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INTRODUCTION
Nanoparticles are the primary nanoparticulate drug delivery systems, offering significant advantages for targeted drug delivery and improving the dissolution rate and bioavailability of poorly water-soluble drugs. The development of drug-loaded nanoparticles is a highly promising strategy. Various techniques are available to reduce particle size to the nanometer scale, and these methods have been extensively studied. The poor solubility and low dissolution rate of Biopharmaceutical Classification System (BCS) Class II drugs in aqueous gastrointestinal fluids often lead to inadequate bioavailability. Enhancing solubility and dissolution rate through advanced techniques is essential to overcoming this limitation. Several approaches, such as solid dispersion, inclusion complex formation, and the use of microparticles and nanoparticles, have been employed to improve drug dissolution rates (Rizvi and Saleh, 2021; Bhalani et al., 2022).
The nanoprecipitation technique, also known as the solvent displacement method, is a simple, rapid, and efficient approach to nanoparticle formulation. It involves the precipitation of a preformed polymer from an organic solution, followed by the diffusion of the organic solvent into an aqueous medium, with or without the presence of a surfactant. This method requires two mutually miscible solvents, where both the polymer and the drug should be soluble in one solvent but insoluble in the other (non-solvent) (Bhalani et al., 2055). Nanoprecipitation occurs through rapid desolvation of the polymer when the polymer solution is introduced into the aqueous phase. As the organic solvent diffuses into the aqueous medium, the polymer precipitates, leading to the immediate entrapment of the drug. Polymer deposition at the interface of the aqueous and organic phases, driven by rapid solvent diffusion, results in the spontaneous formation of a colloidal suspension. This technique is particularly suitable for hydrophobic compounds that are soluble in ethanol or acetone but exhibit minimal solubility in water (Beck-Broichsitter et al., 2010).
Arginine, or L-arginine, is a semi-essential amino acid involved in various physiological processes. It plays a key role in protein synthesis, immune function, and wound healing. Arginine is a precursor for Nitric Oxide (NO), a vital molecule that regulates blood vessel dilation, improving circulation and cardiovascular health. It also contributes to ammonia detoxification through the urea cycle and enhances hormone secretion, including insulin and growth hormone (Salatin et al., 2017). The Central Composite Design (CCD) enables systematic optimization of arginine-loaded nanoparticles by assessing the impact of key formulation variables. It ensures maximum drug entrapment, controlled release, and enhanced bioavailabilitywhile minimizing experimental runs. CCD improves formulation efficiency, making the process cost-effective, reproducible, and scalable. This approach enhances nanoparticle performance for nutraceutical and pharmaceutical applications (Wu et al., 2009).
MATERIALS AND METHODS
L-arginine was obtained from CDH Laboratory Chemicals, Eudragit L-100 was purchased from BFC Laboratories, Banglore, India, poly vinyl alcohol, methanol and acetone were purchased from Molychem, (Mumbai, India). All other materials or chemicals used were of analytical grade.
Preparation of nanoparticles
Arginine-loaded nanoparticles were formulated using the nanoprecipitation technique with varying polymer concentrations, optimized through a 3² Face-Centered Central Composite Design (FCCCD) (Table 1). Eudragit L100 was dissolved in methanol, while arginine was dissolved in acetone, and the two solutions were mixed. The resulting mixture was added dropwise into an aqueous polyvinyl alcohol solution under stirring at 700 rpm for 120 min. After completing organic solvent evaporation, the dispersion was centrifuged at 13,000 rpm, and the collected nanoparticles were dried at 40ºC for 1 hr to obtain the final product (Becker et al., 1967).
| Batch Code | Eudragit L 100 (X1) | Sol: Antisol ratio (X2) | ||
|---|---|---|---|---|
| Coded value | Actual Value (mg) | Coded Value | Actual Value | |
| F1 | 0 | 5 | -1 | 1:3 |
| F2 | 0 | 5 | 0 | 1:4 |
| F3 | 0 | 5 | 0 | 1:4 |
| F4 | 0 | 5 | 0 | 1:4 |
| F5 | 0 | 5 | 0 | 1:4 |
| F6 | 0 | 5 | 1 | 1:5 |
| F7 | 0 | 5 | 0 | 1:4 |
| F8 | 1 | 6 | 0 | 1:4 |
| F9 | -1 | 4 | 0 | 1:4 |
| F10 | 1 | 6 | 1 | 1:5 |
| F11 | -1 | 4 | -1 | 1:3 |
| F12 | 1 | 6 | -1 | 1:3 |
| F13 | -1 | 4 | 1 | 1:5 |
Designing of experiment
A Face centered 3² Central Composite Design (FCCCD) was employed for the optimization study, focusing on two independent variables: Eudragit L100 concentration (X1) and solvent: antisolvent ratio (X2). Their effects were evaluated on key dependent responses, including Particle Size (nm), average zeta Potential (mV), Drug Loading (%), and Entrapment Efficiency (%). The experimental design points are detailed in Table 1.
Polynomial equations were formulated to describe the relationship between independent and dependent variables. The general polynomial equation used to assess these effects is:
where Y1 represents the dependent response, and b0 is the arithmetic mean response across 13experimental runs. This statistical approach allows for efficient optimization and evaluation of nanoparticle formulation parameters (López-Ruiz et al., 2022).
Characterization of nanoparticles
Particle size analysis
The mean particle size of nanoparticles was determined using the Malvern instrument (ver. 6.12). The analysis was conducted by introducing 0.3 mL of the sample into the viewing unit, where Dynamic Light Scattering (DLS) was utilized to measure both particle size and molecular size. The measurements were taken at a scattering angle of 90º at room temperature. The particle size was recorded as the mean±standard deviation, based on three parallel measurements. This technique estimates particle size distribution by analyzing Brownian motion and converting the diffusion data into size parameters (Sadhukhan et al., 2016).
Zeta potential measurement
The zeta potential analysis was conducted using the Malvern Zeta sizer (ver. 6.12, Malvern Instruments, UK). The electrophoretic mobility of the nano particles was measured and converted into zeta potential. For the analysis, the nano particle samples were diluted with 0.1 mM KCl and placed in an electrophoretic cell, where an electric field of 15.2 V/cm was applied. All measurements were performed in triplicate to ensure accuracy and reproducibility (Stetefeld et al., 2016).
Percent drug loading and entrapment efficiency
A precisely weighed sample of 10 mg drug-loaded nanoparticles was dispersed in 10 mL of dichloromethane and subjected to sonication for 1 hr. The resulting solution was then filtered using a membrane filter and analyzed using HPLC with UV detector (Carissimi et al., 2020). The drug loading percentage and Entrapment Efficiency (EE) were calculated using the following formulas:
To ensure precision and reproducibility, all measurements were conducted in triplicate.
Scanning Electron Microscopy (SEM)
The nanoparticles were subjected to gold-palladium coating using a sputter coater unit (VG-Microtech, UK) to enhance conductivity. The surface morphology was then analyzed using a Cambridge Stereoscan S120 Scanning Electron Microscope (SEM, Cambridge, UK), operated at an acceleration voltage of 10 kV (Goldstein et al., 2014).
In vitro drug release studies
For the drug release study, nanoparticles were enclosed in dialysis bags, securely sealed, and immersed in a Phosphate Buffer (pH 6.8) containing 1% sodium lauryl sulfate. The dissolution process was carried out using a USP Type II apparatus, set at 37±0.5ºC with a stirring speed of 100 rpm for 2 hr. At designated time intervals, 5 mL of the medium was withdrawn and replaced with fresh buffer to maintain sink conditions. The withdrawn samples were then filtered using Whatman filter No. 41, appropriately diluted, and analyzed using a UV-vis spectrophotometer (Mast et al., 2021).
Factorial design/statistical analysis
Experimental design with a coded level of variables and their actual values are illustrated as the effects of the different concentrations of drug and polymer ratio (X1), the concentration of surfactant (X2) and the ratio of organic solvents (X3) on the responses.
Optimization and data validation
Eudragit L 100 concentrations were varied at 4 mg, 5 mg, and 6 mg, while the solvent-to-antisolvent ratio was adjusted to 1:3, 1:4, and 1:5. A total of thirteen formulations were designed, incorporating nine different combinations, with the centre point replicated four times, and the average value was considered for further evaluation. The dependent responses were analysed using Design Expert® 8.0.7.1 (trial version), and statistical models were assessed for significance. The optimised formulation was selected based on the desired dependent response values. Additionally, four formulations (VC1 to VC4) were developed alongside the optimized batch and validated using response surface methodology. The predicted and observed responses were critically compared using linear correlation plots. Residual graphs depicting the differences between predicted and observed values were generated, and the percent prediction error (% bias) was determined. A total of four formulations were selected as checkpoints to validate the optimized formulation (Tian et al., 2018).
RESULTS
Visual inspection of the L-Arginine sample revealed it to be a white, crystalline powder. The melting point of the sample was observed to be 228ºC, which lies within the reported range of 222-233ºC, confirming the expected physical characteristics of the compound.
The FTIR (Fourier Transform Infrared) spectrum of the formulated L-arginine nanoparticles was recorded to evaluate potential drug-polymer interactions. The infrared spectra of both the pure drug and the nanoparticle formulation exhibited similar characteristic peaks, as shown in Table 2 and Figures 1 and 2.
| Functional Group | Characteristic Peak of Pure L-Arginine (cm⁻¹) | Characteristic Peak of Formulated Nanoparticles (cm⁻¹) | Inference |
|---|---|---|---|
| NH Stretch | 3158.57 | 3156.45 | No significant shift, indicating no strong interaction. |
| CH₃ Stretch | 2935.78 | 2934.12 | Minor shift, suggesting stable formulation. |
| NH₂ Bend | 1681.04 | 1679.85 | Negligible shift, confirming structural integrity. |
| CO Stretch | 1576.87 | 1575.42 | Minimal shift, no strong interaction observed. |
| OH Bend | 1517.08 | 1516.3 | No significant change, stable bonding. |
| CH₃ Asymmetric Bend | 1458.25 | 1457.1 | Slight shift, indicating compatibility. |
| CH₃ Symmetric Bend | 1407.13 | 1406.5 | Negligible difference, stable formulation. |
| CH₃ Symmetric Bend | 1356.02 | 1355.78 | No major shift, maintaining structural properties. |
| OH Bend | 1320.33 | 1319.92 | Stable formulation confirmed. |
| Symmetric Stretch CCC Bond | 1176.56 | 1175.9 | No significant interaction observed. |
| NH₂ Bend | 899.46 | 898.75 | Negligible shift, ensuring stability. |
| CO Bend | 521.77 | 520.88 | Minimal change, no strong interaction. |
| NH₂ Rock | 447.5 | 446.9 | Stable peak, confirming formulation integrity. |
Formulation Optimization Using Central Composite Design (CCD)
A 3² Central Composite Design (CCD) was employed for the systematic optimization of Nanoparticles of L-Arginine (ARG), as shown in Tables 1. A total of thirteen experimental batches were formulated based on the CCD matrix. The data obtained were fitted to a second-order quadratic polynomial model using Multiple Linear Regression Analysis (MLRA).
The MLRA yielded mathematical models for key Critical Quality Attributes (CQAs) such as particle size, zeta potential, drug loading, and entrapment efficiency. The high correlation coefficients (R² values ranging from 0.9497, p<0.001 to 0.9985, p<0.0001) confirmed the strong predictive capability of the models generated through Response Surface Methodology (RSM).
Particle Size
Zeta Potential
Drug Loading
Entrapment Efficiency
The results were statistically significant (p<0.01) as evaluated by ANOVA using Design Expert® software.
Particle Size
The particle size of the L-Arginine formulations ranged from 89 nm to 128 nm (Table 3 & Figure 3), with the smallest size observed in F10 (89 nm) and the largest in F6 (128±0.44 nm). Smaller particle sizes were achieved with higher polymer concentrations (e.g., 6% Eudragit L100), as seen in F10 (89 nm). Formulations with a lower Solvent: Antisolvent ratio (1:3) produced larger particles, while the 1:5 ratio in F6 led to the largest particle size. The optimized particle size of 114±1.11 nm was achieved in F7, which had a polymer concentration of 5% and a 1:4 Solvent: Antisolvent ratio.
| Batch Code | Particle Size (nm) | Avg Zeta potential (Mv) | Drug Loading (%) | Entrapment Efficiency (%) |
|---|---|---|---|---|
| F1 | 109±.1.03 | 0.658±0.022 | 25.85±0.12 | 75.51±1.33 |
| F2 | 116±0.98 | 0.825±0.013 | 24.45±0.18 | 77.55±1.06 |
| F3 | 114±0.36 | 0.827±0.011 | 24.39±0.17 | 77.81±0.82 |
| F4 | 114±1.11 | 0.826±0.027 | 24.49±0.21 | 77.77±0.58 |
| F5 | 115±0.75 | 0.825±0.011 | 24.53±0.06 | 77.69±0.47 |
| F6 | 128±0.44 | 0.656±0.032 | 25.45±0.21 | 74.85±0.86 |
| F7 | 114±0.73 | 0.828±0.010 | 24.83±0.15 | 77.86±0.93 |
| F8 | 97±0.58 | 0.661±0.014 | 24.75±0.19 | 72.15±0.72 |
| F9 | 93±0.89 | 0.669±0.009 | 25.13±0.07 | 70.09±1.05 |
| F10 | 89±1.01 | 0.637±0.021 | 25.00±0.22 | 69.39±0.99 |
| F11 | 96±0.67 | 0.662±0.028 | 25.98±0.08 | 64.54±1.02 |
| F12 | 96±0.17 | 0.782±0.029 | 25.68±0.11 | 73.44±.88 |
| F13 | 103±0.36 | 0.652±0.036 | 25.21±0.19 | 66.78±0.67 |
Zeta Potential
The zeta potential of the L-arginine nanoparticles ranged from 0.637 mV to 0.828 mV (Table 3 & Figure 3), with the highest value observed in batch F7 (0.828 mV). Formulations with a 1:4 solvent-to-antisolvent ratio consistently showed higher zeta potential values (~0.825-0.828 mV), indicating improved colloidal stability. The lowest value (0.637 mV) was recorded in F10, which deviated from the optimal solvent-to-antisolvent ratio.
Entrapment Efficiency
The entrapment efficiency of the L-arginine nanoparticles ranged from 64.54±1.02% (F11) to 77.86±0.93% (F7) (Table 3 & Figure 3). Formulations with 5% Eudragit L100 and a 1:4 solvent-to-antisolvent ratio showed the highest entrapment efficiencies, with F7 reaching 77.86±0.93%. Lower polymer concentrations (4%) and non-optimal solvent ratios (1:3 or 1:5) resulted in reduced entrapment efficiencies, as seen in F11 (64.54±1.02%) and F10 (69.39±0.99%).
Drug Loading
The drug loading of the L-arginine nanoparticles ranged from 24.39±0.17% to 25.98% (Table 3 & Figure 3). The highest drug loading (25.98±0.08%) was observed in F11, although it had lower entrapment efficiency, indicating a trade-off between the two parameters. Formulations with a 1:4 solvent-to-antisolvent ratio showed stable drug loading, confirming that this ratio optimizes drug incorporation without significant loss.
Response surface Methodology
The response surface plot (Figure 4A) demonstrates the effect of Eudragit L100 concentration (A) and Solvent-to-Antisolvent ratio (B) on particle size. An increase in the solvent-to-antisolvent ratio resulted in larger particle sizes, while higher Eudragit L100 concentration initially reduced particle size, with the effect stabilizing at higher concentrations. The smallest particle size (~89 nm) was achieved with a moderate polymer concentration and an optimized solvent-to-antisolvent ratio. The contour plot from Design-Expert® Software also confirms that higher solvent-to-antisolvent ratios lead to larger particle sizes.
The response surface plot (Figure 4B) illustrates the combined effect of Eudragit L100 concentration (X1) and Solvent-to-Antisolvent ratio (X2) on the average zeta potential (Y-axis). The plot reveals a peak region, indicating optimal formulation conditions that result in the highest zeta potential, which suggests maximum colloidal stability. Deviation from this peak-either by changing the Eudragit L100 concentration or the Solvent-to-Antisolvent ratio-causes a decrease in zeta potential, reflecting reduced colloidal stability.
The response surface plot (Figure 4C) demonstrates the impact of Eudragit L100 concentration (A) and Solvent-to-Antisolvent ratio (B) on drug loading (%) in nanoparticles. Higher polymer concentrations and optimized solvent-to-antisolvent ratios led to increased drug loading. The concave surface indicates a nonlinear relationship between the formulation parameters, emphasizing the need for an optimal balance between these variables to maximize drug incorporation. The contour plot further suggests that drug loading progressively increases as the formulation approaches the optimal region.
The response surface plot (Figure 4D) shows the influence of Eudragit L100 concentration (A) and Solvent-to-Antisolvent ratio (B) on entrapment efficiency (%). It indicates that increasing both variables led to higher entrapment efficiency. The curved surface suggests that an optimal balance between polymer concentration and solvent ratio is crucial for maximizing drug entrapment. The contour plot emphasizes the central region with the highest entrapment efficiency, indicating the optimal formulation range.
Overlay Plot
The overlay plot (Figure 4) demonstrates the combined effects of Eudragit L100 concentration (A) and solvent-to-antisolvent ratio (B) on key formulation parameters, including particle size, zeta potential, drug loading, and entrapment efficiency. It highlights the optimal design space where all responses meet the required criteria, with data labels providing specific values for particle size and zeta potential at selected points.
Validation of optimized formulation batch
The optimized formulations were validated by evaluating their Critical Quality Attributes (CQAs), as detailed in Table 4. A comparison between the observed and predicted responses, generated using Design-Expert® Software, demonstrated a strong correlation. Statistical analysis (Table 4 and Figure 5) indicated high R values (0.964 to 0.999) and a p-value < 0.001, confirming the reliability and accuracy of the model’s predictions. Additionally, the overlay plot (Figure 4) further validated the optimized formulation, reinforcing the robustness of the selected conditions.
| Batch code | X1 | X2 | Response variables | Prediction values | Experimental values | Percentage error |
|---|---|---|---|---|---|---|
| Optimized Batch | -0.66 | 0.363 | Particle Size (nm) | 103.97±1.25 | 103.88±1.18 | 0.0866 |
| Zeta Potential | 0.7712±0.015 | 0.7698±0.014 | 0.1815 | |||
| Drug Loading (%) | 24.826±0.43 | 24.836±0.41 | -0.0403 | |||
| Entrapment Efficiency (%) | 74.106±1.02 | 74.26±1.08 | -0.2078 | |||
| VC1 | 0.26 | -0.47 | Particle Size (nm) | 110.29±1.36 | 110.43±1.29 | -0.1269 |
| Zeta Potential | 0.7745±0.017 | 0.7732±0.018 | 0.1679 | |||
| Drug Loading (%) | 25.006±0.44 | 25.106±0.42 | -0.3999 | |||
| Entrapment Efficiency (%) | 76.981±1.11 | 77.10±1.08 | -0.1546 | |||
| VC2 | 0.83 | -0.46 | Particle Size (nm) | 101.33±1.21 | 101.83±1.19 | -0.4934 |
| Zeta Potential | 0.7611±0.016 | 0.7576±0.017 | 0.4599 | |||
| Drug Loading (%) | 24.869±0.40 | 24.961±0.39 | -0.3699 | |||
| Entrapment Efficiency (%) | 74.461±1.03 | 74.502±1.02 | -0.0551 | |||
| VC3 | 0.64 | -0.77 | Particle Size (nm) | 104.4±1.30 | 105.11±1.25 | -0.6801 |
| Zeta Potential | 0.762±0.018 | 0.759±0.017 | 0.3937 | |||
| Drug Loading (%) | 25.282±0.41 | 25.191±0.43 | 0.3599 | |||
| Entrapment Efficiency (%) | 75.919±1.06 | 75.888±1.03 | 0.0408 | |||
| VC4 | -0.41 | -0.48 | Particle Size (nm) | 107.26±1.32 | 106.98±1.29 | 0.261 |
| Zeta Potential | 0.7656±0.019 | 0.7639±0.018 | 0.222 | |||
| Drug Loading (%) | 25.019±0.46 | 25.88±0.44 | -3.4414 | |||
| Entrapment Efficiency (%) | 75.289±1.05 | 75.201±1.04 | 0.1169 |
Surface topography
The SEM image (Figure 6) of L-arginine nanoparticles, prepared using Eudragit L100 via the solvent evaporation method, revealed aggregated clusters with irregular shapes and varying sizes, characteristic of polymeric nanoparticles. The surface appeared porous and rough, indicating efficient solvent evaporation. Although the nanoparticles were in the nanometer range, the observed aggregation could affect dispersibility and potentially influence controlled drug release.
In vitro drug release
The in vitro drug release profile (Figure 7) showed a sustained-release pattern for the L-arginine nanoparticles. Initially, there was a slow-release phase (0-2 hr), followed by a rapid increase in drug release between 2-4 hr. After 4 hr, the release rate slowed, reaching a plateau, which indicates controlled drug diffusion. This pattern is typical of sustained-release formulations, where drug release is regulated over a longer period to maintain therapeutic levels.
Drug release kinetics
The drug release kinetics were best described by the Korsmeyer-Peppas model (R² = 0.9470, n=0.807), indicating a combination of diffusion and erosion in the release mechanism. The Zero-order model also showed good correlation (R²=0.9277), suggesting near-constant release, while the Higuchi model indicated diffusion control. The First-order model had the weakest fit, suggesting the release was not purely concentration-dependent.
Stability study of optimized formulations
The stability study showed that key parameters (particle size, zeta potential, drug loading, and entrapment efficiency) remained stable over six months under accelerated conditions (40±2ºC, 75±5% RH). The drug release profile at 8 hr also remained unaffected, confirming the formulation's stability (Table 5).
| Conditions | Duration | Particle Size (nm) | Zeta Potential | Drug Loading (%) | Entrapment Efficiency (%) |
|---|---|---|---|---|---|
| Zone II Accelerated (40ºC±2ºC / 75%RH±5%) | 1 month | 296.33±2.14 | 1.634±0.07 | 96.84±0.13 | 96.69±0.18 |
| 3 month | 296.33±2.10 | 1.633±0.06 | 96.83±0.04 | 96.60±0.09 | |
| 6 month | 296.32±2.18 | 1.633±0.08 | 96.83±0.06 | 96.60±0.11 |
DISCUSSION
The observed melting point of 228ºC suggests that the L-Arginine used in the formulation is pure and corresponds well with the reported literature values, affirming the integrity of the compound prior to formulation.
Furthermore, the FTIR analysis revealed that the characteristic functional group peaks remained unaltered in the nanoparticle formulation compared to the pure drug. This indicates that there were no significant chemical interactions between L-Arginine and the polymer used in nanoparticle preparation. The preservation of characteristic peaks in the spectra confirms the compatibility of L-Arginine with the chosen polymer, which is crucial for maintaining the drug's stability and therapeutic efficacy in the nanoparticle form.
The 3² Central Composite Design (CCD) effectively evaluated the impact of formulation variables on the Critical Quality Attributes (CQAs) of L-Arginine Nanoparticles. Significant R² values confirmed the accuracy of the model in predicting outcomes. Particle size was highly influenced by both individual and interactive effects of the variables, with factor A showing strong quadratic effects. Zeta potential demonstrated moderate dependence on the formulation factors. Drug loading was positively affected by the quadratic term of factor B, suggesting optimization of this variable could improve drug incorporation. Entrapment efficiency was sensitive to changes in both interaction and quadratic terms, highlighting the importance of precise formulation control. These results underscore the utility of Response Surface Methodology (RSM) and CCD in optimizing nanoparticulate systems for scale-up and manufacturing.
The particle size of L-arginine nanoparticles was significantly influenced by the polymer concentration and Solvent-to-Antisolvent (S:AS) ratio. Higher Eudragit L100 concentrations (6% in F10 and F8) led to smaller, more uniform particles due to enhanced nucleation and stabilization. The optimal S:AS ratio of 1:4 balanced precipitation, while a higher ratio (1:5 in F6) caused larger particles due to rapid aggregation. Formulation F7 (5% polymer, 1:4 S:AS) achieved a consistent particle size of 114±1.11 nm, ensuring stability for sustained release. These findings underscore the importance of carefully controlling formulation parameters for optimal drug delivery.
Particle size of L-arginine nanoparticles was influenced by Eudragit L100 concentration and solvent-to-antisolvent ratio. Higher polymer concentrations (e.g., 6%) resulted in smaller particles, while a 1:4 solvent-to-antisolvent ratio optimized particle size. Excess antisolvent (1:5) caused aggregation and larger particles. The formulation with 5% polymer and a 1:4 ratio (F7) achieved an ideal particle size of 114±1.11 nm, suitable for sustained-release applications. These results highlight the importance of controlling formulation parameters for consistent nanoparticle characteristics.
Entrapment efficiency of L-arginine nanoparticles was influenced by both polymer concentration and solvent-to-antisolvent ratio. The highest entrapment efficiency (77.86±0.93%) was achieved with 5% Eudragit L100 and a 1:4 S:AS ratio (Batch F7). This optimal combination facilitated the formation of a stable polymer network, enhancing drug encapsulation. Lower polymer concentrations and extreme solvent ratios (1:3 or 1:5) reduced efficiency, likely due to poor matrix formation or premature precipitation. These results highlight the importance of optimizing formulation conditions for improved drug delivery.
Drug loading in L-arginine nanoparticles remained stable across formulations, ranging from 24.39% to 25.98%, indicating consistent drug incorporation. The highest drug loading (25.98%) was observed in F11, but this came with reduced entrapment efficiency, suggesting a trade-off. Formulation F7, which balanced polymer concentration and solvent-to-antisolvent ratio, optimized both parameters. The 1:4 solvent-to-antisolvent ratio proved effective in maintaining drug loading and minimizing loss, ensuring a controlled nanoparticle formation environment. Future efforts should focus on enhancing entrapment efficiency while maintaining optimal drug loading.
The response surface plots for L-arginine nanoparticles highlight that particle size increases with a higher solvent-to-antisolvent ratio, while Eudragit L100 concentration initially decreases particle size, stabilizing at higher concentrations. Zeta potential showed optimal colloidal stability with balanced conditions, while deviations led to aggregation. Drug loading improved with higher polymer concentrations and optimized solvent ratios, requiring careful balance. Entrapment efficiency also increased with higher polymer concentration and solvent ratio, emphasizing the need for precise formulation optimization to achieve stable, high-quality nanoparticles. The overlay plot effectively visualizes the combined effects of Eudragit L100 concentration and solvent-to-antisolvent ratio on key formulation parameters. It identifies regions where formulation adjustments are needed to optimize particle size, stability, and drug encapsulation. The yellow region highlights the optimal conditions, ensuring balanced, efficient nanoparticle properties. The validation of the optimized formulations confirmed the accuracy of Design-Expert® Software in predicting nanoparticle CQAs. Strong linear correlations (R values of 0.964 to 0.999) and low p-values (<0.001) demonstrated the model’s precision. The overlay plot and brute force method further validated the optimized formulation, ensuring it meets required criteria. These findings provide a solid foundation for future pharmaceutical applications.
SEM analysis showed nanoparticle aggregation, typical in solvent evaporation methods, with irregular shapes and a porous surface. This aggregation may affect dispersibility and controlled release, suggesting that further optimization is needed for improved formulation stability and drug release.
The in vitro drug release profile showed sustained-release behavior, with an initial slow release, a rapid phase, and a plateau, indicating controlled drug diffusion. The Korsmeyer-Peppas model best described the release, suggesting both diffusion and erosion control. The Zero-order model supported a constant release rate, ideal for sustained-release systems, while the Higuchi model indicated diffusion control. These findings confirm that the formulation is suitable for controlled drug delivery through a combination of diffusion and erosion mechanisms.
The optimized formulation demonstrated excellent stability, with negligible changes in key parameters over six months. The consistent drug release profile under accelerated conditions suggests the formulation’s robustness and suitability for long-term storage.
CONCLUSION
The study successfully employed the Quality by Design (QbD) approach, utilizing a Face Centered Central Composite Design (FCCCD), to develop and optimize a prolonged-release L-arginine nanoparticle dosage form via the nanoprecipitation technique. The optimized formulation, achieved by controlling the concentration of Eudragit L 100 and the solvent-to-antisolvent ratio, exhibited desirable characteristics: a particle size of 103.97 nm, a zeta potential of 0.771, drug loading of 24.826%, and an entrapment efficiency of 74.26%. Validation of the optimized data demonstrated a close agreement between predicted and experimental values, confirming the robustness and reliability of the developed formulation. Ultimately, this research validates the efficiency of the QbD methodology in rationalizing the optimization of complex L-arginine drug delivery systems, minimizing extensive experimentation while ensuring high-quality, controlled-release nutraceutical formulations.