Preparation and evaluation of risperidone slow-release injectable microspheres using chitosan

Preparation and evaluation of risperidone slow-release injectable microspheres using chitosan | 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 Preparation and evaluation of risperidone slow-release injectable microspheres using chitosan Parisa Latifi, Amirali Jahanshahi, Aysan Jamalara, Arezou Jammanesh, and 1 more This is a preprint; it has not been peer reviewed by a journal. https://doi.org/ 10.21203/rs.3.rs-5742734/v1 This work is licensed under a CC BY 4.0 License Status: Posted Version 1 posted You are reading this latest preprint version Abstract The aim of this study was preparation and evaluation of parenteral chitosan microspheres for sustained release of risperidone. Risperidone, a second-generation antipsychotic, is an effective in the treatment of schizophrenia and has a low incidence of adverse effect. Sustained release microsphere due to ascertain levels of drug in plasma, reduced toxicity and improving the efficacy of the drug. This study is done to consider the effect of four independent variables including, the drug to polymer ratio, TPP concentration, stirring speed and stirring time on the four dependent variables including, encapsulation efficiency, drug loading, particle size and the extent of initial burst in vitro release. In this study chitosan microspheres containing risperidone carried out by double emulsification/ solvent evaporation with two-step solidification process. The microspheres have been analyzed for their shape, size and surface characteristics. Interactions inside microspheres were investigated by differential scanning calorimetry and FTIR. In the following encapsulation efficiency, drug loading and burst release was investigated. The optimized formulation showed a narrow size distribution with an average of 115 ± 2 µm. The SEM image showed that microsphere was round in shape and discrete. The DSC analysis indicated the conversion of drug from crystalline state to molecular state in the optimized lyophilized formulation. FTIR analysis showed no changes in the chemical structure of risperidone in formulation. The in vitro release profile exhibited 92.3% drug release over 21 days in the sink condition. Nanoscience Sustained release Risperidone chitosan parenteral microsphere Figures Figure 1 Figure 2 Figure 3 Figure 4 Figure 5 Figure 6 Introduction Risperidone, a dopamine antagonist, is prescribed in the treatment of schizophrenia. Among second-generation antipsychotic drugs, it is known to have the fewest side effects, such as weight gain, extrapyramidal side effects, and drowsiness, compared to other drugs in the same class. ( 1 ) Schizophrenia is a chronic disease that often affects patients for a lifetime. ( 2 – 3 ) In addition to the significant social and emotional toll it takes on families, it also imposes substantial costs on the healthcare budgets of countries. ( 4 ) Considering the chronic nature of mental illnesses, particularly schizophrenia, and the challenges of patient non-compliance with consistent medication over lifelong treatment, there is a need for injectable sustained release systems. These systems would help control symptoms in schizophrenia patients, improve patient and family comfort, and reduce hospitalization costs and the need for specialized nursing care. ( 5 – 6 ) Developing such a system is essential to increase patient cooperation and overall quality of life. One of the sustained release systems being studied is the microsphere system, which is utilized also in chemotherapy, cardiovascular diseases, hormone therapy and protein delivery. The primary focus of this research is to develop and refine this system. The microsphere system offers numerous benefits, such as sustained drug release over an extended period and a decrease in the frequency of doses. This system can effectively deliver the required drug concentration to the specific target area with minimal adverse effects. ( 7 ) On the other hand, due to the large surface-to-volume ratio of microsphere particles, this system can be a suitable platform for loading insoluble drugs such as Risperidone. ( 8 ) Chitosan is a copolymer of β-[1–4]-linked 2-acetamido-2-deoxy-D-glucopyranose and 2-amino-2-deoxy- D-glucopyranose which is widely used in different drug delivery formulations due to its biocompatibility biodegradability, low toxicity, easy accessibility and suitable cost. Due to its crosslinking matrix with certain chemical crosslinking agents, such as glutaraldehyde, or by using ionic crosslinking interactions with tripolyphosphate can improve the sustained release rate and increase the drug loading efficiency of a drug. ( 9 – 10 ) In this system, particles of different sizes can be prepared, enabling the loading of various doses of medicine in different forms, including oral, injectable, and topical applications. It has always been attempted to develop drug delivery systems based on zero-order kinetics, where the release of the drug is not dependent on its concentration or other variables. Most extended-release systems exhibit behavior similar to this. ( 11 ) The term sustained-release is used for formulations that slowly release drugs over an extended period. This results in gradual drug absorption and a delayed onset of action. Additionally, the drug remains in the bloodstream for a longer duration, leading to a prolonged therapeutic effect. Controlled Release formulations not only offer extended drug delivery but also have predictable and consistent kinetics of drug release. ( 12 – 13 ) There are several reasons for the development of new drug delivery systems: 1. Some drugs can be more effectively administered using new methods, and in some cases, new therapeutic properties can be achieved. 2. New drugs for chronic conditions such as diabetes and psychiatric disorders necessitate long-term drug delivery systems. 3. The manner in which drugs are released significantly impacts the therapeutic response. Materials and Methods In this study, the double emulsion-solvent evaporation method with two hardening steps was used to produce risperidone microspheres. The first hardening stage involved the gelation method, which depends on the electrostatic interaction between the negative charge of TPP and the positive charge of the amino group of chitosan. In the second step, a precise amount of glutaraldehyde was added. The incorporation of a small yet adequate quantity of the covalent binder (glutaraldehyde) was essential for stabilizing the gel particles formed in the initial step. Materials Chitosan with the brand name Chitoclear ® (medium molecular weight, Mw = 275,000, degree of deacetylation 95%) was purchased from Primex Company in Ireland. Risperidone was generously provided by Dr. Abidi Pharmaceutical Company in Iran. Sodium Tripolyphosphate (TPP) and Glutaraldehyde (25% aqueous solution), Span ® 80, Tween ® 40, and Glacial Acetic Acid were purchased from Merck in Germany. All other reagents and solvents were of analytical grade and used as received. Method Preparation of microspheres In this study, the double emulsion-solvent evaporation method with two hardening steps was used to prepare risperidone microspheres. Initially, the chitosan solution 1.5% v/v was dissolved in aqueous acetic acid 5% containing Tween 40 (1% v/v). The pH of the solution was then adjusted to fall within the range of 3.85–4.30. Risperidone, a hydrophobic drug with poor solubility in water, needed to be optimized for encapsulation in microspheres. To achieve this, 3.75 mg of risperidone powder was dissolved in 2 ml of dichloromethane and added slowly, drop by drop, to the prepared polymer solution. The mixture was then placed under a homogenizer at 1000 rpm for 20 seconds to create the primary emulsion. A definite volume of paraffin containing Span 80 (3% v/v) was placed in a beaker on a stirrer (external phase). A clear solution was created by slowly adding the initial emulsion dropwise with a mechanical stirrer. Once all of the primary emulsion was added, the stirring continued for 28 minutes at 500 rpm (secondary emulsion). The temperature was then gradually raised over 90 minutes to evaporate the dispersed phase solvent from the formulation. To harden the obtained micro-emulsions, a TPP solution (8.63%) was prepared and added drop by drop. It was then placed on a stirrer for 60 minutes at a certain speed and at room temperature. Next, glutaraldehyde solution was added and placed on the stirrer for 80 minutes. The microspheres were separated by centrifugation at 6000 rpm for 20 minutes. They were then washed with diethyl ether, acetone, ethyl acetate, normal hexane, a 5% sodium metabisulfite solution, and finally deionized water (twice each). Afterward, the microspheres were dried at room temperature in the laboratory for 48 hours. Characterization of microspheres Particle Size Determination The particle size of the prepared microspheres was determined using a laser diffraction technique (Malvern Zeta Sizer series, UK). The microspheres were prepared in a non-dissolving dispersion medium. The Malvern Zeta Sizer series operates based on the diffraction of laser beams. Particle size is reported based on the average volume diameter (µm) of the particles. DSC (Differential Scanning Calorimetry) Thermal measurements of the prepared microspheres were conducted using a Metler-Toledo (Greifensee, Switzerland) differential scanning calorimeter. The analysis was performed in an aluminum hermetic pan where the sample was placed and covered with a lid, purging nitrogen gas to prevent any oxidation reaction. The heat range for the study was set from 30 to 200°C, with a temperature increase rate of 20°C per minute. In addition to the lyophilized powder of the formulation, thermal measurements were also performed on a physical mixture of the formulation components with the same proportions as the original formulation. By comparing the thermal diagrams, any changes during the formulation process of the drug in the microsphere could be identified. FTIR (Fourier Transform Infrared Spectroscopy) An FTIR using a Magna-IR550 Nicolet FTIR spectrophotometer test is conducted during microsphere manufacturing to detect any undesired or harmful reactions. Each sample and KBr were ground via mortar and pestle, and a thin tablet was made for analysis. The spectra were scanned at room temperature in the 500 to 4000 cm -1 wavelength range with a resolution of 4 cm -1 . The test involves analyzing the polymer, drug, TPP, and final formulation. Scanning Electron Microscopy (SEM) The surface morphology of microspheres was observed by SEM followed by coating with thin layer of gold by means of sputter coater (SCD 005, Bal–Ted, Switzerland) for 1 minute before imaging. Images were obtained using SEM XL 30, Philips (The Netherlands). Determination of Drug Contents The Risperidone content was analyzed using an Agilent 1260 ® system equipped with a 1260 Quat VL pump, automatic sampler (1260 ALS), and 1260 DAD VL detectors set at a wavelength of 275 nm. A C18 chromatographic column packed with octadecyl silica, a standard industrial material for column filling, was used in this study. The column had a length of 150 mm and a diameter of 3 mm. The mobile phase consisted of a mixture of methanol, water, and trimethylamine in a ratio of 0.5:19.5:80. The flow rate was set at 1 ml/min. Encapsulation Efficiency (EE %) and Drug Loading Coefficient (DL) To calculate the encapsulation efficiency of each drug in the formulation, each sample was dissolved in 5 ml of acetic acid aqueous solution, filtered through a 0.22-µm filter. To break down the microspheres and release the entrapped drug, the resulting sample solution was vigorously sonicated and then stirred with a predetermined mixing cycle. Following this, 5 ml of methanol was added to the solution and vigorously stirred. The solution was then centrifuged, allowing the supernatant containing the drug to be carefully separated. Each sample was injected into the RP-HPLC (Agilent Technologies ® ). The mobile phase and the conditions of the instrument were the same as mentioned above. Encapsulation Efficiency was calculated using the following equation: Encapsulation Efficiency% = ( \(\:\frac{\:\text{d}\text{r}\text{u}\text{g}\:\text{a}\text{m}\text{o}\text{u}\text{n}\text{t}\:entrapped\:in\:microspheres}{\text{i}\text{n}\text{i}\text{t}\text{i}\text{a}\text{l}\:\text{a}\text{m}\text{o}\text{u}\text{n}\text{t}\:\text{o}\text{f}\:\text{d}\text{r}\text{u}\text{g}}\) ) × 100 The purpose of this test is to determine the amount of active pharmaceutical ingredient in a pharmaceutical form. It is crucial to perform this test because the occurrence of therapeutic effects is directly related to the amount of the active substance in the pharmaceutical form. Drug Loading coefficient was calculated by the following equation: Drug Loading coefficient = \(\:\:\:\:\frac{\text{a}\text{m}\text{o}\text{u}\text{n}\text{t}\:\text{o}\text{f}\:\text{d}\text{r}\text{u}\text{g}\:\text{i}\text{n}\:\text{m}\text{i}\text{c}\text{r}\text{o}\text{s}\text{p}\text{h}\text{e}\text{r}\text{e}\text{s}}{\text{a}\text{m}\text{o}\text{u}\text{n}\text{t}\:\text{o}\text{f}\:\text{m}\text{i}\text{c}\text{r}\text{o}\text{s}\text{p}\text{h}\text{e}\text{r}\text{e}\text{s}\:}\) In vitro release studies To evaluate the release profile, all formulations were placed on a shaker bath (100 rpm) in 500 mL phosphate buffer medium (pH = 7.4, T = 37°C). At specified time intervals, a 1.0 ml aliquot was withdrawn for analysis by RP-HPLC. After each sample removal, 1.0 ml of fresh buffer was added to maintain sink conditions. Microspheres were tested using a 12 kDa dialysis bag (Molecular Weight cut off). Before the study, the dialysis bag was soaked in deionized water for 24 hours. One end of the bag was sealed with a clamp to prevent liquid leakage before adding the formulation. Mathematical Study of Drug Release Kinetics from Microspheres To investigate the drug release kinetic model, we studied the zero-order, first-order, Higuchi, and Korsmeyer-Peppas models based on the following equations: Zero-order equation: Q 0 - Q t = K 0t Zero-order kinetics refer to uniform release from a polymeric matrix, where the drug is released independently of its concentration in the matrix. Q 0 represents the initial drug amount in solution, and Q t is the amount of drug dissolved over time. ( 14 ) First-order equation: logC = logC 0 - Kt/2.303 First-order kinetics release discussed that the amount of released drug is dependent on the loaded drug in the formulation. ( 15 ) Higuchi equation: Q = K H t (1/2) In this equation, KH represents the Higuchi dissolution constant, and Q represents the amount of drug released at time t. The Higuchi model is the first mathematical model that explains drug release from a polymeric matrix based on Fick's law. According to this model, the amount of drug released is directly related to the square root of time. ( 16 ) Korsmeyer-Peppas equation: M t /M ∞ = Kt n Mt/M ∞ represents the fraction of drug released over time, where K is the release rate constant and n is the release power. The Korsmeyer-Peppas model is frequently utilized when there is an exponential relationship between drug release and time. This model is particularly useful for differentiating between release mechanisms of Fickian diffusion and non-Fickian diffusion models, which deviate from the Fickian model. ( 17 ) All the information obtained from the collection of samples was studied and analyzed using Sigma Plot software to obtain a model with the highest R 2 coefficient, indicating the best kinetics for drug release from the formulation. The data obtained from the release of the optimal formulation were analyzed to determine the appropriateness of the drug release pattern with a mathematical model. To achieve this, a graph was created for each series of data obtained from the release, following the mathematical rule of each release model. The R 2 coefficient, the correlation coefficient of each model, was then calculated. In each mathematical model, the level of significance was 0.05 (p < 0.05). Results and Discussion In this study, several runs of microspheres were fabricated to investigate the role of the following parameters in the formulation. X 1 to X 4 represent the independent variables, which are defined as the drug-to-polymer ratio, TPP concentration, stirring speed, and stirring time with a mechanical stirrer. Y 1 to Y 4 represent the dependent variables, which indicate the Drug loading coefficient, Encapsulation Efficiency, Burst Release in in-vitro studies, and particle size (see table 1). Table 1: Defined Variables Variables Independent Dependent Factor Name Unit Range Levels Response Name Unit Constrains Min Max X 1 Drug/Polymer ratio w/w % 0.50 5.00 Y 1 DL - Max X 2 TPP Conc. g/ml 5.00 10.00 Y 2 EE % Max X 3 Stirring speed rpm 500.00 1000.00 Y 3 BR % Min X 4 Stirring time min 10 30 Y 4 PS µm In range Table 2 shows the values obtained for the independent variables in 25 proposed experiments resulting from the study design. Table 2: Independent variables of different microsphere formulations Run No Independent variables Run No Independent variables X 1 X 2 X 3 X 4 X 1 X 2 X 3 X 4 1 5.00 9.02 500.00 30.00 11 5.00 8.07 807.50 17.70 2 5.00 5.00 900.00 30.00 12 1.15 7.82 785.00 18.63 3 0.50 10.00 1000.00 10.00 13 0.50 5.00 500.00 30.00 4 2.18 5.00 576.44 19.06 14 5.00 5.00 500.00 13.60 5 0.50 5.00 762.50 10.00 15 0.50 7.62 1000.00 30.00 6 5.00 5.00 1000.00 10.00 16 5.00 10.00 500.00 10.00 7 5.00 5.00 500.00 13.60 17 2.16 10.00 740.00 30.00 8 3.45 6.87 685.14 27.90 18 2.13 5.00 1000.00 20.40 9 0.50 10.00 500.00 19.70 19 5.00 5.00 1000.00 10.00 10 1.24 8.11 500.00 29.90 20 2.94 10.00 999.56 19.13 Run No Independent variables X 1 X 2 X 3 X 4 21 2.14 7.42 500.00 10.00 22 0.50 10.00 1000.00 10.00 23 0.50 10.00 500.00 19.70 24 5.00 10.00 500.00 10.00 25 5.00 10.00 1000.00 30.00 Investigation of Encapsulation Efficiency and Drug Loading Coefficient in Different Formulations The encapsulation efficiency of Risperidone (Y 2 ) ranges from 37.88% to 87.11%. This wide range indicates the significant influence of Risperidone encapsulation efficiency on laboratory parameters. The regression coefficient for this model is 0.9867, showing a strong relationship between the response and the selected variable. The p-value of the model is significant and less than 0.05. The lack of fit for the model has a p-value higher than 0.05, indicating its lack of significance. The proposed model for the response of Risperidone encapsulation efficiency is a Quadratic model. The results highlight the significant impact of (X 4 ), as well as (X 1 ), (X 2 ), and (X 3 ) on encapsulation efficiency. Several factors influence encapsulation efficiency, including the drug's nature, the microsphere preparation process, the drug-to-polymer ratio, the concentration of chitosan, and the rotation speed during emulsion formation. The encapsulation efficiency of Risperidone decreases as the initial amount of drug in the formulation increases. This is because with a constant amount of polymer and less drug, the polymer hinders the release of drug crystals from the droplets, resulting in increased drug entrapment. This phenomenon has been observed in various research studies. The encapsulation efficiency of Risperidone was found to increase with higher TPP concentration. This is attributed to the denser matrix formed between chitosan and TPP in the formulation. Yang Wei and his colleagues demonstrated that as TPP concentration increased, the encapsulation percentage of Theophylline also increased. (18) the increase in drug encapsulation efficiency was found to be directly related to the increase in stirring time during the manufacturing process. If the stirring time is prolonged, causing more stress on the matrix, the drug may leak from the matrix and form microspheres, resulting in a decrease in encapsulation efficiency. Additionally, as the speed of the mechanical stirrer increased, there was a gradual increase in drug encapsulation efficiency. The study of Xujing Zhang and et al showed the stirring speed can have a direct impact on the size and distribution of the microspheres. As the stirring speed increases, the size of the microspheres tends to decrease. Slow stirring speeds can result in inadequate emulsification and poor coating of the oil and water phases, leading to unevenly sized microspheres with lower drug loading and release rates. On the other hand, higher stirring speeds can cause microspheres to collide more frequently, promoting agglomeration and adhesion, which can result in clustering, reduced drug encapsulation, and faster drug release. (19) According to Diagram 1, the three-dimensional diagram (a) shows the drug encapsulation efficiency based on X 1 and X 2 at constant values of X 3 and X 4 . The graph illustrates an increase in encapsulation efficiency with increasing TPP concentration up to approximately 7.8%. However, as the concentration of TPP increases, the encapsulation efficiency decreases. The highest Y 2 was achieved at 7.82% X 2 . The graph illustrates that Y 2 increases when X 1 in the formulation is reduced, indicating an inverse relationship between Y 2 and X 1 . The three-dimensional graph (b) displays the drug loading coefficient based on X 1 and X 2 at fixed values of X 3 and X 4 . The drug loading coefficient ranged from 0.25 to 4.84, demonstrating the influence of Y 1 on laboratory parameters. The regression coefficient for this model was 0.9826, indicating a strong relationship between the response and the selected variable in Diagram 1. The p-value of the model is 0.0001, confirming the validity and significance of the proposed model. The lack of fit for the model, with a p-value above 0.05, suggests that it is not significant. The proposed model for the drug loading coefficient response is a Quadratic model. The results indicate that X 1 , X 2 , X 3 , and X 4 all have an impact on the risperidone drug loading coefficient (Y 1 ). The equation represents the best model for the risperidone drug loading coefficient, expressed as a second-degree polynomial model. The graph demonstrates a decrease in the drug loading coefficient as X 2 increases. The increase in Y 1 is linked to a decrease in X 1 , mirroring the pattern of Y 2 . Diagram 2 shows a three-dimensional representation of the encapsulation efficiency based on X 3 and X 4 at fixed values of X 1 and X 2 . The graph illustrates that Y 2 increases with stirring time in the manufacturing process up to 23 minutes, but then decreases with further increases in stirring time. Additionally, increasing the mechanical stirrer speed results in a gradual increase in drug encapsulation efficiency. Diagram 3-4 displays the drug loading coefficient based on X 3 and X 4 at fixed values of X 1 and X 2 . The graph shows that the slope of the drug loading coefficient changes gradually with an increase in X 1 and X 2 . The drug loading coefficient is significantly influenced by the preparation conditions of microspheres. Factors that impact the preparation of microspheres include the physicochemical properties of the drug, the molecular weight and concentration of chitosan, and the concentration of stabilizing agents. The drug loading coefficient of risperidone decreases as the TPP concentration increases. In this method, the drug loading coefficient depends on the swelling of particles in water. The drug loading coefficient decreases with an increase in the concentration of the crosslinking agent due to reduced swelling. In another study Samah Attia Algharib and et al reported an increase in drug loading with an increase in the crosslinking agent. The formation of agglomeration in the aqueous environment is also reported as an important parameter, especially for hydrophobic drugs. This may explain the limitation in the encapsulation percentage of risperidone. The possibility of the formation of these agglomeration increases with the initial amount of the drug. (20) Investigating Microspheres Particle Size in Different Formulations The particle size of the formulations ranged from 53.87 to 220.56 microns. This wide range of responses indicates the impact of microsphere particle size (Y 4 ) on laboratory parameters. The regression coefficient for this model was 0.9539, suggesting a strong relationship between the response and the selected variable. The p-value of the model is significant and less than 0.05. The lack of fit model has a p-value higher than 0.05, indicating that it is not significant. The proposed model for particle size is the Quadratic model. The results in the table demonstrate the significant effects of (X 2 ), (X 1 ), (X 4 ), and (X 3 ) on (Y 3 ). Where Y 4 represents the particle size and X 1 to X 4 are independent variables. 3D diagram 3 illustrates the size of the microspheres in relation to X 1 and X 2 , with constant values for (X 3 ), (X 4 ). Increasing X 1 results in larger microsphere particle sizes. Additionally, increasing X 2 initially decreases particle size by up to 7.2%. However, at higher levels of X 2 , the particle size increases. In 3D diagram 4, the size of the microspheres is shown in relation to (X 3 ) and (X 4 ), with fixed values for (X 1 ) and (X 2 ). The graph illustrates that particle size increases with a decrease in (X 4 ) and (X 3 ). The Particle size plays an essential role in various functions of particle drug delivery systems. These functions include drug release behavior and product syringeability, drug encapsulation, and its fate in the body (21). The size of the final particles depends on factors such as, the amount of crosslinking agent, and stirring rate during hardening, homogenization speed, and the polymer and surfactant type, the concentration of polymer in the organic phase, volume fraction of dispersed phase. Increasing the initial amount of the drug in the formulations results in larger particle size microspheres. Which is due to the viscosity of the droplets formed in the internal phase increases, leading to a higher drug amount. Additionally, reducing the ratio of drug to polymer yields smaller particle sizes. By increasing the concentration of TPP solution which is a multivalent anion and non-toxic ionic cross-linker, the particle size decreased by up to 7.2%. This reduction in particle size is attributed to the interaction between positively charged amino groups of chitosan and negatively charged phosphate groups of TPP. Firstly, the formation of a chitosan and TPP matrix with higher density is a result of the presence of more TPP negative ions during microsphere formation. Secondly, Higher concentrations of TPP can lead to the formation of aggregated particles within the microspheres, resulting in may promote inter-microspheres adhesion, which results in the fusion of small microspheres and formation of the larger size of the microspheres. Additionally, the formation of porous matrices and weak transverse connection structures can also contribute to an increase in particle size. The other factors such as acidic pH level, purity and molecular weight of chitosan are crucial in achieving particles with the appropriate size and morphology. Independent variables that impact the morphology of microspheres should be chosen based on the specific characteristics of each type of chitosan. The size of the microspheres in this study was consistent with findings from previous similar studies. However, due to the use of chitosan as the polymer, the microspheres tended to aggregate. The formation of larger nanoparticles at higher pH levels using the ionic gelation method. It appears that at lower pH levels of the polymer, the electrostatic interaction between the polymer and TPP increases, resulting in denser microparticles with smaller sizes and higher loading percentages. At low pH, the likelihood of protonation of free amines in the polymer increases. This causes the positive charge of the polymer to strengthen, creating a strong electrostatic attraction between the positively charged polymer and TPP charge. This interaction leads to the formation of dense particles that are smaller in size. The particle size was found to increase with a decrease in stirring time and the speed of the mechanical stirrer. The reason for this is that the particles are in contact with the shear force for a shorter amount of time, leading to an increase in particle size. Additionally, increasing the stirring rate of the mechanical stirrer increases the shearing force, thereby reducing the size of the particles. (21-23) Investigating Burst Release in Different Formulations The burst release in the formulations varied from 3.56% to 9.63% of the loaded drug. This wide range of responses demonstrates the significant impact of the burst release of the drug from the formulation (Y 3 ) on the laboratory parameters. The regression coefficient for this model was 0.9620, indicating a strong relationship between the response and the selected variable. The p-value of the model is significant and less than 0.05. The lack of fit model has a p-value higher than 0.05, indicating that it is not significant. The proposed model for the response of the initial drug release rate from the formulation was the Quadratic model. The p-value of (X 2 ) was less than 0.001, and the p-value of (X 3 ) on (Y 3 ) was significant and less than 0.05. Diagram 5 illustrates the impact of (X 1 ) and (X 2 ) on (Y 3 ). As (X 2 ) increases, (Y 3 ) decreases significantly. The optimal concentration value for (X 2 ) that results in the lowest (Y 3 ) is 8.63%. Concentrations of (X 2 ) exceeding 9% led to a significant decrease in the final release rate of risperidone from the microspheres. Diagram 6 is a three-dimensional representation of Y 3 based on X 4 and X 3 at constant values. The graph illustrates an increase in Y 3 with an increase in X 3 and X 4 . The study consider the smaller particle size shows an enhanced initial burst release than the large size microspheres due to increased specific surface area. On the other hand, the polymer degradation rate is more prominent with the large microspheres compared with the small ones. Therefore, the increase in the initial release rate of the drug from the formulation was linked to higher mechanical stirrer speed and stirring time, resulting in a decrease in particle size (increase in surface-to-volume ratio). Smaller particles provide a larger surface area for dissolution, leading to faster drug release. Investigating the morphology and particle size distribution and zeta potential of the optimal formulation SEM Image Figure 1: The SEM image shows the prepared microsphere. The size of the microsphere was consistent with the size indicated by the DLS method. The SEM image reveals microsphere particles with a size of approximately 117 microns and well-dispersed particles. As shown in the figure, the spherical microspheres exhibited a smooth and distinct surface. Additionally, the increase in zeta potential of the particles contributes to the stability of the drug. At higher pH levels, protonation decreases, resulting in a decrease in the zeta potential of the particles. Consequently, there is a higher risk of particle aggregation and the production of microparticles with a larger size Particle size distribution of the optimal formulation The average size of the optimal microspheres is 117 microns, which falls within the particle size range of PLGA microspheres containing risperidone (25-150 microns). The Particle Dispersion Coefficient (PDI) was determined to be 0.163. DSC In this study, thermal measurements were conducted on samples of risperidone powder, chitosan powder, a physical mixture of formulation components (with the same ratio as the optimal formulation), and optimal formulation powder. The resulting graphs in Figure 3 are displayed. Thermogram C reveals that risperidone is a crystalline compound with a melting temperature of 170 degrees Celsius. The absence of the endothermic peak of risperidone in graphs D and B, which correspond to the physical mixture of formulation components and the lyophilized powder of the formulation, suggests a transformation of the drug from a crystalline to an amorphous or molecular form. This transformation ultimately leads to an increase in encapsulation efficiency. In the Thermogram of chitosan (A), the peaks at 233.5°C and 103°C represent the amino groups and hydroxyl groups in its structure. The shift of the peak from 233.5 to 269°C in the thermogram of the optimal formulation indicates a reaction between the amino group of chitosan and the phosphate group of TPP, forming ion pairs. In other words, TPP in the optimal formulation has altered the structure of chitosan from crystalline to amorphous, and cross-links have disrupted chitosan from its crystalline form. FTIR Studies FTIR study was conducted to investigate the stability of risperidone during microsphere manufacturing. Figure 4 illustrates that risperidone displayed a peak related to the C-N bond in the 1000-1200 cm -1 region, as well as stretching peaks associated with the C-C bonds in the 2759-3000 cm -1 region. Additionally, risperidone exhibited a strong absorption peak at 1651 cm -1 , corresponding to the C=O double bond. Comparison of the absorption spectrum of risperidone with the optimal formulation indicated no changes in the structure of risperidone during the microsphere manufacturing process. Moreover, no destructive reactions were observed between the drug and other components of the formula. The interaction between the amine groups of the chitosan polymer chain and TPP ions was demonstrated by changes in the peaks of amine and phosphate groups in the spectrum of the optimal formulation (24). TPP exhibits a peak in the region of 3500-3300 cm -1 , which is attributed to the overlap of N-H and O-H stretching peaks. Additionally, there is a strong peak at 1142 cm -1 , representing the stretching peak of the PO2 group (24). In the chitosan spectrum, peaks are observed at 3354 cm -1 and 11061 cm -1 , corresponding to N-H and C-N amine bonds, respectively, which are not present in the absorption spectrum of the optimal formulation. This absence is likely due to the electrostatic bonds between chitosan amine groups and TPP ions. Furthermore, a new stretching peak around 1208-1215 cm -1 associated with the phosphate group has emerged (25). The presence of the P=O peak in the region around 877 to 910 in the optimal formulation indicates a bond between the phosphoric and ammonium groups. In vitro Release Study pattern from the formulations When examining the drug release from the optimal formulation in vitro as shown in Figure 5, it was revealed that the burst release was 5.04%. Following this initial burst, an extended release pattern was observed over the next three weeks of the test. Figure 6, illustrates a two-stage pattern in the release of the drug. The first stage shows a sudden and rapid release within the first 24 hours, followed by a slower release over 2 weeks (formulation F2, which contains 5% TPP) and 3 weeks (formulation F25, which contains 5% TPP). It is likely that the initial rapid release is caused by the separation of drug molecules from the surface of the microspheres. By the end of the first stage, 3.56% of the loaded drug (formulation F25) and 9.63% of the loaded drug (formulation F2) were released into the environment. The release in the subsequent stage occurs at a much slower rate. The drug is released from the swollen matrix as the polymer erodes, eventually leading to the release of drug molecules from the inner layers. By the end of the second stage, 76.1% of the drug (formulation F25) and 92.3% of the drug (formulation F2) had been released from the formulation's drug content in the specified environment. The formulation containing a higher concentration of TPP is released more slowly and over a longer period compared to other formulations, which is attributed to better hardening and the creation of a stronger matrix. Drug release from the system depends on the amount of crosslinking agent, morphology, size, and density of the particle system, as well as physicochemical properties. An increase in TPP concentration leads to a significant decrease in the initial release rate of the drug. This is due to the strong cross-links formed between chitosan and TPP, which hinder the release of the drug from the matrix as we demonstrated above in our study. The swelling and permeability characteristics of chitosan films, as well as the pH and concentration of the TPP solution, in relation to the drug release from the formulation. Determination of Release Kinetics Model in the Optimized Formulation This test was conducted on the optimized formulation. Kinetic models of drug release include the zero-order model, first-order model, Higuchi model, and Korsmeyer-Peppas model. Drug release from therapeutic systems does not follow just one mechanism, such as diffusion and dissolution. It is assumed that the release mechanism is dominant for the sake of simplifying mathematical calculations. However, in practice, the dominant mechanism may overshadow other mechanisms. According to Table 3, the optimized formulation follows the Higuchi model, which is a drug release model based on the diffusion process. The results suggest that in systems with the ability to swell, the drug is dispersed or encapsulated by the polymer with swelling capabilities. When water enters the structure of the chitosan polymer, it causes the polymer to swell. As a result, the drug dissolves in water and then diffuses as a solution through the swollen polymer. The degree of polymer swelling, drug solubility, and drug concentration in the matrix all play a role in regulating the speed and rate of drug release. Table 3: The optimized formulation Correlation coefficient of release data fitting with variousmathematical models (p<0.001) Zero order First order Korsmeyer Peppas Higuchi Optimized Formulation 0.854 0.784 0.922 0.986 In recent studies, chitosan has been identified as a polymer suitable for use in controlled release systems. Chitosan microspheres play a crucial role in the controlled release of protein and peptide drugs because of their adhesive mucus effect and excellent permeability at biological levels (26). Reports also suggest the use of polymer carriers with various cross-linking agents for extended-release drug delivery (27). For this study, chitosan microspheres were chosen for the controlled release delivery of risperidone. Previous studies have used chitosan microspheres for drug delivery (28-30). Various studies have explored the use of different polymers, such as PLGA (31), polycaprolactone (32), and hydroxypropyl methylcellulose phthalate (33), in the preparation of microspheres for drug delivery. Among these polymers, chitosan was selected as a suitable drug carrier because of its unique polycationic property, as well as its biocompatibility and biodegradability. Conclusion The prepared microspheres in this study demonstrate that the appropriate particle size and distribution can be achieved using the double emulsion-solvent evaporation method with two steps of hardening agents. The first step of hardening involves the emulsion transforming into a microgel after the addition of TPP, while the second step of hardening results in the microgel being further strengthened with the addition of glutaraldehyde, ultimately forming a microsphere. 25 formulations of risperidone microspheres were fabricated and their physicochemical properties were compared. The fabrication method employed in this study has been shown to be effective in terms of drug encapsulation efficiency, optimal loading coefficient, and enhancing the extended release pattern. Additionally, the morphology of the microspheres and the initial drug release rate are influenced by the pH and TPP solution concentration. The flawless chemical structure of the drug after microsphere preparation indicates the appropriate fabrication method and compatibility of the API and excipients in the formulation. The optimal conditions for this method are as follows: TPP concentration at 8.63%, pH of TPP solution ranging from 4.03 to 4.83, initial hardening time of 60 minutes, molar ratio of amine group to aldehyde group at 1:1, secondary hardening time of 80 minutes, and a drug to polymer ratio of 5%. This method has the potential to effectively prepare chitosan microspheres containing risperidone. Suggestions : - Investigate the effects of simultaneously acquiring medium and high molecular weights of chitosan polymer. - Inquire about the impact of using other side crosslinking agents. - Verify the effects of alternative fabrication methods to achieve desired results. - Conduct animal studies to investigate toxicity and in vivo release. Declarations Funding The authors received no financial support for this research. Competing interests The authors declare no competing interests. Disclosure The authors declare that there is no conflict of interest in this work. References Bhat A, Ahmad et al (2023) Neuropharmacological effect of risperidone: From chemistry to medicine. Chemico-Biol Interact 369:110296 Constantinides C, Han LK, Alloza C, Antonucci LA, Arango C, Ayesa-Arriola R, Banaj N, Bertolino A, Borgwardt S, Bruggemann J, Bustillo J (2023) Brain ageing in schizophrenia: evidence from 26 international cohorts via the ENIGMA Schizophrenia consortium. Mol Psychiatry 28(3):1201–1209 Kishi T, Ikuta T, Sakuma K, Okuya M, Iwata N (2021) Efficacy and safety of antipsychotic treatments for schizophrenia: A systematic review and network meta-analysis of randomized trials in Japan. J Psychiatr Res 138:444–452 Weber S, Scott JG, Chatterton ML (2022) Healthcare costs and resource use associated with negative symptoms of schizophrenia: A systematic literature review. Schizophr Res 241:251–259 Kishimoto T, Hagi K, Kurokawa S, Kane JM, Correll CU (2021) Long-acting injectable versus oral antipsychotics for the maintenance treatment of schizophrenia: a systematic review and comparative meta-analysis of randomised, cohort, and pre–post studies. Lancet Psychiatry 8(5):387–404 Kotzeva A, Mittal D, Desai S, Judge D, Samanta K (2023) Socioeconomic burden of schizophrenia: a targeted literature review of types of costs and associated drivers across 10 countries. J Med Econ 26(1):70–83 VandenBerg AM (2022) An update on recently approved long-acting injectable second-generation antipsychotics: knowns and unknowns regarding their use. Mental Health Clinician 12(5):270–281 Sharma VK, Sharma PP, Mazumder B, Bhatnagar A, Subramaniyan V, Fuloria S, Fuloria NK (2021) Mucoadhesive microspheres of glutaraldehyde crosslinked mucilage of Isabgol husk for sustained release of gliclazide. J Biomater Sci Polym Ed 32(11):1420–1449 Raizaday A, Yadav HK, Kasina S (2022) Chitosan and its derivatives as a potential nanobiomaterial: Drug delivery and biomedical application. InRecent Trends in Nanomedicine and Tissue Engineering. Sep 1 (pp. 57–94). River Publishers Karakurt I, Ozaltin K, Vargun E, Kucerova L, Suly P, Harea E, Minařík A, Štěpánková K, Lehocky M, Humpolícek P, Vesel A (2021) Controlled release of enrofloxacin by vanillin-crosslinked chitosan-polyvinyl alcohol blends. Mater Sci Engineering: C 126:112125 Kim JH, Ryu CH, Chon CH, Kim S, Lee S, Maharjan R, Kim NA, Jeong SH (2021) Three months extended-release microspheres prepared by multi-microchannel microfluidics in beagle dog models. Int J Pharm 608:121039 Correll CU, Kim E, Sliwa JK, Hamm W, Gopal S, Mathews M, Venkatasubramanian R, Saklad SR (2021) Pharmacokinetic characteristics of long-acting injectable antipsychotics for schizophrenia: an overview. CNS Drugs 35(1):39–59 Desai N, Rana D, Salave S, Gupta R, Patel P, Karunakaran B, Sharma A, Giri J, Benival D, Kommineni N (2023) Chitosan: A potential biopolymer in drug delivery and biomedical applications. Pharmaceutics 15(4):1313 Ritger PL, Peppas NA (1987) A simple equation for description of solute release I. Fickian and non-fickian release from non-swellable devices in the form of slabs, spheres, cylinders or discs. J Controlled Release 5(1):23–36 Huang J, Wigent RJ, Bentzley CM, Schwartz JB (2006) Nifedipine solid dispersion in microparticles of ammonio methacrylate copolymer and ethylcellulose binary blend for controlled drug delivery: Effect of drug loading on release kinetics. Int J Pharm 319(1–2):44–54 Higuchi TJ (1963) Mechanism of sustained-action medication. Theoretical analysis of rate of release of solid drugs dispersed in solid matrices. J Pharm Sci 52(12):1145–1149 Siepmann J, Siepmann F (2008) Mathematical modeling of drug delivery. Int J Pharm 364(2):328–343 Wei Y, Huang YH, Cheng KC, Song YL (2020) Investigations of the influences of processing conditions on the properties of spray dried chitosan-tripolyphosphate particles loaded with theophylline. Sci Rep 10(1):1155 Zhang X, Zhou J, Xu Y (2021) Optimized parameters for the preparation of silk fibroin drug-loaded microspheres based on the response surface method and a genetic algorithm–backpropagation neural network model. J Biomedical Mater Res Part B: Appl Biomaterials 109(1):6–18 Algharib SA, Dawood A, Zhou K, Chen D, Li C, Meng K, Zhang A, Luo W, Ahmed S, Huang L, Xie S (2022) Preparation of chitosan nanoparticles by ionotropic gelation technique: Effects of formulation parameters and in vitro characterization. J Mol Struct 1252:132129 Butreddy A, Gaddam RP, Kommineni N, Dudhipala N, Voshavar C (2021) PLGA/PLA-based long-acting injectable depot microspheres in clinical use: production and characterization overview for protein/peptide delivery. Int J Mol Sci 22(16):8884 Yasin H, Al-Taani B, Salem MS (2021) Preparation and characterization of ethylcellulose microspheres for sustained-release of pregabalin. Res Pharm Sci 16(1):1–5 Zeng W, Hui H, Liu Z, Chang Z, Wang M, He B, Hao D (2021) TPP ionically cross-linked chitosan/PLGA microspheres for the delivery of NGF for peripheral nerve system repair. Carbohydr Polym 258:117684 SKahdestani SA, Shahriari MH, Abdouss M (2021) Synthesis and characterization of chitosan nanoparticles containing teicoplanin using sol–gel. Polym Bull 78(2):1133–1148 Correa RF, Colucci G, Halla N, Pinto JA, Santamaria-Echart A, Blanco SP, Fernandes IP, Barreiro MF (2021) Development of chitosan microspheres through a green dual crosslinking strategy based on tripolyphosphate and vanillin. Molecules 26(8):2325 Ji X, Shao H, Li X, Ullah MW, Luo G, Xu Z, Ma L, He X, Lei Z, Li Q, Jiang X (2022) Injectable immunomodulation-based porous chitosan microspheres/HPCH hydrogel composites as a controlled drug delivery system for osteochondral regeneration. Biomaterials 285:121530 Khan Z, Abourehab MA, Parveen N, Kohli K, Kesharwani P (2023) Recent advances in microbeads-based drug delivery system for achieving controlled drug release. J Biomater Sci Polym Ed 34(4):541–564 Kulkarni N, Jain P, Shindikar A, Suryawanshi P, Thorat N (2022) Advances in the colon-targeted chitosan based multiunit drug delivery systems for the treatment of inflammatory bowel disease. Carbohydr Polym 288:119351 Gulati N, Dua K, Dureja H (2021) Role of chitosan based nanomedicines in the treatment of chronic respiratory diseases. Int J Biol Macromol 185:20–30 Tao F, Ma S, Tao H, Jin L, Luo Y, Zheng J, Xiang W, Deng H (2021) Chitosan-based drug delivery systems: from synthesis strategy to osteomyelitis treatment–a review. Carbohydr Polym 251:117063 Su Y, Zhang B, Sun R, Liu W, Zhu Q, Zhang X, Wang R, Chen C (2021) PLGA-based biodegradable microspheres in drug delivery: recent advances in research and application. Drug Delivery 28(1):1397–1418 Azeem M, Hanif M, Mahmood K, Siddique F, Hashem HE, Aziz M, Ameer N, Abid U, Latif H, Ramzan N, Rawat R (2023) Design, synthesis, spectroscopic characterization, in-vitro antibacterial evaluation and in-silico analysis of polycaprolactone containing chitosan-quercetin microspheres. J Biomol Struct Dynamics 41(15):7084–7103 Khatibi A, Zahedi P, Ghourchian H, Lari AS (2021) Development of microfluidic-based cellulose acetate phthalate nanoparticles containing omeprazole for antiulcer activity: In vitro and in vivo evaluations. Eur Polymer J 147:110294 Additional Declarations The authors declare no competing interests. Cite Share Download PDF Status: Posted Version 1 posted You are reading this latest preprint version Research Square lets you share your work early, gain feedback from the community, and start making changes to your manuscript prior to peer review in a journal. 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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-5742734","acceptedTermsAndConditions":true,"allowDirectSubmit":true,"archivedVersions":[],"articleType":"Research Article","associatedPublications":[],"authors":[{"id":396234727,"identity":"8010ebc1-6eb4-4771-9dec-ba8a252652ce","order_by":0,"name":"Parisa Latifi","email":"","orcid":"","institution":"Faculty of Pharmacy and pharmaceutical sciences, Islamic Azad University Tehran Medical Sciences","correspondingAuthor":false,"prefix":"","firstName":"Parisa","middleName":"","lastName":"Latifi","suffix":""},{"id":396234728,"identity":"d58ae35e-1920-413e-98d8-d6b48e5e5eb3","order_by":1,"name":"Amirali Jahanshahi","email":"","orcid":"","institution":"Department of Surgery, Faculty of Specialized Veterinary Sciences, Science and Research Branch, Islamic Azad University, Tehran, Iran","correspondingAuthor":false,"prefix":"","firstName":"Amirali","middleName":"","lastName":"Jahanshahi","suffix":""},{"id":396234729,"identity":"73a9ac7f-de4a-4a67-a6ed-1b7c5a965fab","order_by":2,"name":"Aysan Jamalara","email":"","orcid":"","institution":"Faculty of Pharmacy and pharmaceutical sciences, Islamic Azad University Tehran Medical Sciences","correspondingAuthor":false,"prefix":"","firstName":"Aysan","middleName":"","lastName":"Jamalara","suffix":""},{"id":396234730,"identity":"b36279b0-7a3f-4847-832b-8af5da8f1629","order_by":3,"name":"Arezou Jammanesh","email":"","orcid":"","institution":"Faculty of Pharmacy and pharmaceutical sciences, Islamic Azad University Tehran Medical 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formulation\u003c/p\u003e","description":"","filename":"1.png","url":"https://assets-eu.researchsquare.com/files/rs-5742734/v1/d963adff37218d94c06693c5.png"},{"id":72741177,"identity":"f3238520-bd94-4576-931c-a0d46c3704b4","added_by":"auto","created_at":"2025-01-01 09:38:34","extension":"png","order_by":2,"title":"Figure 2","display":"","copyAsset":false,"role":"figure","size":69878,"visible":true,"origin":"","legend":"\u003cp\u003eParticle size distribution\u003c/p\u003e","description":"","filename":"2.png","url":"https://assets-eu.researchsquare.com/files/rs-5742734/v1/da91a73c8c2934818e4e56f1.png"},{"id":72741207,"identity":"e16359c9-19b8-4a19-b05b-ed558f4c638a","added_by":"auto","created_at":"2025-01-01 09:38:35","extension":"png","order_by":3,"title":"Figure 3","display":"","copyAsset":false,"role":"figure","size":102926,"visible":true,"origin":"","legend":"\u003cp\u003eDSC Thermogram of a chitosan, optimal formulation, Risperidone, and physical mixture\u003c/p\u003e","description":"","filename":"3.png","url":"https://assets-eu.researchsquare.com/files/rs-5742734/v1/cbc41fd3f4a3ea1a545ad971.png"},{"id":72741180,"identity":"f81165cd-c459-44cf-b90f-1c14068e837f","added_by":"auto","created_at":"2025-01-01 09:38:34","extension":"png","order_by":4,"title":"Figure 4","display":"","copyAsset":false,"role":"figure","size":133803,"visible":true,"origin":"","legend":"\u003cp\u003eFTIR spectrum of a (Chitosan), b (TPP), c (Risperidone), and d (optimal formulation)\u003c/p\u003e","description":"","filename":"4.png","url":"https://assets-eu.researchsquare.com/files/rs-5742734/v1/81b064eee618f810d461b4dd.png"},{"id":72741179,"identity":"05511d6f-3b52-4cc5-880f-b2b27c5ae6c8","added_by":"auto","created_at":"2025-01-01 09:38:34","extension":"png","order_by":5,"title":"Figure 5","display":"","copyAsset":false,"role":"figure","size":14736,"visible":true,"origin":"","legend":"\u003cp\u003eThe cumulative release of the optimal formulation in BPS (pH=7.4, temperature 37 °)\u003c/p\u003e","description":"","filename":"5.png","url":"https://assets-eu.researchsquare.com/files/rs-5742734/v1/1639bda21f5758df4a0a3f52.png"},{"id":72741183,"identity":"f5c1b2de-f3ea-4cc9-b352-33af3357b39a","added_by":"auto","created_at":"2025-01-01 09:38:34","extension":"png","order_by":6,"title":"Figure 6","display":"","copyAsset":false,"role":"figure","size":18496,"visible":true,"origin":"","legend":"\u003cp\u003eThe cumulative release of formulations F2 and F25 in BPS (pH=7.4, temperature 37 °C)\u003c/p\u003e","description":"","filename":"6.png","url":"https://assets-eu.researchsquare.com/files/rs-5742734/v1/729372a4b917459afa430ea1.png"},{"id":72741840,"identity":"c1c429e6-a928-4db9-8f91-501c31a2a435","added_by":"auto","created_at":"2025-01-01 10:02:35","extension":"pdf","order_by":0,"title":"","display":"","copyAsset":false,"role":"manuscript-pdf","size":1999333,"visible":true,"origin":"","legend":"","description":"","filename":"manuscript.pdf","url":"https://assets-eu.researchsquare.com/files/rs-5742734/v1/1265b701-b114-4147-9109-572a129232f2.pdf"}],"financialInterests":"The authors declare no competing interests.","formattedTitle":"\u003cp\u003e\u003cstrong\u003ePreparation and evaluation of risperidone slow-release injectable microspheres using chitosan\u003c/strong\u003e\u003c/p\u003e","fulltext":[{"header":"Introduction","content":"\u003cp\u003eRisperidone, a dopamine antagonist, is prescribed in the treatment of schizophrenia. Among second-generation antipsychotic drugs, it is known to have the fewest side effects, such as weight gain, extrapyramidal side effects, and drowsiness, compared to other drugs in the same class. (\u003cspan class=\"CitationRef\"\u003e1\u003c/span\u003e) Schizophrenia is a chronic disease that often affects patients for a lifetime. (\u003cspan class=\"CitationRef\"\u003e2\u003c/span\u003e\u0026ndash;\u003cspan class=\"CitationRef\"\u003e3\u003c/span\u003e) In addition to the significant social and emotional toll it takes on families, it also imposes substantial costs on the healthcare budgets of countries. (\u003cspan class=\"CitationRef\"\u003e4\u003c/span\u003e) Considering the chronic nature of mental illnesses, particularly schizophrenia, and the challenges of patient non-compliance with consistent medication over lifelong treatment, there is a need for injectable sustained release systems. These systems would help control symptoms in schizophrenia patients, improve patient and family comfort, and reduce hospitalization costs and the need for specialized nursing care. (\u003cspan class=\"CitationRef\"\u003e5\u003c/span\u003e\u0026ndash;\u003cspan class=\"CitationRef\"\u003e6\u003c/span\u003e) Developing such a system is essential to increase patient cooperation and overall quality of life. One of the sustained release systems being studied is the microsphere system, which is utilized also in chemotherapy, cardiovascular diseases, hormone therapy and protein delivery. The primary focus of this research is to develop and refine this system. The microsphere system offers numerous benefits, such as sustained drug release over an extended period and a decrease in the frequency of doses. This system can effectively deliver the required drug concentration to the specific target area with minimal adverse effects. (\u003cspan class=\"CitationRef\"\u003e7\u003c/span\u003e) On the other hand, due to the large surface-to-volume ratio of microsphere particles, this system can be a suitable platform for loading insoluble drugs such as Risperidone. (\u003cspan class=\"CitationRef\"\u003e8\u003c/span\u003e) Chitosan is a copolymer of \u0026beta;-[1\u0026ndash;4]-linked 2-acetamido-2-deoxy-D-glucopyranose and 2-amino-2-deoxy- D-glucopyranose which is widely used in different drug delivery formulations due to its biocompatibility biodegradability, low toxicity, easy accessibility and suitable cost. Due to its crosslinking matrix with certain chemical crosslinking agents, such as glutaraldehyde, or by using ionic crosslinking interactions with tripolyphosphate can improve the sustained release rate and increase the drug loading efficiency of a drug. (\u003cspan class=\"CitationRef\"\u003e9\u003c/span\u003e\u0026ndash;\u003cspan class=\"CitationRef\"\u003e10\u003c/span\u003e) In this system, particles of different sizes can be prepared, enabling the loading of various doses of medicine in different forms, including oral, injectable, and topical applications. It has always been attempted to develop drug delivery systems based on zero-order kinetics, where the release of the drug is not dependent on its concentration or other variables. Most extended-release systems exhibit behavior similar to this. (\u003cspan class=\"CitationRef\"\u003e11\u003c/span\u003e) The term sustained-release is used for formulations that slowly release drugs over an extended period. This results in gradual drug absorption and a delayed onset of action. Additionally, the drug remains in the bloodstream for a longer duration, leading to a prolonged therapeutic effect. Controlled Release formulations not only offer extended drug delivery but also have predictable and consistent kinetics of drug release. (\u003cspan class=\"CitationRef\"\u003e12\u003c/span\u003e\u0026ndash;\u003cspan class=\"CitationRef\"\u003e13\u003c/span\u003e)\u003c/p\u003e\n\u003cp\u003eThere are several reasons for the development of new drug delivery systems:\u003c/p\u003e\n\u003cp\u003e1. Some drugs can be more effectively administered using new methods, and in some cases, new therapeutic properties can be achieved.\u003c/p\u003e\n\u003cp\u003e2. New drugs for chronic conditions such as diabetes and psychiatric disorders necessitate long-term drug delivery systems.\u003c/p\u003e\n\u003cp\u003e3. The manner in which drugs are released significantly impacts the therapeutic response.\u003c/p\u003e"},{"header":"Materials and Methods","content":"\u003cp\u003eIn this study, the double emulsion-solvent evaporation method with two hardening steps was used to produce risperidone microspheres. The first hardening stage involved the gelation method, which depends on the electrostatic interaction between the negative charge of TPP and the positive charge of the amino group of chitosan. In the second step, a precise amount of glutaraldehyde was added. The incorporation of a small yet adequate quantity of the covalent binder (glutaraldehyde) was essential for stabilizing the gel particles formed in the initial step.\u003c/p\u003e \u003cdiv id=\"Sec3\" class=\"Section2\"\u003e \u003ch2\u003eMaterials\u003c/h2\u003e \u003cp\u003eChitosan with the brand name Chitoclear\u003csup\u003e\u0026reg;\u003c/sup\u003e (medium molecular weight, Mw\u0026thinsp;=\u0026thinsp;275,000, degree of deacetylation 95%) was purchased from Primex Company in Ireland. Risperidone was generously provided by Dr. Abidi Pharmaceutical Company in Iran. Sodium Tripolyphosphate (TPP) and Glutaraldehyde (25% aqueous solution), Span\u003csup\u003e\u0026reg;\u003c/sup\u003e80, Tween\u003csup\u003e\u0026reg;\u003c/sup\u003e40, and Glacial Acetic Acid were purchased from Merck in Germany. All other reagents and solvents were of analytical grade and used as received.\u003c/p\u003e \u003c/div\u003e\n\u003ch3\u003eMethod\u003c/h3\u003e\n\u003cdiv id=\"Sec5\" class=\"Section2\"\u003e \u003ch2\u003ePreparation of microspheres\u003c/h2\u003e \u003cp\u003eIn this study, the double emulsion-solvent evaporation method with two hardening steps was used to prepare risperidone microspheres. Initially, the chitosan solution 1.5% v/v was dissolved in aqueous acetic acid 5% containing Tween 40 (1% v/v). The pH of the solution was then adjusted to fall within the range of 3.85\u0026ndash;4.30. Risperidone, a hydrophobic drug with poor solubility in water, needed to be optimized for encapsulation in microspheres. To achieve this, 3.75 mg of risperidone powder was dissolved in 2 ml of dichloromethane and added slowly, drop by drop, to the prepared polymer solution. The mixture was then placed under a homogenizer at 1000 rpm for 20 seconds to create the primary emulsion. A definite volume of paraffin containing Span 80 (3% v/v) was placed in a beaker on a stirrer (external phase). A clear solution was created by slowly adding the initial emulsion dropwise with a mechanical stirrer. Once all of the primary emulsion was added, the stirring continued for 28 minutes at 500 rpm (secondary emulsion). The temperature was then gradually raised over 90 minutes to evaporate the dispersed phase solvent from the formulation. To harden the obtained micro-emulsions, a TPP solution (8.63%) was prepared and added drop by drop. It was then placed on a stirrer for 60 minutes at a certain speed and at room temperature. Next, glutaraldehyde solution was added and placed on the stirrer for 80 minutes. The microspheres were separated by centrifugation at 6000 rpm for 20 minutes. They were then washed with diethyl ether, acetone, ethyl acetate, normal hexane, a 5% sodium metabisulfite solution, and finally deionized water (twice each). Afterward, the microspheres were dried at room temperature in the laboratory for 48 hours.\u003c/p\u003e \u003c/div\u003e\n\u003ch3\u003eCharacterization of microspheres\u003c/h3\u003e\n\u003cdiv id=\"Sec7\" class=\"Section2\"\u003e \u003ch2\u003eParticle Size Determination\u003c/h2\u003e \u003cp\u003eThe particle size of the prepared microspheres was determined using a laser diffraction technique (Malvern Zeta Sizer series, UK). The microspheres were prepared in a non-dissolving dispersion medium. The Malvern Zeta Sizer series operates based on the diffraction of laser beams. Particle size is reported based on the average volume diameter (\u0026micro;m) of the particles.\u003c/p\u003e \u003c/div\u003e \u003cdiv id=\"Sec8\" class=\"Section2\"\u003e \u003ch2\u003eDSC (Differential Scanning Calorimetry)\u003c/h2\u003e \u003cp\u003eThermal measurements of the prepared microspheres were conducted using a Metler-Toledo (Greifensee, Switzerland) differential scanning calorimeter. The analysis was performed in an aluminum hermetic pan where the sample was placed and covered with a lid, purging nitrogen gas to prevent any oxidation reaction. The heat range for the study was set from 30 to 200\u0026deg;C, with a temperature increase rate of 20\u0026deg;C per minute. In addition to the lyophilized powder of the formulation, thermal measurements were also performed on a physical mixture of the formulation components with the same proportions as the original formulation. By comparing the thermal diagrams, any changes during the formulation process of the drug in the microsphere could be identified.\u003c/p\u003e \u003c/div\u003e\n\u003ch3\u003eFTIR (Fourier Transform Infrared Spectroscopy)\u003c/h3\u003e\n\u003cp\u003eAn FTIR using a Magna-IR550 Nicolet FTIR spectrophotometer test is conducted during microsphere manufacturing to detect any undesired or harmful reactions. Each sample and KBr were ground via mortar and pestle, and a thin tablet was made for analysis. The spectra were scanned at room temperature in the 500 to 4000 cm\u003csup\u003e-1\u003c/sup\u003e wavelength range with a resolution of 4 cm\u003csup\u003e-1\u003c/sup\u003e. The test involves analyzing the polymer, drug, TPP, and final formulation.\u003c/p\u003e\n\u003ch3\u003eScanning Electron Microscopy (SEM)\u003c/h3\u003e\n\u003cp\u003eThe surface morphology of microspheres was observed by SEM followed by coating with thin layer of gold by means of sputter coater (SCD 005, Bal\u0026ndash;Ted, Switzerland) for 1 minute before imaging. Images were obtained using SEM XL 30, Philips (The Netherlands).\u003c/p\u003e \u003cdiv id=\"Sec11\" class=\"Section2\"\u003e \u003ch2\u003eDetermination of Drug Contents\u003c/h2\u003e \u003cp\u003eThe Risperidone content was analyzed using an Agilent 1260\u003csup\u003e\u0026reg;\u003c/sup\u003e system equipped with a 1260 Quat VL pump, automatic sampler (1260 ALS), and 1260 DAD VL detectors set at a wavelength of 275 nm. A C18 chromatographic column packed with octadecyl silica, a standard industrial material for column filling, was used in this study. The column had a length of 150 mm and a diameter of 3 mm. The mobile phase consisted of a mixture of methanol, water, and trimethylamine in a ratio of 0.5:19.5:80. The flow rate was set at 1 ml/min.\u003c/p\u003e \u003c/div\u003e \u003cdiv id=\"Sec12\" class=\"Section2\"\u003e \u003ch2\u003eEncapsulation Efficiency (EE %) and Drug Loading Coefficient (DL)\u003c/h2\u003e \u003cp\u003eTo calculate the encapsulation efficiency of each drug in the formulation, each sample was dissolved in 5 ml of acetic acid aqueous solution, filtered through a 0.22-\u0026micro;m filter. To break down the microspheres and release the entrapped drug, the resulting sample solution was vigorously sonicated and then stirred with a predetermined mixing cycle. Following this, 5 ml of methanol was added to the solution and vigorously stirred. The solution was then centrifuged, allowing the supernatant containing the drug to be carefully separated. Each sample was injected into the RP-HPLC (Agilent Technologies\u003csup\u003e\u0026reg;\u003c/sup\u003e). The mobile phase and the conditions of the instrument were the same as mentioned above. Encapsulation Efficiency was calculated using the following equation:\u003c/p\u003e \u003cp\u003eEncapsulation Efficiency% = ( \u003cspan class=\"InlineEquation\"\u003e\u003cspan class=\"mathinline\"\u003e\\(\\:\\frac{\\:\\text{d}\\text{r}\\text{u}\\text{g}\\:\\text{a}\\text{m}\\text{o}\\text{u}\\text{n}\\text{t}\\:entrapped\\:in\\:microspheres}{\\text{i}\\text{n}\\text{i}\\text{t}\\text{i}\\text{a}\\text{l}\\:\\text{a}\\text{m}\\text{o}\\text{u}\\text{n}\\text{t}\\:\\text{o}\\text{f}\\:\\text{d}\\text{r}\\text{u}\\text{g}}\\)\u003c/span\u003e\u003c/span\u003e) \u0026times; 100\u003c/p\u003e \u003cp\u003eThe purpose of this test is to determine the amount of active pharmaceutical ingredient in a pharmaceutical form. It is crucial to perform this test because the occurrence of therapeutic effects is directly related to the amount of the active substance in the pharmaceutical form.\u003c/p\u003e \u003cp\u003eDrug Loading coefficient was calculated by the following equation:\u003c/p\u003e \u003cp\u003eDrug Loading coefficient =\u003cspan class=\"InlineEquation\"\u003e\u003cspan class=\"mathinline\"\u003e\\(\\:\\:\\:\\:\\frac{\\text{a}\\text{m}\\text{o}\\text{u}\\text{n}\\text{t}\\:\\text{o}\\text{f}\\:\\text{d}\\text{r}\\text{u}\\text{g}\\:\\text{i}\\text{n}\\:\\text{m}\\text{i}\\text{c}\\text{r}\\text{o}\\text{s}\\text{p}\\text{h}\\text{e}\\text{r}\\text{e}\\text{s}}{\\text{a}\\text{m}\\text{o}\\text{u}\\text{n}\\text{t}\\:\\text{o}\\text{f}\\:\\text{m}\\text{i}\\text{c}\\text{r}\\text{o}\\text{s}\\text{p}\\text{h}\\text{e}\\text{r}\\text{e}\\text{s}\\:}\\)\u003c/span\u003e\u003c/span\u003e\u003c/p\u003e \u003c/div\u003e \u003cdiv id=\"Sec13\" class=\"Section2\"\u003e \u003ch2\u003eIn vitro release studies\u003c/h2\u003e \u003cp\u003eTo evaluate the release profile, all formulations were placed on a shaker bath (100 rpm) in 500 mL phosphate buffer medium (pH\u0026thinsp;=\u0026thinsp;7.4, T\u0026thinsp;=\u0026thinsp;37\u0026deg;C). At specified time intervals, a 1.0 ml aliquot was withdrawn for analysis by RP-HPLC. After each sample removal, 1.0 ml of fresh buffer was added to maintain sink conditions.\u003c/p\u003e \u003cp\u003eMicrospheres were tested using a 12 kDa dialysis bag (Molecular Weight cut off). Before the study, the dialysis bag was soaked in deionized water for 24 hours. One end of the bag was sealed with a clamp to prevent liquid leakage before adding the formulation.\u003c/p\u003e \u003c/div\u003e \u003cdiv id=\"Sec14\" class=\"Section2\"\u003e \u003ch2\u003eMathematical Study of Drug Release Kinetics from Microspheres\u003c/h2\u003e \u003cp\u003eTo investigate the drug release kinetic model, we studied the zero-order, first-order, Higuchi, and Korsmeyer-Peppas models based on the following equations:\u003c/p\u003e \u003c/div\u003e \u003cdiv id=\"Sec15\" class=\"Section2\"\u003e \u003ch2\u003eZero-order equation: Q\u003csub\u003e0\u003c/sub\u003e - Q\u003csub\u003et\u003c/sub\u003e = K\u003csub\u003e0t\u003c/sub\u003e\u003c/h2\u003e \u003cp\u003eZero-order kinetics refer to uniform release from a polymeric matrix, where the drug is released independently of its concentration in the matrix. Q\u003csub\u003e0\u003c/sub\u003e represents the initial drug amount in solution, and Q\u003csub\u003et\u003c/sub\u003e is the amount of drug dissolved over time. (\u003cspan citationid=\"CR14\" class=\"CitationRef\"\u003e14\u003c/span\u003e)\u003c/p\u003e \u003c/div\u003e \u003cdiv id=\"Sec16\" class=\"Section2\"\u003e \u003ch2\u003eFirst-order equation: logC\u0026thinsp;=\u0026thinsp;logC\u003csub\u003e0\u003c/sub\u003e - Kt/2.303\u003c/h2\u003e \u003cp\u003eFirst-order kinetics release discussed that the amount of released drug is dependent on the loaded drug in the formulation. (\u003cspan citationid=\"CR15\" class=\"CitationRef\"\u003e15\u003c/span\u003e)\u003c/p\u003e \u003c/div\u003e \u003cdiv id=\"Sec17\" class=\"Section2\"\u003e \u003ch2\u003eHiguchi equation: Q\u0026thinsp;=\u0026thinsp;K\u003csub\u003eH\u003c/sub\u003e t \u003csup\u003e(1/2)\u003c/sup\u003e\u003c/h2\u003e \u003cp\u003eIn this equation, KH represents the Higuchi dissolution constant, and Q represents the amount of drug released at time t. The Higuchi model is the first mathematical model that explains drug release from a polymeric matrix based on Fick's law. According to this model, the amount of drug released is directly related to the square root of time. (\u003cspan citationid=\"CR16\" class=\"CitationRef\"\u003e16\u003c/span\u003e)\u003c/p\u003e \u003c/div\u003e \u003cdiv id=\"Sec18\" class=\"Section2\"\u003e \u003ch2\u003eKorsmeyer-Peppas equation: M\u003csub\u003et\u003c/sub\u003e/M\u003csub\u003e\u0026infin;\u003c/sub\u003e= Kt\u003csup\u003en\u003c/sup\u003e\u003c/h2\u003e \u003cp\u003eMt/M\u0026thinsp;\u0026infin;\u0026thinsp;represents the fraction of drug released over time, where K is the release rate constant and n is the release power. The Korsmeyer-Peppas model is frequently utilized when there is an exponential relationship between drug release and time. This model is particularly useful for differentiating between release mechanisms of Fickian diffusion and non-Fickian diffusion models, which deviate from the Fickian model. (\u003cspan citationid=\"CR17\" class=\"CitationRef\"\u003e17\u003c/span\u003e)\u003c/p\u003e \u003cp\u003eAll the information obtained from the collection of samples was studied and analyzed using Sigma Plot software to obtain a model with the highest R\u003csup\u003e2\u003c/sup\u003e coefficient, indicating the best kinetics for drug release from the formulation. The data obtained from the release of the optimal formulation were analyzed to determine the appropriateness of the drug release pattern with a mathematical model. To achieve this, a graph was created for each series of data obtained from the release, following the mathematical rule of each release model. The R\u003csup\u003e2\u003c/sup\u003e coefficient, the correlation coefficient of each model, was then calculated. In each mathematical model, the level of significance was 0.05 (p\u0026thinsp;\u0026lt;\u0026thinsp;0.05).\u003c/p\u003e \u003c/div\u003e"},{"header":"Results and Discussion","content":"\u003cp\u003eIn this study, several runs of microspheres were fabricated to investigate the role of the following parameters in the formulation. X\u003csub\u003e1\u003c/sub\u003e to X\u003csub\u003e4\u003c/sub\u003e represent the independent variables, which are defined as the drug-to-polymer ratio, TPP concentration, stirring speed, and stirring time with a mechanical stirrer. Y\u003csub\u003e1\u003c/sub\u003e to Y\u003csub\u003e4\u003c/sub\u003e represent the dependent variables, which indicate the Drug loading coefficient, Encapsulation Efficiency, Burst Release in in-vitro studies, and particle size (see table 1).\u003c/p\u003e\n\u003cp\u003eTable 1: Defined Variables\u003c/p\u003e\n\u003ctable border=\"1\" cellspacing=\"0\" cellpadding=\"0\" width=\"604\"\u003e\n \u003ctbody\u003e\n \u003ctr\u003e\n \u003ctd colspan=\"12\" valign=\"top\" style=\"width: 600px;\"\u003e\n \u003cp\u003e\u003cstrong\u003eVariables\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 4px;\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd colspan=\"5\" valign=\"top\" style=\"width: 343px;\"\u003e\n \u003cp\u003e\u003cstrong\u003eIndependent\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd colspan=\"7\" valign=\"top\" style=\"width: 257px;\"\u003e\n \u003cp\u003e\u003cstrong\u003eDependent\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 4px;\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd rowspan=\"2\" style=\"width: 54px;\"\u003e\n \u003cp\u003e\u003cstrong\u003eFactor\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd rowspan=\"2\" style=\"width: 106px;\"\u003e\n \u003cp\u003e\u003cstrong\u003eName\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd rowspan=\"2\" style=\"width: 63px;\"\u003e\n \u003cp\u003e\u003cstrong\u003eUnit\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd colspan=\"2\" style=\"width: 120px;\"\u003e\n \u003cp\u003e\u003cstrong\u003eRange Levels\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd colspan=\"2\" rowspan=\"2\" style=\"width: 55px;\"\u003e\n \u003cp\u003e\u003cstrong\u003eResponse\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd colspan=\"2\" rowspan=\"2\" style=\"width: 107px;\"\u003e\n \u003cp\u003e\u003cstrong\u003eName\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd colspan=\"2\" rowspan=\"2\" style=\"width: 33px;\"\u003e\n \u003cp\u003e\u003cstrong\u003eUnit\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd colspan=\"2\" rowspan=\"2\" style=\"width: 66px;\"\u003e\n \u003cp\u003e\u003cstrong\u003eConstrains\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd valign=\"top\" style=\"width: 55px;\"\u003e\n \u003cp\u003eMin\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 66px;\"\u003e\n \u003cp\u003eMax\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd valign=\"top\" style=\"width: 54px;\"\u003e\n \u003cp\u003e\u003cstrong\u003eX\u003csub\u003e1\u003c/sub\u003e\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 106px;\"\u003e\n \u003cp\u003eDrug/Polymer ratio\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 63px;\"\u003e\n \u003cp\u003ew/w %\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 55px;\"\u003e\n \u003cp\u003e0.50\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 66px;\"\u003e\n \u003cp\u003e5.00\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 51px;\"\u003e\n \u003cp\u003e\u003cstrong\u003eY\u003csub\u003e1\u003c/sub\u003e\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd colspan=\"2\" valign=\"top\" style=\"width: 107px;\"\u003e\n \u003cp\u003eDL\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd colspan=\"2\" valign=\"top\" style=\"width: 33px;\"\u003e\n \u003cp\u003e-\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd colspan=\"2\" valign=\"top\" style=\"width: 66px;\"\u003e\n \u003cp\u003eMax\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 4px;\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd valign=\"top\" style=\"width: 54px;\"\u003e\n \u003cp\u003e\u003cstrong\u003eX\u003csub\u003e2\u003c/sub\u003e\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 106px;\"\u003e\n \u003cp\u003eTPP Conc.\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 63px;\"\u003e\n \u003cp\u003eg/ml\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 55px;\"\u003e\n \u003cp\u003e5.00\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 66px;\"\u003e\n \u003cp\u003e10.00\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 51px;\"\u003e\n \u003cp\u003e\u003cstrong\u003eY\u003csub\u003e2\u003c/sub\u003e\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd colspan=\"2\" valign=\"top\" style=\"width: 107px;\"\u003e\n \u003cp\u003eEE\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd colspan=\"2\" valign=\"top\" style=\"width: 33px;\"\u003e\n \u003cp\u003e%\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd colspan=\"2\" valign=\"top\" style=\"width: 66px;\"\u003e\n \u003cp\u003eMax\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 4px;\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd valign=\"top\" style=\"width: 54px;\"\u003e\n \u003cp\u003e\u003cstrong\u003eX\u003csub\u003e3\u003c/sub\u003e\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 106px;\"\u003e\n \u003cp\u003eStirring speed\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 63px;\"\u003e\n \u003cp\u003erpm\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 55px;\"\u003e\n \u003cp\u003e500.00\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 66px;\"\u003e\n \u003cp\u003e1000.00\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 51px;\"\u003e\n \u003cp\u003e\u003cstrong\u003eY\u003csub\u003e3\u003c/sub\u003e\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd colspan=\"2\" valign=\"top\" style=\"width: 107px;\"\u003e\n \u003cp\u003eBR\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd colspan=\"2\" valign=\"top\" style=\"width: 33px;\"\u003e\n \u003cp\u003e%\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd colspan=\"2\" valign=\"top\" style=\"width: 66px;\"\u003e\n \u003cp\u003eMin\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 4px;\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd valign=\"top\" style=\"width: 54px;\"\u003e\n \u003cp\u003e\u003cstrong\u003eX\u003csub\u003e4\u003c/sub\u003e\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 106px;\"\u003e\n \u003cp\u003eStirring time\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 63px;\"\u003e\n \u003cp\u003emin\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 55px;\"\u003e\n \u003cp\u003e10\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 66px;\"\u003e\n \u003cp\u003e30\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 51px;\"\u003e\n \u003cp\u003e\u003cstrong\u003eY\u003csub\u003e4\u003c/sub\u003e\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd colspan=\"2\" valign=\"top\" style=\"width: 107px;\"\u003e\n \u003cp\u003ePS\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd colspan=\"2\" valign=\"top\" style=\"width: 33px;\"\u003e\n \u003cp\u003e\u0026micro;m\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd colspan=\"2\" valign=\"top\" style=\"width: 66px;\"\u003e\n \u003cp\u003eIn range\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 4px;\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003c/tbody\u003e\n\u003c/table\u003e\n\u003cp\u003e\u003cbr\u003e\u003c/p\u003e\n\u003cp\u003eTable 2 shows the values obtained for the independent variables in 25 proposed experiments resulting from the study design.\u003c/p\u003e\n\u003cp\u003eTable 2: Independent variables of different microsphere formulations\u003c/p\u003e\n\u003ctable border=\"1\" cellspacing=\"0\" cellpadding=\"0\"\u003e\n \u003ctbody\u003e\n \u003ctr\u003e\n \u003ctd style=\"width: 57px;\"\u003e\n \u003cp\u003e\u003cstrong\u003eRun No\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd colspan=\"4\" style=\"width: 227px;\"\u003e\n \u003cp\u003e\u003cstrong\u003eIndependent variables\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 57px;\"\u003e\n \u003cp\u003e\u003cstrong\u003eRun No\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd colspan=\"4\" style=\"width: 226px;\"\u003e\n \u003cp\u003e\u003cstrong\u003eIndependent variables\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd style=\"width: 57px;\"\u003e\n \u003cp\u003e\u003cstrong\u003e\u0026nbsp;\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 58px;\"\u003e\n \u003cp\u003e\u003cstrong\u003eX\u003csub\u003e1\u003c/sub\u003e\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 56px;\"\u003e\n \u003cp\u003e\u003cstrong\u003eX\u003csub\u003e2\u003c/sub\u003e\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 57px;\"\u003e\n \u003cp\u003e\u003cstrong\u003eX\u003csub\u003e3\u003c/sub\u003e\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 57px;\"\u003e\n \u003cp\u003e\u003cstrong\u003eX\u003csub\u003e4\u003c/sub\u003e\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 57px;\"\u003e\n \u003cp\u003e\u003cstrong\u003e\u0026nbsp;\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 57px;\"\u003e\n \u003cp\u003e\u003cstrong\u003eX\u003csub\u003e1\u003c/sub\u003e\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 57px;\"\u003e\n \u003cp\u003e\u003cstrong\u003eX\u003csub\u003e2\u003c/sub\u003e\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 57px;\"\u003e\n \u003cp\u003e\u003cstrong\u003eX\u003csub\u003e3\u003c/sub\u003e\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 57px;\"\u003e\n \u003cp\u003e\u003cstrong\u003eX\u003csub\u003e4\u003c/sub\u003e\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd style=\"width: 57px;\"\u003e\n \u003cp\u003e\u003cstrong\u003e1\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 58px;\"\u003e\n \u003cp\u003e5.00\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 56px;\"\u003e\n \u003cp\u003e9.02\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 57px;\"\u003e\n \u003cp\u003e500.00\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 57px;\"\u003e\n \u003cp\u003e30.00\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 57px;\"\u003e\n \u003cp\u003e\u003cstrong\u003e11\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 57px;\"\u003e\n \u003cp\u003e5.00\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 57px;\"\u003e\n \u003cp\u003e8.07\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 57px;\"\u003e\n \u003cp\u003e807.50\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 57px;\"\u003e\n \u003cp\u003e17.70\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd style=\"width: 57px;\"\u003e\n \u003cp\u003e\u003cstrong\u003e2\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 58px;\"\u003e\n \u003cp\u003e5.00\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 56px;\"\u003e\n \u003cp\u003e5.00\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 57px;\"\u003e\n \u003cp\u003e900.00\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 57px;\"\u003e\n \u003cp\u003e30.00\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 57px;\"\u003e\n \u003cp\u003e\u003cstrong\u003e12\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 57px;\"\u003e\n \u003cp\u003e1.15\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 57px;\"\u003e\n \u003cp\u003e7.82\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 57px;\"\u003e\n \u003cp\u003e785.00\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 57px;\"\u003e\n \u003cp\u003e18.63\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd style=\"width: 57px;\"\u003e\n \u003cp\u003e\u003cstrong\u003e3\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 58px;\"\u003e\n \u003cp\u003e0.50\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 56px;\"\u003e\n \u003cp\u003e10.00\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 57px;\"\u003e\n \u003cp\u003e1000.00\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 57px;\"\u003e\n \u003cp\u003e10.00\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 57px;\"\u003e\n \u003cp\u003e\u003cstrong\u003e13\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 57px;\"\u003e\n \u003cp\u003e0.50\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 57px;\"\u003e\n \u003cp\u003e5.00\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 57px;\"\u003e\n \u003cp\u003e500.00\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 57px;\"\u003e\n \u003cp\u003e30.00\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd style=\"width: 57px;\"\u003e\n \u003cp\u003e\u003cstrong\u003e4\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 58px;\"\u003e\n \u003cp\u003e2.18\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 56px;\"\u003e\n \u003cp\u003e5.00\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 57px;\"\u003e\n \u003cp\u003e576.44\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 57px;\"\u003e\n \u003cp\u003e19.06\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 57px;\"\u003e\n \u003cp\u003e\u003cstrong\u003e14\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 57px;\"\u003e\n \u003cp\u003e5.00\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 57px;\"\u003e\n \u003cp\u003e5.00\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 57px;\"\u003e\n \u003cp\u003e500.00\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 57px;\"\u003e\n \u003cp\u003e13.60\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd style=\"width: 57px;\"\u003e\n \u003cp\u003e\u003cstrong\u003e5\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 58px;\"\u003e\n \u003cp\u003e0.50\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 56px;\"\u003e\n \u003cp\u003e5.00\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 57px;\"\u003e\n \u003cp\u003e762.50\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 57px;\"\u003e\n \u003cp\u003e10.00\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 57px;\"\u003e\n \u003cp\u003e\u003cstrong\u003e15\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 57px;\"\u003e\n \u003cp\u003e0.50\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 57px;\"\u003e\n \u003cp\u003e7.62\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 57px;\"\u003e\n \u003cp\u003e1000.00\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 57px;\"\u003e\n \u003cp\u003e30.00\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd style=\"width: 57px;\"\u003e\n \u003cp\u003e\u003cstrong\u003e6\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 58px;\"\u003e\n \u003cp\u003e5.00\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 56px;\"\u003e\n \u003cp\u003e5.00\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 57px;\"\u003e\n \u003cp\u003e1000.00\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 57px;\"\u003e\n \u003cp\u003e10.00\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 57px;\"\u003e\n \u003cp\u003e\u003cstrong\u003e16\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 57px;\"\u003e\n \u003cp\u003e5.00\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 57px;\"\u003e\n \u003cp\u003e10.00\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 57px;\"\u003e\n \u003cp\u003e500.00\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 57px;\"\u003e\n \u003cp\u003e10.00\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd style=\"width: 57px;\"\u003e\n \u003cp\u003e\u003cstrong\u003e7\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 58px;\"\u003e\n \u003cp\u003e5.00\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 56px;\"\u003e\n \u003cp\u003e5.00\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 57px;\"\u003e\n \u003cp\u003e500.00\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 57px;\"\u003e\n \u003cp\u003e13.60\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 57px;\"\u003e\n \u003cp\u003e\u003cstrong\u003e17\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 57px;\"\u003e\n \u003cp\u003e2.16\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 57px;\"\u003e\n \u003cp\u003e10.00\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 57px;\"\u003e\n \u003cp\u003e740.00\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 57px;\"\u003e\n \u003cp\u003e30.00\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd style=\"width: 57px;\"\u003e\n \u003cp\u003e\u003cstrong\u003e8\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 58px;\"\u003e\n \u003cp\u003e3.45\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 56px;\"\u003e\n \u003cp\u003e6.87\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 57px;\"\u003e\n \u003cp\u003e685.14\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 57px;\"\u003e\n \u003cp\u003e27.90\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 57px;\"\u003e\n \u003cp\u003e\u003cstrong\u003e18\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 57px;\"\u003e\n \u003cp\u003e2.13\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 57px;\"\u003e\n \u003cp\u003e5.00\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 57px;\"\u003e\n \u003cp\u003e1000.00\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 57px;\"\u003e\n \u003cp\u003e20.40\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd style=\"width: 57px;\"\u003e\n \u003cp\u003e\u003cstrong\u003e9\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 58px;\"\u003e\n \u003cp\u003e0.50\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 56px;\"\u003e\n \u003cp\u003e10.00\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 57px;\"\u003e\n \u003cp\u003e500.00\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 57px;\"\u003e\n \u003cp\u003e19.70\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 57px;\"\u003e\n \u003cp\u003e\u003cstrong\u003e19\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 57px;\"\u003e\n \u003cp\u003e5.00\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 57px;\"\u003e\n \u003cp\u003e5.00\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 57px;\"\u003e\n \u003cp\u003e1000.00\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 57px;\"\u003e\n \u003cp\u003e10.00\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd style=\"width: 57px;\"\u003e\n \u003cp\u003e\u003cstrong\u003e10\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 58px;\"\u003e\n \u003cp\u003e1.24\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 56px;\"\u003e\n \u003cp\u003e8.11\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 57px;\"\u003e\n \u003cp\u003e500.00\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 57px;\"\u003e\n \u003cp\u003e29.90\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 57px;\"\u003e\n \u003cp\u003e\u003cstrong\u003e20\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 57px;\"\u003e\n \u003cp\u003e2.94\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 57px;\"\u003e\n \u003cp\u003e10.00\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 57px;\"\u003e\n \u003cp\u003e999.56\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 57px;\"\u003e\n \u003cp\u003e19.13\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd style=\"width: 57px;\"\u003e\n \u003cp\u003e\u003cstrong\u003eRun No\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd colspan=\"4\" style=\"width: 227px;\"\u003e\n \u003cp\u003e\u003cstrong\u003eIndependent variables\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd colspan=\"5\" style=\"width: 283px;\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd style=\"width: 57px;\"\u003e\n \u003cp\u003e\u003cstrong\u003e\u0026nbsp;\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 58px;\"\u003e\n \u003cp\u003e\u003cstrong\u003eX\u003csub\u003e1\u003c/sub\u003e\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 56px;\"\u003e\n \u003cp\u003e\u003cstrong\u003eX\u003csub\u003e2\u003c/sub\u003e\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 57px;\"\u003e\n \u003cp\u003e\u003cstrong\u003eX\u003csub\u003e3\u003c/sub\u003e\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 57px;\"\u003e\n \u003cp\u003e\u003cstrong\u003eX\u003csub\u003e4\u003c/sub\u003e\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd colspan=\"5\" style=\"width: 283px;\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd style=\"width: 57px;\"\u003e\n \u003cp\u003e\u003cstrong\u003e21\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 58px;\"\u003e\n \u003cp\u003e2.14\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 56px;\"\u003e\n \u003cp\u003e7.42\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 57px;\"\u003e\n \u003cp\u003e500.00\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 57px;\"\u003e\n \u003cp\u003e10.00\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd colspan=\"5\" style=\"width: 283px;\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd style=\"width: 57px;\"\u003e\n \u003cp\u003e\u003cstrong\u003e22\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 58px;\"\u003e\n \u003cp\u003e0.50\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 56px;\"\u003e\n \u003cp\u003e10.00\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 57px;\"\u003e\n \u003cp\u003e1000.00\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 57px;\"\u003e\n \u003cp\u003e10.00\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd colspan=\"5\" style=\"width: 283px;\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd style=\"width: 57px;\"\u003e\n \u003cp\u003e\u003cstrong\u003e23\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 58px;\"\u003e\n \u003cp\u003e0.50\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 56px;\"\u003e\n \u003cp\u003e10.00\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 57px;\"\u003e\n \u003cp\u003e500.00\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 57px;\"\u003e\n \u003cp\u003e19.70\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd colspan=\"5\" style=\"width: 283px;\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd style=\"width: 57px;\"\u003e\n \u003cp\u003e\u003cstrong\u003e24\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 58px;\"\u003e\n \u003cp\u003e5.00\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 56px;\"\u003e\n \u003cp\u003e10.00\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 57px;\"\u003e\n \u003cp\u003e500.00\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 57px;\"\u003e\n \u003cp\u003e10.00\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd colspan=\"5\" style=\"width: 283px;\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd style=\"width: 57px;\"\u003e\n \u003cp\u003e\u003cstrong\u003e25\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 58px;\"\u003e\n \u003cp\u003e5.00\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 56px;\"\u003e\n \u003cp\u003e10.00\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 57px;\"\u003e\n \u003cp\u003e1000.00\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 57px;\"\u003e\n \u003cp\u003e30.00\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd colspan=\"5\" style=\"width: 283px;\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003c/tbody\u003e\n\u003c/table\u003e\n\u003cp\u003e\u003cbr\u003e\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eInvestigation of Encapsulation Efficiency and Drug Loading Coefficient in Different Formulations\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003eThe encapsulation efficiency of Risperidone (Y\u003csub\u003e2\u003c/sub\u003e) ranges from 37.88% to 87.11%. This wide range indicates the significant influence of Risperidone encapsulation efficiency on laboratory parameters. The regression coefficient for this model is 0.9867, showing a strong relationship between the response and the selected variable.\u003c/p\u003e\n\u003cp\u003eThe p-value of the model is significant and less than 0.05. The lack of fit for the model has a p-value higher than 0.05, indicating its lack of significance. The proposed model for the response of Risperidone encapsulation efficiency is a Quadratic model. The results highlight the significant impact of (X\u003csub\u003e4\u003c/sub\u003e), as well as (X\u003csub\u003e1\u003c/sub\u003e), (X\u003csub\u003e2\u003c/sub\u003e), and (X\u003csub\u003e3\u003c/sub\u003e) on encapsulation efficiency. Several factors influence encapsulation efficiency, including the drug\u0026apos;s nature, the microsphere preparation process, the drug-to-polymer ratio, the concentration of chitosan, and the rotation speed during emulsion formation. The encapsulation efficiency of Risperidone decreases as the initial amount of drug in the formulation increases. This is because with a constant amount of polymer and less drug, the polymer hinders the release of drug crystals from the droplets, resulting in increased drug entrapment. This phenomenon has been observed in various research studies. The encapsulation efficiency of Risperidone was found to increase with higher TPP concentration. This is attributed to the denser matrix formed between chitosan and TPP in the formulation. Yang Wei and his colleagues demonstrated that as TPP concentration increased, the encapsulation percentage of Theophylline also increased. (18) \u0026nbsp;\u003c/p\u003e\n\u003cp\u003ethe increase in drug encapsulation efficiency was found to be directly related to the increase in stirring time during the manufacturing process. If the stirring time is prolonged, causing more stress on the matrix, the drug may leak from the matrix and form microspheres, resulting in a decrease in encapsulation efficiency. Additionally, as the speed of the mechanical stirrer increased, there was a gradual increase in drug encapsulation efficiency. The study of Xujing Zhang and et al showed the stirring speed can have a direct impact on the size and distribution of the microspheres. As the stirring speed increases, the size of the microspheres tends to decrease. Slow stirring speeds can result in inadequate emulsification and poor coating of the oil and water phases, leading to unevenly sized microspheres with lower drug loading and release rates. On the other hand, higher stirring speeds can cause microspheres to collide more frequently, promoting agglomeration and adhesion, which can result in clustering, reduced drug encapsulation, and faster drug release. (19)\u0026nbsp;\u003c/p\u003e\n\u003cp\u003eAccording to Diagram 1, the three-dimensional diagram (a) shows the drug encapsulation efficiency based on X\u003csub\u003e1\u0026nbsp;\u003c/sub\u003eand X\u003csub\u003e2\u003c/sub\u003e at constant values of X\u003csub\u003e3\u003c/sub\u003e and X\u003csub\u003e4\u003c/sub\u003e. The graph illustrates an increase in encapsulation efficiency with increasing TPP concentration up to approximately 7.8%. However, as the concentration of TPP increases, the encapsulation efficiency decreases. The highest Y\u003csub\u003e2\u003c/sub\u003e was achieved at 7.82% X\u003csub\u003e2\u003c/sub\u003e. The graph illustrates that Y\u003csub\u003e2\u0026nbsp;\u003c/sub\u003eincreases when X\u003csub\u003e1\u003c/sub\u003e in the formulation is reduced, indicating an inverse relationship between Y\u003csub\u003e2\u003c/sub\u003e and X\u003csub\u003e1\u003c/sub\u003e.\u003c/p\u003e\n\u003cp\u003eThe three-dimensional graph (b) displays the drug loading coefficient based on X\u003csub\u003e1\u003c/sub\u003e and X\u003csub\u003e2\u003c/sub\u003e at fixed values of X\u003csub\u003e3\u003c/sub\u003e and X\u003csub\u003e4\u003c/sub\u003e. The drug loading coefficient ranged from 0.25 to 4.84, demonstrating the influence of Y\u003csub\u003e1\u003c/sub\u003e on laboratory parameters. The regression coefficient for this model was 0.9826, indicating a strong relationship between the response and the selected variable in Diagram 1.\u003c/p\u003e\n\u003cp\u003eThe p-value of the model is 0.0001, confirming the validity and significance of the proposed model. The lack of fit for the model, with a p-value above 0.05, suggests that it is not significant. The proposed model for the drug loading coefficient response is a Quadratic model. The results indicate that X\u003csub\u003e1\u003c/sub\u003e, X\u003csub\u003e2\u003c/sub\u003e, X\u003csub\u003e3\u003c/sub\u003e, and X\u003csub\u003e4\u003c/sub\u003e all have an impact on the risperidone drug loading coefficient (Y\u003csub\u003e1\u003c/sub\u003e). The equation represents the best model for the risperidone drug loading coefficient, expressed as a second-degree polynomial model.\u0026nbsp;\u003c/p\u003e\n\u003cp\u003eThe graph demonstrates a decrease in the drug loading coefficient as X\u003csub\u003e2\u003c/sub\u003e increases. The increase in Y\u003csub\u003e1\u003c/sub\u003e is linked to a decrease in X\u003csub\u003e1\u003c/sub\u003e, mirroring the pattern of Y\u003csub\u003e2\u003c/sub\u003e. Diagram 2 shows a three-dimensional representation of the encapsulation efficiency based on X\u003csub\u003e3\u003c/sub\u003e and X\u003csub\u003e4\u003c/sub\u003e at fixed values of X\u003csub\u003e1\u003c/sub\u003e and X\u003csub\u003e2\u003c/sub\u003e. The graph illustrates that Y\u003csub\u003e2\u003c/sub\u003e increases with stirring time in the manufacturing process up to 23 minutes, but then decreases with further increases in stirring time. Additionally, increasing the mechanical stirrer speed results in a gradual increase in drug encapsulation efficiency. Diagram 3-4 displays the drug loading coefficient based on X\u003csub\u003e3\u003c/sub\u003e and X\u003csub\u003e4\u003c/sub\u003e at fixed values of X\u003csub\u003e1\u003c/sub\u003e and X\u003csub\u003e2\u003c/sub\u003e. The graph shows that the slope of the drug loading coefficient changes gradually with an increase in X\u003csub\u003e1\u003c/sub\u003e and X\u003csub\u003e2\u003c/sub\u003e.\u003c/p\u003e\n\u003cp\u003eThe drug loading coefficient is significantly influenced by the preparation conditions of microspheres. Factors that impact the preparation of microspheres include the physicochemical properties of the drug, the molecular weight and concentration of chitosan, and the concentration of stabilizing agents. The drug loading coefficient of risperidone decreases as the TPP concentration increases. In this method, the drug loading coefficient depends on the swelling of particles in water. The drug loading coefficient decreases with an increase in the concentration of the crosslinking agent due to reduced swelling. In another study Samah Attia Algharib and et al reported an increase in drug loading with an increase in the crosslinking agent. The formation of agglomeration in the aqueous environment is also reported as an important parameter, especially for hydrophobic drugs. This may explain the limitation in the encapsulation percentage of risperidone. The possibility of the formation of these agglomeration increases with the initial amount of the drug. (20)\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eInvestigating Microspheres Particle Size in Different Formulations\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003eThe particle size of the formulations ranged from 53.87 to 220.56 microns. This wide range of responses indicates the impact of microsphere particle size (Y\u003csub\u003e4\u003c/sub\u003e) on laboratory parameters. The regression coefficient for this model was 0.9539, suggesting a strong relationship between the response and the selected variable.\u003c/p\u003e\n\u003cp\u003eThe p-value of the model is significant and less than 0.05. The lack of fit model has a p-value higher than 0.05, indicating that it is not significant. The proposed model for particle size is the Quadratic model. The results in the table demonstrate the significant effects of (X\u003csub\u003e2\u003c/sub\u003e), (X\u003csub\u003e1\u003c/sub\u003e), (X\u003csub\u003e4\u003c/sub\u003e), and (X\u003csub\u003e3\u003c/sub\u003e) on (Y\u003csub\u003e3\u003c/sub\u003e).\u003c/p\u003e\n\u003cp\u003eWhere Y\u003csub\u003e4\u003c/sub\u003e represents the particle size and X\u003csub\u003e1\u003c/sub\u003e to X\u003csub\u003e4\u003c/sub\u003e are independent variables.\u003c/p\u003e\n\u003cp\u003e3D diagram 3 illustrates the size of the microspheres in relation to X\u003csub\u003e1\u003c/sub\u003e and X\u003csub\u003e2\u003c/sub\u003e, with constant values for (X\u003csub\u003e3\u003c/sub\u003e), (X\u003csub\u003e4\u003c/sub\u003e).\u003c/p\u003e\n\u003cp\u003eIncreasing X\u003csub\u003e1\u003c/sub\u003e results in larger microsphere particle sizes. Additionally, increasing X\u003csub\u003e2\u003c/sub\u003e initially decreases particle size by up to 7.2%. However, at higher levels of X\u003csub\u003e2\u003c/sub\u003e, the particle size increases.\u003c/p\u003e\n\u003cp\u003eIn 3D diagram 4, the size of the microspheres is shown in relation to (X\u003csub\u003e3\u003c/sub\u003e) and (X\u003csub\u003e4\u003c/sub\u003e), with fixed values for (X\u003csub\u003e1\u003c/sub\u003e) and (X\u003csub\u003e2\u003c/sub\u003e). The graph illustrates that particle size increases with a decrease in (X\u003csub\u003e4\u003c/sub\u003e) and (X\u003csub\u003e3\u003c/sub\u003e).\u003c/p\u003e\n\u003cp\u003eThe Particle size plays an essential role in various functions of particle drug delivery systems. These functions include drug release behavior and product syringeability, drug encapsulation, and its fate in the body (21).\u0026nbsp;\u003c/p\u003e\n\u003cp\u003eThe size of the final particles depends on factors such as, the amount of crosslinking agent, and stirring rate during hardening, homogenization speed, and the polymer and surfactant type, the concentration of polymer in the organic phase, volume fraction of dispersed phase. Increasing the initial amount of the drug in the formulations results in larger particle size microspheres. Which is due to the viscosity of the droplets formed in the internal phase increases, leading to a higher drug amount. Additionally, reducing the ratio of drug to polymer yields smaller particle sizes.\u003c/p\u003e\n\u003cp\u003eBy increasing the concentration of TPP solution which is a multivalent anion and non-toxic ionic cross-linker, the particle size decreased by up to 7.2%. This reduction in particle size is attributed to the interaction between positively charged amino groups of chitosan and negatively charged phosphate groups of TPP. Firstly, the formation of a chitosan and TPP matrix with higher density is a result of the presence of more TPP negative ions during microsphere formation. Secondly, Higher concentrations of TPP can lead to the formation of aggregated particles within the microspheres, resulting in may promote inter-microspheres adhesion, which results in the fusion of small microspheres and formation of the larger size of the microspheres. Additionally, the formation of porous matrices and weak transverse connection structures can also contribute to an increase in particle size.\u0026nbsp;\u003c/p\u003e\n\u003cp\u003eThe other factors such as acidic pH level, purity and molecular weight of chitosan are crucial in achieving particles with the appropriate size and morphology. Independent variables that impact the morphology of microspheres should be chosen based on the specific characteristics of each type of chitosan. The size of the microspheres in this study was consistent with findings from previous similar studies. However, due to the use of chitosan as the polymer, the microspheres tended to aggregate. The formation of larger nanoparticles at higher pH levels using the ionic gelation method. It appears that at lower pH levels of the polymer, the electrostatic interaction between the polymer and TPP increases, resulting in denser microparticles with smaller sizes and higher loading percentages. At low pH, the likelihood of protonation of free amines in the polymer increases. This causes the positive charge of the polymer to strengthen, creating a strong electrostatic attraction between the positively charged polymer and TPP charge. This interaction leads to the formation of dense particles that are smaller in size.\u003c/p\u003e\n\u003cp\u003eThe particle size was found to increase with a decrease in stirring time and the speed of the mechanical stirrer. The reason for this is that the particles are in contact with the shear force for a shorter amount of time, leading to an increase in particle size. Additionally, increasing the stirring rate of the mechanical stirrer increases the shearing force, thereby reducing the size of the particles. (21-23)\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eInvestigating Burst Release in Different Formulations\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003eThe burst release in the formulations varied from 3.56% to 9.63% of the loaded drug. This wide range of responses demonstrates the significant impact of the burst release of the drug from the formulation (Y\u003csub\u003e3\u003c/sub\u003e) on the laboratory parameters. The regression coefficient for this model was 0.9620, indicating a strong relationship between the response and the selected variable.\u003c/p\u003e\n\u003cp\u003eThe p-value of the model is significant and less than 0.05. The lack of fit model has a p-value higher than 0.05, indicating that it is not significant. The proposed model for the response of the initial drug release rate from the formulation was the Quadratic model.\u003c/p\u003e\n\u003cp\u003eThe p-value of (X\u003csub\u003e2\u003c/sub\u003e) was less than 0.001, and the p-value of (X\u003csub\u003e3\u003c/sub\u003e) on (Y\u003csub\u003e3\u003c/sub\u003e) was significant and less than 0.05. Diagram 5 illustrates the impact of (X\u003csub\u003e1\u003c/sub\u003e) and (X\u003csub\u003e2\u003c/sub\u003e) on (Y\u003csub\u003e3\u003c/sub\u003e). As (X\u003csub\u003e2\u003c/sub\u003e) increases, (Y\u003csub\u003e3\u003c/sub\u003e) decreases significantly.\u003c/p\u003e\n\u003cp\u003eThe optimal concentration value for (X\u003csub\u003e2\u003c/sub\u003e) that results in the lowest (Y\u003csub\u003e3\u003c/sub\u003e) is 8.63%. Concentrations of (X\u003csub\u003e2\u003c/sub\u003e) exceeding 9% led to a significant decrease in the final release rate of risperidone from the microspheres.\u003c/p\u003e\n\u003cp\u003eDiagram 6 is a three-dimensional representation of Y\u003csub\u003e3\u003c/sub\u003e based on X\u003csub\u003e4\u003c/sub\u003e and X\u003csub\u003e3\u003c/sub\u003e at constant values. The graph illustrates an increase in Y\u003csub\u003e3\u003c/sub\u003e with an increase in X\u003csub\u003e3\u003c/sub\u003e and X\u003csub\u003e4\u003c/sub\u003e.\u003c/p\u003e\n\u003cp\u003eThe study consider the smaller particle size shows an enhanced initial burst release than the large size microspheres due to increased specific surface area. On the other hand, the polymer degradation rate is more prominent with the large microspheres compared with the small ones. Therefore, the increase in the initial release rate of the drug from the formulation was linked to higher mechanical stirrer speed and stirring time, resulting in a decrease in particle size (increase in surface-to-volume ratio). Smaller particles provide a larger surface area for dissolution, leading to faster drug release.\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eInvestigating the morphology and particle size distribution and zeta potential of the optimal formulation\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eSEM Image\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003eFigure 1: The SEM image shows the prepared microsphere. The size of the microsphere was consistent with the size indicated by the DLS method. The SEM image reveals microsphere particles with a size of approximately 117 microns and well-dispersed particles. As shown in the figure, the spherical microspheres exhibited a smooth and distinct surface. Additionally, the increase in zeta potential of the particles contributes to the stability of the drug. At higher pH levels, protonation decreases, resulting in a decrease in the zeta potential of the particles. Consequently, there is a higher risk of particle aggregation and the production of microparticles with a larger size\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eParticle size distribution of the optimal formulation\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003eThe average size of the optimal microspheres is 117 microns, which falls within the particle size range of PLGA microspheres containing risperidone (25-150 microns). The Particle Dispersion Coefficient (PDI) was determined to be 0.163.\u0026nbsp;\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eDSC\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003eIn this study, thermal measurements were conducted on samples of risperidone powder, chitosan powder, a physical mixture of formulation components (with the same ratio as the optimal formulation), and optimal formulation powder. The resulting graphs in Figure 3 are displayed.\u003c/p\u003e\n\u003cp\u003eThermogram C reveals that risperidone is a crystalline compound with a melting temperature of 170 degrees Celsius. The absence of the endothermic peak of risperidone in graphs D and B, which correspond to the physical mixture of formulation components and the lyophilized powder of the formulation, suggests a transformation of the drug from a crystalline to an amorphous or molecular form. This transformation ultimately leads to an increase in encapsulation efficiency. In the Thermogram of chitosan (A), the peaks at 233.5\u0026deg;C and 103\u0026deg;C represent the amino groups and hydroxyl groups in its structure.\u003c/p\u003e\n\u003cp\u003eThe shift of the peak from 233.5 to 269\u0026deg;C in the thermogram of the optimal formulation indicates a reaction between the amino group of chitosan and the phosphate group of TPP, forming ion pairs. In other words, TPP in the optimal formulation has altered the structure of chitosan from crystalline to amorphous, and cross-links have disrupted chitosan from its crystalline form.\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eFTIR Studies\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003e\u0026nbsp;FTIR study was conducted to investigate the stability of risperidone during microsphere manufacturing. Figure 4 illustrates that risperidone displayed a peak related to the C-N bond in the 1000-1200 cm\u003csup\u003e-1\u003c/sup\u003e region, as well as stretching peaks associated with the C-C bonds in the 2759-3000 cm\u003csup\u003e-1\u003c/sup\u003e region. Additionally, risperidone exhibited a strong absorption peak at 1651 cm\u003csup\u003e-1\u003c/sup\u003e, corresponding to the C=O double bond.\u003c/p\u003e\n\u003cp\u003eComparison of the absorption spectrum of risperidone with the optimal formulation indicated no changes in the structure of risperidone during the microsphere manufacturing process. Moreover, no destructive reactions were observed between the drug and other components of the formula.\u003c/p\u003e\n\u003cp\u003eThe interaction between the amine groups of the chitosan polymer chain and TPP ions was demonstrated by changes in the peaks of amine and phosphate groups in the spectrum of the optimal formulation (24).\u003c/p\u003e\n\u003cp\u003eTPP exhibits a peak in the region of 3500-3300 cm\u003csup\u003e-1\u003c/sup\u003e, which is attributed to the overlap of N-H and O-H stretching peaks. Additionally, there is a strong peak at 1142 cm\u003csup\u003e-1\u003c/sup\u003e, representing the stretching peak of the PO2 group (24). In the chitosan spectrum, peaks are observed at 3354 cm\u003csup\u003e-1\u003c/sup\u003e and 11061 cm\u003csup\u003e-1\u003c/sup\u003e, corresponding to N-H and C-N amine bonds, respectively, which are not present in the absorption spectrum of the optimal formulation. This absence is likely due to the electrostatic bonds between chitosan amine groups and TPP ions. Furthermore, a new stretching peak around 1208-1215 cm\u003csup\u003e-1\u003c/sup\u003e associated with the phosphate group has emerged (25). The presence of the P=O peak in the region around 877 to 910 in the optimal formulation indicates a bond between the phosphoric and ammonium groups.\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eIn vitro Release Study pattern from the formulations\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003eWhen examining the drug release from the optimal formulation in vitro as shown in Figure 5, it was revealed that the burst release was 5.04%. Following this initial burst, an extended release pattern was observed over the next three weeks of the test.\u003c/p\u003e\n\u003cp\u003eFigure 6, illustrates a two-stage pattern in the release of the drug. The first stage shows a sudden and rapid release within the first 24 hours, followed by a slower release over 2 weeks (formulation F2, which contains 5% TPP) and 3 weeks (formulation F25, which contains 5% TPP). It is likely that the initial rapid release is caused by the separation of drug molecules from the surface of the microspheres. By the end of the first stage, 3.56% of the loaded drug (formulation F25) and 9.63% of the loaded drug (formulation F2) were released into the environment. The release in the subsequent stage occurs at a much slower rate. The drug is released from the swollen matrix as the polymer erodes, eventually leading to the release of drug molecules from the inner layers. By the end of the second stage, 76.1% of the drug (formulation F25) and 92.3% of the drug (formulation F2) had been released from the formulation\u0026apos;s drug content in the specified environment. The formulation containing a higher concentration of TPP is released more slowly and over a longer period compared to other formulations, which is attributed to better hardening and the creation of a stronger matrix.\u003c/p\u003e\n\u003cp\u003eDrug release from the system depends on the amount of crosslinking agent, morphology, size, and density of the particle system, as well as physicochemical properties. An increase in TPP concentration leads to a significant decrease in the initial release rate of the drug. This is due to the strong cross-links formed between chitosan and TPP, which hinder the release of the drug from the matrix as we demonstrated above in our study. The swelling and permeability characteristics of chitosan films, as well as the pH and concentration of the TPP solution, in relation to the drug release from the formulation.\u0026nbsp;\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eDetermination of Release Kinetics Model in the Optimized Formulation\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003eThis test was conducted on the optimized formulation. Kinetic models of drug release include the zero-order model, first-order model, Higuchi model, and Korsmeyer-Peppas model. Drug release from therapeutic systems does not follow just one mechanism, such as diffusion and dissolution. It is assumed that the release mechanism is dominant for the sake of simplifying mathematical calculations. However, in practice, the dominant mechanism may overshadow other mechanisms. According to Table 3, the optimized formulation follows the Higuchi model, which is a drug release model based on the diffusion process. The results suggest that in systems with the ability to swell, the drug is dispersed or encapsulated by the polymer with swelling capabilities. When water enters the structure of the chitosan polymer, it causes the polymer to swell. As a result, the drug dissolves in water and then diffuses as a solution through the swollen polymer. The degree of polymer swelling, drug solubility, and drug concentration in the matrix all play a role in regulating the speed and rate of drug release.\u003c/p\u003e\n\u003cp\u003eTable 3: The optimized formulation Correlation coefficient of release data fitting with variousmathematical models (p\u0026lt;0.001)\u003c/p\u003e\n\u003cdiv\u003e\n \u003ctable border=\"1\" cellspacing=\"0\" cellpadding=\"0\" width=\"571\"\u003e\n \u003ctbody\u003e\n \u003ctr\u003e\n \u003ctd\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd\u003e\n \u003cp\u003eZero order\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd\u003e\n \u003cp\u003eFirst order\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd\u003e\n \u003cp\u003eKorsmeyer Peppas\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd\u003e\n \u003cp\u003eHiguchi\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd\u003e\n \u003cp\u003eOptimized Formulation\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd\u003e\n \u003cp\u003e0.854\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd\u003e\n \u003cp\u003e0.784\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd\u003e\n \u003cp\u003e0.922\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd\u003e\n \u003cp\u003e0.986\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003c/tbody\u003e\n \u003c/table\u003e\n\u003c/div\u003e\n\u003cp\u003e\u003cbr\u003e\u003c/p\u003e\n\u003cp\u003eIn recent studies, chitosan has been identified as a polymer suitable for use in controlled release systems. Chitosan microspheres play a crucial role in the controlled release of protein and peptide drugs because of their adhesive mucus effect and excellent permeability at biological levels (26). Reports also suggest the use of polymer carriers with various cross-linking agents for extended-release drug delivery (27).\u003c/p\u003e\n\u003cp\u003eFor this study, chitosan microspheres were chosen for the controlled release delivery of risperidone. Previous studies have used chitosan microspheres for drug delivery (28-30). Various studies have explored the use of different polymers, such as PLGA (31), polycaprolactone (32), and hydroxypropyl methylcellulose phthalate (33), in the preparation of microspheres for drug delivery. Among these polymers, chitosan was selected as a suitable drug carrier because of its unique polycationic property, as well as its biocompatibility and biodegradability.\u003c/p\u003e"},{"header":"Conclusion","content":"\u003cp\u003eThe prepared microspheres in this study demonstrate that the appropriate particle size and distribution can be achieved using the double emulsion-solvent evaporation method with two steps of hardening agents. The first step of hardening involves the emulsion transforming into a microgel after the addition of TPP, while the second step of hardening results in the microgel being further strengthened with the addition of glutaraldehyde, ultimately forming a microsphere. 25 formulations of risperidone microspheres were fabricated and their physicochemical properties were compared. The fabrication method employed in this study has been shown to be effective in terms of drug encapsulation efficiency, optimal loading coefficient, and enhancing the extended release pattern.\u003c/p\u003e \u003cp\u003eAdditionally, the morphology of the microspheres and the initial drug release rate are influenced by the pH and TPP solution concentration. The flawless chemical structure of the drug after microsphere preparation indicates the appropriate fabrication method and compatibility of the API and excipients in the formulation.\u003c/p\u003e \u003cp\u003eThe optimal conditions for this method are as follows: TPP concentration at 8.63%, pH of TPP solution ranging from 4.03 to 4.83, initial hardening time of 60 minutes, molar ratio of amine group to aldehyde group at 1:1, secondary hardening time of 80 minutes, and a drug to polymer ratio of 5%. This method has the potential to effectively prepare chitosan microspheres containing risperidone.\u003c/p\u003e \u003cp\u003e \u003cb\u003eSuggestions\u003c/b\u003e:\u003c/p\u003e \u003cp\u003e \u003cul\u003e \u003cli\u003e \u003cp\u003e- Investigate the effects of simultaneously acquiring medium and high molecular weights of chitosan polymer.\u003c/p\u003e \u003c/li\u003e \u003cli\u003e \u003cp\u003e- Inquire about the impact of using other side crosslinking agents.\u003c/p\u003e \u003c/li\u003e \u003cli\u003e \u003cp\u003e- Verify the effects of alternative fabrication methods to achieve desired results.\u003c/p\u003e \u003c/li\u003e \u003cli\u003e \u003cp\u003e- Conduct animal studies to investigate toxicity and in vivo release.\u003c/p\u003e \u003c/li\u003e \u003c/ul\u003e \u003c/p\u003e"},{"header":"Declarations","content":"\u003cp\u003e\u003cstrong\u003eFunding\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003eThe authors received no financial support for this research.\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eCompeting interests\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003eThe authors declare no competing interests.\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eDisclosure\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003eThe authors declare that there is no conflict of interest in this work.\u003c/p\u003e"},{"header":"References","content":"\u003col\u003e\u003cli\u003e\u003cspan\u003eBhat A, Ahmad et al (2023) Neuropharmacological effect of risperidone: From chemistry to medicine. 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Eur Polymer J 147:110294\u003c/span\u003e\u003c/li\u003e\u003c/ol\u003e"}],"fulltextSource":"","fullText":"","funders":[],"hasAdminPriorityOnWorkflow":false,"hasManuscriptDocX":true,"hasOptedInToPreprint":true,"hasPassedJournalQc":"","hasAnyPriority":true,"hideJournal":true,"highlight":"","institution":"","isAcceptedByJournal":false,"isAuthorSuppliedPdf":false,"isDeskRejected":"","isHiddenFromSearch":false,"isInQc":false,"isInWorkflow":false,"isPdf":false,"isPdfUpToDate":true,"isWithdrawnOrRetracted":false,"journal":{"display":true,"email":"[email protected]","identity":"researchsquare","isNatureJournal":false,"hasQc":true,"allowDirectSubmit":true,"externalIdentity":"","sideBox":"","snPcode":"","submissionUrl":"/submission","title":"Research Square","twitterHandle":"researchsquare","acdcEnabled":true,"dfaEnabled":false,"editorialSystem":"","reportingPortfolio":"","inReviewEnabled":false,"inReviewRevisionsEnabled":true},"keywords":"Sustained release, Risperidone, chitosan, parenteral microsphere","lastPublishedDoi":"10.21203/rs.3.rs-5742734/v1","lastPublishedDoiUrl":"https://doi.org/10.21203/rs.3.rs-5742734/v1","license":{"name":"CC BY 4.0","url":"https://creativecommons.org/licenses/by/4.0/"},"manuscriptAbstract":"\u003cp\u003eThe aim of this study was preparation and evaluation of parenteral chitosan microspheres for sustained release of risperidone. Risperidone, a second-generation antipsychotic, is an effective in the treatment of schizophrenia and has a low incidence of adverse effect. Sustained release microsphere due to ascertain levels of drug in plasma, reduced toxicity and improving the efficacy of the drug. This study is done to consider the effect of four independent variables including, the drug to polymer ratio, TPP concentration, stirring speed and stirring time on the four dependent variables including, encapsulation efficiency, drug loading, particle size and the extent of initial burst in vitro release. In this study chitosan microspheres containing risperidone carried out by double emulsification/ solvent evaporation with two-step solidification process. The microspheres have been analyzed for their shape, size and surface characteristics. Interactions inside microspheres were investigated by differential scanning calorimetry and FTIR. In the following encapsulation efficiency, drug loading and burst release was investigated. The optimized formulation showed a narrow size distribution with an average of 115\u0026thinsp;\u0026plusmn;\u0026thinsp;2 \u0026micro;m. The SEM image showed that microsphere was round in shape and discrete. The DSC analysis indicated the conversion of drug from crystalline state to molecular state in the optimized lyophilized formulation. FTIR analysis showed no changes in the chemical structure of risperidone in formulation. The in vitro release profile exhibited 92.3% drug release over 21 days in the sink condition.\u003c/p\u003e","manuscriptTitle":"Preparation and evaluation of risperidone slow-release injectable microspheres using chitosan","msid":"","msnumber":"","nonDraftVersions":[{"code":1,"date":"2025-01-01 09:38:29","doi":"10.21203/rs.3.rs-5742734/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":"77aa70eb-24cc-4bd5-80f3-63ef5c9f0885","owner":[],"postedDate":"January 1st, 2025","published":true,"recentEditorialEvents":[],"rejectedJournal":[],"revision":"","amendment":"","status":"posted","subjectAreas":[{"id":42214214,"name":"Nanoscience"}],"tags":[],"updatedAt":"2025-01-01T09:38:29+00:00","versionOfRecord":[],"versionCreatedAt":"2025-01-01 09:38:29","video":"","vorDoi":"","vorDoiUrl":"","workflowStages":[]},"version":"v1","identity":"rs-5742734","journalConfig":"researchsquare"},"__N_SSP":true},"page":"/article/[identity]/[[...version]]","query":{"redirect":"/article/rs-5742734","identity":"rs-5742734","version":["v1"]},"buildId":"8U1c8b4HqxoKbykW_rLl7","isFallback":false,"isExperimentalCompile":false,"dynamicIds":[84888],"gssp":true,"scriptLoader":[]}