Design and Optimization of Inclusion Complexes Using QbD Principles: A strategy to enhance solubility and dissolution of Voriconazole

Design and Optimization of Inclusion Complexes Using QbD Principles: A strategy to enhance solubility and dissolution of Voriconazole | Research Square window.SnipcartSettings = { analytics: { enabled: false } }; (function() { var accessVector = localStorage.getItem('access_vector') || ''; window.dataLayer = window.dataLayer || []; if (accessVector) { window.dataLayer.push({ user: { profile: { profileInfo: { snid: accessVector } } } }); } })(); (function(w,d,s,l,i){w[l]=w[l]||[];w[l].push({'gtm.start':new Date().getTime(),event:'gtm.js'});var f=d.getElementsByTagName(s)[0],j=d.createElement(s),dl=l!='dataLayer'?'&l='+l:'';j.async=true;j.src='https://www.googletagmanager.com/gtm.js?id='+i+dl;f.parentNode.insertBefore(j,f);})(window,document,'script','dataLayer','GTM-K279D39R'); Browse Preprints In Review Journals COVID-19 Preprints AJE Video Bytes Research Tools Research Promotion AJE Professional Editing AJE Rubriq About Preprint Platform In Review Editorial Policies Our Team Advisory Board Help Center Sign In Submit a Preprint Cite Share Download PDF Research Article Design and Optimization of Inclusion Complexes Using QbD Principles: A strategy to enhance solubility and dissolution of Voriconazole Bhaskar Daravath, Madhavi Vasamsetti, Naveen Chella, Sateesh Kumar Vemula This is a preprint; it has not been peer reviewed by a journal. https://doi.org/ 10.21203/rs.3.rs-6642975/v1 This work is licensed under a CC BY 4.0 License Status: Published Journal Publication published 24 Oct, 2025 Read the published version in Journal of Pharmaceutical Innovation → Version 1 posted You are reading this latest preprint version Abstract Purpose: This current study aimed to increase the solubility and dissolution of poor aqueous soluble drug voriconazole (VC), a lipophilic antifungal drug, through the inclusion complexes with β-cyclodextrin (β-CD) by a solvent-free technology like co-grinding method. Methods: By using RSM (Response Surface Methodology), a CCD (central composite design) was utilized to analyse the impact of β-CD amount and grinding time on VC solubility and % CDR (percentage cumulative drug release). Results: Phase solubility studies confirmed the formation of an A L -type complex. Statistical analysis revealed that both factors significantly and positively affected the responses. The optimized formulation, comprising 600 mg of β-CD and a grinding time of 30 minutes, exhibited a substantial increase in solubility (70.64 mg/ml) and achieved a high dissolution rate (98.47%) with a desirability value of 0.969. The formation of the inclusion complex was further validated through comprehensive characterization studies. FTIR spectra showed minor shifts in characteristic VC peaks. DSC thermograms revealed the disappearance of the characteristic sharp melting endotherm of VC in the optimized complex, suggesting molecular dispersion within β-CD. XRD patterns revealed a significant reduction in VC crystallinity. SEM images display a distinct morphological change from crystalline VC to smooth-surfaced complex particles. Conclusion: These findings demonstrate that VC/β-CD inclusion complex is a promising approach to significantly increase the aqueous solubility and dissolution of voriconazole, potentially enhancing its therapeutic efficacy. Solvent-free technology β-cyclodextrin Inclusion complex Response Surface Methodology Central composite design Dissolution enhancement Figures Figure 1 Figure 2 Figure 3 Figure 4 Figure 5 Figure 6 Figure 7 Figure 8 Figure 9 Figure 10 Figure 11 INTRODUCTION The high-throughput screening, widely utilized in drug development, has led to a growing number of lipophilic compounds among emerging drug candidates. The limited solubility of these compounds poses significant challenges for formulation scientists during their development and manufacturing. This solubility issue affects a substantial proportion of pharmaceutical compounds. More than 40% of the currently marketed drug products and 70% of the active molecules in development are poorly soluble[ 1 , 2 ]. The aqueous solubility and dissolution kinetics of active pharmaceutical ingredients (APIs) are critical physicochemical parameters that significantly influence their bioavailability and biodistribution profiles. The ability of a drug molecule to dissolve in aqueous media is a fundamental characteristic that directly modulates its absorption following oral administration. Despite the advances in pharmaceutical science, the development of APIs with optimal solubility profiles remains the greatest challenge in contemporary drug discovery and development [ 3 ]. Enhancing the bioavailability of poorly soluble APIs can be achieved through diverse pharmaceutical interventions targeting their limited aqueous solubility, ultimately enabling dose reduction and minimizing adverse effects. Researchers have successfully implemented multiple methodologies, including salt formation [ 4 ], use of non-volatile solvents [ 5 , 6 ], particle size reduction[ 7 ], surfactant incorporation [ 8 ], molecular complexation [ 9 ], hot-melt extrusion [ 10 ] and solid dispersions[ 11 ]). Among these, cyclodextrin complexation has proven to be the most effective [ 12 ]. Cyclodextrins (CDs) are unique toroidal-shaped cyclic oligosaccharides with an interior hydrophobic core and exterior hydrophilic property. This structure allows CDs to encapsulate hydrophobic API in their cavities. Complexation relies on precise matching of the cavity size with the API molecule. A cavity that is too small cannot form a complex, whereas a cavity that is too large will create weak, unstable complexes. Therefore, the formation of optimal complexes relies on suitable API-sized cavities in the CDs that maximize API encapsulation while minimizing the release potential. When the API-cyclodextrin complex was added to water, the hydrophilic hydroxyl groups on the exterior of the CD interacted with the water molecules through hydrogen bonding. This allowed for easy dissolution of the complex [ 13 ]. Cyclodextrins have proven effective in enhancing the solubility of various drugs, including ibuprofen [ 14 ], telmisartan [ 15 ], diclofenac [ 16 ], lisinopril [ 17 ], and catechin [ 18 ]. Preparation of inclusion complexes has been performed by grinding [ 19 ], kneading [ 20 ], co-precipitation [ 9 ], freeze-drying [ 21 ], microwave irradiation [ 22 ], and spray drying [ 23 ]. These methods can effectively produce inclusion complexes but have a few limitations, such as the use of organic solvents (coprecipitation), low recovery, excessive particle growth (spray drying), high cost and processing time (freeze drying), degradation of thermolabile substances, and content uniformity issues (microwave irradiation) [ 24 – 27 ]. Cyclodextrin complexation by co-grinding is preferred over other methods. Co-grinding uses a solvent-free approach that potentially minimizes environmental impact and reduces production costs [ 28 ]. This method is relatively straightforward and highly scalable, making it suitable for commercial applications [ 29 ]. A few authors have used this approach to enhance the solubility and dissolution rate of poor soluble drugs [ 30 , 31 ]. In pharmaceutical development, QbD (Quality by Design) addresses formulation challenges by emphasizing the set goals from the product creation and process implementation stages [ 32 , 33 ]. Design of Experiments (DoE) enables a systematic analysis of the impact of multiple factors within particular limits on a given outcome. Moreover, a RSM (Response Surface Methodology) serves as a powerful statistical method for drug formulation optimization because it allows the predictive modelling of relationships between factors and responses from data collected through experiments and regression analysis [ 34 ]. Applying QbD and DoE was effective in achieving the targeted product profile. Voriconazole (VC) has broad-spectrum antifungal activity against pathogenic fungi such as Candida, Aspergillus, and Fusarium. VC is classified as a (BCS) Biopharmaceutics Classification System Class II drug, characterized by low aqueous solubility and high permeability. However, its limited aqueous solubility and potential for variable bioavailability pose challenges for formulation development [ 35 ]. These constraints require approaches, such as cyclodextrin complexation, to enhance its delivery and efficacy. So far, only a few studies have explored the using cyclodextrin inclusion complexes to enhance voriconazole (VC) solubility. However, no research has used the co-grinding method, nor has it examined how the amount of carrier and grinding time affect VC release [ 36 – 38 ]. Additionally, they conducted studies that examined a single factor at a time. Voriconazole (VC) solubility and drug release are dependent on the carrier concentration and grinding time. The carrier amount (A) and grinding time (B) were deemed critical material and process parameters and were studied as independent variables. This study examined the impact of these factors on the solubility and drug release of VC inclusion complexes developed using a co-grinding technique. Solubility and cumulative drug release were selected as outcome measures. A Central Composite Design (CCD) was utilized under a QbD strategy to refine the formulation with regard to VC solubility, dissolution, and therapeutic efficacy. Parameters A and B were adjusted, and the corresponding VC solubility (Y1) and cumulative % drug release (Q; Y2) were measured. The purpose was to look for a design space to improve the solubility, dissolution, and therapeutic efficacy of VC. A new cyclodextrin complexation method was developed to enhance voriconazole’s solubility and dissolution. The study implemented QbD featuring RSM and CCD to suggest optimization related to the amount of carrier and grinding time on the release of VC aimed at increasing both solubility and dissolution of VC. MATERIALS AND METHODS Materials Voriconazole was gifted by MSN Laboratories Pvt., Ltd. (Hyderabad, Telangana, India). The other reagents were procured from SD Fine Chemicals (Mumbai, India). Methods Phase solubility study of voriconazole The voriconazole phase solubility study was conducted following procedures provided in literature [ 39 ]. An excess amount of VC was added to aqueous solutions containing varying amounts of β-cyclodextrin (β-CD) (0, 1, 2, 3, 4, and 5% w/v). The suspensions were rotated at 25°C for 48 hours using an orbital shaker set at 300 rpm. After equilibrium was reached, the samples were filtered through a 0.45 µm membrane filter, and the concentration of VC was determined spectrophotometrically at 250 nm using a UV–visible spectrophotometer. Development of a mathematical model through Experimental Design Design-Expert® (DOE) software (version 13) was employed to construct a CCD (central Composite Design) for the systematic optimization of two formulation variables and to evaluate their effects on key response parameters. Table 1 shows that the independent variables, ‘A’ (carrier amount) and ‘B’ (grinding time), were investigated at five levels (−α, − 1, 0, + 1, +α), with both actual and coded values. The dependent factors (responses) assessed were solubility (Y₁) and cumulative percentage e drug release (%CDR, Y₂). A total of 13 experimental runs were executed (Table 2 ). Table 1 Factors and their levels as per the Central Composite Design Factors (Independent Variables) Levels -α -1 0 + 1 +α Amount of Carrier (β – cyclodextrin) 117.157 200 400 600 682.843 Grinding time 2.23654 7 18.5 30 34.7635 Table 2 Results of Central Composite Design and the Responses Obtained for Experiments Experimental Run Factor A: Amount of Carrier (β – cyclodextrin) (mg) Factor B: Grinding time (min) Solubility (mg/ml) % CDR (%) 1 600 7 52.79 79.56 2 400 18.5 47.37 71.48 3 400 18.5 42.23 67.37 4 600 30 65.86 85.93 5 400 18.5 46.7 76.48 6 200 7 21.5 33.89 7 400 34.7635 52.28 79.33 8 400 18.5 37.15 62.15 9 200 30 35.64 57.03 10 400 18.5 41.56 66.46 11 682.843 18.5 74.63 95.73 12 117.157 18.5 17.31 26.08 13 400 2.23654 9.83 25.31 The second-order polynomial model equation is as follows: Y = β0 + β1 A + β2 B + β11 A2 + β22 B2 + β12 AB ------ 1 Where “Y” represents dependent responses (solubility or %CDR), intercept is β0; linear coefficients are β1, β2; square coefficients are β11, β22; interaction coefficient is β12 for independent variables A and B. Statistical analysis, including p-values, F-values, and regression coefficients for the model terms, are summarized in Table 3 . Table 3 F-values, P-values, and the estimated coefficient values obtained from the regression models Source Y 1 : Solubility Y 3 : Cumulative % drug release F-value p-value Coefficient Estimate F-value p-value Coefficient Estimate Model 32.12 < 0.0001 - 27.40 < 0.0001 - Intercept - - 41.91 - - 63.60 A: Amount of Carrier (β – cyclodextrin) 46.74 < 0.0001 17.82 39.87 < 0.0001 21.63 B: Grinding time 17.50 0.0019 10.91 14.93 0.0031 13.24 AB - - - - - - A² - - - - - - B² - - - - - - Lack of Fit 4.53 0.825 - 4.64 0.0795 - Model p 0.05: Lack of Fit if not significant Model validation was performed by comparing the observed values with predicted responses (Table 4 ). Based on the desirability and optimization criteria of the responses, a flexible design space was established. An optimized formulation batch was subsequently prepared using the identified optimal conditions within the design space. Table 4 Comparison of the Predicted and Observed Experimental Values Check points Independent variables Factor A: Factor B: Y 1 : Solubility Y 4 : Cumulative % drug release Amount of Carrier (β – cyclodextrin) (mg) Grinding time (mg) Predicted value (mg/ml) Experimental value (mg/ml) Standard Error Predicted value ( o ) Experimental value ( o ) Standard Error R1 600 7 48.82 52.79 4.216 71.99 79.56 5.541 R3 400 18.5 41.91 42.23 2.045 63.60 67.37 2.688 R9 200 30 34.99 35.64 4.216 55.20 57.03 5.541 Optimized 600 30 70.64 65.86 4.216 98.47 85.93 5.541 Table 5 Stability studies of optimized formulation (n = 6) Time (min) Before storage After 6 months t -test at 0.05 LS Similarity Factor (F2) 0 0 0 Not Significant 71.24 5 30.62 ± 1.42 30.27 ± 1.47 10 38.78 ± 1.47 36.35 ± 1.32 15 47.24 ± 1.36 44.25 ± 1.73 30 57.32 ± 1.27 54.37 ± 1.54 45 64.98 ± 1.68 61.53 ± 1.54 60 70.21 ± 1.16 68.42 ± 1.28 75 74.98 ± 2.46 72.68 ± 1.47 90 81.06 ± 1.62 79.61 ± 1.16 120 85.93 ± 2.35 83.46 ± 1.76 Formulation VC/β-cyclodextrin inclusion complexes The co-grinding method was used to prepare VC/β-cyclodextrin inclusion complexes using VC and β-cyclodextrin. Formulations were prepared using a mortar and pestle at different carrier concentrations (117.1, 200, 400, 600, and 682.8 mg). 200 mg of VC was added to all the formulations. The samples were ground for grinding times (2.2, 7, 18.5, 30, and 34.7 min). The accurately weighed amounts of powder samples were placed in a mortar and ground using a pestle for a specified time. A physical mixture (PM) of VC/β-cyclodextrin was formulated by mixing VC and β-cyclodextrin for 5 min with a spatula. Samples were withdrawn and stored for further analysis. Solubility studies Solubility studies were conducted for the prepared formulations. Solubility studies of VC/β-cyclodextrin inclusion complexes were carried out using the procedure described by Daravath et al. in phosphate buffer pH 7.2 [ 40 ]. The drug sample was then analyzed using UV-visible spectrophotometry at a λ max of 250 nm. Cumulative % drug release studies In-vitro dissolution studies were conducted on formulations, VC, and PM employing a USP dissolution Type II apparatus in pH 7.2 phosphate buffer, at 37 ± 0.5°C at 50 rpm. The samples were examined at 250 nm by UV spectrophotometry. Characterization of VC/β-cyclodextrin inclusion complexes The prepared formulations, pure drug, and physical mixture were further evaluated. Fourier transform infrared spectroscopy (FTIR) FTIR spectra of VC, β-CD, physical mixture, and the optimized formulations were obtained employing the potassium bromide (KBr) pellet method in the range of 400–4000 cm⁻¹. Potassium bromide (KBr) pellets were prepared by compressing the mixture at 12,000 psi under vacuum for 3 min [ 41 ]. Differential scanning calorimetry (DSC) DSC was performed on VC, β-CD, PM, and optimized formulations. 5 mg of powder was hermetically sealed in aluminum crucibles. The samples were heated from 25 to 350°C under a nitrogen atmosphere (50 mL/min) at a rate of 10°C/min [ 42 ]. X-ray diffraction (XRD) XRD patterns of VC, β-CD, physical mixture, and the optimized complex were recorded using Cu radiation (wavelength 1.540 Å). The instrument was operated at 45 kV and 30 mA, and data were collected over a 2θ range of 0–90°[ 42 ]. Scanning electron microscopy (SEM) A field emission scanning electron microscope (FESEM) was used to acquire images at a voltage of 20 kV. Before FESEM analysis, the samples were gold-coated to ensure electrical conductivity under vacuum [ 43 ]. Stability studies of VC/β-cyclodextrin inclusion complexes The optimized complex was subjected to stability studies at 40 ± 2°C and relative humidity of 75 ± 5%. The sample was stored for six months[ 5 ]. By using the dissolution profile of the optimized complex, the similarity factor ( f2 ) was calculated. RESULTS Voriconazole is a lipophilic drug having low water solubility (0.5 mg/ml and log P value of 1.65, which makes voriconazole a more appropriate model drug for the preparation of β-cyclodextrin inclusion complexation. Phase solubility study of voriconazole Figure 1 illustrates the phase solubility profile of VC in β-cyclodextrin solutions (0–5% w/v). The linear increment in VC solubility with an increase in β-cyclodextrin indicates A L -type complex formation. This linear VC/β-cyclodextrin correlation, with a slope of 0.8113, which was less than 1, and an R 2 value of 0.9935, suggests 1:1 stoichiometric formation of VC and β-cyclodextrin inclusion complexation. Manca et al reported similar results [ 16 ]. Development of a mathematical model through Experimental Design A CCD was used to optimize two formulation factors, namely, the amount of β-cyclodextrin (A) and grinding time (B), and their impact on solubility (Y1) and %CDR (Y2) was studied. In the present study, these factors were investigated at five levels: α, − 1, 0, + 1, and + α. Design-Expert® software (version 13) was used to conduct analysis of variance (ANOVA). The p-values, F-values, and estimated coefficients of the regression models for the responses were analyzed, and the results were summarized in Table 3 . The regression model will be considered significant when the model F value is greater than 1 and value p-value is less than 0.05. The lack-of-fit should be non-significant, and the p-value should be more than 0.05. The predicted R 2 and adjusted R 2 difference should be less than 0.2, and the adequate precision should be more than 4. Solubility studies of VC/β-cyclodextrin inclusion complexes Table 3 shows that solubility ranged from 9.8 mg/ml to 74.63 mg/ml. The statistical analysis indicated that a linear regression model best described the influence of the formulation variables on solubility (Y₁). The model demonstrated robust significance, with F-value of 32.12 and p-value of < 0.0001, confirming the model terms were significant. The lack of fit was not significant (p-value = 0.825), thus supporting the adequacy of the model. Both the carrier amount (A) and grinding time (B) were significant factors affecting the solubility, with p-values < 0.0001 and 0.0019, respectively. Regression analysis revealed positive coefficients for both variables (17.82 for A and 10.91 for B), indicating that a significant enhancement in solubility occurred with increasing β-cyclodextrin amount and/or grinding time. The reliability of the model was further supported by the close agreement between the predicted R² (0.7220) and adjusted R² (0.8384), with a difference is less than 0.2, which is acceptable. The model exhibited an adequate precision value of 16.22, which is well above the recommended minimum of four, indicating a strong signal-to-noise ratio. These findings were visually corroborated by 2D and 3D response surface plots showed in Fig. 2 and Fig. 3 ). The relative influence of the formulation variables on the solubility was studied by examining the coefficients in the coded regression equation. Y1 = 41.91 + 17.82 A + 10.91 B -------- 2 where, Y1: represents the predicted solubility A: denotes the amount of β-cyclodextrin B: corresponds to the grinding time This equation indicates that both the amount of carrier and grinding time exert a significant positive effect on the solubility, with the carrier amount having a comparatively greater impact, as reflected by its higher coefficient (17.82 vs. 10.91). Consequently, increasing either the carrier amount or the grinding time enhanced solubility of the VC/β-cyclodextrin inclusion complexes (Fig. 4 i and ii). % CDR (Cumulative percentage drug release) of VC/β-cyclodextrin inclusion complexes Figure 5 illustrates the in vitro dissolution profiles of inclusion complexes, physical mixture, and VC. The prepared inclusion complexes showed increased drug release in comparison to PM and plain VC. Table 3 shows the percentage cumulative drug release (%CDR), which ranges from 25.31–95.73%. The statistical analysis indicated that a linear regression model best described the impact of the formulation factors on %CDR (Y₂). The model demonstrated strong significance, with a p-value of < 0.0001 and a F-value of 27.40. The lack of fit was not significant (p-value = 0.0795), indicating the model’s adequacy. The β-cyclodextrin amount (A) and grinding time (B) were significant factors affecting the %CDR, with p-values < 0.0001 and 0.0031, respectively. Regression analysis revealed positive coefficients for both variables (21.63 for A and 13.24 for B), demonstrating that increasing the carrier amount and/or grinding time significantly increased the %CDR. The reliability of the model was supported by the close agreement between the adjusted R² (0.8148) and predicted R² (0.7022), with a difference is less than 0.2, which is acceptable. The model exhibited an adequate precision value of 14.9812, well above the recommended minimum of 4, indicating a strong signal-to-noise ratio. These findings were further corroborated by the 2D and 3D response surface plots (Fig. 2 and Fig. 3 ). The relative influence of the formulation variables on %CDR was studied by examining the coefficients in the coded regression equation. Y2 = 63.60 + 23.63 A + 13.24 B -------- 3 Where, Y2: represents the predicted %CDR A: denotes the amount of carrier B: corresponds to the grinding time This equation indicates that both the amount of carrier and grinding time exert a significant positive effect on % CDR, with the carrier amount having a comparatively greater impact than grinding time, as reflected by its higher coefficient (23.63 vs. 13.24). Consequently, increasing either the carrier amount or the grinding time leads to an enhanced % CDR of the VC/β-cyclodextrin inclusion complexes (Fig. 4 i and ii). Multiple Response Optimization Study The optimal combination of β-cyclodextrin amount (A) and grinding time (B) that simultaneously maximizes solubility (Y₁) and percentage cumulative drug release (%CDR, Y₂) was identified by implementing a multiple-response optimization strategy. This approach combines statistical modelling, desirability functions, and numerical algorithms to effectively balance multiple responses while ensuring a robust formulation performance. Based on the desirability function, numerical and graphical optimization techniques were used to refine the responses. The desirability function, scaled from “0 to 1” [ 44 ], was employed to simultaneously optimize solubility (Y₁) and % cumulative drug release (%CDR, Y₂). Numerical optimization aimed to maximize both responses while constraining the independent variables within their experimental ranges, i.e., carrier amount (A) and grinding time (B). Graphical optimization further defines acceptable limits for responses and factors, enabling the identification of a flexible design space. A flexible design space was established by setting the criteria for maximum solubility and enhanced dissolution rate. The overlay plot (Fig. 6 ) highlights this design space as a yellow region. An optimized formulation, with 600 mg of B-cyclodextrin and 30 min grinding time, was identified using the desirability "0.969" (Fig. 6 ). This formulation exhibits a maximum solubility of 70.64 mg/ml and highest dissolution of 98.47%. The formulation was subsequently subjected to further characterization for comprehensive evaluation. Verification of model adequacy The model’s adequacy for Y 1 and Y 2 was analyzed by observing the plots of the predicted versus experimental values, as depicted in Table 4 and Fig. 7 . The comparison between the values of predicted and actual demonstrates that the linear regression model closely fits the data, indicating strong and reliable predictive performance. (Chella et al. 2024). Characterization of VC/β-cyclodextrin inclusion complexes Figure 8 presents the FTIR spectra of voriconazole (VC), its physical mixture, and optimized formulation. VC displayed characteristic functional group vibrations: O-H stretching at 3198.28 cm − 1, C-F stretching at 1460.70, and C-N stretching at 1280.58 cm − 1. The physical mixture spectrum retained key bands: O-H stretching vibration at 3320.96 cm − 1 , C-F stretching at 1454.77 cm − 1 , and C-N stretching at 1279.38 cm − 1 , showing similar peaks as VC. Whereas the optimized formulation exhibits O-H stretching slightly shifted to 3324.58 cm − 1 , C-F stretching at 1455.32 cm − 1 , and C-N stretching at 1280.28 cm − 1 . These observations indicate the formation of inclusion complexes. DSC and XRD analyses further confirmed this. Figure 9 displays the DSC thermograms of VC, PM, and the optimized formulation. The pure drug exhibited a sharp endothermic peak at 131.19°C, indicating the melting point of VC, while a broader peak at 111.16°C corresponds to the loss of water from the β-cyclodextrin cavity and its thermal decomposition at 316.56°C. In the physical mixture, the drug peak remained visible at 130.71°C, with reduced intensity, suggesting the drug was not fully dispersed in the carrier. Conversely, in the optimized formulation, the characteristic melting point peak of the drug was absent, indicating complete molecular dispersion of VC within β-cyclodextrin. Figure 10 displays intense and sharp peaks at diffraction angles 2θ of 14 0 , 20 0 , 26 0 , 28 0 , and 35 0 , indicating the crystalline nature of VC. The peak intensity decreased to some extent in the physical mixture, indicating that VC was not completely converted into an amorphous form. However, in the optimized formulation, the peak intensity decreased or disappeared, indicating that VC has been changed into an amorphous form. Figure 11 shows that VC is present as irregular particles with a crystalline structure, whereas β-cyclodextrin exists as smooth-surfaced particles. A PM was present in the original morphology. VC and β-CD inclusion complexes showed smooth surfaces, indicating the formation of an inclusion complex between VC and β-CD. Stability studies The stability study of the optimized formulation showed a similarity factor (f2) of 71.24 at P < 0.05 after six months of storage, indicating no significant difference. DISCUSSION The present study successfully aimed to enhance the water solubility and dissolution of voriconazole (VC), a lipophilic antifungal drug, by the formation of inclusion complexes with β-cyclodextrin (β-CD). Initial characterization of the physicochemical properties of voriconazole, specifically its low water solubility and favorable log P value, supported its selection as a suitable model drug for β-CD complexation. The phase solubility study provided compelling evidence for forming an inclusion complex between VC and β-CD. The observed AL-type profile was characterized by a linear increase in VC solubility with increasing β-CD concentration. This enhanced solubility was attributed to the ability of the hydrophobic cavity of β-CD to encapsulate lipophilic VC molecules, thereby increasing its interaction with the aqueous environment. To further optimize the formulation of the VC/β-CD inclusion complex, a central composite design (CCD) was employed. This statistical approach allowed for the systematic investigation of the impact of two critical formulation variables, the amount of carrier (β-CD) and the grinding time, on key performance attributes, namely solubility (Y1) and percentage cumulative drug release (% CDR, Y2). ANOVA revealed significant regression models for both responses (p < 0.0001), indicating a strong correlation between the independent and dependent factors. The non-significant lack-of-fit values further validated the adequacy of the chosen linear models for predicting solubility and % CDR. Statistical analysis of the solubility data demonstrated that both the amount of carrier (A) and grinding time (B) had a significant positive effect on the solubility of VC. The positive coefficients in the coded equation (Y1 = 41.91 + 17.82 A + 10.91 B) clearly indicate that increasing the amount of β-CD and/or the grinding time leads to a substantial enhancement in VC solubility. This may be due to higher availability of β-CD molecules to form inclusion complexes, which potentially increases the efficiency of the complexation process with prolonged grinding. The consistency between the adjusted and predicted R² values, along with the high precision, further strengthens the reliability of the model. The visual representation of these effects is seen in the 2D and 3D response surface plots (Figs. 2 and 3 ), which provide a clear understanding of the relationship between formulation variables and solubility. Similarly, the results for % CDR showed that both the amount of carrier (A) and grinding time (B) significantly and positively influenced the drug-release profile. The coded equation (Y2 = 63.60 + 23.63 A + 13.24 B) highlights the substantial positive contribution of both factors to the percentage of drug released. A higher amount of β-CD likely led to better solubilization of VC, facilitating its dissolution and subsequent release. Increased grinding time may increase the homogeneity of the mixture and the extent of complex formation, thereby improving drug release. The statistical parameters (significant F-value, non-significant lack of fit, acceptable difference between R² values, and high adequate precision) confirmed the robustness of the model for predicting % CDR. The corresponding response surface plots (Figs. 2 and 3 ) visually depict these relationships. The multiple response optimization study successfully identified an optimized formulation with 600 mg of β-CD and a grinding time of 30 min, yielding a high desirability value of 0.969. This optimized formulation exhibited a remarkable maximum solubility of 70.64 mg/ml and highest dissolution of 98.47%. This significant improvement in both solubility and dissolution rate compared to those of the pure drug underscores the effectiveness of β-CD inclusion complexation and the optimization process. Characterization studies provided further evidence for forming VC/β-CD inclusion complex. The FTIR spectra showed slight shifts in characteristic peaks of VC in the optimized inclusion complex. These subtle changes suggest interactions at the molecular level, supporting the formation of inclusion complexes. However, these observations alone are inconclusive and require further confirmation. DSC thermograms provided definitive evidence of the complex formation. The presence of the VC melting peak, albeit with reduced intensity, in the physical mixture suggests incomplete interaction between VC and β-cyclodextrin. The disappearance of a sharp endothermic peak of pure VC in the optimized inclusion complex suggests that VC is no longer present in its crystalline form, but is molecularly dispersed within the β-CD cavity. The XRD patterns corroborate the DSC results. The crystallinity of VC, as evident from the numerous sharp and intense peaks, was significantly reduced or absent in the optimized formulation. This transformation from a crystalline to an amorphous or molecularly dispersed state within the β-cyclodextrin cavity is a well-known consequence of the forming inclusion complex and contributes to the enhanced solubility and dissolution rate. Scanning Electron Microscopy (SEM) images confirmed the morphological changes upon complexation. The irregular crystalline particles of pure VC and the distinct morphologies in the physical mixture contrasted with the smooth-surfaced particles of VC/β-CD inclusion complex, suggesting that a new entity was formed through the interaction between VC and β-CD. Finally, stability studies on the optimized VC/β-CD inclusion complex demonstrated good stability over a six-month storage period, as indicated by the high similarity factor (f2) value within the acceptable range (P < 0.05). This suggests that the enhanced solubility and dissolution characteristics of the optimized formulation were maintained over time, highlighting its potential for pharmaceutical applications. CONCLUSION This study demonstrated the effective application of β-CD in increasing the aqueous solubility and dissolution rate of the poorly water-soluble antifungal voriconazole (VC). The formation of the AL-type inclusion complexation was confirmed by phase solubility studies. A central composite design (CCD) was strategically employed to optimize the formulation variables, specifically the amount of β-CD and grinding time, which affected VC solubility and percentage cumulative drug release (% CDR). Statistical analysis revealed significant positive effects of both carrier amount and grinding time on both responses. The optimized formulation, comprising 600 mg of β-CD and a 30-minute grinding time, exhibited a substantial increase in solubility (70.64 mg/ml) and achieved a high dissolution rate (98.47%) with a desirability value of "0.969". Characterization studies, including FTIR, DSC, XRD, and SEM, provided evidence of the successful formation of an inclusion complex. FTIR spectra showed minor shifts in characteristic peaks, while DSC thermograms indicate the absence of the crystalline melting peak of VC in the optimized formulation, suggesting its molecular dispersion within the β-CD cavity. XRD patterns confirm a reduction in VC crystallinity, further supporting the formation of a complex. SEM images revealed distinct morphological changes in the optimized formulations. Stability studies conducted over six months demonstrated the robustness of the optimized VC/β-CD inclusion complex, with no significant changes in its dissolution profile. The significant enhancement in solubility and dissolution coupled with good stability underscores the potential of β-CD inclusion complexation as a promising pharmaceutical strategy to improve the bioavailability and therapeutic efficacy of voriconazole. This approach offers a viable pathway for overcoming the limitations associated with the poor aqueous solubility of this important antifungal agent. Declarations FUNDING The authors declare that no external funding or grants were received for the research, preparation, and submission of this manuscript. Author Contribution Bhaskar Daravath: Conceptualization, Formulation development, Experimental work, Original draft writing, Manuscript review, and editing.Madhavi: Experimental work, Original draft writing.Naveen Chella: Conceptualization, Manuscript review, and editingSateesh Kumar Vemula: Conceptualization, Manuscript review, and editing Acknowledgement The authors gratefully acknowledge the management of the GITAM School of Pharmacy, GITAM Deemed to be University, Hyderabad, and MSN Laboratories Pvt Ltd, Hyderabad, Telangana, India, for generously providing the facility and voriconazole sample crucial for the successful completion of this research. References Munnangi SR, Youssef AAA, Narala N, Lakkala P, Vemula SK, et al. Continuous Manufacturing of Solvent-Free Cyclodextrin Inclusion Complexes for Enhanced Drug Solubility via Hot-Melt Extrusion: A Quality by Design Approach. Pharmaceutics. 2023;15(9):2203. Thiry J, Krier F, Ratwatte S, Thomassin JM, Jerome C, et al. Hot-melt extrusion as a continuous manufacturing process to form ternary cyclodextrin inclusion complexes. Eur J Pharm Sci. 2017;96:590–7. Lipinski CA, Lombardo F, Dominy BW, Feeney PJ. Experimental and computational approaches to estimate solubility and permeability in drug discovery and development settings. Adv Drug Deliv Rev. 2012;64:4–17. Serajuddin ATM. Salt formation to improve drug solubility. Adv Drug Deliv Rev. 2007;59(7):603–16. Daravath B, Vemula SK, Chella N. Optimizing Nateglinide Liquisolid Compacts: Achieving Formulation Excellence Through the Quality by Design Approach. J Pharm Innov. 2024;19(5). Daravath B, Somalanka S. Enhancement of dissolution rate of racecadotril by liquisolid compact technology. Brazilian J Pharm Sci. 2022;58. Sonoda R, Horibe M, Oshima T, Iwasaki T, Watano S. Improvement of Dissolution Property of Poorly Water-Soluble Drug by Novel Dry Coating Method Using Planetary Ball Mill. Chem Pharm Bull (Tokyo). 2008;56(9):1243–7. Neslihan Gursoy R, Benita S. Self-emulsifying drug delivery systems (SEDDS) for improved oral delivery of lipophilic drugs. Biomed Pharmacother. 2004;58(3):173–82. Das S, Subuddhi U. Studies on the complexation of diclofenac sodium with β–cyclodextrin: Influence of method of preparation. J Mol Struct. 2015;1099:482–9. Alzahrani A, Nyavanandi D, Mandati P, Youssef AAA, Narala S, et al. A systematic and robust assessment of hot-melt extrusion-based amorphous solid dispersions: Theoretical prediction to practical implementation. Int J Pharm. 2022;624:121951. Daravath B, Tadikonda RR, Vemula SK. Formulation and pharmacokinetics of gelucire solid dispersions of flurbiprofen. Drug Dev Ind Pharm. 2015;41(8):1254–62. Brewster ME, Loftsson T. Cyclodextrins as pharmaceutical solubilizers. Adv Drug Deliv Rev. 2007;59(7):645–66. Aiassa V, Garnero C, Longhi MR, Zoppi A. Cyclodext Multicomponent Complexes: Pharm Appl Pharm. 2021;13(7):1099. Pereva S, Nikolova V, Sarafska T, Angelova S, Spassov T, et al. Inclusion complexes of ibuprofen and β-cyclodextrin: Supramolecular structure and stability. J Mol Struct. 2020;1205:127575. Borba PAA, Pinotti M, Andrade GRS, Da Costa NB, Olchanheski LR, et al. The effect of mechanical grinding on the formation, crystalline changes and dissolution behaviour of the inclusion complex of telmisartan and β-cyclodextrins. Carbohydr Polym. 2015;133:373–83. Manca ML, Zaru M, Ennas G, Valenti D, Sinico C, Loy G, Fadda AM. Diclofenac-beta-cyclodextrin binary systems: physicochemical characterization and in vitro dissolution and diffusion studies. AAPS PharmSciTech. 2005;6(3):E464–72. Mohammad A, Singh S, Swain S, Ghose D. Quality by Design Enabled β-Cyclodextrin Complexes of Lisinopril by Kneading Method: Improved Solubility and Bioavailability. Curr Mater Sci. 2023;17(2):135–47. Jiang L, Yang J, Wang Q, Ren L, Zhou J. Physicochemical properties of catechin/β-cyclodextrin inclusion complex obtained via co-precipitation. CyTA - J Food. 2019;17(1):544–51. Jug M, Mura PA. Grinding as Solvent-Free Green Chemistry Approach for Cyclodextrin Inclusion Complex Preparation in the Solid State. Pharmaceutics. 2018;10(4):189. Ghosh A, Biswas S, Ghosh T. Preparation and Evaluation of Silymarin β-cyclodextrin Molecular Inclusion Complexes. J Young Pharmacists. 2011;3(3):205–10. Zingone G, Rubessa F. Preformulation study of the inclusion complex warfarin-β-cyclodextrin. Int J Pharm. 2005;291(1–2):3–10. Hernández-Sánchez P, López-Miranda S, Guardiola L, Serrano-Martínez A, Gabaldón JA, et al. Optimization of a method for preparing solid complexes of essential clove oil with β-cyclodextrins. J Sci Food Agric. 2017;97(2):420–6. Liu Y, Chen Y, Gao X, Fu J, Hu L. Application of cyclodextrin in food industry. Crit Rev Food Sci Nutr. 2022;62(10):2627–40. Hedges AR. Industrial Applications of Cyclodextrins. Chem Rev. 1998;98(5):2035–44. Del Valle EMM. Cyclodextrins and their uses: a review. Process Biochem. 2004;39(9):1033–46. Walters RH, Bhatnagar B, Tchessalov S, Izutsu KI, Tsumoto K, et al. Next Generation Drying Technologies for Pharmaceutical Applications. J Pharm Sci. 2014;103(9):2673–95. Kulkarni NS, Gite PD, Munde MK, Dhole SN, Khiste RH. A comprehensive review on application of microwave irradiation for preparation of inclusion complexes with cyclodextrins. Res J Pharm Technol. 2021;14(2):1131–6. Barzegar-Jalali M, Valizadeh H, Shadbad MRS, Adibkia K, Mohammadi G, et al. Cogrinding as an approach to enhance dissolution rate of a poorly water-soluble drug (gliclazide). Powder Technol. 2010;197(3):150–8. Borba PAA, Pinotti M, Andrade GRS, da Costa NB, Olchanheski LR, et al. The effect of mechanical grinding on the formation, crystalline changes and dissolution behaviour of the inclusion complex of telmisartan and β-cyclodextrins. Carbohydr Polym. 2015;133:373–83. Lin HL, Lin SY, Lin CC, Hsu CH, Wu TK, et al. Mechanical grinding effect on thermodynamics and inclusion efficiency of loratadine–cyclodextrin inclusion complex formation. Carbohydr Polym. 2012;87(1):512–7. Colombo I, Grassi G, Grassi M. Drug mechanochemical activation. J Pharm Sci. 2009;98(11):3961–86. Yu LX, Amidon G, Khan MA, Hoag SW, Polli J, et al. Understanding Pharmaceutical Quality by Design. AAPS J. 2014;16(4):771–83. Vemula SK, Daravath B, Repka M. Quality by design (QbD) approach to develop fast-dissolving tablets using melt-dispersion paired with surface-adsorption method: formulation and pharmacokinetics of flurbiprofen melt-dispersion granules. Drug Deliv Transl Res. 2023;13(12):3204–22. Vemula SK, Narala S, Uttreja P, Narala N, Daravath B, et al. Quality by Design (QbD) Approach to Develop Colon-Specific Ketoprofen Hot-Melt Extruded Pellets: Impact of Eudragit® S 100 Coating on the In Vitro Drug Release. Pharmaceutics. 2024;16(10):1265. Soe HMSH, Kerdpol K, Rungrotmongkol T, Pruksakorn P, Autthateinchai R, et al. Voriconazole Eye Drops: Enhanced Solubility and Stability through Ternary Voriconazole/Sulfobutyl Ether β-Cyclodextrin/Polyvinyl Alcohol Complexes. Int J Mol Sci. 2023;24(3):2343. Suvarna P, Chaudhari P, Birangal S, Mallela LS, Roy S, et al. Voriconazole–Cyclodextrin Supramolecular Ternary Complex-Loaded Ocular Films for Management of Fungal Keratitis. Mol Pharm. 2022;19(1):258–73. Farooq M, Usman F, Naseem M, Aati HY, Ahmad H, et al. Voriconazole Cyclodextrin Based Polymeric Nanobeads for Enhanced Solubility and Activity: In Vitro/In Vivo and Molecular Simulation Approach. Pharmaceutics. 2023;15(2):389. Başaran E, Aykaç K, Yenilmez E, Büyükköroğlu G, Tunali Y, et al. Formulation and characterization studies of inclusion complexes of voriconazole for possible ocular application. Pharm Dev Technol. 2022;27(2):228–41. Daravath B, Naveen C, Vemula SK, Tadikonda RR. Solubility and dissolution enhancement of flurbiprofen by solid dispersion using hydrophilic carriers. Brazilian J Pharm Sci. 2018;53(4). Daravath B. Surface solid dispersion: A novel method for improving in-vitro dissolution and in-vivo pharmacokinetics of meclizine hydrochloride. Res J Pharm Technol. 2021;14(2):685–93. Chella N, Daravath B, Kumar D, Tadikonda RR. Formulation and Pharmacokinetic Evaluation of Polymeric Dispersions Containing Valsartan. Eur J Drug Metab Pharmacokinet. 2016;41(5):517–26. Daravath B, Kumari G. Improvement of bioavailability of poorly soluble racecadotril by solid dispersion with surface adsorption method: A case study. J Rep Pharm Sci. 2021;10(1):77. Kondoros BA, Berkesi O, Tóth Z, Aigner Z, Ambrus R, et al. Cyclodextrin Complexation of Fenofibrate by Co-Grinding Method and Monitoring the Process Using Complementary Analytical Tools. Pharmaceutics. 2022;14(7):1329. Maha FE, Ahmed AE, Nadia MM, Laila HE. Optimization of Meloxicam Solid Dispersion Formulations for Dissolution Enhancement and Storage Stability Using 3 3 Full Factorial Design Based on Response Surface Methodology. AAPS PharmSciTech. 2022;23(7):248. Additional Declarations No competing interests reported. Cite Share Download PDF Status: Published Journal Publication published 24 Oct, 2025 Read the published version in Journal of Pharmaceutical Innovation → 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. 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Also discoverable on Platform About Our Team In Review Editorial Policies Advisory Board Help Center Resources Author Services Accessibility API Access RSS feed Manage Cookie Preferences © Research Square 2026 | ISSN 2693-5015 (online) Privacy Policy Terms of Service Do Not Sell My Personal Information {"props":{"pageProps":{"initialData":{"identity":"rs-6642975","acceptedTermsAndConditions":true,"allowDirectSubmit":true,"archivedVersions":[],"articleType":"Research Article","associatedPublications":[],"authors":[{"id":459043342,"identity":"cc23571b-b7e4-4838-8784-613ebf93da3b","order_by":0,"name":"Bhaskar Daravath","email":"data:image/png;base64,iVBORw0KGgoAAAANSUhEUgAAAZAAAAAyAQMAAABI0h/eAAAABlBMVEX///8AAABVwtN+AAAACXBIWXMAAA7EAAAOxAGVKw4bAAAA2UlEQVRIiWNgGAWjYBACA4YECIOfgbEBwjpApBYJyQbGxgbStBgcgFlDSIs5e/KzDz/b7OqMzy9uf/Azh0GO70YCfi2WPc+MZ/a2JUuY3XjY2Ni7jcFYkpAWgxsJxgw8Z5iBWg42NvBuY0jcQFhL+mfGP2fqJYxnHGxs/LuNoZ4ILTnGzDwVhyUM+Bsbm4G2JBgQ9subYmaZiuOSM24wNs6W3SZhOPPMA/xazNnTNzO+Majm5+8//uDj22028nzHCdiCABJglRLEKgcB/gOkqB4Fo2AUjIKRBADfT0t97BAKyAAAAABJRU5ErkJggg==","orcid":"","institution":"GITAM Deemed to be University","correspondingAuthor":true,"prefix":"","firstName":"Bhaskar","middleName":"","lastName":"Daravath","suffix":""},{"id":459043343,"identity":"4db34f5c-16d8-4acb-8eea-0f93afd43ce9","order_by":1,"name":"Madhavi Vasamsetti","email":"","orcid":"","institution":"GITAM Deemed to be University","correspondingAuthor":false,"prefix":"","firstName":"Madhavi","middleName":"","lastName":"Vasamsetti","suffix":""},{"id":459043344,"identity":"40c0059e-353f-43d5-bb5e-8225e1a02254","order_by":2,"name":"Naveen Chella","email":"","orcid":"","institution":"National Institute of Pharmaceutical Education and Research (NIPER)","correspondingAuthor":false,"prefix":"","firstName":"Naveen","middleName":"","lastName":"Chella","suffix":""},{"id":459043345,"identity":"f63a3197-7ed2-4a3d-b091-bc2280a46a80","order_by":3,"name":"Sateesh Kumar Vemula","email":"","orcid":"","institution":"The University of Mississippi, University","correspondingAuthor":false,"prefix":"","firstName":"Sateesh","middleName":"Kumar","lastName":"Vemula","suffix":""}],"badges":[],"createdAt":"2025-05-12 05:23:07","currentVersionCode":1,"declarations":"","doi":"10.21203/rs.3.rs-6642975/v1","doiUrl":"https://doi.org/10.21203/rs.3.rs-6642975/v1","draftVersion":[],"editorialEvents":[{"content":"https://doi.org/10.1007/s12247-025-10214-1","type":"published","date":"2025-10-24T16:16:42+00:00"}],"editorialNote":"","failedWorkflow":false,"files":[{"id":83281789,"identity":"ea367743-5376-4516-b2e5-15f8a295be5b","added_by":"auto","created_at":"2025-05-22 10:28:01","extension":"jpg","order_by":1,"title":"Figure 1","display":"","copyAsset":false,"role":"figure","size":19751,"visible":true,"origin":"","legend":"\u003cp\u003ePhase solubility study of voriconazole in β-cyclodextrin aqueous solutions\u003c/p\u003e","description":"","filename":"Figure1...jpg","url":"https://assets-eu.researchsquare.com/files/rs-6642975/v1/b40385fe035cb0a5d177a1b2.jpg"},{"id":83281795,"identity":"7d213f42-5bd0-449a-85b0-c80efae7ca16","added_by":"auto","created_at":"2025-05-22 10:28:02","extension":"jpg","order_by":2,"title":"Figure 2","display":"","copyAsset":false,"role":"figure","size":172675,"visible":true,"origin":"","legend":"\u003cp\u003e2-D contour plots of surface plots of Y1: solubility (mg/ml); Y2: %CDR (%)\u003c/p\u003e","description":"","filename":"Figure2..jpg","url":"https://assets-eu.researchsquare.com/files/rs-6642975/v1/b83937ac49e16c9bd458c9e2.jpg"},{"id":83281790,"identity":"00308cde-1506-416d-bda9-c3ea6e132d78","added_by":"auto","created_at":"2025-05-22 10:28:01","extension":"jpg","order_by":3,"title":"Figure 3","display":"","copyAsset":false,"role":"figure","size":305891,"visible":true,"origin":"","legend":"\u003cp\u003e3-D response surface plots of Y1: solubility (mg/ml); Y2: %CDR (%)\u003c/p\u003e","description":"","filename":"Figure3.jpg","url":"https://assets-eu.researchsquare.com/files/rs-6642975/v1/6005166fe750e3ce0df67550.jpg"},{"id":83282374,"identity":"3bef8d88-ab37-48ee-b33e-1833d0b8052d","added_by":"auto","created_at":"2025-05-22 10:36:02","extension":"jpg","order_by":4,"title":"Figure 4","display":"","copyAsset":false,"role":"figure","size":23894,"visible":true,"origin":"","legend":"\u003cp\u003eCoefficient estimates of the factors a) Amount of Carrier b) Grinding time\u003c/p\u003e","description":"","filename":"Figure4.jpg","url":"https://assets-eu.researchsquare.com/files/rs-6642975/v1/819ac4441631a5ed4ea6b310.jpg"},{"id":83282376,"identity":"610a8459-9835-4b0f-9bfc-5c46ef1a1336","added_by":"auto","created_at":"2025-05-22 10:36:02","extension":"jpg","order_by":5,"title":"Figure 5","display":"","copyAsset":false,"role":"figure","size":105964,"visible":true,"origin":"","legend":"\u003cp\u003eDissolution profiles of\u003cstrong\u003e \u003c/strong\u003eprepared\u003cstrong\u003e \u003c/strong\u003einclusion complexes, PM, and pure VC\u003c/p\u003e","description":"","filename":"Figure5.jpg","url":"https://assets-eu.researchsquare.com/files/rs-6642975/v1/0154b3e6dfbcce9a20a7d035.jpg"},{"id":83281793,"identity":"7faf69cb-14a4-4bc8-98cf-18d8b18c0ebb","added_by":"auto","created_at":"2025-05-22 10:28:02","extension":"jpg","order_by":6,"title":"Figure 6","display":"","copyAsset":false,"role":"figure","size":116412,"visible":true,"origin":"","legend":"\u003cp\u003eDesirability, design space, and factor levels for the optimized formula and their responses\u003c/p\u003e","description":"","filename":"Figure6..jpg","url":"https://assets-eu.researchsquare.com/files/rs-6642975/v1/337d435d53f2d3a988cc93df.jpg"},{"id":83282379,"identity":"5a125bbc-a9e7-47c3-b91c-f50b31d6fc4e","added_by":"auto","created_at":"2025-05-22 10:36:02","extension":"jpg","order_by":7,"title":"Figure 7","display":"","copyAsset":false,"role":"figure","size":87960,"visible":true,"origin":"","legend":"\u003cp\u003ePredicted vs actual plots Y1: solubility (mg/ml); Y2: %CDR (%)\u003c/p\u003e","description":"","filename":"Figure7.jpg","url":"https://assets-eu.researchsquare.com/files/rs-6642975/v1/59227f24ea97ae465351b0dd.jpg"},{"id":83281800,"identity":"1e833446-cd72-47c5-880d-a0c8bc8ff288","added_by":"auto","created_at":"2025-05-22 10:28:02","extension":"jpg","order_by":8,"title":"Figure 8","display":"","copyAsset":false,"role":"figure","size":301817,"visible":true,"origin":"","legend":"\u003cp\u003eFTIR spectrums of A) VC B) β-cyclodextrin C) PM D) Optimized formulation\u003c/p\u003e","description":"","filename":"Figure8..jpg","url":"https://assets-eu.researchsquare.com/files/rs-6642975/v1/9346179cd4145365e14923a2.jpg"},{"id":83282375,"identity":"f82f809d-cca7-40b9-861c-a8b897f5fdfd","added_by":"auto","created_at":"2025-05-22 10:36:02","extension":"jpg","order_by":9,"title":"Figure 9","display":"","copyAsset":false,"role":"figure","size":147586,"visible":true,"origin":"","legend":"\u003cp\u003eDSC thermograms of A) VC B) β-cyclodextrin C) PM D) Optimized formulation\u003c/p\u003e","description":"","filename":"Figure9..jpg","url":"https://assets-eu.researchsquare.com/files/rs-6642975/v1/c1ea95870790c2b4dc3b8d9c.jpg"},{"id":83282386,"identity":"2afec95c-a37c-4323-88b0-93f485f2dcfd","added_by":"auto","created_at":"2025-05-22 10:36:02","extension":"jpg","order_by":10,"title":"Figure 10","display":"","copyAsset":false,"role":"figure","size":2376132,"visible":true,"origin":"","legend":"\u003cp\u003eX-ray diffraction patterns of A) VC B) β-cyclodextrin C) PM D) Optimized formulation\u003c/p\u003e","description":"","filename":"Figure10..jpg","url":"https://assets-eu.researchsquare.com/files/rs-6642975/v1/a5e6e2f9b7fadc6347e7477a.jpg"},{"id":83281813,"identity":"7627d663-bac8-4331-bdf4-6dbbc85af672","added_by":"auto","created_at":"2025-05-22 10:28:02","extension":"jpg","order_by":11,"title":"Figure 11","display":"","copyAsset":false,"role":"figure","size":1405737,"visible":true,"origin":"","legend":"\u003cp\u003eSEM images of A) VC B) β-cyclodextrin C) PM D) Optimized formulation\u003c/p\u003e","description":"","filename":"Figure11.jpg","url":"https://assets-eu.researchsquare.com/files/rs-6642975/v1/3aaefbb3b68b003766fc50f8.jpg"},{"id":94490488,"identity":"1557c62b-60a7-4649-9db3-d16c1fcde3ea","added_by":"auto","created_at":"2025-10-27 17:11:03","extension":"pdf","order_by":0,"title":"","display":"","copyAsset":false,"role":"manuscript-pdf","size":6336508,"visible":true,"origin":"","legend":"","description":"","filename":"manuscript.pdf","url":"https://assets-eu.researchsquare.com/files/rs-6642975/v1/d4dc3ffc-0b4e-4559-9775-a70066ee45b7.pdf"}],"financialInterests":"No competing interests reported.","formattedTitle":"Design and Optimization of Inclusion Complexes Using QbD Principles: A strategy to enhance solubility and dissolution of Voriconazole","fulltext":[{"header":"INTRODUCTION","content":"\u003cp\u003eThe high-throughput screening, widely utilized in drug development, has led to a growing number of lipophilic compounds among emerging drug candidates. The limited solubility of these compounds poses significant challenges for formulation scientists during their development and manufacturing. This solubility issue affects a substantial proportion of pharmaceutical compounds. More than 40% of the currently marketed drug products and 70% of the active molecules in development are poorly soluble[\u003cspan citationid=\"CR1\" class=\"CitationRef\"\u003e1\u003c/span\u003e, \u003cspan citationid=\"CR2\" class=\"CitationRef\"\u003e2\u003c/span\u003e].\u003c/p\u003e \u003cp\u003eThe aqueous solubility and dissolution kinetics of active pharmaceutical ingredients (APIs) are critical physicochemical parameters that significantly influence their bioavailability and biodistribution profiles. The ability of a drug molecule to dissolve in aqueous media is a fundamental characteristic that directly modulates its absorption following oral administration. Despite the advances in pharmaceutical science, the development of APIs with optimal solubility profiles remains the greatest challenge in contemporary drug discovery and development [\u003cspan citationid=\"CR3\" class=\"CitationRef\"\u003e3\u003c/span\u003e].\u003c/p\u003e \u003cp\u003eEnhancing the bioavailability of poorly soluble APIs can be achieved through diverse pharmaceutical interventions targeting their limited aqueous solubility, ultimately enabling dose reduction and minimizing adverse effects. Researchers have successfully implemented multiple methodologies, including salt formation [\u003cspan citationid=\"CR4\" class=\"CitationRef\"\u003e4\u003c/span\u003e], use of non-volatile solvents [\u003cspan citationid=\"CR5\" class=\"CitationRef\"\u003e5\u003c/span\u003e, \u003cspan citationid=\"CR6\" class=\"CitationRef\"\u003e6\u003c/span\u003e], particle size reduction[\u003cspan citationid=\"CR7\" class=\"CitationRef\"\u003e7\u003c/span\u003e], surfactant incorporation [\u003cspan citationid=\"CR8\" class=\"CitationRef\"\u003e8\u003c/span\u003e], molecular complexation [\u003cspan citationid=\"CR9\" class=\"CitationRef\"\u003e9\u003c/span\u003e], hot-melt extrusion [\u003cspan citationid=\"CR10\" class=\"CitationRef\"\u003e10\u003c/span\u003e] and solid dispersions[\u003cspan citationid=\"CR11\" class=\"CitationRef\"\u003e11\u003c/span\u003e]). Among these, cyclodextrin complexation has proven to be the most effective [\u003cspan citationid=\"CR12\" class=\"CitationRef\"\u003e12\u003c/span\u003e].\u003c/p\u003e \u003cp\u003eCyclodextrins (CDs) are unique toroidal-shaped cyclic oligosaccharides with an interior hydrophobic core and exterior hydrophilic property. This structure allows CDs to encapsulate hydrophobic API in their cavities. Complexation relies on precise matching of the cavity size with the API molecule. A cavity that is too small cannot form a complex, whereas a cavity that is too large will create weak, unstable complexes. Therefore, the formation of optimal complexes relies on suitable API-sized cavities in the CDs that maximize API encapsulation while minimizing the release potential. When the API-cyclodextrin complex was added to water, the hydrophilic hydroxyl groups on the exterior of the CD interacted with the water molecules through hydrogen bonding. This allowed for easy dissolution of the complex [\u003cspan citationid=\"CR13\" class=\"CitationRef\"\u003e13\u003c/span\u003e].\u003c/p\u003e \u003cp\u003eCyclodextrins have proven effective in enhancing the solubility of various drugs, including ibuprofen [\u003cspan citationid=\"CR14\" class=\"CitationRef\"\u003e14\u003c/span\u003e], telmisartan [\u003cspan citationid=\"CR15\" class=\"CitationRef\"\u003e15\u003c/span\u003e], diclofenac [\u003cspan citationid=\"CR16\" class=\"CitationRef\"\u003e16\u003c/span\u003e], lisinopril [\u003cspan citationid=\"CR17\" class=\"CitationRef\"\u003e17\u003c/span\u003e], and catechin [\u003cspan citationid=\"CR18\" class=\"CitationRef\"\u003e18\u003c/span\u003e]. Preparation of inclusion complexes has been performed by grinding [\u003cspan citationid=\"CR19\" class=\"CitationRef\"\u003e19\u003c/span\u003e], kneading [\u003cspan citationid=\"CR20\" class=\"CitationRef\"\u003e20\u003c/span\u003e], co-precipitation [\u003cspan citationid=\"CR9\" class=\"CitationRef\"\u003e9\u003c/span\u003e], freeze-drying [\u003cspan citationid=\"CR21\" class=\"CitationRef\"\u003e21\u003c/span\u003e], microwave irradiation [\u003cspan citationid=\"CR22\" class=\"CitationRef\"\u003e22\u003c/span\u003e], and spray drying [\u003cspan citationid=\"CR23\" class=\"CitationRef\"\u003e23\u003c/span\u003e]. These methods can effectively produce inclusion complexes but have a few limitations, such as the use of organic solvents (coprecipitation), low recovery, excessive particle growth (spray drying), high cost and processing time (freeze drying), degradation of thermolabile substances, and content uniformity issues (microwave irradiation) [\u003cspan additionalcitationids=\"CR25 CR26\" citationid=\"CR24\" class=\"CitationRef\"\u003e24\u003c/span\u003e\u0026ndash;\u003cspan citationid=\"CR27\" class=\"CitationRef\"\u003e27\u003c/span\u003e].\u003c/p\u003e \u003cp\u003eCyclodextrin complexation by co-grinding is preferred over other methods. Co-grinding uses a solvent-free approach that potentially minimizes environmental impact and reduces production costs [\u003cspan citationid=\"CR28\" class=\"CitationRef\"\u003e28\u003c/span\u003e]. This method is relatively straightforward and highly scalable, making it suitable for commercial applications [\u003cspan citationid=\"CR29\" class=\"CitationRef\"\u003e29\u003c/span\u003e]. A few authors have used this approach to enhance the solubility and dissolution rate of poor soluble drugs [\u003cspan citationid=\"CR30\" class=\"CitationRef\"\u003e30\u003c/span\u003e, \u003cspan citationid=\"CR31\" class=\"CitationRef\"\u003e31\u003c/span\u003e].\u003c/p\u003e \u003cp\u003eIn pharmaceutical development, QbD (Quality by Design) addresses formulation challenges by emphasizing the set goals from the product creation and process implementation stages [\u003cspan citationid=\"CR32\" class=\"CitationRef\"\u003e32\u003c/span\u003e, \u003cspan citationid=\"CR33\" class=\"CitationRef\"\u003e33\u003c/span\u003e]. Design of Experiments (DoE) enables a systematic analysis of the impact of multiple factors within particular limits on a given outcome. Moreover, a RSM (Response Surface Methodology) serves as a powerful statistical method for drug formulation optimization because it allows the predictive modelling of relationships between factors and responses from data collected through experiments and regression analysis [\u003cspan citationid=\"CR34\" class=\"CitationRef\"\u003e34\u003c/span\u003e]. Applying QbD and DoE was effective in achieving the targeted product profile.\u003c/p\u003e \u003cp\u003eVoriconazole (VC) has broad-spectrum antifungal activity against pathogenic fungi such as Candida, Aspergillus, and Fusarium. VC is classified as a (BCS) Biopharmaceutics Classification System Class II drug, characterized by low aqueous solubility and high permeability. However, its limited aqueous solubility and potential for variable bioavailability pose challenges for formulation development [\u003cspan citationid=\"CR35\" class=\"CitationRef\"\u003e35\u003c/span\u003e]. These constraints require approaches, such as cyclodextrin complexation, to enhance its delivery and efficacy.\u003c/p\u003e \u003cp\u003eSo far, only a few studies have explored the using cyclodextrin inclusion complexes to enhance voriconazole (VC) solubility. However, no research has used the co-grinding method, nor has it examined how the amount of carrier and grinding time affect VC release [\u003cspan additionalcitationids=\"CR37\" citationid=\"CR36\" class=\"CitationRef\"\u003e36\u003c/span\u003e\u0026ndash;\u003cspan citationid=\"CR38\" class=\"CitationRef\"\u003e38\u003c/span\u003e]. Additionally, they conducted studies that examined a single factor at a time.\u003c/p\u003e \u003cp\u003eVoriconazole (VC) solubility and drug release are dependent on the carrier concentration and grinding time. The carrier amount (A) and grinding time (B) were deemed critical material and process parameters and were studied as independent variables. This study examined the impact of these factors on the solubility and drug release of VC inclusion complexes developed using a co-grinding technique. Solubility and cumulative drug release were selected as outcome measures. A Central Composite Design (CCD) was utilized under a QbD strategy to refine the formulation with regard to VC solubility, dissolution, and therapeutic efficacy. Parameters A and B were adjusted, and the corresponding VC solubility (Y1) and cumulative % drug release (Q; Y2) were measured. The purpose was to look for a design space to improve the solubility, dissolution, and therapeutic efficacy of VC.\u003c/p\u003e \u003cp\u003eA new cyclodextrin complexation method was developed to enhance voriconazole\u0026rsquo;s solubility and dissolution. The study implemented QbD featuring RSM and CCD to suggest optimization related to the amount of carrier and grinding time on the release of VC aimed at increasing both solubility and dissolution of VC.\u003c/p\u003e"},{"header":"MATERIALS AND METHODS","content":"\u003cdiv id=\"Sec3\" class=\"Section2\"\u003e \u003ch2\u003eMaterials\u003c/h2\u003e \u003cp\u003eVoriconazole was gifted by MSN Laboratories Pvt., Ltd. (Hyderabad, Telangana, India). The other reagents were procured from SD Fine Chemicals (Mumbai, India).\u003c/p\u003e \u003c/div\u003e\n\u003ch3\u003eMethods\u003c/h3\u003e\n\u003cdiv id=\"Sec5\" class=\"Section2\"\u003e \u003ch2\u003ePhase solubility study of voriconazole\u003c/h2\u003e \u003cp\u003eThe voriconazole phase solubility study was conducted following procedures provided in literature [\u003cspan citationid=\"CR39\" class=\"CitationRef\"\u003e39\u003c/span\u003e]. An excess amount of VC was added to aqueous solutions containing varying amounts of β-cyclodextrin (β-CD) (0, 1, 2, 3, 4, and 5% w/v). The suspensions were rotated at 25\u0026deg;C for 48 hours using an orbital shaker set at 300 rpm. After equilibrium was reached, the samples were filtered through a 0.45 \u0026micro;m membrane filter, and the concentration of VC was determined spectrophotometrically at 250 nm using a UV\u0026ndash;visible spectrophotometer.\u003c/p\u003e \u003c/div\u003e\n\u003ch3\u003eDevelopment of a mathematical model through Experimental Design\u003c/h3\u003e\n\u003cp\u003eDesign-Expert\u0026reg; (DOE) software (version 13) was employed to construct a CCD (central Composite Design) for the systematic optimization of two formulation variables and to evaluate their effects on key response parameters. Table\u0026nbsp;\u003cspan refid=\"Tab1\" class=\"InternalRef\"\u003e1\u003c/span\u003e shows that the independent variables, \u0026lsquo;A\u0026rsquo; (carrier amount) and \u0026lsquo;B\u0026rsquo; (grinding time), were investigated at five levels (\u0026minus;α, \u0026minus;\u0026thinsp;1, 0, +\u0026thinsp;1, +α), with both actual and coded values. The dependent factors (responses) assessed were solubility (Y₁) and cumulative percentage e drug release (%CDR, Y₂). A total of 13 experimental runs were executed (Table\u0026nbsp;\u003cspan refid=\"Tab2\" class=\"InternalRef\"\u003e2\u003c/span\u003e).\u003c/p\u003e \u003cp\u003e \u003cdiv class=\"gridtable\"\u003e\u003ctable float=\"Yes\" id=\"Tab1\" border=\"1\"\u003e \u003ccaption language=\"En\"\u003e \u003cdiv class=\"CaptionNumber\"\u003eTable 1\u003c/div\u003e \u003cdiv class=\"CaptionContent\"\u003e \u003cp\u003eFactors and their levels as per the Central Composite Design\u003c/p\u003e \u003c/div\u003e \u003c/caption\u003e \u003ccolgroup cols=\"6\"\u003e \u003cdiv align=\"left\" class=\"colspec\" colname=\"c1\" colnum=\"1\"\u003e\u003c/div\u003e \u003cdiv align=\"char\" char=\".\" class=\"colspec\" colname=\"c2\" colnum=\"2\"\u003e\u003c/div\u003e \u003cdiv align=\"char\" char=\".\" class=\"colspec\" colname=\"c3\" colnum=\"3\"\u003e\u003c/div\u003e \u003cdiv align=\"left\" class=\"colspec\" colname=\"c4\" colnum=\"4\"\u003e\u003c/div\u003e \u003cdiv align=\"char\" char=\".\" class=\"colspec\" colname=\"c5\" colnum=\"5\"\u003e\u003c/div\u003e \u003cdiv align=\"char\" char=\".\" class=\"colspec\" colname=\"c6\" colnum=\"6\"\u003e\u003c/div\u003e \u003cthead\u003e \u003ctr\u003e \u003cth align=\"left\" colname=\"c1\" morerows=\"1\" rowspan=\"2\"\u003e \u003cp\u003eFactors (Independent Variables)\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colspan=\"5\" nameend=\"c6\" namest=\"c2\"\u003e \u003cp\u003eLevels\u003c/p\u003e \u003c/th\u003e \u003c/tr\u003e \u003ctr\u003e \u003cth align=\"left\" colname=\"c2\"\u003e \u003cp\u003e-α\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c3\"\u003e \u003cp\u003e-1\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c4\"\u003e \u003cp\u003e0\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c5\"\u003e \u003cp\u003e+\u0026thinsp;1\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c6\"\u003e \u003cp\u003e+α\u003c/p\u003e \u003c/th\u003e \u003c/tr\u003e \u003c/thead\u003e \u003ctbody\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003e\u003cb\u003eAmount of Carrier (β \u0026ndash; cyclodextrin)\u003c/b\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e \u003cp\u003e117.157\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e \u003cp\u003e200\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e400\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c5\"\u003e \u003cp\u003e600\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c6\"\u003e \u003cp\u003e682.843\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003e\u003cb\u003eGrinding time\u003c/b\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e \u003cp\u003e2.23654\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e \u003cp\u003e7\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e18.5\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c5\"\u003e \u003cp\u003e30\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c6\"\u003e \u003cp\u003e34.7635\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003c/tbody\u003e \u003c/colgroup\u003e \u003c/table\u003e\u003c/div\u003e \u003c/p\u003e \u003cp\u003e \u003cdiv class=\"gridtable\"\u003e\u003ctable float=\"Yes\" id=\"Tab2\" border=\"1\"\u003e \u003ccaption language=\"En\"\u003e \u003cdiv class=\"CaptionNumber\"\u003eTable 2\u003c/div\u003e \u003cdiv class=\"CaptionContent\"\u003e \u003cp\u003eResults of Central Composite Design and the Responses Obtained for Experiments\u003c/p\u003e \u003c/div\u003e \u003c/caption\u003e \u003ccolgroup cols=\"5\"\u003e \u003cdiv align=\"left\" class=\"colspec\" colname=\"c1\" colnum=\"1\"\u003e\u003c/div\u003e \u003cdiv align=\"left\" class=\"colspec\" colname=\"c2\" colnum=\"2\"\u003e\u003c/div\u003e \u003cdiv align=\"left\" class=\"colspec\" colname=\"c3\" colnum=\"3\"\u003e\u003c/div\u003e \u003cdiv align=\"char\" char=\".\" class=\"colspec\" colname=\"c4\" colnum=\"4\"\u003e\u003c/div\u003e \u003cdiv align=\"char\" char=\".\" class=\"colspec\" colname=\"c5\" colnum=\"5\"\u003e\u003c/div\u003e \u003cthead\u003e \u003ctr\u003e \u003cth align=\"left\" colname=\"c1\"\u003e \u003cp\u003eExperimental Run\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c2\"\u003e \u003cp\u003eFactor A: Amount of Carrier (β \u0026ndash; cyclodextrin) (mg)\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c3\"\u003e \u003cp\u003eFactor B: Grinding time (min)\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c4\"\u003e \u003cp\u003eSolubility (mg/ml)\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c5\"\u003e \u003cp\u003e% CDR (%)\u003c/p\u003e \u003c/th\u003e \u003c/tr\u003e \u003c/thead\u003e \u003ctbody\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003e1\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e600\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e7\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e \u003cp\u003e52.79\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c5\"\u003e \u003cp\u003e79.56\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003e2\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e400\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e18.5\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e \u003cp\u003e47.37\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c5\"\u003e \u003cp\u003e71.48\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003e3\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e400\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e18.5\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e \u003cp\u003e42.23\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c5\"\u003e \u003cp\u003e67.37\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003e4\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e600\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e30\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e \u003cp\u003e65.86\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c5\"\u003e \u003cp\u003e85.93\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003e5\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e400\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e18.5\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e \u003cp\u003e46.7\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c5\"\u003e \u003cp\u003e76.48\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003e6\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e200\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e7\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e \u003cp\u003e21.5\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c5\"\u003e \u003cp\u003e33.89\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003e7\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e400\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e34.7635\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e \u003cp\u003e52.28\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c5\"\u003e \u003cp\u003e79.33\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003e8\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e400\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e18.5\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e \u003cp\u003e37.15\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c5\"\u003e \u003cp\u003e62.15\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003e9\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e200\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e30\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e \u003cp\u003e35.64\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c5\"\u003e \u003cp\u003e57.03\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003e10\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e400\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e18.5\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e \u003cp\u003e41.56\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c5\"\u003e \u003cp\u003e66.46\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003e11\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e682.843\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e18.5\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e \u003cp\u003e74.63\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c5\"\u003e \u003cp\u003e95.73\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003e12\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e117.157\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e18.5\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e \u003cp\u003e17.31\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c5\"\u003e \u003cp\u003e26.08\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003e13\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e400\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e2.23654\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e \u003cp\u003e9.83\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c5\"\u003e \u003cp\u003e25.31\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003c/tbody\u003e \u003c/colgroup\u003e \u003c/table\u003e\u003c/div\u003e \u003c/p\u003e \u003cp\u003eThe second-order polynomial model equation is as follows:\u003c/p\u003e \u003cp\u003e \u003cem\u003eY\u0026thinsp;=\u0026thinsp;β0\u0026thinsp;+\u0026thinsp;β1 A\u0026thinsp;+\u0026thinsp;β2 B\u0026thinsp;+\u0026thinsp;β11 A2\u0026thinsp;+\u0026thinsp;β22 B2\u0026thinsp;+\u0026thinsp;β12 AB ------\u003c/em\u003e 1\u003c/p\u003e \u003cp\u003eWhere \u0026ldquo;Y\u0026rdquo; represents dependent responses (solubility or %CDR), intercept is β0; linear coefficients are β1, β2; square coefficients are β11, β22; interaction coefficient is β12 for independent variables A and B. Statistical analysis, including p-values, F-values, and regression coefficients for the model terms, are summarized in Table\u0026nbsp;\u003cspan refid=\"Tab3\" class=\"InternalRef\"\u003e3\u003c/span\u003e.\u003c/p\u003e \u003cp\u003e \u003cdiv class=\"gridtable\"\u003e\u003ctable float=\"Yes\" id=\"Tab3\" border=\"1\"\u003e \u003ccaption language=\"En\"\u003e \u003cdiv class=\"CaptionNumber\"\u003eTable 3\u003c/div\u003e \u003cdiv class=\"CaptionContent\"\u003e \u003cp\u003eF-values, P-values, and the estimated coefficient values obtained from the regression models\u003c/p\u003e \u003c/div\u003e \u003c/caption\u003e \u003ccolgroup cols=\"7\"\u003e \u003cdiv align=\"left\" class=\"colspec\" colname=\"c1\" colnum=\"1\"\u003e\u003c/div\u003e \u003cdiv align=\"left\" class=\"colspec\" colname=\"c2\" colnum=\"2\"\u003e\u003c/div\u003e \u003cdiv align=\"left\" class=\"colspec\" colname=\"c3\" colnum=\"3\"\u003e\u003c/div\u003e \u003cdiv align=\"left\" class=\"colspec\" colname=\"c4\" colnum=\"4\"\u003e\u003c/div\u003e \u003cdiv align=\"left\" class=\"colspec\" colname=\"c5\" colnum=\"5\"\u003e\u003c/div\u003e \u003cdiv align=\"left\" class=\"colspec\" colname=\"c6\" colnum=\"6\"\u003e\u003c/div\u003e \u003cdiv align=\"left\" class=\"colspec\" colname=\"c7\" colnum=\"7\"\u003e\u003c/div\u003e \u003cthead\u003e \u003ctr\u003e \u003cth align=\"left\" colname=\"c1\" morerows=\"1\" rowspan=\"2\"\u003e \u003cp\u003eSource\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colspan=\"3\" nameend=\"c4\" namest=\"c2\"\u003e \u003cp\u003eY\u003csub\u003e1\u003c/sub\u003e: Solubility\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colspan=\"3\" nameend=\"c7\" namest=\"c5\"\u003e \u003cp\u003eY\u003csub\u003e3\u003c/sub\u003e: Cumulative % drug release\u003c/p\u003e \u003c/th\u003e \u003c/tr\u003e \u003ctr\u003e \u003cth align=\"left\" colname=\"c2\"\u003e \u003cp\u003eF-value\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c3\"\u003e \u003cp\u003ep-value\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c4\"\u003e \u003cp\u003eCoefficient Estimate\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c5\"\u003e \u003cp\u003eF-value\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c6\"\u003e \u003cp\u003ep-value\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c7\"\u003e \u003cp\u003eCoefficient Estimate\u003c/p\u003e \u003c/th\u003e \u003c/tr\u003e \u003c/thead\u003e \u003ctbody\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eModel\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e32.12\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e\u0026lt;\u0026thinsp;0.0001\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e-\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e27.40\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e \u003cp\u003e\u0026lt;\u0026thinsp;0.0001\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c7\"\u003e \u003cp\u003e-\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eIntercept\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e-\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e-\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e41.91\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e-\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e \u003cp\u003e-\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c7\"\u003e \u003cp\u003e63.60\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eA: Amount of Carrier (β \u0026ndash; cyclodextrin)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e46.74\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e\u0026lt;\u0026thinsp;0.0001\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e17.82\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e39.87\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e \u003cp\u003e\u0026lt;\u0026thinsp;0.0001\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c7\"\u003e \u003cp\u003e21.63\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eB: Grinding time\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e17.50\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e0.0019\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e10.91\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e14.93\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e \u003cp\u003e0.0031\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c7\"\u003e \u003cp\u003e13.24\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eAB\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e-\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e-\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e-\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e-\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e \u003cp\u003e-\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c7\"\u003e \u003cp\u003e-\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eA\u0026sup2;\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e-\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e-\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e-\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e-\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e \u003cp\u003e-\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c7\"\u003e \u003cp\u003e-\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eB\u0026sup2;\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e-\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e-\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e-\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e-\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e \u003cp\u003e-\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c7\"\u003e \u003cp\u003e-\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eLack of Fit\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e4.53\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e0.825\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e-\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e4.64\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e \u003cp\u003e0.0795\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c7\"\u003e \u003cp\u003e-\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003c/tbody\u003e \u003c/colgroup\u003e \u003ctfoot\u003e \u003ctr\u003e\u003ctd colspan=\"7\"\u003eModel p\u0026thinsp;\u0026lt;\u0026thinsp;0.05: Statistically significant Lack of Fit p\u0026thinsp;\u0026gt;\u0026thinsp;0.05: Lack of Fit if not significant\u003c/td\u003e\u003c/tr\u003e \u003c/tfoot\u003e \u003c/table\u003e\u003c/div\u003e \u003c/p\u003e \u003cp\u003eModel validation was performed by comparing the observed values with predicted responses (Table\u0026nbsp;\u003cspan refid=\"Tab4\" class=\"InternalRef\"\u003e4\u003c/span\u003e). Based on the desirability and optimization criteria of the responses, a flexible design space was established. An optimized formulation batch was subsequently prepared using the identified optimal conditions within the design space.\u003c/p\u003e \u003cp\u003e \u003cdiv class=\"gridtable\"\u003e\u003ctable float=\"Yes\" id=\"Tab4\" border=\"1\"\u003e \u003ccaption language=\"En\"\u003e \u003cdiv class=\"CaptionNumber\"\u003eTable 4\u003c/div\u003e \u003cdiv class=\"CaptionContent\"\u003e \u003cp\u003eComparison of the Predicted and Observed Experimental Values\u003c/p\u003e \u003c/div\u003e \u003c/caption\u003e \u003ccolgroup cols=\"9\"\u003e \u003cdiv align=\"left\" class=\"colspec\" colname=\"c1\" colnum=\"1\"\u003e\u003c/div\u003e \u003cdiv align=\"char\" char=\".\" class=\"colspec\" colname=\"c2\" colnum=\"2\"\u003e\u003c/div\u003e \u003cdiv align=\"left\" class=\"colspec\" colname=\"c3\" colnum=\"3\"\u003e\u003c/div\u003e \u003cdiv align=\"char\" char=\".\" class=\"colspec\" colname=\"c4\" colnum=\"4\"\u003e\u003c/div\u003e \u003cdiv align=\"char\" char=\".\" class=\"colspec\" colname=\"c5\" colnum=\"5\"\u003e\u003c/div\u003e \u003cdiv align=\"char\" char=\".\" class=\"colspec\" colname=\"c6\" colnum=\"6\"\u003e\u003c/div\u003e \u003cdiv align=\"char\" char=\".\" class=\"colspec\" colname=\"c7\" colnum=\"7\"\u003e\u003c/div\u003e \u003cdiv align=\"char\" char=\".\" class=\"colspec\" colname=\"c8\" colnum=\"8\"\u003e\u003c/div\u003e \u003cdiv align=\"char\" char=\".\" class=\"colspec\" colname=\"c9\" colnum=\"9\"\u003e\u003c/div\u003e \u003cthead\u003e \u003ctr\u003e \u003cth align=\"left\" colname=\"c1\" morerows=\"2\" rowspan=\"3\"\u003e \u003cp\u003eCheck points\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colspan=\"2\" nameend=\"c3\" namest=\"c2\"\u003e \u003cp\u003eIndependent variables\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colspan=\"6\" nameend=\"c9\" namest=\"c4\"\u003e\u0026nbsp;\u003c/th\u003e \u003c/tr\u003e \u003ctr\u003e \u003cth align=\"left\" colname=\"c2\"\u003e \u003cp\u003eFactor A:\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c3\"\u003e \u003cp\u003eFactor B:\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colspan=\"3\" nameend=\"c6\" namest=\"c4\"\u003e \u003cp\u003eY\u003csub\u003e1\u003c/sub\u003e: Solubility\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colspan=\"3\" nameend=\"c9\" namest=\"c7\"\u003e \u003cp\u003eY\u003csub\u003e4\u003c/sub\u003e: Cumulative % drug release\u003c/p\u003e \u003c/th\u003e \u003c/tr\u003e \u003ctr\u003e \u003cth align=\"left\" colname=\"c2\"\u003e \u003cp\u003eAmount of Carrier (β \u0026ndash; cyclodextrin) (mg)\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c3\"\u003e \u003cp\u003eGrinding time (mg)\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c4\"\u003e \u003cp\u003ePredicted value (mg/ml)\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c5\"\u003e \u003cp\u003eExperimental value (mg/ml)\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c6\"\u003e \u003cp\u003eStandard Error\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c7\"\u003e \u003cp\u003ePredicted value (\u003csup\u003eo\u003c/sup\u003e)\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c8\"\u003e \u003cp\u003eExperimental value (\u003csup\u003eo\u003c/sup\u003e)\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c9\"\u003e \u003cp\u003eStandard Error\u003c/p\u003e \u003c/th\u003e \u003c/tr\u003e \u003c/thead\u003e \u003ctbody\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eR1\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e \u003cp\u003e600\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e7\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e \u003cp\u003e48.82\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c5\"\u003e \u003cp\u003e52.79\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c6\"\u003e \u003cp\u003e4.216\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c7\"\u003e \u003cp\u003e71.99\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c8\"\u003e \u003cp\u003e79.56\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c9\"\u003e \u003cp\u003e5.541\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eR3\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e \u003cp\u003e400\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e18.5\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e \u003cp\u003e41.91\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c5\"\u003e \u003cp\u003e42.23\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c6\"\u003e \u003cp\u003e2.045\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c7\"\u003e \u003cp\u003e63.60\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c8\"\u003e \u003cp\u003e67.37\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c9\"\u003e \u003cp\u003e2.688\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eR9\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e \u003cp\u003e200\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e30\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e \u003cp\u003e34.99\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c5\"\u003e \u003cp\u003e35.64\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c6\"\u003e \u003cp\u003e4.216\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c7\"\u003e \u003cp\u003e55.20\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c8\"\u003e \u003cp\u003e57.03\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c9\"\u003e \u003cp\u003e5.541\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eOptimized\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e \u003cp\u003e600\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e30\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e \u003cp\u003e70.64\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c5\"\u003e \u003cp\u003e65.86\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c6\"\u003e \u003cp\u003e4.216\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c7\"\u003e \u003cp\u003e98.47\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c8\"\u003e \u003cp\u003e85.93\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c9\"\u003e \u003cp\u003e5.541\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003c/tbody\u003e \u003c/colgroup\u003e \u003c/table\u003e\u003c/div\u003e \u003c/p\u003e \u003cp\u003e \u003cdiv class=\"gridtable\"\u003e\u003ctable float=\"Yes\" id=\"Tab5\" border=\"1\"\u003e \u003ccaption language=\"En\"\u003e \u003cdiv class=\"CaptionNumber\"\u003eTable 5\u003c/div\u003e \u003cdiv class=\"CaptionContent\"\u003e \u003cp\u003eStability studies of optimized formulation (n\u0026thinsp;=\u0026thinsp;6)\u003c/p\u003e \u003c/div\u003e \u003c/caption\u003e \u003ccolgroup cols=\"5\"\u003e \u003cdiv align=\"left\" class=\"colspec\" colname=\"c1\" colnum=\"1\"\u003e\u003c/div\u003e \u003cdiv align=\"left\" class=\"colspec\" colname=\"c2\" colnum=\"2\"\u003e\u003c/div\u003e \u003cdiv align=\"left\" class=\"colspec\" colname=\"c3\" colnum=\"3\"\u003e\u003c/div\u003e \u003cdiv align=\"left\" class=\"colspec\" colname=\"c4\" colnum=\"4\"\u003e\u003c/div\u003e \u003cdiv align=\"left\" class=\"colspec\" colname=\"c5\" colnum=\"5\"\u003e\u003c/div\u003e \u003ctbody\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eTime (min)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003eBefore storage\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003eAfter 6 months\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e\u003cem\u003et\u003c/em\u003e-test at 0.05 LS\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003eSimilarity Factor (F2)\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003e0\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e0\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e0\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\" morerows=\"9\" rowspan=\"10\"\u003e \u003cp\u003eNot Significant\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\" morerows=\"9\" rowspan=\"10\"\u003e \u003cp\u003e71.24\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003e5\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e30.62\u0026thinsp;\u0026plusmn;\u0026thinsp;1.42\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e30.27\u0026thinsp;\u0026plusmn;\u0026thinsp;1.47\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003e10\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e38.78\u0026thinsp;\u0026plusmn;\u0026thinsp;1.47\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e36.35\u0026thinsp;\u0026plusmn;\u0026thinsp;1.32\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003e15\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e47.24\u0026thinsp;\u0026plusmn;\u0026thinsp;1.36\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e44.25\u0026thinsp;\u0026plusmn;\u0026thinsp;1.73\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003e30\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e57.32\u0026thinsp;\u0026plusmn;\u0026thinsp;1.27\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e54.37\u0026thinsp;\u0026plusmn;\u0026thinsp;1.54\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003e45\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e64.98\u0026thinsp;\u0026plusmn;\u0026thinsp;1.68\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e61.53\u0026thinsp;\u0026plusmn;\u0026thinsp;1.54\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003e60\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e70.21\u0026thinsp;\u0026plusmn;\u0026thinsp;1.16\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e68.42\u0026thinsp;\u0026plusmn;\u0026thinsp;1.28\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003e75\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e74.98\u0026thinsp;\u0026plusmn;\u0026thinsp;2.46\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e72.68\u0026thinsp;\u0026plusmn;\u0026thinsp;1.47\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003e90\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e81.06\u0026thinsp;\u0026plusmn;\u0026thinsp;1.62\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e79.61\u0026thinsp;\u0026plusmn;\u0026thinsp;1.16\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003e120\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e85.93\u0026thinsp;\u0026plusmn;\u0026thinsp;2.35\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e83.46\u0026thinsp;\u0026plusmn;\u0026thinsp;1.76\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003c/tbody\u003e \u003c/colgroup\u003e \u003c/table\u003e\u003c/div\u003e \u003c/p\u003e\n\u003ch3\u003eFormulation VC/β-cyclodextrin inclusion complexes\u003c/h3\u003e\n\u003cp\u003eThe co-grinding method was used to prepare VC/β-cyclodextrin inclusion complexes using VC and β-cyclodextrin. Formulations were prepared using a mortar and pestle at different carrier concentrations (117.1, 200, 400, 600, and 682.8 mg). 200 mg of VC was added to all the formulations. The samples were ground for grinding times (2.2, 7, 18.5, 30, and 34.7 min). The accurately weighed amounts of powder samples were placed in a mortar and ground using a pestle for a specified time.\u003c/p\u003e \u003cp\u003eA physical mixture (PM) of VC/β-cyclodextrin was formulated by mixing VC and β-cyclodextrin for 5 min with a spatula. Samples were withdrawn and stored for further analysis.\u003c/p\u003e \u003cdiv id=\"Sec8\" class=\"Section2\"\u003e \u003ch2\u003eSolubility studies\u003c/h2\u003e \u003cp\u003eSolubility studies were conducted for the prepared formulations. Solubility studies of VC/β-cyclodextrin inclusion complexes were carried out using the procedure described by Daravath et al. in phosphate buffer pH 7.2 [\u003cspan citationid=\"CR40\" class=\"CitationRef\"\u003e40\u003c/span\u003e]. The drug sample was then analyzed using UV-visible spectrophotometry at a λ\u003csub\u003emax\u003c/sub\u003e of 250 nm.\u003c/p\u003e \u003c/div\u003e\n\u003ch3\u003eCumulative % drug release studies\u003c/h3\u003e\n\u003cp\u003eIn-vitro dissolution studies were conducted on formulations, VC, and PM employing a USP dissolution Type II apparatus in pH 7.2 phosphate buffer, at 37\u0026thinsp;\u0026plusmn;\u0026thinsp;0.5\u0026deg;C at 50 rpm. The samples were examined at 250 nm by UV spectrophotometry.\u003c/p\u003e\n\u003ch3\u003eCharacterization of VC/β-cyclodextrin inclusion complexes\u003c/h3\u003e\n\u003cp\u003eThe prepared formulations, pure drug, and physical mixture were further evaluated.\u003c/p\u003e \u003cdiv id=\"Sec11\" class=\"Section2\"\u003e \u003ch2\u003eFourier transform infrared spectroscopy (FTIR)\u003c/h2\u003e \u003cp\u003eFTIR spectra of VC, β-CD, physical mixture, and the optimized formulations were obtained employing the potassium bromide (KBr) pellet method in the range of 400\u0026ndash;4000 cm⁻\u0026sup1;. Potassium bromide (KBr) pellets were prepared by compressing the mixture at 12,000 psi under vacuum for 3 min [\u003cspan citationid=\"CR41\" class=\"CitationRef\"\u003e41\u003c/span\u003e].\u003c/p\u003e \u003c/div\u003e \u003cdiv id=\"Sec12\" class=\"Section2\"\u003e \u003ch2\u003eDifferential scanning calorimetry (DSC)\u003c/h2\u003e \u003cp\u003eDSC was performed on VC, β-CD, PM, and optimized formulations. 5 mg of powder was hermetically sealed in aluminum crucibles. The samples were heated from 25 to 350\u0026deg;C under a nitrogen atmosphere (50 mL/min) at a rate of 10\u0026deg;C/min [\u003cspan citationid=\"CR42\" class=\"CitationRef\"\u003e42\u003c/span\u003e].\u003c/p\u003e \u003c/div\u003e \u003cdiv id=\"Sec13\" class=\"Section2\"\u003e \u003ch2\u003eX-ray diffraction (XRD)\u003c/h2\u003e \u003cp\u003eXRD patterns of VC, β-CD, physical mixture, and the optimized complex were recorded using Cu radiation (wavelength 1.540 \u0026Aring;). The instrument was operated at 45 kV and 30 mA, and data were collected over a 2θ range of 0\u0026ndash;90\u0026deg;[\u003cspan citationid=\"CR42\" class=\"CitationRef\"\u003e42\u003c/span\u003e].\u003c/p\u003e \u003c/div\u003e \u003cdiv id=\"Sec14\" class=\"Section2\"\u003e \u003ch2\u003eScanning electron microscopy (SEM)\u003c/h2\u003e \u003cp\u003eA field emission scanning electron microscope (FESEM) was used to acquire images at a voltage of 20 kV. Before FESEM analysis, the samples were gold-coated to ensure electrical conductivity under vacuum [\u003cspan citationid=\"CR43\" class=\"CitationRef\"\u003e43\u003c/span\u003e].\u003c/p\u003e \u003c/div\u003e \u003cdiv id=\"Sec15\" class=\"Section2\"\u003e \u003ch2\u003eStability studies of VC/β-cyclodextrin inclusion complexes\u003c/h2\u003e \u003cp\u003eThe optimized complex was subjected to stability studies at 40\u0026thinsp;\u0026plusmn;\u0026thinsp;2\u0026deg;C and relative humidity of 75\u0026thinsp;\u0026plusmn;\u0026thinsp;5%. The sample was stored for six months[\u003cspan citationid=\"CR5\" class=\"CitationRef\"\u003e5\u003c/span\u003e]. By using the dissolution profile of the optimized complex, the similarity factor (\u003cem\u003ef2\u003c/em\u003e) was calculated.\u003c/p\u003e \u003c/div\u003e"},{"header":"RESULTS","content":"\u003cp\u003eVoriconazole is a lipophilic drug having low water solubility (0.5 mg/ml and log P value of 1.65, which makes voriconazole a more appropriate model drug for the preparation of \u0026beta;-cyclodextrin inclusion complexation.\u003c/p\u003e\n\u003cdiv id=\"Sec17\" class=\"Section2\"\u003e\n \u003ch2\u003ePhase solubility study of voriconazole\u003c/h2\u003e\n \u003cp\u003eFigure \u003cspan class=\"InternalRef\"\u003e1\u003c/span\u003e illustrates the phase solubility profile of VC in \u0026beta;-cyclodextrin solutions (0\u0026ndash;5% w/v). The linear increment in VC solubility with an increase in \u0026beta;-cyclodextrin indicates A\u003csub\u003eL\u003c/sub\u003e-type complex formation. This linear VC/\u0026beta;-cyclodextrin correlation, with a slope of 0.8113, which was less than 1, and an R\u003csup\u003e2\u003c/sup\u003e value of 0.9935, suggests 1:1 stoichiometric formation of VC and \u0026beta;-cyclodextrin inclusion complexation. Manca et al reported similar results [\u003cspan class=\"CitationRef\"\u003e16\u003c/span\u003e].\u003c/p\u003e\n \u003cp\u003e\u003cbr\u003e\u003c/p\u003e\n\u003c/div\u003e\n\u003cdiv id=\"Sec18\" class=\"Section2\"\u003e\n \u003ch2\u003eDevelopment of a mathematical model through Experimental Design\u003c/h2\u003e\n \u003cp\u003eA CCD was used to optimize two formulation factors, namely, the amount of \u0026beta;-cyclodextrin (A) and grinding time (B), and their impact on solubility (Y1) and %CDR (Y2) was studied. In the present study, these factors were investigated at five levels: \u0026alpha;, \u0026minus;\u0026thinsp;1, 0, +\u0026thinsp;1, and\u0026thinsp;+\u0026thinsp;\u0026alpha;.\u003c/p\u003e\n \u003cp\u003eDesign-Expert\u0026reg; software (version 13) was used to conduct analysis of variance (ANOVA). The p-values, F-values, and estimated coefficients of the regression models for the responses were analyzed, and the results were summarized in Table\u0026nbsp;\u003cspan class=\"InternalRef\"\u003e3\u003c/span\u003e. The regression model will be considered significant when the model F value is greater than 1 and value p-value is less than 0.05. The lack-of-fit should be non-significant, and the p-value should be more than 0.05. The predicted R\u003csup\u003e2\u003c/sup\u003e and adjusted R\u003csup\u003e2\u003c/sup\u003e difference should be less than 0.2, and the adequate precision should be more than 4.\u003c/p\u003e\n\u003c/div\u003e\n\u003cdiv id=\"Sec19\" class=\"Section2\"\u003e\n \u003ch2\u003eSolubility studies of VC/\u0026beta;-cyclodextrin inclusion complexes\u003c/h2\u003e\n \u003cp\u003eTable\u0026nbsp;\u003cspan class=\"InternalRef\"\u003e3\u003c/span\u003e shows that solubility ranged from 9.8 mg/ml to 74.63 mg/ml. The statistical analysis indicated that a linear regression model best described the influence of the formulation variables on solubility (Y₁). The model demonstrated robust significance, with F-value of 32.12 and p-value of \u0026lt;\u0026thinsp;0.0001, confirming the model terms were significant. The lack of fit was not significant (p-value\u0026thinsp;=\u0026thinsp;0.825), thus supporting the adequacy of the model.\u003c/p\u003e\n \u003cp\u003eBoth the carrier amount (A) and grinding time (B) were significant factors affecting the solubility, with p-values\u0026thinsp;\u0026lt;\u0026thinsp;0.0001 and 0.0019, respectively. Regression analysis revealed positive coefficients for both variables (17.82 for A and 10.91 for B), indicating that a significant enhancement in solubility occurred with increasing \u0026beta;-cyclodextrin amount and/or grinding time. The reliability of the model was further supported by the close agreement between the predicted R\u0026sup2; (0.7220) and adjusted R\u0026sup2; (0.8384), with a difference is less than 0.2, which is acceptable. The model exhibited an adequate precision value of 16.22, which is well above the recommended minimum of four, indicating a strong signal-to-noise ratio. These findings were visually corroborated by 2D and 3D response surface plots showed in Fig.\u0026nbsp;\u003cspan class=\"InternalRef\"\u003e2\u003c/span\u003e and Fig.\u0026nbsp;\u003cspan class=\"InternalRef\"\u003e3\u003c/span\u003e).\u003c/p\u003e\n \u003cp\u003e\u003cbr\u003e\u003c/p\u003e\n \u003cp\u003e\u003cbr\u003e\u003c/p\u003e\n \u003cp\u003eThe relative influence of the formulation variables on the solubility was studied by examining the coefficients in the coded regression equation.\u003c/p\u003e\n\u003c/div\u003e\n\u003cdiv id=\"Sec20\" class=\"Section2\"\u003e\n \u003cp\u003eY1\u0026thinsp;=\u0026thinsp;41.91\u0026thinsp;+\u0026thinsp;17.82 A\u0026thinsp;+\u0026thinsp;10.91 B -------- 2\u003c/p\u003e\n \u003cp\u003ewhere,\u003c/p\u003e\n \u003cp\u003eY1: represents the predicted solubility\u003c/p\u003e\n \u003cp\u003eA: denotes the amount of \u0026beta;-cyclodextrin\u003c/p\u003e\n \u003cp\u003eB: corresponds to the grinding time\u003c/p\u003e\n \u003cp\u003eThis equation indicates that both the amount of carrier and grinding time exert a significant positive effect on the solubility, with the carrier amount having a comparatively greater impact, as reflected by its higher coefficient (17.82 vs. 10.91). Consequently, increasing either the carrier amount or the grinding time enhanced solubility of the VC/\u0026beta;-cyclodextrin inclusion complexes (Fig. \u003cspan class=\"InternalRef\"\u003e4\u003c/span\u003ei and ii).\u003c/p\u003e\n\u003c/div\u003e\n\u003cdiv id=\"Sec21\" class=\"Section2\"\u003e\n \u003ch2\u003e% CDR (Cumulative percentage drug release) of VC/\u0026beta;-cyclodextrin inclusion complexes\u003c/h2\u003e\n \u003cp\u003eFigure \u003cspan class=\"InternalRef\"\u003e5\u003c/span\u003e illustrates the in vitro dissolution profiles of inclusion complexes, physical mixture, and VC. The prepared inclusion complexes showed increased drug release in comparison to PM and plain VC.\u003c/p\u003e\n \u003cp\u003eTable \u003cspan class=\"InternalRef\"\u003e3\u003c/span\u003e shows the percentage cumulative drug release (%CDR), which ranges from 25.31\u0026ndash;95.73%. The statistical analysis indicated that a linear regression model best described the impact of the formulation factors on %CDR (Y₂). The model demonstrated strong significance, with a p-value of \u0026lt;\u0026thinsp;0.0001 and a F-value of 27.40. The lack of fit was not significant (p-value\u0026thinsp;=\u0026thinsp;0.0795), indicating the model\u0026rsquo;s adequacy.\u003c/p\u003e\n \u003cp\u003eThe \u0026beta;-cyclodextrin amount (A) and grinding time (B) were significant factors affecting the %CDR, with p-values\u0026thinsp;\u0026lt;\u0026thinsp;0.0001 and 0.0031, respectively. Regression analysis revealed positive coefficients for both variables (21.63 for A and 13.24 for B), demonstrating that increasing the carrier amount and/or grinding time significantly increased the %CDR. The reliability of the model was supported by the close agreement between the adjusted R\u0026sup2; (0.8148) and predicted R\u0026sup2; (0.7022), with a difference is less than 0.2, which is acceptable. The model exhibited an adequate precision value of 14.9812, well above the recommended minimum of 4, indicating a strong signal-to-noise ratio. These findings were further corroborated by the 2D and 3D response surface plots (Fig.\u0026nbsp;\u003cspan class=\"InternalRef\"\u003e2\u003c/span\u003e and Fig.\u0026nbsp;\u003cspan class=\"InternalRef\"\u003e3\u003c/span\u003e).\u003c/p\u003e\n \u003cp\u003eThe relative influence of the formulation variables on %CDR was studied by examining the coefficients in the coded regression equation.\u003c/p\u003e\n\u003c/div\u003e\n\u003cdiv id=\"Sec22\" class=\"Section2\"\u003e\n \u003cp\u003eY2\u0026thinsp;=\u0026thinsp;63.60\u0026thinsp;+\u0026thinsp;23.63 A\u0026thinsp;+\u0026thinsp;13.24 B -------- 3\u003c/p\u003e\n \u003cp\u003eWhere,\u003c/p\u003e\n \u003cp\u003eY2: represents the predicted %CDR\u003c/p\u003e\n \u003cp\u003eA: denotes the amount of carrier\u003c/p\u003e\n \u003cp\u003eB: corresponds to the grinding time\u003c/p\u003e\n \u003cp\u003eThis equation indicates that both the amount of carrier and grinding time exert a significant positive effect on % CDR, with the carrier amount having a comparatively greater impact than grinding time, as reflected by its higher coefficient (23.63 vs. 13.24). Consequently, increasing either the carrier amount or the grinding time leads to an enhanced % CDR of the VC/\u0026beta;-cyclodextrin inclusion complexes (Fig.\u0026nbsp;\u003cspan class=\"InternalRef\"\u003e4\u003c/span\u003ei and ii).\u003c/p\u003e\n \u003cdiv id=\"Sec23\" class=\"Section3\"\u003e\n \u003ch2\u003eMultiple Response Optimization Study\u003c/h2\u003e\n \u003cp\u003eThe optimal combination of \u0026beta;-cyclodextrin amount (A) and grinding time (B) that simultaneously maximizes solubility (Y₁) and percentage cumulative drug release (%CDR, Y₂) was identified by implementing a multiple-response optimization strategy.\u003c/p\u003e\n \u003cp\u003eThis approach combines statistical modelling, desirability functions, and numerical algorithms to effectively balance multiple responses while ensuring a robust formulation performance. Based on the desirability function, numerical and graphical optimization techniques were used to refine the responses.\u003c/p\u003e\n \u003cp\u003eThe desirability function, scaled from \u0026ldquo;0 to 1\u0026rdquo; [\u003cspan class=\"CitationRef\"\u003e44\u003c/span\u003e], was employed to simultaneously optimize solubility (Y₁) and % cumulative drug release (%CDR, Y₂). Numerical optimization aimed to maximize both responses while constraining the independent variables within their experimental ranges, i.e., carrier amount (A) and grinding time (B). Graphical optimization further defines acceptable limits for responses and factors, enabling the identification of a flexible design space.\u003c/p\u003e\n \u003cp\u003eA flexible design space was established by setting the criteria for maximum solubility and enhanced dissolution rate. The overlay plot (Fig. \u003cspan class=\"InternalRef\"\u003e6\u003c/span\u003e) highlights this design space as a yellow region. An optimized formulation, with 600 mg of B-cyclodextrin and 30 min grinding time, was identified using the desirability \u0026quot;0.969\u0026quot; (Fig. \u003cspan class=\"InternalRef\"\u003e6\u003c/span\u003e). This formulation exhibits a maximum solubility of 70.64 mg/ml and highest dissolution of 98.47%. The formulation was subsequently subjected to further characterization for comprehensive evaluation.\u003c/p\u003e\n \u003c/div\u003e\n\u003c/div\u003e\n\u003cdiv id=\"Sec24\" class=\"Section2\"\u003e\n \u003ch2\u003eVerification of model adequacy\u003c/h2\u003e\n \u003cp\u003eThe model\u0026rsquo;s adequacy for Y\u003csub\u003e1\u003c/sub\u003e and Y\u003csub\u003e2\u003c/sub\u003e was analyzed by observing the plots of the predicted versus experimental values, as depicted in Table \u003cspan class=\"InternalRef\"\u003e4\u003c/span\u003e and Fig. \u003cspan class=\"InternalRef\"\u003e7\u003c/span\u003e. The comparison between the values of predicted and actual demonstrates that the linear regression model closely fits the data, indicating strong and reliable predictive performance. (Chella et al. 2024).\u003c/p\u003e\n \u003cdiv id=\"Sec25\" class=\"Section3\"\u003e\n \u003ch2\u003eCharacterization of VC/\u0026beta;-cyclodextrin inclusion complexes\u003c/h2\u003e\n \u003cp\u003eFigure \u003cspan class=\"InternalRef\"\u003e8\u003c/span\u003e presents the FTIR spectra of voriconazole (VC), its physical mixture, and optimized formulation. VC displayed characteristic functional group vibrations: O-H stretching at 3198.28 cm\u0026thinsp;\u0026minus;\u0026thinsp;1, C-F stretching at 1460.70, and C-N stretching at 1280.58 cm\u0026thinsp;\u0026minus;\u0026thinsp;1. The physical mixture spectrum retained key bands: O-H stretching vibration at 3320.96 cm\u003csup\u003e\u0026minus;\u0026thinsp;1\u003c/sup\u003e, C-F stretching at 1454.77 cm\u003csup\u003e\u0026minus;\u0026thinsp;1\u003c/sup\u003e, and C-N stretching at 1279.38 cm\u003csup\u003e\u0026minus;\u0026thinsp;1\u003c/sup\u003e, showing similar peaks as VC. Whereas the optimized formulation exhibits O-H stretching slightly shifted to 3324.58 cm\u003csup\u003e\u0026minus;\u0026thinsp;1\u003c/sup\u003e, C-F stretching at 1455.32 cm\u003csup\u003e\u0026minus;\u0026thinsp;1\u003c/sup\u003e, and C-N stretching at 1280.28 cm\u003csup\u003e\u0026minus;\u0026thinsp;1\u003c/sup\u003e. These observations indicate the formation of inclusion complexes. DSC and XRD analyses further confirmed this.\u003c/p\u003e\n \u003cp\u003eFigure \u003cspan class=\"InternalRef\"\u003e9\u003c/span\u003e displays the DSC thermograms of VC, PM, and the optimized formulation. The pure drug exhibited a sharp endothermic peak at 131.19\u0026deg;C, indicating the melting point of VC, while a broader peak at 111.16\u0026deg;C corresponds to the loss of water from the \u0026beta;-cyclodextrin cavity and its thermal decomposition at 316.56\u0026deg;C. In the physical mixture, the drug peak remained visible at 130.71\u0026deg;C, with reduced intensity, suggesting the drug was not fully dispersed in the carrier. Conversely, in the optimized formulation, the characteristic melting point peak of the drug was absent, indicating complete molecular dispersion of VC within \u0026beta;-cyclodextrin.\u003c/p\u003e\n \u003cp\u003e\u003cbr\u003e\u003c/p\u003e\n \u003cp\u003eFigure \u003cspan class=\"InternalRef\"\u003e10\u003c/span\u003e displays intense and sharp peaks at diffraction angles 2\u0026theta; of 14\u003csup\u003e0\u003c/sup\u003e, 20\u003csup\u003e0\u003c/sup\u003e, 26\u003csup\u003e0\u003c/sup\u003e, 28\u003csup\u003e0\u003c/sup\u003e, and 35\u003csup\u003e0\u003c/sup\u003e, indicating the crystalline nature of VC. The peak intensity decreased to some extent in the physical mixture, indicating that VC was not completely converted into an amorphous form. However, in the optimized formulation, the peak intensity decreased or disappeared, indicating that VC has been changed into an amorphous form.\u003c/p\u003e\n \u003cp\u003eFigure \u003cspan class=\"InternalRef\"\u003e11\u003c/span\u003e shows that VC is present as irregular particles with a crystalline structure, whereas \u0026beta;-cyclodextrin exists as smooth-surfaced particles. A PM was present in the original morphology. VC and \u0026beta;-CD inclusion complexes showed smooth surfaces, indicating the formation of an inclusion complex between VC and \u0026beta;-CD.\u003c/p\u003e\n \u003cp\u003e\u003cbr\u003e\u003c/p\u003e\n \u003c/div\u003e\n \u003cdiv id=\"Sec26\" class=\"Section3\"\u003e\n \u003ch2\u003eStability studies\u003c/h2\u003e\n \u003cp\u003eThe stability study of the optimized formulation showed a similarity factor (f2) of 71.24 at P\u0026thinsp;\u0026lt;\u0026thinsp;0.05 after six months of storage, indicating no significant difference.\u003c/p\u003e\n \u003c/div\u003e\n\u003c/div\u003e"},{"header":"DISCUSSION","content":"\u003cp\u003eThe present study successfully aimed to enhance the water solubility and dissolution of voriconazole (VC), a lipophilic antifungal drug, by the formation of inclusion complexes with β-cyclodextrin (β-CD). Initial characterization of the physicochemical properties of voriconazole, specifically its low water solubility and favorable log P value, supported its selection as a suitable model drug for β-CD complexation.\u003c/p\u003e \u003cp\u003eThe phase solubility study provided compelling evidence for forming an inclusion complex between VC and β-CD. The observed AL-type profile was characterized by a linear increase in VC solubility with increasing β-CD concentration. This enhanced solubility was attributed to the ability of the hydrophobic cavity of β-CD to encapsulate lipophilic VC molecules, thereby increasing its interaction with the aqueous environment.\u003c/p\u003e \u003cp\u003eTo further optimize the formulation of the VC/β-CD inclusion complex, a central composite design (CCD) was employed. This statistical approach allowed for the systematic investigation of the impact of two critical formulation variables, the amount of carrier (β-CD) and the grinding time, on key performance attributes, namely solubility (Y1) and percentage cumulative drug release (% CDR, Y2). ANOVA revealed significant regression models for both responses (p\u0026thinsp;\u0026lt;\u0026thinsp;0.0001), indicating a strong correlation between the independent and dependent factors. The non-significant lack-of-fit values further validated the adequacy of the chosen linear models for predicting solubility and % CDR.\u003c/p\u003e \u003cp\u003eStatistical analysis of the solubility data demonstrated that both the amount of carrier (A) and grinding time (B) had a significant positive effect on the solubility of VC. The positive coefficients in the coded equation (Y1\u0026thinsp;=\u0026thinsp;41.91\u0026thinsp;+\u0026thinsp;17.82 A\u0026thinsp;+\u0026thinsp;10.91 B) clearly indicate that increasing the amount of β-CD and/or the grinding time leads to a substantial enhancement in VC solubility. This may be due to higher availability of β-CD molecules to form inclusion complexes, which potentially increases the efficiency of the complexation process with prolonged grinding. The consistency between the adjusted and predicted R\u0026sup2; values, along with the high precision, further strengthens the reliability of the model. The visual representation of these effects is seen in the 2D and 3D response surface plots (Figs.\u0026nbsp;\u003cspan refid=\"Fig2\" class=\"InternalRef\"\u003e2\u003c/span\u003e and \u003cspan refid=\"Fig3\" class=\"InternalRef\"\u003e3\u003c/span\u003e), which provide a clear understanding of the relationship between formulation variables and solubility.\u003c/p\u003e \u003cp\u003eSimilarly, the results for % CDR showed that both the amount of carrier (A) and grinding time (B) significantly and positively influenced the drug-release profile. The coded equation (Y2\u0026thinsp;=\u0026thinsp;63.60\u0026thinsp;+\u0026thinsp;23.63 A\u0026thinsp;+\u0026thinsp;13.24 B) highlights the substantial positive contribution of both factors to the percentage of drug released. A higher amount of β-CD likely led to better solubilization of VC, facilitating its dissolution and subsequent release. Increased grinding time may increase the homogeneity of the mixture and the extent of complex formation, thereby improving drug release. The statistical parameters (significant F-value, non-significant lack of fit, acceptable difference between R\u0026sup2; values, and high adequate precision) confirmed the robustness of the model for predicting % CDR. The corresponding response surface plots (Figs.\u0026nbsp;\u003cspan refid=\"Fig2\" class=\"InternalRef\"\u003e2\u003c/span\u003e and \u003cspan refid=\"Fig3\" class=\"InternalRef\"\u003e3\u003c/span\u003e) visually depict these relationships.\u003c/p\u003e \u003cp\u003eThe multiple response optimization study successfully identified an optimized formulation with 600 mg of β-CD and a grinding time of 30 min, yielding a high desirability value of 0.969. This optimized formulation exhibited a remarkable maximum solubility of 70.64 mg/ml and highest dissolution of 98.47%. This significant improvement in both solubility and dissolution rate compared to those of the pure drug underscores the effectiveness of β-CD inclusion complexation and the optimization process.\u003c/p\u003e \u003cp\u003eCharacterization studies provided further evidence for forming VC/β-CD inclusion complex. The FTIR spectra showed slight shifts in characteristic peaks of VC in the optimized inclusion complex. These subtle changes suggest interactions at the molecular level, supporting the formation of inclusion complexes. However, these observations alone are inconclusive and require further confirmation.\u003c/p\u003e \u003cp\u003eDSC thermograms provided definitive evidence of the complex formation. The presence of the VC melting peak, albeit with reduced intensity, in the physical mixture suggests incomplete interaction between VC and β-cyclodextrin. The disappearance of a sharp endothermic peak of pure VC in the optimized inclusion complex suggests that VC is no longer present in its crystalline form, but is molecularly dispersed within the β-CD cavity.\u003c/p\u003e \u003cp\u003eThe XRD patterns corroborate the DSC results. The crystallinity of VC, as evident from the numerous sharp and intense peaks, was significantly reduced or absent in the optimized formulation. This transformation from a crystalline to an amorphous or molecularly dispersed state within the β-cyclodextrin cavity is a well-known consequence of the forming inclusion complex and contributes to the enhanced solubility and dissolution rate.\u003c/p\u003e \u003cp\u003eScanning Electron Microscopy (SEM) images confirmed the morphological changes upon complexation. The irregular crystalline particles of pure VC and the distinct morphologies in the physical mixture contrasted with the smooth-surfaced particles of VC/β-CD inclusion complex, suggesting that a new entity was formed through the interaction between VC and β-CD.\u003c/p\u003e \u003cp\u003eFinally, stability studies on the optimized VC/β-CD inclusion complex demonstrated good stability over a six-month storage period, as indicated by the high similarity factor (f2) value within the acceptable range (P\u0026thinsp;\u0026lt;\u0026thinsp;0.05). This suggests that the enhanced solubility and dissolution characteristics of the optimized formulation were maintained over time, highlighting its potential for pharmaceutical applications.\u003c/p\u003e"},{"header":"CONCLUSION","content":"\u003cp\u003eThis study demonstrated the effective application of β-CD in increasing the aqueous solubility and dissolution rate of the poorly water-soluble antifungal voriconazole (VC). The formation of the AL-type inclusion complexation was confirmed by phase solubility studies. A central composite design (CCD) was strategically employed to optimize the formulation variables, specifically the amount of β-CD and grinding time, which affected VC solubility and percentage cumulative drug release (% CDR).\u003c/p\u003e \u003cp\u003eStatistical analysis revealed significant positive effects of both carrier amount and grinding time on both responses. The optimized formulation, comprising 600 mg of β-CD and a 30-minute grinding time, exhibited a substantial increase in solubility (70.64 mg/ml) and achieved a high dissolution rate (98.47%) with a desirability value of \"0.969\".\u003c/p\u003e \u003cp\u003eCharacterization studies, including FTIR, DSC, XRD, and SEM, provided evidence of the successful formation of an inclusion complex. FTIR spectra showed minor shifts in characteristic peaks, while DSC thermograms indicate the absence of the crystalline melting peak of VC in the optimized formulation, suggesting its molecular dispersion within the β-CD cavity. XRD patterns confirm a reduction in VC crystallinity, further supporting the formation of a complex. SEM images revealed distinct morphological changes in the optimized formulations.\u003c/p\u003e \u003cp\u003eStability studies conducted over six months demonstrated the robustness of the optimized VC/β-CD inclusion complex, with no significant changes in its dissolution profile. The significant enhancement in solubility and dissolution coupled with good stability underscores the potential of β-CD inclusion complexation as a promising pharmaceutical strategy to improve the bioavailability and therapeutic efficacy of voriconazole. This approach offers a viable pathway for overcoming the limitations associated with the poor aqueous solubility of this important antifungal agent.\u003c/p\u003e"},{"header":"Declarations","content":"\u003ch2\u003eFUNDING\u003c/h2\u003e \u003cp\u003eThe authors declare that no external funding or grants were received for the research, preparation, and submission of this manuscript.\u003c/p\u003e\u003ch2\u003eAuthor Contribution\u003c/h2\u003e\u003cp\u003eBhaskar Daravath: Conceptualization, Formulation development, Experimental work, Original draft writing, Manuscript review, and editing.Madhavi: Experimental work, Original draft writing.Naveen Chella: Conceptualization, Manuscript review, and editingSateesh Kumar Vemula: Conceptualization, Manuscript review, and editing\u003c/p\u003e\u003ch2\u003eAcknowledgement\u003c/h2\u003e\u003cp\u003eThe authors gratefully acknowledge the management of the GITAM School of Pharmacy, GITAM Deemed to be University, Hyderabad, and MSN Laboratories Pvt Ltd, Hyderabad, Telangana, India, for generously providing the facility and voriconazole sample crucial for the successful completion of this research.\u003c/p\u003e"},{"header":"References","content":"\u003col\u003e\u003cli\u003e\u003cspan\u003eMunnangi SR, Youssef AAA, Narala N, Lakkala P, Vemula SK, et al. 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AAPS PharmSciTech. 2022;23(7):248.\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":true,"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":"Solvent-free technology, β-cyclodextrin, Inclusion complex, Response Surface Methodology, Central composite design, Dissolution enhancement","lastPublishedDoi":"10.21203/rs.3.rs-6642975/v1","lastPublishedDoiUrl":"https://doi.org/10.21203/rs.3.rs-6642975/v1","license":{"name":"CC BY 4.0","url":"https://creativecommons.org/licenses/by/4.0/"},"manuscriptAbstract":"\u003cp\u003e\u003cstrong\u003ePurpose:\u003c/strong\u003e This current study aimed to increase the solubility and dissolution of poor aqueous soluble drug voriconazole (VC), a lipophilic antifungal drug, through the inclusion complexes with β-cyclodextrin (β-CD) by a solvent-free technology like co-grinding method.\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eMethods:\u003c/strong\u003e By using RSM (Response Surface Methodology), a CCD (central composite design) was utilized to analyse the impact of β-CD amount and grinding time on VC solubility and % CDR (percentage cumulative drug release).\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eResults:\u003c/strong\u003e Phase solubility studies confirmed the formation of an A\u003csub\u003eL\u003c/sub\u003e-type complex. Statistical analysis revealed that both factors significantly and positively affected the responses. The optimized formulation, comprising 600 mg of β-CD and a grinding time of 30 minutes, exhibited a substantial increase in solubility (70.64 mg/ml) and achieved a high dissolution rate (98.47%) with a desirability value of 0.969. The formation of the inclusion complex was further validated through comprehensive characterization studies. FTIR spectra showed minor shifts in characteristic VC peaks. DSC thermograms revealed the disappearance of the characteristic sharp melting endotherm of VC in the optimized complex, suggesting molecular dispersion within β-CD. XRD patterns revealed a significant reduction in VC crystallinity. SEM images display a distinct morphological change from crystalline VC to smooth-surfaced complex particles.\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eConclusion:\u003c/strong\u003e These findings demonstrate that VC/β-CD inclusion complex is a promising approach to significantly increase the aqueous solubility and dissolution of voriconazole, potentially enhancing its therapeutic efficacy.\u003c/p\u003e","manuscriptTitle":"Design and Optimization of Inclusion Complexes Using QbD Principles: A strategy to enhance solubility and dissolution of Voriconazole","msid":"","msnumber":"","nonDraftVersions":[{"code":1,"date":"2025-05-22 10:27:57","doi":"10.21203/rs.3.rs-6642975/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":"df56cfb7-5756-4c69-b291-52bdfdb3a383","owner":[],"postedDate":"May 22nd, 2025","published":true,"recentEditorialEvents":[],"rejectedJournal":[],"revision":"","amendment":"","status":"posted","subjectAreas":[],"tags":[],"updatedAt":"2025-10-27T16:33:21+00:00","versionOfRecord":{"articleIdentity":"rs-6642975","link":"https://doi.org/10.1007/s12247-025-10214-1","journal":{"identity":"journal-of-pharmaceutical-innovation","isVorOnly":false,"title":"Journal of Pharmaceutical Innovation"},"publishedOn":"2025-10-24 16:16:42","publishedOnDateReadable":"October 24th, 2025"},"versionCreatedAt":"2025-05-22 10:27:57","video":"","vorDoi":"10.1007/s12247-025-10214-1","vorDoiUrl":"https://doi.org/10.1007/s12247-025-10214-1","workflowStages":[]},"version":"v1","identity":"rs-6642975","journalConfig":"researchsquare"},"__N_SSP":true},"page":"/article/[identity]/[[...version]]","query":{"redirect":"/article/rs-6642975","identity":"rs-6642975","version":["v1"]},"buildId":"8U1c8b4HqxoKbykW_rLl7","isFallback":false,"isExperimentalCompile":false,"dynamicIds":[84888],"gssp":true,"scriptLoader":[]}