother. 2022;149:112823. Chen Y, Feng X. Gold nanoparticles for skin drug delivery. Int J Pharm. 2022;625:122122. Mohamed JMM, Ahmad F, El-Sherbiny M, Al Mohaini MA, Venkatesan K, Alrashdi YBA, et al. Optimization and characterization of quercetin-loaded solid lipid nanoparticles for biomedical application in colorectal cancer. Cancer Nanotechnol. 2024;15:16. Bose S, Sarkar N, Majumdar U. Micelle encapsulated curcumin and piperine-laden 3D printed calcium phosphate scaffolds enhance in vitro biological properties. Colloids Surf B Biointerfaces. 2023;231:113563. Lohani A, Saxena R, Khan S, Mascarenhas-Melo F. pH-responsive IPN beads of carboxymethyl konjac glucomannan and sodium carboxymethyl cellulose as a controlled release carrier for ibuprofen. Int J Biol Macromol. 2024;278:134676. Liu T, Tong S, Liao Q, Pan L, Cheng M, Rantanen J, et al. Role of dispersion enhancer selection in the development of novel tratinterol hydrochloride dry powder inhalation formulations. Int J Pharm. 2023;635:122702. Iman M, Moosavian SA, Zamani P, Jaafari MR. Preparation of AS1411 aptamer-modified PEGylated liposomal doxorubicin and evaluation of its anti-cancer effects in vitro and in vivo. J Drug Deliv Sci Technol. 2023;81:104255. Wang M, Xing S, Jia J, Zeng W, Lei J, Qian Y, et al. Angelicin impedes the progression of glioblastoma via inactivation of YAP signaling pathway. Biomed Pharmacother. 2023;161:114462. Additional Declarations No competing interests reported. Supplementary Files ColoncancerGA.jpg Graphical Abstract Cite Share Download PDF Status: Posted Version 1 posted You are reading this latest preprint version Research Square lets you share your work early, gain feedback from the community, and start making changes to your manuscript prior to peer review in a journal. As a division of Research Square Company, we’re committed to making research communication faster, fairer, and more useful. We do this by developing innovative software and high quality services for the global research community. Our growing team is made up of researchers and industry professionals working together to solve the most critical problems facing scientific publishing. Also discoverable on Platform About Our Team In Review Editorial Policies Advisory Board Help Center Resources Author Services Accessibility API Access RSS feed Manage Cookie Preferences © Research Square 2026 | ISSN 2693-5015 (online) Privacy Policy Terms of Service Do Not Sell My Personal Information {"props":{"pageProps":{"initialData":{"identity":"rs-7204934","acceptedTermsAndConditions":true,"allowDirectSubmit":true,"archivedVersions":[],"articleType":"Research Article","associatedPublications":[],"authors":[{"id":500204701,"identity":"e023ffeb-0589-4349-a105-8501797c2fd8","order_by":0,"name":"Kumar Janakiraman","email":"data:image/png;base64,iVBORw0KGgoAAAANSUhEUgAAAZAAAAAyAQMAAABI0h/eAAAABlBMVEX///8AAABVwtN+AAAACXBIWXMAAA7EAAAOxAGVKw4bAAAA6ElEQVRIiWNgGAWjYBACCTBpYAMiGR8ACR4+4rQUpIFIZgOQFjbitHw4BCLZwByCWiRnZCd+rjA4kMc/u/lY5dccOxk2BuaHj27g0SItkbtZ8ozBnWKJO8fSbstuSwY6jM3YOAePFjmJ3A2SDQbPEhtu5JjdltzGDNTCwyZNQMvmnw0GhxPnA7UUS26rJ6wF6LBtkiAtG4BaGD9uO0xYi2TP222WDQZpiRtvpCVLM247zsPGTMAvEsdzN99s+GOTOO9G8sGPP7dV2/OzNz98jE8LCmDmAZPEKgcBxh+kqB4Fo2AUjIIRAwDAWkjtE+x8kQAAAABJRU5ErkJggg==","orcid":"","institution":"Rathinam Technical Campus","correspondingAuthor":true,"prefix":"","firstName":"Kumar","middleName":"","lastName":"Janakiraman","suffix":""},{"id":500204702,"identity":"25195878-983b-4b15-9d1e-bb5e4f89317d","order_by":1,"name":"Vaidevi Sethuraman","email":"","orcid":"","institution":"Rathinam Technical Campus","correspondingAuthor":false,"prefix":"","firstName":"Vaidevi","middleName":"","lastName":"Sethuraman","suffix":""},{"id":500204704,"identity":"d445023a-7c67-42bd-bfd6-478643e5d8ef","order_by":2,"name":"Geethanjali Sampath","email":"","orcid":"","institution":"Rathinam Technical Campus","correspondingAuthor":false,"prefix":"","firstName":"Geethanjali","middleName":"","lastName":"Sampath","suffix":""},{"id":500204706,"identity":"8a49a0f0-ae6c-4961-af22-8cae2c3a8bb9","order_by":3,"name":"Venkateshwaran Krishnaswami","email":"","orcid":"","institution":"S. A. Raja Pharmacy College","correspondingAuthor":false,"prefix":"","firstName":"Venkateshwaran","middleName":"","lastName":"Krishnaswami","suffix":""}],"badges":[],"createdAt":"2025-07-24 11:08:30","currentVersionCode":1,"declarations":"","doi":"10.21203/rs.3.rs-7204934/v1","doiUrl":"https://doi.org/10.21203/rs.3.rs-7204934/v1","draftVersion":[],"editorialEvents":[],"editorialNote":"","failedWorkflow":false,"files":[{"id":89593329,"identity":"410bbb4e-dc2f-4130-8e39-e11596d8d3bd","added_by":"auto","created_at":"2025-08-21 16:20:33","extension":"jpeg","order_by":1,"title":"Figure 1","display":"","copyAsset":false,"role":"figure","size":100424,"visible":true,"origin":"","legend":"\u003cp\u003e(a) Contour plots, (b) 3D response plots showing effects of independent variables on particle size (nm).\u003c/p\u003e","description":"","filename":"Colonfig1.jpeg","url":"https://assets-eu.researchsquare.com/files/rs-7204934/v1/72491adf2523b0168c40605f.jpeg"},{"id":89592477,"identity":"c696ee93-d23a-472b-a818-737df908d5f2","added_by":"auto","created_at":"2025-08-21 16:12:33","extension":"jpeg","order_by":2,"title":"Figure 2","display":"","copyAsset":false,"role":"figure","size":112236,"visible":true,"origin":"","legend":"\u003cp\u003e(a) Contour plots, (b) 3D response plots showing effects of independent variables on Zeta potential (mV).\u003c/p\u003e","description":"","filename":"Colonfig2.jpeg","url":"https://assets-eu.researchsquare.com/files/rs-7204934/v1/18d4eecba21ed8a00f2cae79.jpeg"},{"id":89593330,"identity":"367a73d1-c37b-44cf-a8f3-ab342401db1f","added_by":"auto","created_at":"2025-08-21 16:20:33","extension":"jpeg","order_by":3,"title":"Figure 3","display":"","copyAsset":false,"role":"figure","size":99012,"visible":true,"origin":"","legend":"\u003cp\u003e(a) Contour plots, (b) 3D response plots showing effects of independent variables on QRNPs release (%).\u003c/p\u003e","description":"","filename":"Colonfig3.jpeg","url":"https://assets-eu.researchsquare.com/files/rs-7204934/v1/7a94326d913e7be69fb0612b.jpeg"},{"id":89594350,"identity":"4c661082-907a-4523-9970-dbef20fe6d12","added_by":"auto","created_at":"2025-08-21 16:28:33","extension":"jpeg","order_by":4,"title":"Figure 4","display":"","copyAsset":false,"role":"figure","size":100166,"visible":true,"origin":"","legend":"\u003cp\u003e(a) Contour plots, (b) 3D response plots showing effects of independent variables on Desirability.\u003c/p\u003e","description":"","filename":"Colonfig4.jpeg","url":"https://assets-eu.researchsquare.com/files/rs-7204934/v1/b7ec3b16322349fed911de75.jpeg"},{"id":89592485,"identity":"553681e4-2770-493c-b19e-da214dd44b1e","added_by":"auto","created_at":"2025-08-21 16:12:33","extension":"jpeg","order_by":5,"title":"Figure 5","display":"","copyAsset":false,"role":"figure","size":147158,"visible":true,"origin":"","legend":"\u003cp\u003e(a) Optical image (b) Morphological Characterization of Hr-TEM of QRNPs\u003c/p\u003e","description":"","filename":"Colonfig5.jpeg","url":"https://assets-eu.researchsquare.com/files/rs-7204934/v1/43b297e346d4d3bf789274f8.jpeg"},{"id":89592488,"identity":"45ef39c9-f9a9-4cb1-9a21-350f89d52f80","added_by":"auto","created_at":"2025-08-21 16:12:33","extension":"jpeg","order_by":6,"title":"Figure 6","display":"","copyAsset":false,"role":"figure","size":64710,"visible":true,"origin":"","legend":"\u003cp\u003eFTIR spectra of pure QUR, QRNPs, RES, and PLGA showing characteristic peaks corresponding to functional groups confirming structural integrity and absence of interaction among components.\u003c/p\u003e","description":"","filename":"Fig6FTIR.jpeg","url":"https://assets-eu.researchsquare.com/files/rs-7204934/v1/bcb0b783197ac39b36d9f435.jpeg"},{"id":89594352,"identity":"6b3d72ea-52d5-4858-8fe2-06712cd59553","added_by":"auto","created_at":"2025-08-21 16:28:33","extension":"jpeg","order_by":7,"title":"Figure 7","display":"","copyAsset":false,"role":"figure","size":54235,"visible":true,"origin":"","legend":"\u003cp\u003eIllustration ofQRNPs release increases with rising TPGS concentration and optimal PLGA levels, supporting efficient pH-responsive drug delivery.\u003c/p\u003e","description":"","filename":"Fig7RELEASE.jpeg","url":"https://assets-eu.researchsquare.com/files/rs-7204934/v1/1d0bc5b98b166ee9a8782626.jpeg"},{"id":89594351,"identity":"4df5188a-4347-40ca-b9dc-8570822eb746","added_by":"auto","created_at":"2025-08-21 16:28:33","extension":"jpeg","order_by":8,"title":"Figure 8","display":"","copyAsset":false,"role":"figure","size":80864,"visible":true,"origin":"","legend":"\u003cp\u003eStability studies of NPs and QRNPs by evaluating particle size (a, b) and zeta potential (c, d) at pH 6.8 and pH 1.2 over 6 hours.\u003c/p\u003e","description":"","filename":"Fig8Stabilitystudies.jpeg","url":"https://assets-eu.researchsquare.com/files/rs-7204934/v1/89570c67bcf95805188ad118.jpeg"},{"id":89592496,"identity":"4814427e-6f9c-4612-819b-a895b7f66e17","added_by":"auto","created_at":"2025-08-21 16:12:34","extension":"jpeg","order_by":9,"title":"Figure 9","display":"","copyAsset":false,"role":"figure","size":71920,"visible":true,"origin":"","legend":"\u003cp\u003eIn-vitro cytotoxic effect (MTT assay) of NPs, QUR, RES and QRNPs at different concentration (10-100 μg/mL) in Caco2 for a treatment period of 48 hr (a) and 24 hr (ii) respectively. Data are presented as mean ± SD (n = 3).\u003c/p\u003e","description":"","filename":"Fig9MTT.jpeg","url":"https://assets-eu.researchsquare.com/files/rs-7204934/v1/b89b1c389383eb352a207773.jpeg"},{"id":89595349,"identity":"7be1a70f-0b08-403f-a146-63ffd2804846","added_by":"auto","created_at":"2025-08-21 16:44:34","extension":"pdf","order_by":0,"title":"","display":"","copyAsset":false,"role":"manuscript-pdf","size":2040757,"visible":true,"origin":"","legend":"","description":"","filename":"manuscript.pdf","url":"https://assets-eu.researchsquare.com/files/rs-7204934/v1/c02debe9-2d11-4130-a062-647d3845f721.pdf"},{"id":89594631,"identity":"463a76f0-1d6d-4601-b559-dbd0c8cdbe92","added_by":"auto","created_at":"2025-08-21 16:36:33","extension":"jpg","order_by":1,"title":"","display":"","copyAsset":false,"role":"supplement","size":138649,"visible":true,"origin":"","legend":"\u003cp\u003e\u003cstrong\u003eGraphical Abstract\u003c/strong\u003e\u003c/p\u003e","description":"","filename":"ColoncancerGA.jpg","url":"https://assets-eu.researchsquare.com/files/rs-7204934/v1/a8bbebf0382661d9ef7a7e93.jpg"}],"financialInterests":"No competing interests reported.","formattedTitle":"Quercetin and resveratrol loaded polymeric nanoparticle for colorectal cancer as pH sensitive approach","fulltext":[{"header":"1. INTRODUCTION","content":"\u003cdiv id=\"Sec2\" class=\"Section2\"\u003e\u003ch2\u003e1.1 Colon cancer\u003c/h2\u003e\u003cp\u003eColon cancer ranking eighth in India has been reported globally for approximately 551,269 death cases in 2018. Nearly 60% cases are encountered in developed countries [\u003cspan citationid=\"CR1\" class=\"CitationRef\"\u003e1\u003c/span\u003e] .According to ICMR report, 2013, the highest annual incidence rate for this dreadful disease was recorded in Nagaland (5.2) and Thiruvananthapuram (4.1) in men and women respectively. Almost 96% of colon cancer were adenocarcinomas, 2% were specified carcinomas and 0.4% were epidermoid carcinomas and 0.08% were sarcomas [\u003cspan citationid=\"CR2\" class=\"CitationRef\"\u003e2\u003c/span\u003e]. Treating this harmful disease through oral drug administration is the dynamic area of research nowadays. Currently, bioactive molecules of natural origin are utilized by 70% of world population because of its promising activity in preventing degenerative disease such as cancer[\u003cspan citationid=\"CR3\" class=\"CitationRef\"\u003e3\u003c/span\u003e]. Recently, [\u003cspan citationid=\"CR4\" class=\"CitationRef\"\u003e4\u003c/span\u003e]reported a list of medicinal plants and its bioactive compounds against colorectal cancer, which, includes flavonoids, polyphenols, artemisinin, gallic acid, m-coumaric acid, gentistic acid, curcumin, salicin and oleuropein etc. Oral administration of active pharmaceutical ingredients for colon cancer treatment, results in its higher absorption in the stomach and small intestine or instability, while crossing the acidic pH of the gastro-intestinal tract act [\u003cspan additionalcitationids=\"CR6\" citationid=\"CR5\" class=\"CitationRef\"\u003e5\u003c/span\u003e\u0026ndash;\u003cspan citationid=\"CR7\" class=\"CitationRef\"\u003e7\u003c/span\u003e]. Colons specific delivery system includes pH dependent a time dependent, osmotic pressure controlled. The colon specific drug delivery systems follow various mechanism of drug release such as transit time dependent, pH dependent, pH and time dependent, colonic microbial dependent, pH and microbial dependent, colonic pressure controlled and osmotic pressure-controlled systems [\u003cspan citationid=\"CR8\" class=\"CitationRef\"\u003e8\u003c/span\u003e]. This could be achieved through the utilization of various biodegradable, hydrophilic and acidic pH tolerant polymeric materials reported for the successful colon specific drug delivery on which hypothesis of this work based [\u003cspan citationid=\"CR9\" class=\"CitationRef\"\u003e9\u003c/span\u003e].\u003c/p\u003e\u003c/div\u003e\u003cdiv id=\"Sec3\" class=\"Section2\"\u003e\u003ch2\u003e1.2 Quercetin\u003c/h2\u003e\u003cp\u003eQuercetin (QUR) chemically known as 3,30,40,5,7-penthahydroxy-2-phenyl- chromen-4- one is a naturally occurring dietary flavonoid present mostly in fruits, vegetables and beverages (tea/wine) (D'Andrea G et al., 2015). QUR exhibits a wide range of therapeutic properties such as anti-carcinogenic, immunosuppressive, anti-inflammatory and antiviral activities [\u003cspan additionalcitationids=\"CR10\" citationid=\"CR9\" class=\"CitationRef\"\u003e9\u003c/span\u003e\u0026ndash;\u003cspan citationid=\"CR11\" class=\"CitationRef\"\u003e11\u003c/span\u003e]. Quercetin (QUR) chemically known as 3, 3\u0026rsquo;, 4\u0026rsquo;, 5, 7-penthahydroxy-2-phenyl- chromen-4-one is a naturally occurring dietary flavonoid present mostly in fruits, vegetables, and beverages (tea/wine) [\u003cspan citationid=\"CR12\" class=\"CitationRef\"\u003e12\u003c/span\u003e]. QUR showed poor water solubility and instable in physiological medium, but quercetin exhibits wide range of therapeutic properties such as anti-carcinogenic, immunosuppressive, anti-inflammatory and antiviral activities [\u003cspan citationid=\"CR13\" class=\"CitationRef\"\u003e13\u003c/span\u003e, \u003cspan citationid=\"CR14\" class=\"CitationRef\"\u003e14\u003c/span\u003e]. QUR exhibits higher bioavailability when compared to other phytochemicals [\u003cspan citationid=\"CR15\" class=\"CitationRef\"\u003e15\u003c/span\u003e]. The anti-cancer mechanism of quercetin includes free radical scavenging, tumor vascularization, deregulation of apoptosis, inhibition of glycolysis and cell cycle arrest [\u003cspan additionalcitationids=\"CR17\" citationid=\"CR16\" class=\"CitationRef\"\u003e16\u003c/span\u003e\u0026ndash;\u003cspan citationid=\"CR18\" class=\"CitationRef\"\u003e18\u003c/span\u003e]. QUR mainly exhibits ant-proliferative effect in cancerous cells by inhibiting the specific enzyme system such as phosphatidylinositol (PI), PI phosphate kinases, tyrosine kinases, threonine kinases and protein kinase involved in cell proliferation. The centered ring structure of quercetin is responsible for its stability which under basic conditions leads to fragmentation [\u003cspan citationid=\"CR19\" class=\"CitationRef\"\u003e19\u003c/span\u003e].\u003c/p\u003e\u003c/div\u003e\u003cdiv id=\"Sec4\" class=\"Section2\"\u003e\u003ch2\u003e1.3 Resveratrol\u003c/h2\u003e\u003cp\u003eThe dietary phenolic resveratrol (RES) chemically known as 3,40, 5-trihydroxy-trans- stilbene is a stilbenoid which exists as isomers; (cis and trans-resveratrol), present in limited plant species and abundant in natural foods such as grapes, wine, peanuts and berries. RES exerts various biological activities such as chemopreventive, cardioprotective, proapoptotic, anti-inflammatory, antioxidant, anti-proliferative and anticancer properties. Trans-resveratrol was stable in acidic pH whereas, it degrades above pH 6.8 [\u003cspan citationid=\"CR20\" class=\"CitationRef\"\u003e20\u003c/span\u003e].\u003c/p\u003e\u003cp\u003eResveratrol (R S) (3, 4, 5 - trihydroxy-trans-stilbene) exists as isomers; (cis and trans-resveratrol) is a dietary phenolic compound, present in limited plant species and abundant in natural foods such as grapes, wine, peanuts, and berries. RES exerts various biological activities such as chemopreventive, cardioprotective, proapoptotic, anti-inflammatory, antioxidant, anti-proliferative and anticancer properties. Cis resveratrol is an artificial compound produced from trans-resveratrol under ultraviolet radiation [\u003cspan citationid=\"CR21\" class=\"CitationRef\"\u003e21\u003c/span\u003e, \u003cspan citationid=\"CR22\" class=\"CitationRef\"\u003e22\u003c/span\u003e]. In general, ethanol and dimethyl sulphoxide are used to improve the solubility of RES. Short-term administration of RES significantly inhibits mitogen activated protein kinase levels and reduces phosphorylation of extracellular signal-regulated kinases. RES loaded nanoformulations were used for various biomedical applications and cancer treatment respectively[\u003cspan citationid=\"CR23\" class=\"CitationRef\"\u003e23\u003c/span\u003e, \u003cspan citationid=\"CR24\" class=\"CitationRef\"\u003e24\u003c/span\u003e] reported the estimation of trans-RES in lipidic nanoparticles using C18 column by isocratic elution utilizing the mobile phase of 2% acetic acid and acetonitrile (80:20% v/v) by fluorometric detection at an excitation wavelength of 330 nm and emission wavelength of 374 nm. Further they validated the method for the quantification of trans-RES in lipidic nanoparticles emphazing its application for in-vitro intestinal permeability.\u003c/p\u003e\u003cp\u003eColons specific delivery system includes pH dependent a time dependent, osmotic pressure controlled. The colon specific drug delivery systems follow various mechanism of drug release such as transit time dependent, pH dependent, pH and time dependent, colonic microbial dependent, pH and microbial dependent, colonic pressure controlled and osmotic pressure-controlled systems [\u003cspan citationid=\"CR25\" class=\"CitationRef\"\u003e25\u003c/span\u003e].This could be achieved through the utilization of various biodegradable, hydrophilic and acidic pH tolerant polymeric materials reported for the successful colon specific drug delivery on which hypothesis of this work based. pH sensitivity of resveratrol and PLGA may aid selective release in colon cancer specific area. The prepared formulations were subjected to various in vitro evaluations to confirm the improve the solubility of resveratrol and QRNPs in addressing the objective of this study [\u003cspan citationid=\"CR26\" class=\"CitationRef\"\u003e26\u003c/span\u003e].\u003c/p\u003e\u003c/div\u003e"},{"header":"2. MATERIALS AND METHODS","content":"\u003cdiv id=\"Sec6\" class=\"Section2\"\u003e\u003ch2\u003e2.1 Materials\u003c/h2\u003e\u003cp\u003eQuercetin, resveratrol PLGA, D-α-Tocopheryl polyethylene glycol succinate (TPGS), Acetone, ethanol, DMSO, PBS, Tween 80 MTT (3-(4,5-dimethylthiazol-2-yl)-2,5-diphenyl tetrazolium bromide), and acetic acid were used as solvents. Milli-Q water was purchased from Thermo fisher, India.\u003c/p\u003e\u003c/div\u003e\u003cdiv id=\"Sec7\" class=\"Section2\"\u003e\u003ch2\u003e2.2 Development of Quercetin and Resveratrol Loaded Nanoparticles (QRNPs).\u003c/h2\u003e\u003cp\u003eQuercetin resveratrol loaded nanoparticles (QRNPs) was prepared as per previously reported emulsification method [\u003cspan citationid=\"CR27\" class=\"CitationRef\"\u003e27\u003c/span\u003e, \u003cspan citationid=\"CR28\" class=\"CitationRef\"\u003e28\u003c/span\u003e]. Briefly, organic phase was prepared by dissolving 10 mg of poly (lactic-co-glycolic acid) in acetone (5 mL) sonicated for 5 mins to form uniform mixture. Further, quercetin (2 mg) and RES (2 mg) was transferred to above mixture. In another tube 2.5% of aqueous D-α-Tocopherol polyethylene glycol (TPGS) acts as the aqueous phase which kept under stirring at 700 rpm for 30 mins to form uniform dispersion. The resultant organic phase was injected into aqueous phase under magnetic stirring 700 rpm for 24 h under light protection. The formed nanoparticles (QRNPs) were collected by centrifugation at 15,000 rpm for 15 mins at 4\u0026deg;C, washed and concentrated into final volume of 1 mL. The average particle size and zeta potential of developed QRNPs were measured using Zetasizer (Nano ZS90 series, Malvern, UK) [\u003cspan citationid=\"CR29\" class=\"CitationRef\"\u003e29\u003c/span\u003e]\u003c/p\u003e\u003c/div\u003e\u003cdiv id=\"Sec8\" class=\"Section2\"\u003e\u003ch2\u003e2.3 Optimization using CDD\u003c/h2\u003e\u003cp\u003eThe formulation of QRNPs was optimized using Central Composite Design (CCD) via Design Expert software (Version 12). Independent variables were PLGA and TPGS, and the response variables were particle size, zeta potential, and drug release percentage. The experimental runs were 13 generated. Statistical models including ANOVA, regression analysis, and lack-of-fit tests were used to assess the significance and adequacy of the responses. Desirability plots were used to predict the optimal formulation [\u003cspan citationid=\"CR30\" class=\"CitationRef\"\u003e30\u003c/span\u003e].\u003c/p\u003e\u003c/div\u003e\u003cdiv id=\"Sec9\" class=\"Section2\"\u003e\u003ch2\u003e2.4 In-vitro characterization techniques for QRNPs\u003c/h2\u003e\u003cp\u003eParticle size distribution and zeta potential of QRNPs were recorded using Malvern Zeta sizer (Nano ZS90 series, Malvern Instruments, UK) by diluting MTCs (1:10) using Milli Q water sonicated for 2 min and further analysed. pH of QRNPs was checked using pH meter (AD8000 Mumbai) [\u003cspan citationid=\"CR31\" class=\"CitationRef\"\u003e31\u003c/span\u003e].\u003c/p\u003e\u003c/div\u003e\u003cdiv id=\"Sec10\" class=\"Section2\"\u003e\u003ch2\u003e2.5 Morphology characterization (TEM)\u003c/h2\u003e\u003cp\u003eOptical microscopy was also employed for initial observation at 10\u0026times; magnification [\u003cspan citationid=\"CR32\" class=\"CitationRef\"\u003e32\u003c/span\u003e]. The surface morphology and structure of QRNPs were examined by High-Resolution Transmission Electron Microscopy (HR-TEM). A diluted drop of nanoparticle dispersion was placed on a carbon-coated copper grid and allowed to air dry. Imaging was at an accelerating voltage of 200 kV to monitor particle shape and homogeneity.\u003c/p\u003e\u003c/div\u003e\u003cdiv id=\"Sec11\" class=\"Section2\"\u003e\u003ch2\u003e2.6 Fourier transform infrared spectroscopy analysis\u003c/h2\u003e\u003cp\u003eFTIR analysis for QUR, RES, and QRNPs was performed using an FTIR spectrometer (Jasco 6300) by the KBr pellet press technique at a scanning range of 4000 cm⁻\u0026sup1; to 400 cm⁻\u0026sup1; [\u003cspan citationid=\"CR33\" class=\"CitationRef\"\u003e33\u003c/span\u003e]. The samples were finely powdered, combined with dry potassium bromide (KBr) at a 1:100 ratio, and compacted into translucent pellets with a hydraulic press. The spectra were collected at a resolution of 4 cm⁻\u0026sup1;. The distinctive peaks corresponding to functional groups were analysed to determine structural properties of the pure pharmaceuticals as well as potential interactions between the medications and polymeric excipients in the nanoparticle formulation.\u003c/p\u003e\u003c/div\u003e\u003cdiv id=\"Sec12\" class=\"Section2\"\u003e\u003ch2\u003e2.7 In Vitro Release study for QRNPs\u003c/h2\u003e\u003cp\u003eThe kinetics of quercetin release from QRNPs was followed with in vitro release studies by dialysis bag diffusion method after [\u003cspan citationid=\"CR34\" class=\"CitationRef\"\u003e34\u003c/span\u003e] study. About 2 mL of QRNPs suspension (equivalent to a known quercetin concentration) was introduced into a pre-soaked dialysis membrane (MWCO 12\u0026ndash;14 kDa) and sealed. The dialysis bag prepared was placed in 50 mL of phosphate-buffered saline (PBS, pH 7.4) containing 0.5% Tween 80 for sink maintenance, and kept between 37\u0026thinsp;\u0026plusmn;\u0026thinsp;0.5\u0026deg;C, stirring continuously at 100 rpm. At definite intervals of 0, 5, 10, 15, 20hrs, the aliquots of 1 mL were drawn from the release medium, which were immediately replaced with an equal volume of freshly prepared PBS. The samples were analyzed for the released quercetin content using UV\u0026ndash;Vis spectrophotometry set at 370 nm. All tests were done in triplicate, with the data expressed as cumulative percentage drug release.\u003c/p\u003e\u003c/div\u003e\u003cdiv id=\"Sec13\" class=\"Section2\"\u003e\u003ch2\u003e2.8 Stability Studies\u003c/h2\u003e\u003cp\u003eQRNPs were incubated in simulated gastric fluid (SGF, pH 1.2) and simulated intestinal fluid (SIF, pH 6.8) under mild shaking at 37\u0026deg;C for 2 hrs. After incubation, particle size and zeta potential were measured using DLS (Malvern Zetasizer Nano ZS90). Changes in size distribution used to assess colloidal stability under physiological pH conditions [\u003cspan citationid=\"CR35\" class=\"CitationRef\"\u003e35\u003c/span\u003e].\u003c/p\u003e\u003c/div\u003e\u003cdiv id=\"Sec14\" class=\"Section2\"\u003e\u003ch2\u003e2.9 In Vitro Cytotoxicity Assay (MTT)\u003c/h2\u003e\u003cp\u003eThe cytotoxicity of QRNPs, free QUR, and RES against Caco-2 colon cancer cells was assessed using the MTT assay. Cells are seeded in 1\u0026times;10⁴ cells/well in 96-well plates and treated with test formulations of different concentrations (10\u0026ndash;100 \u0026micro;g/mL) for 24\u0026ndash;48 hrs. After treatment, add 20 \u0026micro;L of MTT solution (5 mg/mL) and incubation for 4 hours. Formazan crystals were dissolved in DMSO, and the absorbance was read at 570 nm using a microplate reader. CC₅₀ values were calculated from the dose-response curve [\u003cspan citationid=\"CR36\" class=\"CitationRef\"\u003e36\u003c/span\u003e].\u003c/p\u003e\u003c/div\u003e"},{"header":"3. RESULT","content":"\u003cp\u003e\u003cstrong\u003e3.1 Optimization\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003eUsing central composite design with Design Expert (Version 12), the tests are optimised. PLGA (Factor A) and TPGS (Factor B) at two levels low and high were chosen as the independent variables. Particle size (nm), Zeta potential (mV)and QRNPs release (%) are the dependent or response variables [37] (Table 1). Regarding several statistical metrics, including probability (P value), regression coefficient (R\u003csup\u003e2\u003c/sup\u003e value), model Fischer\u0026apos;s value (F value), and lack of fit model, the design was optimized. The experimental portion was carried out based on the outcome of the design [30]. Table 2 contains the final formulation.\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eTable 1:\u003c/strong\u003e Independent variables and their coded levels used in CCD optimization\u003c/p\u003e\n\u003ctable border=\"1\" cellspacing=\"0\" cellpadding=\"0\" width=\"519\"\u003e\n \u003ctbody\u003e\n \u003ctr\u003e\n \u003ctd valign=\"top\" style=\"width: 95px;\"\u003e\n \u003cp\u003eFactors\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 123px;\"\u003e\n \u003cp\u003eDesignation\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 82px;\"\u003e\n \u003cp\u003eUnits\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 109px;\"\u003e\n \u003cp\u003eLow level\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 110px;\"\u003e\n \u003cp\u003eHigh level\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd valign=\"top\" style=\"width: 95px;\"\u003e\n \u003cp\u003eA\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 123px;\"\u003e\n \u003cp\u003ePLGA\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 82px;\"\u003e\n \u003cp\u003emg\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 109px;\"\u003e\n \u003cp\u003e0.5\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 110px;\"\u003e\n \u003cp\u003e1.5\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd valign=\"top\" style=\"width: 95px;\"\u003e\n \u003cp\u003eB\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 123px;\"\u003e\n \u003cp\u003eTPGS\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 82px;\"\u003e\n \u003cp\u003e%\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 109px;\"\u003e\n \u003cp\u003e0.5\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 110px;\"\u003e\n \u003cp\u003e1.0\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003c/tbody\u003e\n\u003c/table\u003e\n\u003cp\u003e\u0026nbsp;\u003c/p\u003e\n\u003ctable border=\"0\" cellspacing=\"0\" cellpadding=\"0\" width=\"391\"\u003e\n \u003ctbody\u003e\n \u003ctr\u003e\n \u003ctd valign=\"top\" style=\"width: 133px;\"\u003e\n \u003cp\u003e\u0026nbsp; \u0026nbsp; \u0026nbsp; Response\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 181px;\"\u003e\n \u003cp\u003e\u0026nbsp; \u0026nbsp; \u0026nbsp; \u0026nbsp; \u0026nbsp; Designation\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 77px;\"\u003e\n \u003cp\u003eUnits\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd valign=\"top\" style=\"width: 133px;\"\u003e\n \u003cp\u003e\u0026nbsp; \u0026nbsp; \u0026nbsp; R1\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 181px;\"\u003e\n \u003cp\u003e\u0026nbsp; \u0026nbsp; \u0026nbsp; \u0026nbsp; \u0026nbsp; Particle Size\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 77px;\"\u003e\n \u003cp\u003e\u0026nbsp;nm\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd valign=\"top\" style=\"width: 133px;\"\u003e\n \u003cp\u003e\u0026nbsp; \u0026nbsp; \u0026nbsp; R2\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 181px;\"\u003e\n \u003cp\u003e\u0026nbsp; \u0026nbsp; \u0026nbsp; \u0026nbsp; \u0026nbsp; Zeta Potential\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 77px;\"\u003e\n \u003cp\u003emV\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd valign=\"top\" style=\"width: 133px;\"\u003e\n \u003cp\u003e\u0026nbsp; \u0026nbsp; \u0026nbsp; R3\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 181px;\"\u003e\n \u003cp\u003e\u0026nbsp; \u0026nbsp; \u0026nbsp; \u0026nbsp; \u0026nbsp; QRNPs release\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 77px;\"\u003e\n \u003cp\u003e\u0026nbsp;%\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003c/tbody\u003e\n\u003c/table\u003e\n\u003cp\u003e\u0026nbsp;\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eTable 2:\u003c/strong\u003e Central Composite Design (CCD) based trial formulation runs for QRNPs\u003c/p\u003e\n\u003ctable border=\"1\" cellspacing=\"0\" cellpadding=\"0\" width=\"411\"\u003e\n \u003ctbody\u003e\n \u003ctr\u003e\n \u003ctd style=\"width: 125px;\"\u003e\n \u003cp\u003eRun\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 145px;\"\u003e\n \u003cp\u003eFactor A\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 141px;\"\u003e\n \u003cp\u003eFactor B\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd style=\"width: 125px;\"\u003e\u003cbr\u003e\u003c/td\u003e\n \u003ctd style=\"width: 145px;\"\u003e\n \u003cp\u003ePLGA (mg)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 141px;\"\u003e\n \u003cp\u003eTPGS (%)\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd style=\"width: 125px;\"\u003e\n \u003cp\u003e1\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 145px;\"\u003e\n \u003cp\u003e15\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 141px;\"\u003e\n \u003cp\u003e3.31\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd style=\"width: 125px;\"\u003e\n \u003cp\u003e2\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 145px;\"\u003e\n \u003cp\u003e15\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 141px;\"\u003e\n \u003cp\u003e1.18\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd style=\"width: 125px;\"\u003e\n \u003cp\u003e3\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 145px;\"\u003e\n \u003cp\u003e22.07\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 141px;\"\u003e\n \u003cp\u003e2.25\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd style=\"width: 125px;\"\u003e\n \u003cp\u003e4\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 145px;\"\u003e\n \u003cp\u003e7.92\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 141px;\"\u003e\n \u003cp\u003e2.25\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd style=\"width: 125px;\"\u003e\n \u003cp\u003e5\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 145px;\"\u003e\n \u003cp\u003e15\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 141px;\"\u003e\n \u003cp\u003e2.25\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd style=\"width: 125px;\"\u003e\n \u003cp\u003e6\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 145px;\"\u003e\n \u003cp\u003e15\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 141px;\"\u003e\n \u003cp\u003e2.25\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd style=\"width: 125px;\"\u003e\n \u003cp\u003e7\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 145px;\"\u003e\n \u003cp\u003e15\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 141px;\"\u003e\n \u003cp\u003e2.25\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd style=\"width: 125px;\"\u003e\n \u003cp\u003e8\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 145px;\"\u003e\n \u003cp\u003e10\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 141px;\"\u003e\n \u003cp\u003e1.5\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd style=\"width: 125px;\"\u003e\n \u003cp\u003e9\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 145px;\"\u003e\n \u003cp\u003e15\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 141px;\"\u003e\n \u003cp\u003e2.25\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd style=\"width: 125px;\"\u003e\n \u003cp\u003e10\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 145px;\"\u003e\n \u003cp\u003e15\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 141px;\"\u003e\n \u003cp\u003e2.25\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd style=\"width: 125px;\"\u003e\n \u003cp\u003e11\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 145px;\"\u003e\n \u003cp\u003e20\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 141px;\"\u003e\n \u003cp\u003e1.5\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd style=\"width: 125px;\"\u003e\n \u003cp\u003e12\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 145px;\"\u003e\n \u003cp\u003e20\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 141px;\"\u003e\n \u003cp\u003e3\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd style=\"width: 125px;\"\u003e\n \u003cp\u003e13\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 145px;\"\u003e\n \u003cp\u003e10\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 141px;\"\u003e\n \u003cp\u003e3\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003c/tbody\u003e\n\u003c/table\u003e\n\u003cp\u003e\u0026nbsp;\u003c/p\u003e\n\u003cp\u003e13 trial formulation have been obtained from CCD design and are put in table 2. (Table.3) gives the result of all 13 trials formulation for response factor particle size (nm), zeta potential (mV), QRNPs release (%) [38]\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eTable 3:\u003c/strong\u003e Experimental results for particle size, zeta potential, and QRNPs release percentage\u003c/p\u003e\n\u003ctable border=\"1\" cellspacing=\"0\" cellpadding=\"0\" width=\"627\"\u003e\n \u003ctbody\u003e\n \u003ctr\u003e\n \u003ctd style=\"width: 63px;\"\u003e\u003cbr\u003e\u003c/td\u003e\n \u003ctd style=\"width: 107px;\"\u003e\n \u003cp\u003eFactor 1\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 123px;\"\u003e\n \u003cp\u003eFactor 2\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 109px;\"\u003e\n \u003cp\u003eResponse 1\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 118px;\"\u003e\n \u003cp\u003eResponse 2\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 108px;\"\u003e\n \u003cp\u003eResponse 3\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd style=\"width: 63px;\"\u003e\n \u003cp\u003eRun\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 107px;\"\u003e\n \u003cp\u003ePLGA (mg)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 123px;\"\u003e\n \u003cp\u003eTPGS (%)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 109px;\"\u003e\n \u003cp\u003eParticle size (nm)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 118px;\"\u003e\n \u003cp\u003eZeta Potential (mV)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 108px;\"\u003e\n \u003cp\u003eQRNPs release (%)\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd style=\"width: 63px;\"\u003e\n \u003cp\u003e1\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 107px;\"\u003e\n \u003cp\u003e15\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 123px;\"\u003e\n \u003cp\u003e3.31\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 109px;\"\u003e\n \u003cp\u003e174\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 118px;\"\u003e\n \u003cp\u003e-22\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 108px;\"\u003e\n \u003cp\u003e84\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd style=\"width: 63px;\"\u003e\n \u003cp\u003e2\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 107px;\"\u003e\n \u003cp\u003e15\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 123px;\"\u003e\n \u003cp\u003e1.18\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 109px;\"\u003e\n \u003cp\u003e173\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 118px;\"\u003e\n \u003cp\u003e-22\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 108px;\"\u003e\n \u003cp\u003e82\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd style=\"width: 63px;\"\u003e\n \u003cp\u003e3\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 107px;\"\u003e\n \u003cp\u003e22.07\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 123px;\"\u003e\n \u003cp\u003e2.25\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 109px;\"\u003e\n \u003cp\u003e172\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 118px;\"\u003e\n \u003cp\u003e-22\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 108px;\"\u003e\n \u003cp\u003e82\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd style=\"width: 63px;\"\u003e\n \u003cp\u003e4\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 107px;\"\u003e\n \u003cp\u003e7.92\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 123px;\"\u003e\n \u003cp\u003e2.25\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 109px;\"\u003e\n \u003cp\u003e170\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 118px;\"\u003e\n \u003cp\u003e-23\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 108px;\"\u003e\n \u003cp\u003e83\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd style=\"width: 63px;\"\u003e\n \u003cp\u003e5\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 107px;\"\u003e\n \u003cp\u003e15\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 123px;\"\u003e\n \u003cp\u003e2.25\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 109px;\"\u003e\n \u003cp\u003e176\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 118px;\"\u003e\n \u003cp\u003e-22\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 108px;\"\u003e\n \u003cp\u003e83\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd style=\"width: 63px;\"\u003e\n \u003cp\u003e6\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 107px;\"\u003e\n \u003cp\u003e15\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 123px;\"\u003e\n \u003cp\u003e2.25\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 109px;\"\u003e\n \u003cp\u003e176\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 118px;\"\u003e\n \u003cp\u003e-23\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 108px;\"\u003e\n \u003cp\u003e85\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd style=\"width: 63px;\"\u003e\n \u003cp\u003e7\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 107px;\"\u003e\n \u003cp\u003e15\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 123px;\"\u003e\n \u003cp\u003e2.25\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 109px;\"\u003e\n \u003cp\u003e174\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 118px;\"\u003e\n \u003cp\u003e-22\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 108px;\"\u003e\n \u003cp\u003e86\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd style=\"width: 63px;\"\u003e\n \u003cp\u003e8\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 107px;\"\u003e\n \u003cp\u003e10\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 123px;\"\u003e\n \u003cp\u003e1.5\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 109px;\"\u003e\n \u003cp\u003e171\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 118px;\"\u003e\n \u003cp\u003e-23\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 108px;\"\u003e\n \u003cp\u003e82\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd style=\"width: 63px;\"\u003e\n \u003cp\u003e9\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 107px;\"\u003e\n \u003cp\u003e15\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 123px;\"\u003e\n \u003cp\u003e2.25\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 109px;\"\u003e\n \u003cp\u003e175\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 118px;\"\u003e\n \u003cp\u003e-23\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 108px;\"\u003e\n \u003cp\u003e85\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd style=\"width: 63px;\"\u003e\n \u003cp\u003e10\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 107px;\"\u003e\n \u003cp\u003e15\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 123px;\"\u003e\n \u003cp\u003e2.25\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 109px;\"\u003e\n \u003cp\u003e177\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 118px;\"\u003e\n \u003cp\u003e-22\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 108px;\"\u003e\n \u003cp\u003e85\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd style=\"width: 63px;\"\u003e\n \u003cp\u003e11\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 107px;\"\u003e\n \u003cp\u003e20\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 123px;\"\u003e\n \u003cp\u003e1.5\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 109px;\"\u003e\n \u003cp\u003e174\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 118px;\"\u003e\n \u003cp\u003e-22\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 108px;\"\u003e\n \u003cp\u003e82\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd style=\"width: 63px;\"\u003e\n \u003cp\u003e12\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 107px;\"\u003e\n \u003cp\u003e20\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 123px;\"\u003e\n \u003cp\u003e3\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 109px;\"\u003e\n \u003cp\u003e176\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 118px;\"\u003e\n \u003cp\u003e-24\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 108px;\"\u003e\n \u003cp\u003e84\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd style=\"width: 63px;\"\u003e\n \u003cp\u003e13\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 107px;\"\u003e\n \u003cp\u003e10\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 123px;\"\u003e\n \u003cp\u003e3\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 109px;\"\u003e\n \u003cp\u003e173\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 118px;\"\u003e\n \u003cp\u003e-21\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 108px;\"\u003e\n \u003cp\u003e84\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003c/tbody\u003e\n\u003c/table\u003e\n\u003cp\u003e\u003cstrong\u003e\u0026nbsp;\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003ea) Particle size:\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003eThe response was constructed based on a polynomial equation in which independent factors are coded. The coded equation is as follows:\u0026nbsp;\u003c/p\u003e\n\u003cp\u003eIn this polynomial equation PLGA is coded as A and TPGS is coded as B.\u003c/p\u003e\n\u003cp\u003eANNOVA table (table 4) shows the significance of the model. The model is apparently significant given its Model F-value of 5.71. An F-value this great might be caused by noise with a mere 2.05% probability. Significant model terms are those with P-values less 0.0500, here, the model terms A and A\u003csup\u003e2\u003c/sup\u003e are significant. Indicators of the model terms\u0026apos; significance are values larger than 0.1000. According to the Lack of Fit (F-value) of 1.34, the lack of fit is not significant in comparison to pure error. A significant Lack of Fit F-value has a 38.01% likelihood of being the result of noise. We want the model to fit; thus, a negligible lack of fit is ideal.\u003c/p\u003e\n\u003cp\u003eParticle size = 175.60 + 1.10 *A + 0.6768 *B + 0.00 *AB - 1.99 *A\u0026sup2; - 0.7375 *B\u0026sup2;.\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eTable 4:\u003c/strong\u003e ANOVA results for particle size response\u003c/p\u003e\n\u003ctable border=\"0\" cellspacing=\"0\" cellpadding=\"0\" width=\"662\"\u003e\n \u003ctbody\u003e\n \u003ctr\u003e\n \u003ctd style=\"width: 93px;\"\u003e\n \u003cp\u003e\u003cstrong\u003eSource\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 92px;\"\u003e\n \u003cp\u003e\u003cstrong\u003eSum of Squares\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 84px;\"\u003e\n \u003cp\u003e\u003cstrong\u003edf\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 92px;\"\u003e\n \u003cp\u003e\u003cstrong\u003eMean Square\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 92px;\"\u003e\n \u003cp\u003e\u003cstrong\u003eF-value\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 90px;\"\u003e\n \u003cp\u003e\u003cstrong\u003ep-value\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 119px;\"\u003e\u003cbr\u003e\u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd style=\"width: 93px;\"\u003e\n \u003cp\u003e\u003cstrong\u003eModel\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 92px;\"\u003e\n \u003cp\u003e42.50\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 84px;\"\u003e\n \u003cp\u003e5\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 92px;\"\u003e\n \u003cp\u003e8.50\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 92px;\"\u003e\n \u003cp\u003e5.71\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 90px;\"\u003e\n \u003cp\u003e0.0205\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 119px;\"\u003e\n \u003cp\u003esignificant\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd style=\"width: 93px;\"\u003e\n \u003cp\u003eA-PLGA\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 92px;\"\u003e\n \u003cp\u003e9.74\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 84px;\"\u003e\n \u003cp\u003e1\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 92px;\"\u003e\n \u003cp\u003e9.74\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 92px;\"\u003e\n \u003cp\u003e6.55\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 90px;\"\u003e\n \u003cp\u003e0.0376\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 119px;\"\u003e\u003cbr\u003e\u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd style=\"width: 93px;\"\u003e\n \u003cp\u003eB-TPGS\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 92px;\"\u003e\n \u003cp\u003e3.66\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 84px;\"\u003e\n \u003cp\u003e1\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 92px;\"\u003e\n \u003cp\u003e3.66\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 92px;\"\u003e\n \u003cp\u003e2.46\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 90px;\"\u003e\n \u003cp\u003e0.1606\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 119px;\"\u003e\u003cbr\u003e\u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd style=\"width: 93px;\"\u003e\n \u003cp\u003eAB\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 92px;\"\u003e\n \u003cp\u003e7.105E-15\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 84px;\"\u003e\n \u003cp\u003e1\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 92px;\"\u003e\n \u003cp\u003e7.105E-15\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 92px;\"\u003e\n \u003cp\u003e4.774E-15\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 90px;\"\u003e\n \u003cp\u003e1.0000\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 119px;\"\u003e\u003cbr\u003e\u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd style=\"width: 93px;\"\u003e\n \u003cp\u003eA\u0026sup2;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 92px;\"\u003e\n \u003cp\u003e27.48\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 84px;\"\u003e\n \u003cp\u003e1\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 92px;\"\u003e\n \u003cp\u003e27.48\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 92px;\"\u003e\n \u003cp\u003e18.46\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 90px;\"\u003e\n \u003cp\u003e0.0036\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 119px;\"\u003e\u003cbr\u003e\u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd style=\"width: 93px;\"\u003e\n \u003cp\u003eB\u0026sup2;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 92px;\"\u003e\n \u003cp\u003e3.78\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 84px;\"\u003e\n \u003cp\u003e1\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 92px;\"\u003e\n \u003cp\u003e3.78\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 92px;\"\u003e\n \u003cp\u003e2.54\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 90px;\"\u003e\n \u003cp\u003e0.1549\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 119px;\"\u003e\u003cbr\u003e\u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd style=\"width: 93px;\"\u003e\n \u003cp\u003e\u003cstrong\u003eResidual\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 92px;\"\u003e\n \u003cp\u003e10.42\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 84px;\"\u003e\n \u003cp\u003e7\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 92px;\"\u003e\n \u003cp\u003e1.49\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 92px;\"\u003e\u003cbr\u003e\u003c/td\u003e\n \u003ctd style=\"width: 90px;\"\u003e\u003cbr\u003e\u003c/td\u003e\n \u003ctd style=\"width: 119px;\"\u003e\u003cbr\u003e\u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd style=\"width: 93px;\"\u003e\n \u003cp\u003eLack of Fit\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 92px;\"\u003e\n \u003cp\u003e5.22\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 84px;\"\u003e\n \u003cp\u003e3\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 92px;\"\u003e\n \u003cp\u003e1.74\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 92px;\"\u003e\n \u003cp\u003e1.34\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 90px;\"\u003e\n \u003cp\u003e0.3801\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 119px;\"\u003e\n \u003cp\u003eNot significant\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd style=\"width: 93px;\"\u003e\n \u003cp\u003ePure Error\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 92px;\"\u003e\n \u003cp\u003e5.20\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 84px;\"\u003e\n \u003cp\u003e4\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 92px;\"\u003e\n \u003cp\u003e1.30\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 92px;\"\u003e\u003cbr\u003e\u003c/td\u003e\n \u003ctd style=\"width: 90px;\"\u003e\u003cbr\u003e\u003c/td\u003e\n \u003ctd style=\"width: 119px;\"\u003e\u003cbr\u003e\u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd style=\"width: 93px;\"\u003e\n \u003cp\u003e\u003cstrong\u003eCor Total\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 92px;\"\u003e\n \u003cp\u003e52.92\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 84px;\"\u003e\n \u003cp\u003e12\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 92px;\"\u003e\u003cbr\u003e\u003c/td\u003e\n \u003ctd style=\"width: 92px;\"\u003e\u003cbr\u003e\u003c/td\u003e\n \u003ctd style=\"width: 90px;\"\u003e\u003cbr\u003e\u003c/td\u003e\n \u003ctd style=\"width: 119px;\"\u003e\u003cbr\u003e\u003c/td\u003e\n \u003c/tr\u003e\n \u003c/tbody\u003e\n\u003c/table\u003e\n\u003cp\u003e\u0026nbsp;\u003c/p\u003e\n\u003cp\u003eFit statistics (Table 5) showed correlation coefficient (R\u003csup\u003e2\u003c/sup\u003e) value of 0.8031, coefficient of variance % of 0.7014 for response factor particle size (nm). The predicted R\u003csup\u003e2\u003c/sup\u003e for particle size is 0.4453, the adjusted R\u003csup\u003e2\u003c/sup\u003e for particle size is 0.6625 and their difference is \u0026lt;0.2. The desirable adeq precision should be \u0026gt;4 and the obtained adeq precision is 6.6791. As shown in Fig. 1, both contour and 3D response surface plots demonstrated that increasing PLGA concentration led to a marginal increase in particle size, whereas TPGS had a subtler effect.\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eTable 5:\u0026nbsp;\u003c/strong\u003eFit statistics for particle size response\u003c/p\u003e\n\u003ctable border=\"0\" cellspacing=\"0\" cellpadding=\"0\" width=\"609\"\u003e\n \u003ctbody\u003e\n \u003ctr\u003e\n \u003ctd style=\"width: 152px;\"\u003e\n \u003cp\u003e\u003cstrong\u003eStd. Dev.\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 152px;\"\u003e\n \u003cp\u003e1.22\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 152px;\"\u003e\n \u003cp\u003e\u003cstrong\u003eR\u0026sup2;\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 152px;\"\u003e\n \u003cp\u003e0.8031\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd style=\"width: 152px;\"\u003e\n \u003cp\u003e\u003cstrong\u003eMean\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 152px;\"\u003e\n \u003cp\u003e173.92\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 152px;\"\u003e\n \u003cp\u003e\u003cstrong\u003eAdjusted R\u0026sup2;\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 152px;\"\u003e\n \u003cp\u003e0.6625\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd style=\"width: 152px;\"\u003e\n \u003cp\u003e\u003cstrong\u003eC.V. %\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 152px;\"\u003e\n \u003cp\u003e0.7014\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 152px;\"\u003e\n \u003cp\u003e\u003cstrong\u003ePredicted R\u0026sup2;\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 152px;\"\u003e\n \u003cp\u003e0.4453\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd style=\"width: 152px;\"\u003e\u003cbr\u003e\u003c/td\u003e\n \u003ctd style=\"width: 152px;\"\u003e\u003cbr\u003e\u003c/td\u003e\n \u003ctd style=\"width: 152px;\"\u003e\n \u003cp\u003e\u003cstrong\u003eAdeq Precision\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 152px;\"\u003e\n \u003cp\u003e6.6791\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003c/tbody\u003e\n\u003c/table\u003e\n\u003cp\u003e\u0026nbsp;\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eb) Zeta Potential:\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003eThe response Zeta potential was constructed based on a polynomial equation in which independent factors are coded. The coded equation is as follows:\u003c/p\u003e\n\u003cp\u003eZeta Potential = - 22.38 - 0.0732 A + 0.0 B - 1.0 AB.\u003c/p\u003e\n\u003cp\u003eIn this polynomial equation PLGA is coded as A and TPGS is coded as B.\u003c/p\u003e\n\u003cp\u003eANNOVA table (table 6) shows the significance of the model. The model is apparently significant given its Model F-value of 4.00. An F-value this great might be caused by noise with a mere 4.61% probability. Significant model terms are those with P-values less 0.0500, here, the model terms A and A\u003csup\u003e2\u003c/sup\u003e are significant. Indicators of the model terms\u0026apos; significance are values larger than 0.1000. According to the Lack of Fit (F-value) of 1.22, the lack of fit is not significant in comparison to pure error. A significant Lack of Fit F-value has a 43.50% likelihood of being the result of noise. We want the model to fit, thus a negligible lack of fit is ideal [39].\u003c/p\u003e\n\u003cp\u003eFit statistics (Table 7) showed Predicted R\u003csup\u003e2\u003c/sup\u003e value of -0.1442 which shows that overall mean is the better predictor of the model, coefficient of variance % of 2.59 for response factor Zeta potential (mV). The Correlation coefficient (R\u003csup\u003e2)\u003c/sup\u003e for Zeta potential is 0.5731, the adjusted R\u003csup\u003e2\u003c/sup\u003e for Zeta potential is 0.4284. The desirable adeq precision should be \u0026gt;4 and the obtained adeq precision is 6.6646. These are the statistical parameters that supports the chosen model is significant for the formulation of QRNPs. Fig. 2, shows that surface charge remained consistently negative across the formulation range, indicating good colloidal stability, with a notable influence from PLGA concentration.\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eTable 6:\u0026nbsp;\u003c/strong\u003eANOVA results for zeta potential response\u003c/p\u003e\n\u003ctable border=\"0\" cellspacing=\"0\" cellpadding=\"0\" width=\"609\"\u003e\n \u003ctbody\u003e\n \u003ctr\u003e\n \u003ctd style=\"width: 152px;\"\u003e\n \u003cp\u003e\u003cstrong\u003eStd. Dev.\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 152px;\"\u003e\n \u003cp\u003e0.5806\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 152px;\"\u003e\n \u003cp\u003e\u003cstrong\u003eR\u0026sup2;\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 152px;\"\u003e\n \u003cp\u003e0.5713\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd style=\"width: 152px;\"\u003e\n \u003cp\u003e\u003cstrong\u003eMean\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 152px;\"\u003e\n \u003cp\u003e-22.36\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 152px;\"\u003e\n \u003cp\u003e\u003cstrong\u003eAdjusted R\u0026sup2;\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 152px;\"\u003e\n \u003cp\u003e0.4284\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd style=\"width: 152px;\"\u003e\n \u003cp\u003e\u003cstrong\u003eC.V. %\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 152px;\"\u003e\n \u003cp\u003e2.59\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 152px;\"\u003e\n \u003cp\u003e\u003cstrong\u003ePredicted R\u0026sup2;\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 152px;\"\u003e\n \u003cp\u003e-0.1442\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd style=\"width: 152px;\"\u003e\u003cbr\u003e\u003c/td\u003e\n \u003ctd style=\"width: 152px;\"\u003e\u003cbr\u003e\u003c/td\u003e\n \u003ctd style=\"width: 152px;\"\u003e\n \u003cp\u003e\u003cstrong\u003eAdeq Precision\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 152px;\"\u003e\n \u003cp\u003e6.6646\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003c/tbody\u003e\n\u003c/table\u003e\n\u003cp\u003e\u003cstrong\u003e\u0026nbsp;\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eTable 7:\u0026nbsp;\u003c/strong\u003eFit statistics for zeta potential response\u003c/p\u003e\n\u003ctable border=\"1\" cellspacing=\"0\" cellpadding=\"0\" width=\"646\"\u003e\n \u003ctbody\u003e\n \u003ctr\u003e\n \u003ctd style=\"width: 92px;\"\u003e\n \u003cp\u003e\u003cstrong\u003eSource\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 92px;\"\u003e\n \u003cp\u003e\u003cstrong\u003eSum of Squares\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 92px;\"\u003e\n \u003cp\u003e\u003cstrong\u003edf\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 92px;\"\u003e\n \u003cp\u003e\u003cstrong\u003eMean Square\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 92px;\"\u003e\n \u003cp\u003e\u003cstrong\u003eF-value\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 92px;\"\u003e\n \u003cp\u003e\u003cstrong\u003ep-value\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 92px;\"\u003e\u003cbr\u003e\u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd style=\"width: 92px;\"\u003e\n \u003cp\u003e\u003cstrong\u003eModel\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 92px;\"\u003e\n \u003cp\u003e4.04\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 92px;\"\u003e\n \u003cp\u003e3\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 92px;\"\u003e\n \u003cp\u003e1.35\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 92px;\"\u003e\n \u003cp\u003e4.00\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 92px;\"\u003e\n \u003cp\u003e0.0461\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 92px;\"\u003e\n \u003cp\u003esignificant\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd style=\"width: 92px;\"\u003e\n \u003cp\u003eA-PLGA\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 92px;\"\u003e\n \u003cp\u003e0.0429\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 92px;\"\u003e\n \u003cp\u003e1\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 92px;\"\u003e\n \u003cp\u003e0.0429\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 92px;\"\u003e\n \u003cp\u003e0.1272\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 92px;\"\u003e\n \u003cp\u003e0.7295\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 92px;\"\u003e\u003cbr\u003e\u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd style=\"width: 92px;\"\u003e\n \u003cp\u003eB-TPGS\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 92px;\"\u003e\n \u003cp\u003e0.0000\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 92px;\"\u003e\n \u003cp\u003e1\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 92px;\"\u003e\n \u003cp\u003e0.0000\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 92px;\"\u003e\n \u003cp\u003e0.0000\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 92px;\"\u003e\n \u003cp\u003e1.0000\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 92px;\"\u003e\u003cbr\u003e\u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd style=\"width: 92px;\"\u003e\n \u003cp\u003eAB\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 92px;\"\u003e\n \u003cp\u003e4.00\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 92px;\"\u003e\n \u003cp\u003e1\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 92px;\"\u003e\n \u003cp\u003e4.00\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 92px;\"\u003e\n \u003cp\u003e11.87\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 92px;\"\u003e\n \u003cp\u003e0.0073\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 92px;\"\u003e\u003cbr\u003e\u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd style=\"width: 92px;\"\u003e\n \u003cp\u003e\u003cstrong\u003eResidual\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 92px;\"\u003e\n \u003cp\u003e3.03\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 92px;\"\u003e\n \u003cp\u003e9\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 92px;\"\u003e\n \u003cp\u003e0.3371\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 92px;\"\u003e\u003cbr\u003e\u003c/td\u003e\n \u003ctd style=\"width: 92px;\"\u003e\u003cbr\u003e\u003c/td\u003e\n \u003ctd style=\"width: 92px;\"\u003e\u003cbr\u003e\u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd style=\"width: 92px;\"\u003e\n \u003cp\u003eLack of Fit\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 92px;\"\u003e\n \u003cp\u003e1.83\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 92px;\"\u003e\n \u003cp\u003e5\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 92px;\"\u003e\n \u003cp\u003e0.3668\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 92px;\"\u003e\n \u003cp\u003e1.22\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 92px;\"\u003e\n \u003cp\u003e0.4350\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 92px;\"\u003e\n \u003cp\u003enot significant\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd style=\"width: 92px;\"\u003e\n \u003cp\u003ePure Error\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 92px;\"\u003e\n \u003cp\u003e1.20\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 92px;\"\u003e\n \u003cp\u003e4\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 92px;\"\u003e\n \u003cp\u003e0.3000\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 92px;\"\u003e\u003cbr\u003e\u003c/td\u003e\n \u003ctd style=\"width: 92px;\"\u003e\u003cbr\u003e\u003c/td\u003e\n \u003ctd style=\"width: 92px;\"\u003e\u003cbr\u003e\u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd style=\"width: 92px;\"\u003e\n \u003cp\u003e\u003cstrong\u003eCor Total\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 92px;\"\u003e\n \u003cp\u003e7.08\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 92px;\"\u003e\n \u003cp\u003e12\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 92px;\"\u003e\u003cbr\u003e\u003c/td\u003e\n \u003ctd style=\"width: 92px;\"\u003e\u003cbr\u003e\u003c/td\u003e\n \u003ctd style=\"width: 92px;\"\u003e\u003cbr\u003e\u003c/td\u003e\n \u003ctd style=\"width: 92px;\"\u003e\u003cbr\u003e\u003c/td\u003e\n \u003c/tr\u003e\n \u003c/tbody\u003e\n\u003c/table\u003e\n\u003cp\u003e\u0026nbsp;\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003ec) QRNPs Release:\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003eThe response QRNPs release was constructed based on a polynomial equation in which independent factors are coded. The coded equation is as follows:\u003c/p\u003e\n\u003cp\u003eQRNPs release = 84.80 - 0.1768 *A + 0.8536 *B + 0.0 *AB - 1.09 *A\u0026sup2; - 0.08375 *B\u0026sup2;\u003c/p\u003e\n\u003cp\u003eIn this polynomial equation PLGA is coded as A and TPGS is coded as B.\u003c/p\u003e\n\u003cp\u003eANNOVA table (table 8) shows the significance of the model. The model is apparently significant given its Model F-value of 4.64. An F-value this great might be caused by noise with a mere 3.45% probability. Significant model terms are those with P-values less 0.0500, here, the model terms A and A\u003csup\u003e2\u003c/sup\u003e are significant. Indicators of the model terms\u0026apos; significance are values larger than 0.1000. According to the Lack of Fit (F-value) of 0.15, the lack of fit is not significant in comparison to pure error. A significant Lack of Fit F-value has a 92.33% likelihood of being the result of noise. We want the model to fit; thus, a negligible lack of fit is ideal [34,39].\u003c/p\u003e\n\u003cp\u003eFit statistics (Table 9) showed correlation coefficient (R\u003csup\u003e2\u003c/sup\u003e) value of 0.7683, coefficient of variance % of 1.05 for response factor QRNPs release (%). The predicted R\u003csup\u003e2\u003c/sup\u003e is 0.5066, the adjusted R\u003csup\u003e2\u003c/sup\u003e for particle size is 0.6028 and their difference is \u0026lt;0.2. The desirable adeq precision should be \u0026gt;4 and the obtained adeq precision is 4.9755. These are statistical parameters that supports the designed model is significant for the formulation. Fig 3, a and b show that QRNPs release (%) increases with higher TPGS and moderate PLGA levels, indicating optimal polymer\u0026ndash;surfactant balance enhances drug release.\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eTable 8:\u003c/strong\u003e\u003cstrong\u003e\u0026nbsp;\u003c/strong\u003eANOVA results for QRNPs release response\u003c/p\u003e\n\u003ctable border=\"1\" cellspacing=\"0\" cellpadding=\"0\" width=\"679\"\u003e\n \u003ctbody\u003e\n \u003ctr\u003e\n \u003ctd style=\"width: 103px;\"\u003e\n \u003cp\u003e\u003cstrong\u003eSource\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 98px;\"\u003e\n \u003cp\u003e\u003cstrong\u003eSum of Squares\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 83px;\"\u003e\n \u003cp\u003e\u003cstrong\u003edf\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 95px;\"\u003e\n \u003cp\u003e\u003cstrong\u003eMean Square\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 93px;\"\u003e\n \u003cp\u003e\u003cstrong\u003eF-value\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 93px;\"\u003e\n \u003cp\u003e\u003cstrong\u003ep-value\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 113px;\"\u003e\u003cbr\u003e\u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd style=\"width: 103px;\"\u003e\n \u003cp\u003e\u003cstrong\u003eModel\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 98px;\"\u003e\n \u003cp\u003e17.73\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 83px;\"\u003e\n \u003cp\u003e5\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 95px;\"\u003e\n \u003cp\u003e3.55\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 93px;\"\u003e\n \u003cp\u003e4.64\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 93px;\"\u003e\n \u003cp\u003e0.0345\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 113px;\"\u003e\n \u003cp\u003esignificant\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd style=\"width: 103px;\"\u003e\n \u003cp\u003eA-PLGA\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 98px;\"\u003e\n \u003cp\u003e0.2500\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 83px;\"\u003e\n \u003cp\u003e1\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 95px;\"\u003e\n \u003cp\u003e0.2500\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 93px;\"\u003e\n \u003cp\u003e0.3273\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 93px;\"\u003e\n \u003cp\u003e0.5851\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 113px;\"\u003e\u003cbr\u003e\u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd style=\"width: 103px;\"\u003e\n \u003cp\u003eB-TPGS\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 98px;\"\u003e\n \u003cp\u003e5.83\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 83px;\"\u003e\n \u003cp\u003e1\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 95px;\"\u003e\n \u003cp\u003e5.83\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 93px;\"\u003e\n \u003cp\u003e7.63\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 93px;\"\u003e\n \u003cp\u003e0.0280\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 113px;\"\u003e\u003cbr\u003e\u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd style=\"width: 103px;\"\u003e\n \u003cp\u003eAB\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 98px;\"\u003e\n \u003cp\u003e0.0000\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 83px;\"\u003e\n \u003cp\u003e1\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 95px;\"\u003e\n \u003cp\u003e0.0000\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 93px;\"\u003e\n \u003cp\u003e0.0000\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 93px;\"\u003e\n \u003cp\u003e1.0000\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 113px;\"\u003e\u003cbr\u003e\u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd style=\"width: 103px;\"\u003e\n \u003cp\u003eA\u0026sup2;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 98px;\"\u003e\n \u003cp\u003e8.23\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 83px;\"\u003e\n \u003cp\u003e1\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 95px;\"\u003e\n \u003cp\u003e8.23\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 93px;\"\u003e\n \u003cp\u003e10.77\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 93px;\"\u003e\n \u003cp\u003e0.0135\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 113px;\"\u003e\u003cbr\u003e\u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd style=\"width: 103px;\"\u003e\n \u003cp\u003eB\u0026sup2;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 98px;\"\u003e\n \u003cp\u003e4.88\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 83px;\"\u003e\n \u003cp\u003e1\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 95px;\"\u003e\n \u003cp\u003e4.88\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 93px;\"\u003e\n \u003cp\u003e6.39\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 93px;\"\u003e\n \u003cp\u003e0.0394\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 113px;\"\u003e\u003cbr\u003e\u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd style=\"width: 103px;\"\u003e\n \u003cp\u003e\u003cstrong\u003eResidual\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 98px;\"\u003e\n \u003cp\u003e5.35\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 83px;\"\u003e\n \u003cp\u003e7\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 95px;\"\u003e\n \u003cp\u003e0.7638\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 93px;\"\u003e\u003cbr\u003e\u003c/td\u003e\n \u003ctd style=\"width: 93px;\"\u003e\u003cbr\u003e\u003c/td\u003e\n \u003ctd style=\"width: 113px;\"\u003e\u003cbr\u003e\u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd style=\"width: 103px;\"\u003e\n \u003cp\u003eLack of Fit\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 98px;\"\u003e\n \u003cp\u003e0.5466\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 83px;\"\u003e\n \u003cp\u003e3\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 95px;\"\u003e\n \u003cp\u003e0.1822\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 93px;\"\u003e\n \u003cp\u003e0.1518\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 93px;\"\u003e\n \u003cp\u003e0.9233\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 113px;\"\u003e\n \u003cp\u003enot significant\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd style=\"width: 103px;\"\u003e\n \u003cp\u003ePure Error\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 98px;\"\u003e\n \u003cp\u003e4.80\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 83px;\"\u003e\n \u003cp\u003e4\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 95px;\"\u003e\n \u003cp\u003e1.20\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 93px;\"\u003e\u003cbr\u003e\u003c/td\u003e\n \u003ctd style=\"width: 93px;\"\u003e\u003cbr\u003e\u003c/td\u003e\n \u003ctd style=\"width: 113px;\"\u003e\u003cbr\u003e\u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd style=\"width: 103px;\"\u003e\n \u003cp\u003e\u003cstrong\u003eCor Total\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 98px;\"\u003e\n \u003cp\u003e23.08\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 83px;\"\u003e\n \u003cp\u003e12\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 95px;\"\u003e\u003cbr\u003e\u003c/td\u003e\n \u003ctd style=\"width: 93px;\"\u003e\u003cbr\u003e\u003c/td\u003e\n \u003ctd style=\"width: 93px;\"\u003e\u003cbr\u003e\u003c/td\u003e\n \u003ctd style=\"width: 113px;\"\u003e\u003cbr\u003e\u003c/td\u003e\n \u003c/tr\u003e\n \u003c/tbody\u003e\n\u003c/table\u003e\n\u003cp\u003e\u0026nbsp;\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eTable 9\u003c/strong\u003e: Fit statistics for QRNPs release response\u003c/p\u003e\n\u003ctable border=\"0\" cellspacing=\"0\" cellpadding=\"0\" width=\"555\"\u003e\n \u003ctbody\u003e\n \u003ctr\u003e\n \u003ctd style=\"width: 115px;\"\u003e\n \u003cp\u003e\u003cstrong\u003eStd. Dev.\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 140px;\"\u003e\n \u003cp\u003e0.8704\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 143px;\"\u003e\n \u003cp\u003e\u003cstrong\u003eR\u0026sup2;\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 157px;\"\u003e\n \u003cp\u003e0.7683\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd style=\"width: 115px;\"\u003e\n \u003cp\u003e\u003cstrong\u003eMean\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 140px;\"\u003e\n \u003cp\u003e83.62\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 143px;\"\u003e\n \u003cp\u003e\u003cstrong\u003eAdjusted R\u0026sup2;\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 157px;\"\u003e\n \u003cp\u003e0.6028\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd style=\"width: 115px;\"\u003e\n \u003cp\u003e\u003cstrong\u003eC.V. %\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 140px;\"\u003e\n \u003cp\u003e1.05\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 143px;\"\u003e\n \u003cp\u003e\u003cstrong\u003ePredicted R\u0026sup2;\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 157px;\"\u003e\n \u003cp\u003e0.5066\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd style=\"width: 115px;\"\u003e\u003cbr\u003e\u003c/td\u003e\n \u003ctd style=\"width: 140px;\"\u003e\u003cbr\u003e\u003c/td\u003e\n \u003ctd style=\"width: 143px;\"\u003e\n \u003cp\u003e\u003cstrong\u003eAdeq Precision\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 157px;\"\u003e\n \u003cp\u003e4.9755\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003c/tbody\u003e\n\u003c/table\u003e\n\u003cp\u003e\u0026nbsp;\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003ed) Desirability:\u0026nbsp;\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003eThe overall desirability was calculated using a multi-response optimization method, and the response surface was fitted to a second-order polynomial equation with coded variables. The coding equation for desirability (D) is given below:\u003c/p\u003e\n\u003cp\u003eDesirability (D) is 0.75 + 0.045A + 0.030B - 0.012A\u0026sup2; - 0.020B\u0026sup2; - 0.018*AB\u003c/p\u003e\n\u003cp\u003eThe desirability plots (Figure 4) illustrate the combined effect of PLGA (mg) and TPGS (%) on the optimization response.\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003e3.2\u0026nbsp;\u003c/strong\u003e\u003cstrong\u003eMorphology characterization (TEM)\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003eThe spherical morphology of prepared QRNPs showed was confirmed by HR-TEM analysis (Figure 5). The optical microscopic image of prepared QRNPs at 10x magnification were shown in figure 7a. a) Optical microscopic image of prepared QRNPs at 10 X magnification. (b) HR- TEM image of QRNPs [40].\u003c/p\u003e\n\u003cp\u003e\u003cbr\u003e\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003e3.3 FTIR\u0026nbsp;spectroscopic analysis\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003eThe FTIR spectrum for pure quercetin is shown in figure 6, where its characteristic bands were detected. OH, groups stretching were detectable at 3406 and 3283 cm\u003csup\u003e\u0026minus;1\u003c/sup\u003e, whereas OH bending of the phenol function was detectable at 1379 cm\u003csup\u003e\u0026minus;1\u003c/sup\u003e. The C=O aryl ketonic stretch absorption was evident at 1666 cm\u003csup\u003e\u0026minus;1\u003c/sup\u003e. C=C aromatic ring stretch bands were detectable at 1610, 1560, and 1510 cm\u003csup\u003e\u0026minus;1\u003c/sup\u003e. The in-plane bending band of C\u0026ndash;H in aromatic hydrocarbon was detectable at 1317 cm\u003csup\u003e\u0026minus;1\u003c/sup\u003e, and out-of-plane bending bands were evident at 933, 820, 679, and 600 cm\u003csup\u003e\u0026minus;1\u003c/sup\u003e. Bands at 1263, 1200, and 1165 cm\u003csup\u003e\u0026minus;1\u003c/sup\u003e were attributable due to to the C\u0026ndash;O stretching in the aryl ether ring, the C\u0026ndash;O stretching in phenol, and the C\u0026ndash;CO\u0026ndash;C stretch and bending in ketone, respectively (Fig 4). The strong band characteristic for trans-resveratrol can observed at 3290 cm\u003csup\u003e-1\u003c/sup\u003e, which originates due to valence \u0026nu;(OH)vibrations of phenols (Fig 1 RES). The band at 3021 cm-1 is the result of the valence vibration of vinyl group (=C-H), while the bands at 2924 and 2852 cm-1 originate from the valence vibrations of C-H bond from CH and CH2 groups. The valence vibrations \u0026nu;(C=C) of the benzene ring were observed at 1606, 1587, 1512 and 1444 cm\u003csup\u003e-1\u003c/sup\u003e. The in-plane deformational vibrations of OH group appeared at 1384 and 1325 cm\u003csup\u003e-1\u003c/sup\u003e which is responsible for OH group identification. Additional bands of valence vibrations of C-C bond at 1248 cm\u003csup\u003e-1\u003c/sup\u003e and of C-O bond from the phenol group at 1154 cm\u003csup\u003e-1\u003c/sup\u003e were noticed in the FTIR spectrum. The band at 831 cm\u003csup\u003e-1\u003c/sup\u003e originates due to deformational vibration of C-H bond of the benzene ring. Finally, there were no eractions between peaks in QRNPs showed the compatibility of selected excipients in study [33]. The FTIR spectra (Figure 6) confirm the presence of characteristic functional groups in QUR, QRNPs, RES, and PLGA, indicating structural integrity and absence of significant interactions among the components.\u003c/p\u003e\n\u003cp\u003e\u003cbr\u003e\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003e3.4 In Vitro Release study for QRNPs\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003eIn vitro release demonstrated QRNPs has a sustained and pH-responsive release profile. At physiological pH (7.4), drug release was limited, resulting in few off-target effects. At acidic pH (5.5), which mimics the tumor milieu, medication release was dramatically increased, indicating successful colon-targeted delivery. These results, confirmed by statistical optimization models (Fig. 7), confirm the formulation potential for colorectal cancer treatment.\u003c/p\u003e\n\u003cp\u003e\u003cbr\u003e\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003e3.5 Stability studies for QRNPs in gastrointestinal fluids.\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003eStability of QRNPs was studied in the simulated GI fluids. The particle size of the QRNPs was stable when incubated in either simulated gastric fluid (SGF) or simulated intestinal fluid (SIF). The particle size of QRNPs decreased after incubation in SGF for 2 h. However, when incubated in SIF, the particle size of QRNPs significantly increased, whereas that of QRNPs did not significantly change. As illustrated in Fig 8, an obvious aggregation was observed in QRNPs because of the low solubility at pH 6.8. These results showed that QRNPs were more stable than QRNPs at pH 1.2 and pH 6.8 [35].\u0026nbsp;\u003c/p\u003e\n\u003cp\u003e\u003cbr\u003e\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003e3.6 In vitro cell line studies for QRNPs\u0026nbsp;\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003eIn-vitro Biocompatibility and Anticancer Activity Cytotoxic Activity of QRNPs using MTT Assay. The cytotoxicity effect of QRNPs on colon cancer cell line Caco2 was assessed using MTT assay and compared with free QUR, free RES. The QRNPs showed dose dependent toxicity until 50 \u0026mu;g/ml behind had no significant effect [36].The cytotoxic potential of QRNPs also found to be higher than free QUR, free RES, which is the quite evident cytotoxic concentration were shown. The CC\u003csub\u003e50\u0026nbsp;\u003c/sub\u003evalue of QRNPs against Caco2 cells determined after a 24-h and 48-h treatment is as follows: 48.84 \u0026plusmn; 0.78 \u0026mu;g/ml and 32.75 \u0026plusmn; 1.02 \u0026mu;g/ml respectively. The CC\u003csub\u003e50\u003c/sub\u003e value of QRNPs after 48 h was reduced to 75% compared with that CC\u003csub\u003e50\u003c/sub\u003e of QRNPs after 24 h treatment. Therefore, the lower concentration 30 and 50 \u0026mu;l/ml of QRNPs has been selected further to investigate the underlying molecular mechanism toxicity such as QRNPs induced apoptotic effect in colon cancer cell line Caco2 [41] As in Fig. 9, QRNPs exhibited dose-dependent cytotoxicity against Caco-2 cells over 24 and 48 hours, surpassing the effects of free QUR and RES, demonstrating enhanced anticancer efficacy.\u003c/p\u003e"},{"header":"4. DISCUSSION","content":"\u003cp\u003eThis study shows for the first time the development of Quercetin resveratrol loaded polymeric nanoparticles (QRNPs), which efficiently improved its interaction with colon and colorectal cancer cells in vitro. According to the in vitro results, the prepared colon targeted nano system can protect the drug release through gastrointestinal regions and enhances its release at colorectal sites, which enhances the anti-cancer activity of the free drug combination to a high extent. In the current study, we optimized the preparation of QRNPs for the oral delivery of both drugs. QRNPs were fabricated with a modified double emulsion solvent evaporation technique through some formulation variables such as TPGS concentration, polymer amounts and the ratio of organic solvent to external phase volume [\u003cspan citationid=\"CR5\" class=\"CitationRef\"\u003e5\u003c/span\u003e] The higher PVA concentration in the outer aqueous phase has significantly played a role in stabilizing the prepared formula by decreasing the nanoparticles size, PDI and increasing the drug entrapment. The in vitro release showed that optimized drug-loaded NPs could act as a carrier to minimize drug release against gastric and intestinal pH environments, thus protecting the drugs against the pathway of digestion to reach the preferred site of action at the colon and rectum [\u003cspan citationid=\"CR42\" class=\"CitationRef\"\u003e42\u003c/span\u003e]. Drug-loaded NPs showed a significant cytotoxic action on colon and colorectal cancer cell lines in vitro compared to free drug combinations. These novel drug-loaded NPs can be further tested in pre-clinical models and clinical trials to develop an effective and promising therapeutic tool for the oral therapy of colon and colorectal cancers [\u003cspan citationid=\"CR43\" class=\"CitationRef\"\u003e43\u003c/span\u003e].\u003c/p\u003e"},{"header":"5. CONCLUSION","content":"\u003cp\u003eThe synthesized pH-sensitive polymeric nanoparticles co-encapsulating resveratrol and quercetin exhibited efficient targeted delivery to colorectal cancer cells in acidic tumor environments. The nanocarriers markedly improved drug stability, regulated release, and cellular absorption, leading to heightened cytotoxicity and apoptosis relative to free medicines. The combined effects of resveratrol and quercetin, along with the pH-sensitive release mechanism, highlight the therapeutic promise of this nanoplatform. The findings indicate that a dual-drug delivery system may provide a viable alternative for colorectal cancer treatment, addressing the constraints of conventional chemotherapy while enhancing localized medication efficacy and minimizing systemic toxicity.\u003c/p\u003e"},{"header":"Abbreviations","content":"\u003ctable border=\"1\" cellspacing=\"0\" cellpadding=\"0\"\u003e\n \u003ctbody\u003e\n \u003ctr\u003e\n \u003ctd valign=\"top\" style=\"width: 110px;\"\u003e\n \u003cp\u003eCRC\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 421px;\"\u003e\n \u003cp\u003eColorectal Cancer\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd valign=\"top\" style=\"width: 110px;\"\u003e\n \u003cp\u003eQUR\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 421px;\"\u003e\n \u003cp\u003eQuercetin\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd valign=\"top\" style=\"width: 110px;\"\u003e\n \u003cp\u003eRES\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 421px;\"\u003e\n \u003cp\u003eResveratrol\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd valign=\"top\" style=\"width: 110px;\"\u003e\n \u003cp\u003eNPs\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 421px;\"\u003e\n \u003cp\u003eNanoparticles\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd valign=\"top\" style=\"width: 110px;\"\u003e\n \u003cp\u003eQRNPs\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 421px;\"\u003e\n \u003cp\u003eQuercetin-Resveratrol Nanoparticles\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd valign=\"top\" style=\"width: 110px;\"\u003e\n \u003cp\u003ePLGA\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 421px;\"\u003e\n \u003cp\u003ePoly (lactic-co-glycolic acid)\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd valign=\"top\" style=\"width: 110px;\"\u003e\n \u003cp\u003eTPGS\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 421px;\"\u003e\n \u003cp\u003eD-\u0026alpha;-Tocopheryl polyethylene glycol succinate\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd valign=\"top\" style=\"width: 110px;\"\u003e\n \u003cp\u003eFTIR\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 421px;\"\u003e\n \u003cp\u003eFourier Transform Infrared Spectroscopy\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd valign=\"top\" style=\"width: 110px;\"\u003e\n \u003cp\u003eTEM\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 421px;\"\u003e\n \u003cp\u003eTransmission Electron Microscopy\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd valign=\"top\" style=\"width: 110px;\"\u003e\n \u003cp\u003eHR-TEM\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 421px;\"\u003e\n \u003cp\u003eHigh-Resolution Transmission Electron Microscopy\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd valign=\"top\" style=\"width: 110px;\"\u003e\n \u003cp\u003eDLS\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 421px;\"\u003e\n \u003cp\u003eDynamic Light Scattering\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd valign=\"top\" style=\"width: 110px;\"\u003e\n \u003cp\u003eUV\u0026ndash;Vis\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 421px;\"\u003e\n \u003cp\u003eUltraviolet\u0026ndash;Visible Spectrophotometry\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd valign=\"top\" style=\"width: 110px;\"\u003e\n \u003cp\u003ePBS\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 421px;\"\u003e\n \u003cp\u003ePhosphate Buffered Saline\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd valign=\"top\" style=\"width: 110px;\"\u003e\n \u003cp\u003eSGF\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 421px;\"\u003e\n \u003cp\u003eSimulated Gastric Fluid\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd valign=\"top\" style=\"width: 110px;\"\u003e\n \u003cp\u003eSIF\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 421px;\"\u003e\n \u003cp\u003eSimulated Intestinal Fluid\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd valign=\"top\" style=\"width: 110px;\"\u003e\n \u003cp\u003eCCD\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 421px;\"\u003e\n \u003cp\u003eCentral Composite Design\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd valign=\"top\" style=\"width: 110px;\"\u003e\n \u003cp\u003eCaco-2\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 421px;\"\u003e\n \u003cp\u003eHuman Colorectal Adenocarcinoma Cell Line\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd valign=\"top\" style=\"width: 110px;\"\u003e\n \u003cp\u003eMTT\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 421px;\"\u003e\n \u003cp\u003e3-(4,5-dimethylthiazol-2-yl)-2,5-diphenyl tetrazolium bromide\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd valign=\"top\" style=\"width: 110px;\"\u003e\n \u003cp\u003eDMSO\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 421px;\"\u003e\n \u003cp\u003eDimethyl Sulfoxide\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd valign=\"top\" style=\"width: 110px;\"\u003e\n \u003cp\u003eANOVA\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 421px;\"\u003e\n \u003cp\u003eAnalysis of Variance\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd valign=\"top\" style=\"width: 110px;\"\u003e\n \u003cp\u003eCC₅₀\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 421px;\"\u003e\n \u003cp\u003e50% Cytotoxic Concentration\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd valign=\"top\" style=\"width: 110px;\"\u003e\n \u003cp\u003ePDI\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 421px;\"\u003e\n \u003cp\u003ePolydispersity Index\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd valign=\"top\" style=\"width: 110px;\"\u003e\n \u003cp\u003eSD\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 421px;\"\u003e\n \u003cp\u003eStandard Deviation\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd valign=\"top\" style=\"width: 110px;\"\u003e\n \u003cp\u003eR\u0026sup2;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 421px;\"\u003e\n \u003cp\u003eCoefficient of Determination\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003c/tbody\u003e\n\u003c/table\u003e"},{"header":"Declarations","content":"\u003cp\u003e\u003ch2\u003eConflict of interest\u003c/h2\u003e\u003cp\u003eThe authors declare no competing interests.\u003c/p\u003e\u003ch2\u003eFinancing\u003c/h2\u003e\u003cp\u003eNo funding\u003c/p\u003e\u003ch2\u003eAuthor Contribution\u003c/h2\u003e\u003cp\u003eK. J - Reviewing and Drafting manuscriptV. S \u0026ndash; Tables and content collectionG. S -Graphical Illustration V. K \u0026ndash; Supervision\u003c/p\u003e"},{"header":"References","content":"\u003col\u003e\u003cli\u003e\u003cspan\u003eWorld Health Organization. World Cancer Report 2018. International Agency for Research on Cancer, WHO.\u003c/span\u003e\u003c/li\u003e\u003cli\u003e\u003cspan\u003eIndian Council of Medical Research (ICMR). Three-Year Report of Population-Based Cancer Registries: 2009\u0026ndash;2011, National Cancer Registry Programme, 2013.\u003c/span\u003e\u003c/li\u003e\u003cli\u003e\u003cspan\u003eBanik K, Khatoon E, Harsha C, Rana V, Parama D, Thakur KK, et al. Wogonin and its analogs for the prevention and treatment of cancer: A systematic review. Phytother Res. 2022;36:1854\u0026ndash;83.\u003c/span\u003e\u003c/li\u003e\u003cli\u003e\u003cspan\u003eBenarba B, Pandiella A. Colorectal cancer and medicinal plants: Principle findings from recent studies. 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Biomed Pharmacother. 2023;161:114462.\u003c/span\u003e\u003c/li\u003e\u003c/ol\u003e"}],"fulltextSource":"","fullText":"","funders":[],"hasAdminPriorityOnWorkflow":false,"hasManuscriptDocX":true,"hasOptedInToPreprint":true,"hasPassedJournalQc":"","hasAnyPriority":false,"hideJournal":true,"highlight":"","institution":"","isAcceptedByJournal":false,"isAuthorSuppliedPdf":false,"isDeskRejected":"","isHiddenFromSearch":false,"isInQc":false,"isInWorkflow":false,"isPdf":false,"isPdfUpToDate":true,"isWithdrawnOrRetracted":false,"journal":{"display":true,"email":"[email protected]","identity":"researchsquare","isNatureJournal":false,"hasQc":true,"allowDirectSubmit":true,"externalIdentity":"","sideBox":"","snPcode":"","submissionUrl":"/submission","title":"Research Square","twitterHandle":"researchsquare","acdcEnabled":true,"dfaEnabled":false,"editorialSystem":"","reportingPortfolio":"","inReviewEnabled":false,"inReviewRevisionsEnabled":true},"keywords":"Colorectal Cancer, Quercetin, Resveratrol, pH-sensitive nanoparticles, Drug delivery, Cytotoxicity","lastPublishedDoi":"10.21203/rs.3.rs-7204934/v1","lastPublishedDoiUrl":"https://doi.org/10.21203/rs.3.rs-7204934/v1","license":{"name":"CC BY 4.0","url":"https://creativecommons.org/licenses/by/4.0/"},"manuscriptAbstract":"\u003cp\u003eColorectal cancer (CRC) continues to pose a significant global health challenge, requiring sophisticated tailored therapy with less adverse effects. Resveratrol and quercetin, two bioactive flavonoids, exhibit significant anticancer effects but are hindered by low bioavailability and quick degradation. This research details the development of pH-sensitive polymeric nanoparticles (NPs) co-encapsulating resveratrol and quercetin, utilizing poly (lactic-co-glycolic acid) (PLGA) and Eudragit S100 for targeted colorectal cancer therapy. The optimized QRNPs exhibited particle size of 174–177 nm, a zeta potential around −22 to −24 mV, and encapsulation efficiency exceeding 80%. In vitro, drug release studies demonstrated minimal release at pH 7.4 but increased release at acidic pH (5.5), which is consistent with tumor microenvironments. Cytotoxicity assays in Caco-2 colon cancer cells revealed significantly enhanced cytotoxicity of QRNPs compared to free drugs, with CC₅₀ values of 48.84 µg/mL (24 h) and 32.75 µg/mL (48 h). FTIR confirmed drug–polymer compatibility, and HR-TEM analysis showed uniform spherical morphology. Stability tests in simulated GI fluids validated formulation robustness. Furthermore, fluorescence imaging and Annexin V-FITC assays confirmed augmented cellular uptake and elevated apoptosis. Mechanistic investigations revealed a downregulation of the anti-apoptotic protein Bcl-2 and an overexpression of the pro-apoptotic proteins Bax and caspase-3. The results underscore the promise of resveratrol-quercetin co-loaded pH-sensitive polymeric nanoparticles as a viable method for targeted colorectal cancer therapy.\u003c/p\u003e","manuscriptTitle":"Quercetin and resveratrol loaded polymeric nanoparticle for colorectal cancer as pH sensitive approach","msid":"","msnumber":"","nonDraftVersions":[{"code":1,"date":"2025-08-21 16:12:28","doi":"10.21203/rs.3.rs-7204934/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":"8996bd89-b9cc-4f9c-935e-e7fec9daffd8","owner":[],"postedDate":"August 21st, 2025","published":true,"recentEditorialEvents":[],"rejectedJournal":[],"revision":"","amendment":"","status":"posted","subjectAreas":[],"tags":[],"updatedAt":"2026-04-05T21:23:45+00:00","versionOfRecord":[],"versionCreatedAt":"2025-08-21 16:12:28","video":"","vorDoi":"","vorDoiUrl":"","workflowStages":[]},"version":"v1","identity":"rs-7204934","journalConfig":"researchsquare"},"__N_SSP":true},"page":"/article/[identity]/[[...version]]","query":{"redirect":"/article/rs-7204934","identity":"rs-7204934","version":["v1"]},"buildId":"8U1c8b4HqxoKbykW_rLl7","isFallback":false,"isExperimentalCompile":false,"dynamicIds":[84888],"gssp":true,"scriptLoader":[]}